CN115423544A - Commodity order data acquisition system and data acquisition method - Google Patents
Commodity order data acquisition system and data acquisition method Download PDFInfo
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- CN115423544A CN115423544A CN202210782192.0A CN202210782192A CN115423544A CN 115423544 A CN115423544 A CN 115423544A CN 202210782192 A CN202210782192 A CN 202210782192A CN 115423544 A CN115423544 A CN 115423544A
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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
- G06Q30/06—Buying, selling or leasing transactions
- G06Q30/0601—Electronic shopping [e-shopping]
- G06Q30/0633—Lists, e.g. purchase orders, compilation or processing
- G06Q30/0635—Processing of requisition or of purchase orders
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/24—Querying
- G06F16/245—Query processing
- G06F16/2457—Query processing with adaptation to user needs
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/24—Querying
- G06F16/248—Presentation of query results
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/26—Visual data mining; Browsing structured data
Abstract
The application relates to a commodity order data acquisition system and a data acquisition method, wherein data in a server of a target webpage form a plurality of time period parameters, and the time period parameters are a set of corresponding browsing amount, added shopping cart number, transaction amount and browsing number of each commodity in a set time period and then are matched with a chart visualization function set; the method has the advantages that appropriate chart visualization functions and time period parameters can be called according to the target requirements, corresponding visualization charts are obtained, and operators can visually analyze data according to the obtained visualization charts, so that operation adjustment is performed.
Description
Technical Field
The present application relates to the field of management systems, and in particular, to a system and a method for acquiring commodity order data.
Background
With the rapid development of the internet, the times and the quantity of shopping in the network are gradually increased. Wherein each successful purchase is determined based on the order. Therefore, the order data is important for any merchant to the performance evaluation, the confirmation of sales, and the like, which is not comparable to other data. In the existing acquisition process of order data, a script code is usually deployed at a client, and then order data of a user in a target webpage is acquired through the script code, wherein orders are generated in a server where the target webpage is located, so that when the order data are acquired in the prior art, the acquisition of the order data is realized through order information fed back by the server where the target webpage is located.
In some related technologies, after the order information is collected, only the information of the category, the quantity, the time and the like of the commodities is known, and the preference of the consumer for each commodity and the transaction situation of the final order purchase are determined. If the operation strategy needs to be adjusted according to the data analysis, operators need to manually export the data from each data acquisition device and arrange the data to form various forms, and the operators need to acquire required information from massive forms obtained through arrangement, so that the operation is complex, errors are easy to occur, and objective and effective operation decisions cannot be finally obtained.
Disclosure of Invention
The embodiment of the application provides a commodity order data acquisition system and a data acquisition method, and aims to solve the problems that in the related art, the operation is complex and errors are easy to occur, and objective and effective operation decisions cannot be finally obtained due to the fact that information of commodity orders needs to be manually arranged and acquired and manually analyzed by operators.
In a first aspect, a method for collecting commodity order data includes the following steps:
acquiring data in a server where a target webpage is located, and forming original data; the original data comprises a plurality of time period parameters, and the time period parameters are a set of corresponding browsing amount, number of shopping carts added, volume of deals, amount of deals and browsing number of people of each commodity in a set time period;
establishing a chart visualization function set, wherein the chart visualization function set comprises a plurality of different chart visualization functions;
calling a corresponding chart visualization function according to the target requirement, and any item of the browsing amount, the number of added shopping carts, the volume of deals, the amount of deals and the number of browsed people of any commodity in the time period parameter;
and inputting any item of the browsing amount of any commodity, the number of the added shopping carts, the transaction amount and the browsing number in the called time period parameters into the called chart visualization function to form a visualization chart.
In some embodiments, the set of chart visualization functions includes a histogram visualization function, a line graph visualization function, a pie graph visualization function, a scatter plot visualization function, and a radar plot visualization function.
In some embodiments, when the target requirement is to obtain the popularity of each commodity in the set time period, a scatter diagram visualization function or a radar diagram visualization function is required to be called, and browsing amount, the number of shopping carts added and the number of browsed people are required to be called;
the visual chart is a scatter chart or a radar chart, and the density of scatter distribution or the proportion of each part in the radar chart is taken as the popularity.
In some embodiments, when the target demand is to obtain the boutique goods within the set time period, a bar graph visualization function or a pie graph visualization function is called, and a volume of bargaining and an amount of bargaining are called;
the visual chart is a column chart or a pie chart, and the commodity with the highest column height or the largest proportion of each part in the pie chart is taken as a main commodity.
In some embodiments, when the target demand is to predict sales of each commodity in a next set time period within a current set time period, a line graph visualization function needs to be called, and a current time period parameter and all time period parameters before the current time period parameter need to be called;
the visual chart is a broken line chart, the trend of each broken line is obtained, and the sales condition in the next set time period is predicted according to the trend condition of the broken lines.
In some embodiments, the trend of the broken line can be used to see the selling condition of the goods in each set time period, and the operator can find out the underestimated selling time period, so as to adjust the selling time period or the gold selling time period.
A data acquisition system used by a commodity order data acquisition method comprises the following steps:
the data acquisition module is in signal connection with a server where the target webpage is located and is used for forming original data;
the storage module is in signal connection with the data acquisition module and is used for storing the original data;
the calling module comprises a memory unit and a calling unit, the calling unit is in signal connection with the storage module, and the memory unit is used for storing a chart visualization function set;
the analysis processing module is in signal connection with the storage module and the calling module and is used for calling original data and a chart visualization function according to a target requirement to form a visualization chart;
and the display module is in signal connection with the analysis processing module, the storage module and the calling module.
In some embodiments, the display module further includes a touch unit and a plurality of requirement selection units corresponding to the purpose requirements, and when one of the requirement selection units is pressed down, the touch unit displays a visual chart of the purpose requirements corresponding to the requirement selection unit.
In some embodiments, the data acquisition system used in the commodity order data acquisition method further includes a printing module, and the printing module is configured to print the visualization map.
In a third aspect, a computer-readable storage medium for a method for commodity order data collection is provided.
The beneficial effect that technical scheme that this application provided brought includes:
the embodiment of the application provides a commodity order data acquisition system and a data acquisition method, wherein data in a server of a target webpage form a plurality of time period parameters, and the time period parameters are a set of corresponding browsing volume, added shopping cart number, transaction volume, transaction amount and browsing number of each commodity in a set time period and then are matched with a chart visualization function set; in the process, the operator does not need to manually search for related data in huge data and manually arrange and analyze the data, so that the desired information set can be automatically and quickly obtained, and the problems that operation is complicated, errors are easy to occur and objective and effective operation decisions cannot be finally obtained due to manual arrangement and acquisition and manual analysis are avoided.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
Fig. 1 is a flowchart illustrating a management method of a commodity order data collection method according to a related art;
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are some embodiments of the present application, but not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
The embodiment of the application provides a commodity order data acquisition system and a data acquisition method, and aims to solve the problems that in the related art, the information of commodity orders needs to be manually sorted and acquired by operators and manually analyzed, so that the operation is complex, errors are easy to occur, and objective and effective operation decisions cannot be finally obtained.
Referring to fig. 1, a method for acquiring commodity order data includes the following steps:
acquiring data in a server where a target webpage is located, and forming original data; the original data comprises a plurality of time period parameters, and the time period parameters are a set of browsing volume, number of shopping carts added, volume of deals, amount of deals and number of browsing people corresponding to each commodity in a set time period;
establishing a chart visualization function set, wherein the chart visualization function set comprises a plurality of different chart visualization functions;
calling a corresponding chart visualization function according to the target requirement, and any item of the browsing amount of any commodity, the number of shopping carts added, the volume of deals, the amount of deals and the number of browsing people in the time period parameter;
and inputting any item of the browsing amount of any commodity, the number of the added shopping carts, the transaction amount and the browsing number in the called time period parameters into the called chart visualization function to form a visualization chart. The chart visualization function set comprises a column chart visualization function, a broken line chart visualization function, a pie chart visualization function, a scatter chart visualization function and a radar chart visualization function. Therefore, huge data are arranged and classified according to the time sequence, operators do not need to search blindly, and data omission is avoided.
The data in the server of the target webpage is formed into a plurality of time period parameters through the steps, wherein the time period parameters are a set of the corresponding browsing volume, the number of the shopping carts added, the volume of the deals, the amount of the deals and the number of the browsing people of each commodity in a set time period, and then a visual function set is matched with the chart; in the process, the operator does not need to manually search for related data in huge data and manually arrange and analyze the data, a desired information set can be automatically and quickly obtained, and the problems that objective and effective operation decisions cannot be finally obtained due to complex operation and error easily caused by manual arrangement and acquisition and manual analysis are avoided.
In some preferred embodiments, the chart visualization function set includes a histogram visualization function, a line chart visualization function, a pie chart visualization function, a scatter chart visualization function, and a radar chart visualization function, and specific purpose requirements are given below for explanation:
when the target requirement is to obtain the popularity of each commodity in a set time period, a scatter diagram visualization function or a radar diagram visualization function is required to be called, and the browsing amount, the number of shopping carts added and the number of browsed people are required to be called; the visual chart is a scatter chart or a radar chart, and the density of scatter distribution or the proportion of each part in the radar chart is taken as the popularity.
When the target requirement is to obtain the main sales commodity in a set time period, calling a column diagram visualization function or a pie diagram visualization function, and calling the volume of the deal and the amount of the deal; the visual chart is a column chart or a pie chart, and the commodity corresponding to the highest column height or the largest proportion of each part in the pie chart is used as a main commodity.
When the target requirement is to predict the sales condition of each commodity in the next set time period in the current set time period, calling a line graph visualization function, and calling a current time period parameter and all time period parameters before the current time period parameter; the visual chart is a broken line chart, the trend of each broken line is obtained, and the sales condition in the next set time period is predicted according to the trend condition of the broken lines.
The selling condition of the commodities in each set time period can be seen by utilizing the trend of the broken lines, and the operator can find out the underestimated selling time period, so that the selling time period or the gold selling time period can be adjusted.
Therefore, through the chart formed by the specific parts, an operator can visually see the selling condition of the commodity, so that the operation strategy is adjusted, for example, the operation of changing the type of the sold commodity, increasing the quantity of sold commodities, adjusting the price, selling time and the like is performed, a clear direction is given for daily operation work, and the workload of the operator is reduced.
The application also provides a data acquisition system used by the commodity order data acquisition method, which comprises the following steps:
the data acquisition module is in signal connection with a server where the target webpage is located and is used for forming original data;
the storage module is in signal connection with the data acquisition module and is used for storing the original data;
the calling module comprises a memory unit and a calling unit, the calling unit is in signal connection with the storage module, and the memory unit is used for storing a chart visualization function set;
the analysis processing module is in signal connection with the storage module and the calling module and is used for calling original data and a chart visualization function according to the target requirement to form a visualization chart;
and the display module is in signal connection with the analysis processing module, the storage module and the calling module.
Through the system, an operator can freely select the time period parameters and the chart visualization functions according to specific requirements to obtain different charts, so that the obtained information is expanded, and it is understood that the charts obtained by the system are not limited to the specific target requirements mentioned above, so that the obtained information can be used more flexibly.
In some preferred embodiments, the display module further includes a touch unit and a plurality of requirement selection units corresponding to the purpose requirements, and after one of the requirement selection units is pressed down, the touch unit displays a visualization chart of the purpose requirements corresponding to the requirement selection unit.
In this embodiment, the operator can press the corresponding requirement selection unit on the touch unit according to the specific purpose requirement, which is more convenient. It should be understood that the requirement selection unit can be set and edited by itself, so as to meet the daily work requirement. For the implementation of this function, there are detailed technical solutions in the related art, and details are not described here.
Further, in order to meet the paper requirements, the data acquisition system used in the commodity order data acquisition method further comprises a printing module, and the printing module is used for printing the visual graph.
The application also provides a computer-readable storage medium, wherein the computer-readable storage medium is used for executing the commodity order data acquisition method, so that data in a server where the target webpage is located form a plurality of time period parameters, and the time period parameters are a set of browsing volume, added shopping cart number, transaction volume, transaction amount and browsing number corresponding to each commodity in a set time period and then are matched with a chart visualization function set; the method has the advantages that appropriate chart visualization functions and time period parameters can be called according to the target requirements, corresponding visual charts are obtained, operators can visually analyze data according to the obtained visual charts, operation adjustment is accordingly conducted, in the process, manual searching for relevant data in huge data and manual arrangement and analysis are not needed, a desired information set can be automatically and quickly obtained, and the problems that operation is complicated and mistakes are easily made due to manual arrangement and acquisition and manual analysis, and objective and effective operation decisions cannot be finally obtained are solved.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and so forth) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto 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 flow or flows and/or block diagram block or blocks. In a typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory. The memory may include forms of volatile memory in a computer readable medium, random Access Memory (RAM) and/or non-volatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). The memory is an example of a computer-readable medium.
Computer-readable media, including both permanent and non-permanent, removable and non-removable media, may implement the information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data.
Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static Random Access Memory (SRAM), dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), read Only Memory (ROM), electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), digital Versatile Disks (DVD) or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transmission medium, which can be used to store information that can be accessed by a computing device. As defined herein, a computer readable medium does not include a transitory computer readable medium such as a modulated data signal or a carrier wave. It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrases "comprising a" \8230; "does not exclude the presence of additional like elements in the process, method, article, or apparatus that comprises the element.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein. The above are merely examples of the present application and are not intended to limit the present application. As will be apparent to those skilled in the art,
various modifications and changes may be made to the present application. Any modification, equivalent replacement, improvement or the like made within the spirit and principle of the present application shall be included within the scope of the claims of the present application. .
In the description of the present application, it should be noted that the terms "upper", "lower", and the like indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings, which are merely for convenience of description and simplification of the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and thus, should not be construed as limiting the present application. Unless expressly stated or limited otherwise, the terms "mounted," "connected," and "connected" are intended to be inclusive and mean, for example, that they may be fixedly connected, detachably connected, or integrally connected; can be mechanically or electrically connected; either directly or indirectly through intervening media, or may be interconnected between two elements. The specific meaning of the above terms in the present application can be understood by those of ordinary skill in the art as the case may be.
It is noted that, in the present application, relational terms such as "first" and "second", and the like, are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrases "comprising a component of' 8230; \8230;" does not exclude the presence of additional like elements in a process, method, article, or apparatus that comprises the element.
The previous description is only an example of the present application, and is provided to enable any person skilled in the art to understand or implement the present application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the application. Thus, the present application is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (10)
1. A commodity order data acquisition method is characterized by comprising the following steps:
acquiring data in a server where a target webpage is located, and forming original data; the original data comprises a plurality of time period parameters, and the time period parameters are a set of browsing volume, the number of shopping carts added, transaction volume, transaction amount and the number of browsing people corresponding to each commodity in a set time period;
establishing a chart visualization function set, wherein the chart visualization function set comprises a plurality of different chart visualization functions;
calling a corresponding chart visualization function according to the target requirement, and any item of the browsing amount of any commodity, the number of shopping carts added, the volume of deals, the amount of deals and the number of browsing people in the time period parameter;
and inputting any item of the browsing amount of any commodity, the number of the added shopping carts, the volume of the deals, the amount of the deals and the number of the browsed people in the called time period parameters into the called visual function of the chart to form a visual chart.
2. The commodity order data collection method according to claim 1, wherein:
the chart visualization function set comprises a bar chart visualization function, a line chart visualization function, a pie chart visualization function, a scatter chart visualization function and a radar chart visualization function.
3. The commodity order data collection method according to claim 2, wherein:
when the target requirement is to obtain the popularity of each commodity in the set time period, a scatter diagram visualization function or a radar diagram visualization function is required to be called, and the browsing amount, the number of shopping carts added and the number of browsed people are required to be called;
the visual chart is a scatter chart or a radar chart, and the density of scatter distribution or the proportion of each part in the radar chart is used as the popularity.
4. The commodity order data collection method according to claim 2, wherein:
when the target requirement is to obtain the principal commodity in the set time period, calling a column diagram visualization function or a pie diagram visualization function, and calling a volume of transaction and a volume of transaction;
the visual chart is a column chart or a pie chart, and the commodity corresponding to the highest column height or the largest proportion of each part in the pie chart is used as a main commodity.
5. The commodity order data collection method according to claim 2, wherein:
when the target demand is used for predicting the sales condition of each commodity in the next set time period in the current set time period, calling a line graph visualization function, and calling a current time period parameter and all time period parameters before the current time period parameter;
the visual chart is a broken line chart, the trend of each broken line is obtained, and the sales condition in the next set time period is predicted according to the trend condition of the broken lines.
6. The commodity order data collection method of claim 5, wherein:
the selling condition of the commodities in each set time slot can be seen by utilizing the trend of the broken lines, and the operator can find out the underestimation period of the selling time, so that the selling time slot or the selling golden time slot can be adjusted.
7. A data acquisition system for use in the commodity order data acquisition method according to any one of claims 1 to 6, comprising:
the data acquisition module is in signal connection with a server where the target webpage is located and is used for forming original data;
the storage module is in signal connection with the data acquisition module and is used for storing the original data;
the calling module comprises a memory unit and a calling unit, the calling unit is in signal connection with the storage module, and the memory unit is used for storing the chart visualization function set;
the analysis processing module is in signal connection with the storage module and the calling module and is used for calling original data and a chart visualization function according to the target requirement to form a visualization chart;
and the display module is in signal connection with the analysis processing module, the storage module and the calling module.
8. The data acquisition system used in the commodity order data acquisition method according to claim 7, characterized in that:
the display module further comprises a touch unit and a plurality of requirement selection units corresponding to the purpose requirements, and after one requirement selection unit is pressed down, the touch unit displays the visual chart of the purpose requirements corresponding to the requirement selection unit.
9. The data acquisition system used in the commodity order data acquisition method according to claim 7, characterized in that:
the data acquisition system used by the commodity order data acquisition method further comprises a printing module, and the printing module is used for printing the visual graph.
10. A computer-readable storage medium, characterized in that: the computer readable storage medium is for performing the commodity order data collection method of any one of claims 1 to 6.
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