CN116523543A - Data processing method, equipment and medium for preformed dish transaction progress display - Google Patents
Data processing method, equipment and medium for preformed dish transaction progress display Download PDFInfo
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Abstract
The application provides a data processing method, equipment and medium for showing progress of a prefabricated vegetable transaction, wherein the method acquires a first classification index set and a second classification index set corresponding to the prefabricated vegetable transaction; the first set of classification indicators corresponds to a first attribute indicator of the prepared vegetable transaction. The second set of classification indicators corresponds to a second attribute indicator of the prepared vegetable transaction. The first attribute index includes a second attribute index. Based on the number relation of the classification index elements in the first classification index set and the second classification index set, corresponding preset sample size is determined. Based on the real-time business data of the prefabricated dish transaction from the data acquisition module, a plurality of predetermined business data sets corresponding to the predetermined sample size are determined. And determining a transaction progress curve corresponding to each preset time period node of the prefabricated vegetable transaction according to each preset service data set and a preset transaction progress calculation model, so as to send the transaction progress curve to the user terminal in real time, and displaying the whole transaction progress information of the prefabricated vegetable transaction.
Description
Technical Field
The application relates to the technical field of internet, in particular to a data processing method, equipment and medium for performing transaction progress display of prefabricated dishes.
Background
Along with the development of science and technology, the intelligent level of each industry is continuously improved, and intelligent equipment is convenient for people in aspects of shopping, traveling, working and other matters. For example, when a person performs a task or job with the aid of a computer, the computer can complete the calculation by its internal logic by simply providing the computer with data related to the task or job.
While the level of intelligence is increasing, people are getting less and less concerned about process data and overall data made up of individual nodes in the process. For example, in the working process of a computer, only the final result is concerned in work for non-computer professionals, and the dependency relationship and the association relationship between data in the using process of the computer are not concerned. The attention degree of the relationship between the process and the whole data of people is weakened, various problems are often caused, and some unexpected problems are brought to work and life.
In the prefabricated vegetable industry, the number of prefabricated vegetable enterprises is rapidly increasing, and the prefabricated vegetable market is rapidly growing. The attention of people to the development of the prefabricated dishes is limited to the selling price of the prefabricated dishes, and the data of each node in the purchasing and selling processes of the prefabricated dishes are lack of attention, so that the development of industry cannot be well promoted.
Based on the relation, how to enable the user to obtain the related process or the whole data relation of the prefabricated vegetable industry more conveniently, enable the user to enable the progress of related transactions (such as purchasing and selling) of the prefabricated vegetable to be more convenient through the relation, and improve the user experience and transaction efficiency.
Disclosure of Invention
The embodiment of the application provides a data processing method, equipment and medium for performing transaction progress display of prefabricated dishes, which are used for solving the problems that the current prefabricated dishes industry is inconvenient to obtain related processes or overall data relations of the prefabricated dishes industry, the progress of the related transactions (such as purchasing and selling) of the prefabricated dishes by a user is unsmooth, the user experience is poor and the transaction handling efficiency is low.
In one aspect, an embodiment of the present application provides a data processing method for performing a transaction progress presentation on a prepared dish, where the method includes:
acquiring a first classification index set and a second classification index set corresponding to the prefabricated vegetable transaction; wherein the first set of classification indicators corresponds to a first attribute indicator of the prepared vegetable transaction; the second classification index set corresponds to a second attribute index of the prepared vegetable transaction; the first attribute index comprises the second attribute index;
Determining a corresponding predetermined sample size based on the number relationship of the classification index elements in the first classification index set and the second classification index set;
determining a plurality of predetermined service data sets corresponding to the predetermined sample size based on real-time service data of the prepared vegetable transaction from a data acquisition module; the real-time service data comprise service data of nodes in each preset period of time of the prefabricated vegetable transaction;
and determining a transaction progress curve corresponding to each preset time period node of the prefabricated vegetable transaction according to each preset service data set and a preset transaction progress calculation model, so as to send the transaction progress curve to a user terminal in real time, and displaying the whole transaction progress information of the prefabricated vegetable transaction.
In an implementation manner of the present application, obtaining a first classification index set and a second classification index set corresponding to a prefabricated vegetable transaction specifically includes:
determining the category of the prefabricated dishes corresponding to the prefabricated dish transaction, wherein the category of the prefabricated dishes is the classification index element of the first classification index set; the prepared dish category at least comprises: sichuan pickle, rouge, huaiyang pickle, yue pickle, zhejiang pickle, mincai pickle, hunan pickle and Hui pickle; and
Determining the classification of the eating modes corresponding to the prefabricated vegetable categories according to the prefabricated vegetable categories and a preset eating mode list, and taking the classification as the classification index elements of the second classification index set; wherein, the edible way classification at least comprises: instant preparation, instant cooking, instant heating and instant eating.
In one implementation manner of the present application, determining the corresponding predetermined sample size based on the number relationship of the classification index elements in the first classification index set and the second classification index set specifically includes:
taking the number of the classification index elements in the first classification index set as a first number;
taking the number of the classified index elements in the second classified index set as a second number;
and determining the preset sample quantity according to a preset sample selected value and a product value of the first quantity and the second quantity.
In one implementation of the present application, determining a plurality of predetermined service data sets corresponding to the predetermined sample size based on real-time service data of the prefabricated dish transaction from a data acquisition module specifically includes:
the data acquisition module is used for crawling real-time service data corresponding to a plurality of prefabricated vegetable transactions from an Internet platform; the prefabricated vegetable transaction at least comprises: counting selling price and selling quantity;
Determining attribute index binary groups of each real-time service data; wherein the attribute index doublet comprises: a first attribute index and the second attribute index;
matching each attribute index binary group with the first classification index set and the second classification index set so as to determine a plurality of preset business data groups corresponding to the preset sample size according to a matching result; the plurality of predetermined time period nodes are different.
In one implementation manner of the present application, each of the attribute index tuples is matched with the first classification index set and the second classification index set, so as to determine, according to a matching result, a plurality of predetermined service data sets corresponding to the predetermined sample size, including:
matching a first attribute index in the attribute index binary group with the first classification index set;
under the condition of successful matching, determining a multi-index business data list corresponding to the first attribute index which is successfully matched; wherein a column of the multi-index business data list corresponds to the second attribute index; the number of lines of the multi-element index service data list is a preset sample selection value; and
Matching a second attribute index in the attribute index binary group successfully matched with the first classification index set with the second classification index set;
under the condition that the matching with the second classification index set is successful, determining whether the element number of a second attribute index column corresponding to the multi-index service data list reaches the sample selected value;
if not, adding the corresponding real-time service data to the corresponding position of the multi-index service data list until the number of the column elements of each multi-index service data list reaches the sample selected value, so as to form each multi-index service data list into the preset service data group corresponding to the preset sample quantity.
In one implementation manner of the present application, determining a transaction progress curve corresponding to each of the predetermined time period nodes of the prefabricated dish transaction according to each of the predetermined service data sets and a preset transaction progress calculation model specifically includes:
determining a calculation base period of the transaction progress curve according to the predetermined period node;
determining the preset service data group corresponding to the calculation base period;
inputting the preset business data set into the transaction progress calculation model, and determining a base period curve value of the calculated base period;
And generating the transaction progress curve based on the base period curve value, the transaction progress calculation model and each preset business data set.
In one implementation of the present application, the transaction progress calculation model includes a curve function value calculation formula of the following transaction progress curve:
wherein I is p A first transaction index of the prefabricated dish representing a first reporting period corresponding to the node of the preset time period, i is a sample number of a preset sample quantity, and p 1i For the service data corresponding to the first transaction of the prefabricated dish in the first reporting period, q 0i For the business data corresponding to the second transaction of the prefabricated dish of the calculated basic period, p 0i And calculating service data corresponding to the first transaction of the prefabricated dish in the basic period.
In one implementation of the present application, after generating the transaction progress curve, the method further includes:
inputting each preset service data group into a weighted average calculation formula of the transaction progress calculation model, and determining a comprehensive average data value of real-time service data corresponding to the first transaction of the preset time period node;
and generating a transaction progress node curve according to each comprehensive average data value so as to show the transaction node progress information to a user.
In another aspect, an embodiment of the present application provides a data processing apparatus for performing a display of a transaction progress of a prepared dish, the apparatus including:
at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to:
acquiring a first classification index set and a second classification index set corresponding to the prefabricated vegetable transaction; wherein the first set of classification indicators corresponds to a first attribute indicator of the prepared vegetable transaction; the second classification index set corresponds to a second attribute index of the prepared vegetable transaction; the first attribute index comprises the second attribute index;
determining a corresponding predetermined sample size based on the number relationship of the classification index elements in the first classification index set and the second classification index set;
determining a plurality of predetermined service data sets corresponding to the predetermined sample size based on real-time service data of the prepared vegetable transaction from a data acquisition module; the real-time service data comprise service data of nodes in each preset period of time of the prefabricated vegetable transaction;
And determining a transaction progress curve corresponding to each preset time period node of the prefabricated vegetable transaction according to each preset service data set and a preset transaction progress calculation model, so as to send the transaction progress curve to a user terminal in real time, and displaying the whole transaction progress information of the prefabricated vegetable transaction.
In yet another aspect, embodiments of the present application further provide a data processing non-volatile computer storage medium for prefabricated dish transaction progress presentation, storing computer-executable instructions configured to:
acquiring a first classification index set and a second classification index set corresponding to the prefabricated vegetable transaction; wherein the first set of classification indicators corresponds to a first attribute indicator of the prepared vegetable transaction; the second classification index set corresponds to a second attribute index of the prepared vegetable transaction; the first attribute index comprises the second attribute index;
determining a corresponding predetermined sample size based on the number relationship of the classification index elements in the first classification index set and the second classification index set;
determining a plurality of predetermined service data sets corresponding to the predetermined sample size based on real-time service data of the prepared vegetable transaction from a data acquisition module; the real-time service data comprise service data of nodes in each preset period of time of the prefabricated vegetable transaction;
And determining a transaction progress curve corresponding to each preset time period node of the prefabricated vegetable transaction according to each preset service data set and a preset transaction progress calculation model, so as to send the transaction progress curve to a user terminal in real time, and displaying the whole transaction progress information of the prefabricated vegetable transaction.
According to the scheme, a more convenient way can be provided for the display of the progress (price index) of the prefabricated vegetable transaction, the overall price trend of the prefabricated vegetable product is intuitively displayed on a macroscopic level, a gripper is provided for the macroscopic regulation market of the government, and a basis is provided for policy establishment; but also reflects the price and the fluctuation range of the product to a certain extent, and provides market guidance in purchasing, selling and the like for enterprises and farmers. Has good use and popularization value. The method and the system can enable the user in the prefabricated vegetable industry to obtain the related process or the whole data relationship in the prefabricated vegetable industry, smoothly complete the adjustment of related matters (such as purchasing and selling) of the prefabricated vegetable, and improve the user experience and the transaction efficiency.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiments of the application and together with the description serve to explain the application and do not constitute an undue limitation to the application. In the drawings:
FIG. 1 is a schematic flow chart of a data processing method for performing a transaction progress presentation for a prepared dish according to an embodiment of the present application;
fig. 2 is a schematic structural diagram of a data processing device for performing a transaction progress presentation of a prepared dish in an embodiment of the present application.
Detailed Description
For the purposes, technical solutions and advantages of the present application, the technical solutions of the present application will be clearly and completely described below with reference to specific embodiments of the present application and corresponding drawings. It will be apparent that the described embodiments are only some, but not all, of the embodiments of the present application. All other embodiments, which can be made by one of ordinary skill in the art without undue burden from the present disclosure, are within the scope of the present disclosure.
The embodiment of the application provides a data processing method, equipment and medium for performing transaction progress display of prefabricated dishes, which are used for solving the problems that the current prefabricated dishes industry is inconvenient to obtain related processes or whole data relations in the prefabricated dishes industry, the progress of the related transactions (such as purchasing and selling) of the prefabricated dishes by a user is unsmooth, the user experience is poor and the transaction handling efficiency is low.
Various embodiments of the present application are described in detail below with reference to the accompanying drawings.
The embodiment of the application provides a data processing method for performing a transaction progress display of a prepared dish, as shown in fig. 1, the method may include steps S101-S104:
s101, a server acquires a first classification index set and a second classification index set corresponding to the prefabricated vegetable transaction.
Wherein the first set of classification indicators corresponds to a first attribute indicator of the prepared vegetable transaction. The second set of classification indicators corresponds to a second attribute indicator of the prepared vegetable transaction. The first attribute index includes a second attribute index.
It should be noted that, the server is merely an exemplary embodiment of an execution body of the data processing method for performing the display of the progress of the prefabricated dish transaction, and the execution body is not limited to the server, and the application is not limited thereto.
The pre-made vegetable transaction may be a transaction that counts the price of the sale, a transaction that counts the amount of the sale. The method for obtaining the first classification index set and the second classification index set corresponding to the prefabricated vegetable transaction specifically comprises the following steps:
the server determines the category of the prefabricated dish corresponding to the prefabricated dish transaction and takes the category index element as the first category index set. The prepared dish category at least comprises: sichuan pickle, rouge, huaiyang pickle, yue pickle, zhejiang pickle, mincai pickle, hunan pickle and Hui pickle. And determining the classification of the eating modes corresponding to the categories of the prefabricated vegetables as the classification index elements of the second classification index set according to the categories of the prefabricated vegetables and the preset eating mode list. Wherein, the classification of the eating modes at least comprises: instant preparation, instant cooking, instant heating and instant eating.
That is, the prefabricated dishes of the application cover the eight major dishes of national dishes, including Sichuan dishes, rouge, huaiyang dishes, yue dishes, zhejiang dishes, min dishes, hunan dishes and Hui dishes, and the eight major dishes are used as the dishes to select the element values corresponding to the first attribute indexes (dishes); the first attribute index is divided into 4 second attribute indexes (edible mode classification) according to the classification of the prepared vegetables, and the method comprises the following steps: instant preparation, instant cooking, instant heating and instant eating, and further two classification index sets are obtained.
According to the vegetables and the eating modes, the calculation and the display of the transaction progress of the prefabricated vegetables can be more comprehensively carried out on the prefabricated vegetables of different types, such as the price of the prefabricated vegetables.
S102, the server determines a corresponding preset sample size based on the number relation of the classification index elements in the first classification index set and the second classification index set.
In this embodiment of the present application, determining a corresponding predetermined sample size based on a number relationship of the classification index elements in the first classification index set and the second classification index set specifically includes:
The server takes the number of the classifying index elements in the first classifying index set as a first number. And taking the number of the classified index elements in the second classified index set as a second number. And determining the predetermined sample amount according to the product value of the preset sample selected value, the first quantity and the second quantity.
The predetermined sample size is the selected sample size when generating the transaction progression curve. For example, the server selects the top 200 prefabricated dishes of the base-period sales ranks in the panning, the jingdong, the trembling and the quick-hand platforms, integrates the product division standard of each platform, 8 first attribute indexes and 4 second attribute indexes, and selects 160 prefabricated dishes with the preset sample size (8×4×5=160) with the top ranking as sample bodies, wherein each different second attribute index has a sample selection value of 5 samples. i represents a sample number, where i=1, 2,3 … ….
S103, the server determines a plurality of preset service data groups corresponding to the preset sample amount based on the real-time service data of the prefabricated vegetable transaction from the data acquisition module.
The real-time traffic data includes traffic data of nodes of each predetermined period of the prefabricated vegetable transaction.
In an embodiment of the present application, determining a plurality of predetermined service data sets corresponding to a predetermined sample size based on real-time service data of a prefabricated dish transaction from a data acquisition module specifically includes:
The server crawls real-time service data corresponding to a plurality of prefabricated vegetable transactions from the Internet platform through the data acquisition module. The prefabricated vegetable transaction at least comprises: and (5) counting selling prices and selling quantity. And determining attribute index binary groups of each real-time service data. Wherein the attribute index binary group comprises: a first attribute index and a second attribute index. And matching each attribute index binary group with the first classification index set and the second classification index set so as to determine a plurality of preset business data sets corresponding to the preset sample size according to the matching result. A plurality of predetermined time period nodes are different.
At present, according to the difference of prefabricated dish sales mode, mainly include modes such as B2B, B2C, C C, the main stream electric business sales platform is selected to the platform, includes: naughty, jingdong, tremble and quick. The data acquisition module can climb data to obtain real-time service data such as price, sales and the like of each prefabricated dish. The crawling mode can be used for crawling according to the nodes in a preset time period, for example, 8 points per day are selected as the nodes in the preset time period for crawling real-time service data for one day. The attribute index binary group is the vegetable series and the eating mode of the prefabricated vegetable.
Wherein, each attribute index binary group is matched with the first classification index set and the second classification index set, so as to determine a plurality of preset business data sets corresponding to preset sample size according to the matching result, and the method specifically comprises the following steps:
the server matches a first attribute index in the attribute index doublet with a first set of classification indexes. And under the condition that the matching is successful, determining a multi-index business data list corresponding to the first attribute index which is successfully matched. Wherein a column of the multi-index service data list corresponds to the second attribute index. The number of lines of the multi-index service data list is a preset sample selection value. And matching the second attribute indexes in the attribute index binary group successfully matched with the first classification index set with the second classification index set.
And under the condition that the matching with the second classification index set is successful, determining whether the element number of the second attribute index column corresponding to the multi-index service data list reaches a sample selected value.
And when the element number of the corresponding second attribute index column of the multi-index service data list does not reach the sample selection value, adding the corresponding real-time service data to the corresponding position of the multi-index service data list until the element number of each column of each multi-index service data list reaches the sample selection value, so that each multi-index service data list forms a preset service data group corresponding to the preset sample number.
That is, the server may match the cuisine with the first classification index set, where the first classification index set includes the 8 cuisines, and among the 8 cuisines in the attribute index binary group, the server loads from the multi-index service data list stored in the memory or the database in advance, and queries to which second attribute index the eating manner of the attribute index binary group belongs. The server determines whether the number of column elements corresponding to the matched second attribute index reaches a sample selection value of 5 in the multi-index service data list. I.e. the server will determine if the number of column elements in the list is full and if not the server will add the real time service data to the corresponding column position. In each multi-index service data list, a predetermined service data set is generated in the case that no empty element exists in each cell.
In addition, the server can acquire real-time service data of the nodes in a plurality of preset time periods and acquire a plurality of preset service data groups.
S104, the server determines transaction progress curves corresponding to the nodes of the preset time periods of the prefabricated vegetable transaction according to the preset service data sets and the preset transaction progress calculation model, so that the transaction progress curves are sent to the user terminal in real time, and the whole transaction progress information of the prefabricated vegetable transaction is displayed.
The user terminal can be a mobile phone, a computer and other devices of the user, the application is not particularly limited to the above, and the transaction progress curve can be sent to the user terminal in the form of an image, so that the user can intuitively see the progress of the prepared dish transaction.
In this embodiment of the present application, determining, according to each predetermined service data set and a predetermined transaction progress calculation model, a transaction progress curve corresponding to each predetermined period node of a prefabricated dish transaction, includes:
first, the server determines a calculation base period of the transaction progress curve according to the predetermined period node.
The server may use one of the predetermined period nodes having the earliest time as the calculation base period, for example, 1 month and 1 day, 1 month and 2 days, 1 month and 3 days, and 1 month and 1 day as the calculation base period.
Next, the server determines a predetermined set of business data corresponding to the calculated base period.
The server then inputs the predetermined set of business data into a business progress calculation model, determining a base curve value for the calculated base.
The calculation formula of the base curve value is as follows:
wherein I is 0 In order to calculate the value of the basal period curve of the basal period, the basal period price index calculated by the prefabricated vegetable affairs such as the price index can be set to be 100, so that when the price index curve (affair progress curve) is constructed by utilizing the Law index in the follow-up process, the data objectivity is high, and a user can easily see price fluctuation, such as a government agency, so that macroscopic regulation and control of the market are performed.
The transaction progress calculation model comprises the following curve function value calculation formulas of transaction progress curves:
wherein I is p A first transaction index of the prefabricated dish representing a first reporting period corresponding to a node of a predetermined period, i is a sample number of a predetermined sample size, p 1i For the service data corresponding to the first transaction of the prefabricated dish in the first reporting period, q 0i To calculate the business data corresponding to the second business of the preliminary dish of the basic period, p 0i And calculating service data corresponding to the first transaction of the prefabricated dish in the basic period. The first transaction of the prepared dish, such as price, and the second transaction of the prepared dish, such as sales of the prepared dish.
The server then generates a transaction progress curve based on the base curve values and the transaction progress calculation model, each of the predetermined sets of business data.
That is, the function value (price index) of the real-time business data corresponding to each node of the predetermined period can be calculated by the curve function value calculation formula, and the server can establish the transaction progress curve according to the function value and the coordinate system established in advance.
In addition, after generating the transaction progress curve, the embodiment of the application further includes:
and the server inputs each preset service data group into a weighted average calculation formula of the transaction progress calculation model, and determines the comprehensive average data value of the real-time service data corresponding to the first transaction of the prefabricated dish of each preset time period node. And generating a transaction progress node curve according to each comprehensive average data value so as to show the transaction node progress information to a user.
The weighted average calculation formula is as follows:
wherein, p average is the comprehensive average value of the real-time business data of a node in a certain preset period, p i Service data of first transaction of prefabricated dish, q i And the service data of the second transaction of the prefabricated dish corresponding to the first transaction of the prefabricated dish. The first transaction of the prepared dish is a price, and the selling quantity (the second transaction of the prepared dish) corresponding to the price.
The transaction progress node curve is a curve of a comprehensive average value corresponding to each node of the preset time period. It will be appreciated that the transaction progress node curve is an average price curve for each of the predetermined time period nodes.
According to the scheme, a more convenient way can be provided for the display of the progress (price index statistics) of the prefabricated vegetable transaction, the overall price trend of the prefabricated vegetable product is intuitively displayed on a macroscopic level, a gripper is provided for the macroscopic regulation market of the government, and a basis is provided for policy establishment; the price curve can reflect the price and the fluctuation range of the product on a microscopic level, and market guidance in purchasing, selling and the like can be provided for enterprises and farmers. Has good use and popularization value. The method and the system can enable the user in the prefabricated vegetable industry to obtain the related process or the whole data relationship in the prefabricated vegetable industry, smoothly complete related transactions (such as purchasing and selling) of the prefabricated vegetable, and improve the user experience and transaction efficiency.
Fig. 2 is a schematic structural diagram of a data processing device for performing a transaction progress presentation of a prepared dish according to an embodiment of the present application, where, as shown in fig. 2, the device includes:
at least one processor. And a memory communicatively coupled to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executable by the at least one processor to enable the at least one processor to:
and acquiring a first classification index set and a second classification index set corresponding to the prefabricated vegetable transaction. Wherein the first set of classification indicators corresponds to a first attribute indicator of the prepared vegetable transaction. The second set of classification indicators corresponds to a second attribute indicator of the prepared vegetable transaction. The first attribute index includes a second attribute index. Based on the number relation of the classification index elements in the first classification index set and the second classification index set, corresponding preset sample size is determined. Based on the real-time business data of the prefabricated dish transaction from the data acquisition module, a plurality of predetermined business data sets corresponding to the predetermined sample size are determined. The real-time traffic data includes traffic data of nodes of each predetermined period of the prefabricated vegetable transaction. And determining a transaction progress curve corresponding to each preset time period node of the prefabricated vegetable transaction according to each preset service data set and a preset transaction progress calculation model, so as to send the transaction progress curve to the user terminal in real time, and displaying the whole transaction progress information of the prefabricated vegetable transaction.
The embodiment of the application provides a data processing nonvolatile computer storage medium for performing dish transaction progress display, which stores computer executable instructions, wherein the computer executable instructions are configured to:
and acquiring a first classification index set and a second classification index set corresponding to the prefabricated vegetable transaction. Wherein the first set of classification indicators corresponds to a first attribute indicator of the prepared vegetable transaction. The second set of classification indicators corresponds to a second attribute indicator of the prepared vegetable transaction. The first attribute index includes a second attribute index. Based on the number relation of the classification index elements in the first classification index set and the second classification index set, corresponding preset sample size is determined. Based on the real-time business data of the prefabricated dish transaction from the data acquisition module, a plurality of predetermined business data sets corresponding to the predetermined sample size are determined. The real-time traffic data includes traffic data of nodes of each predetermined period of the prefabricated vegetable transaction. And determining a transaction progress curve corresponding to each preset time period node of the prefabricated vegetable transaction according to each preset service data set and a preset transaction progress calculation model, so as to send the transaction progress curve to the user terminal in real time, and displaying the whole transaction progress information of the prefabricated vegetable transaction.
In the 90 s of the 20 th century, improvements to one technology could clearly be distinguished as improvements in hardware (e.g., improvements to circuit structures such as diodes, transistors, switches, etc.) or software (improvements to the process flow). However, with the development of technology, many improvements of the current method flows can be regarded as direct improvements of hardware circuit structures. Designers almost always obtain corresponding hardware circuit structures by programming improved method flows into hardware circuits. Therefore, an improvement of a method flow cannot be said to be realized by a hardware entity module. For example, a programmable logic device (Programmable Logic Device, PLD) (e.g., field programmable gate array (Field Programmable Gate Array, FPGA)) is an integrated circuit whose logic function is determined by the programming of the device by a user. A designer programs to "integrate" a digital system onto a PLD without requiring the chip manufacturer to design and fabricate application-specific integrated circuit chips. Moreover, nowadays, instead of manually manufacturing integrated circuit chips, such programming is mostly implemented by using "logic compiler" software, which is similar to the software compiler used in program development and writing, and the original code before the compiling is also written in a specific programming language, which is called hardware description language (Hardware Description Language, HDL), but not just one of the hdds, but a plurality of kinds, such as ABEL (Advanced Boolean Expression Language), AHDL (Altera Hardware Description Language), confluence, CUPL (Cornell University Programming Language), HDCal, JHDL (Java Hardware Description Language), lava, lola, myHDL, PALASM, RHDL (Ruby Hardware Description Language), etc., VHDL (Very-High-Speed Integrated Circuit Hardware Description Language) and Verilog are currently most commonly used. It will also be apparent to those skilled in the art that a hardware circuit implementing the logic method flow can be readily obtained by merely slightly programming the method flow into an integrated circuit using several of the hardware description languages described above.
The controller may be implemented in any suitable manner, for example, the controller may take the form of, for example, a microprocessor or processor and a computer readable medium storing computer readable program code (e.g., software or firmware) executable by the (micro) processor, logic gates, switches, application specific integrated circuits (Application Specific Integrated Circuit, ASIC), programmable logic controllers, and embedded microcontrollers, examples of which include, but are not limited to, the following microcontrollers: ARC 625D, atmel AT91SAM, microchip PIC18F26K20, and Silicone Labs C8051F320, the memory controller may also be implemented as part of the control logic of the memory. Those skilled in the art will also appreciate that, in addition to implementing the controller in a pure computer readable program code, it is well possible to implement the same functionality by logically programming the method steps such that the controller is in the form of logic gates, switches, application specific integrated circuits, programmable logic controllers, embedded microcontrollers, etc. Such a controller may thus be regarded as a kind of hardware component, and means for performing various functions included therein may also be regarded as structures within the hardware component. Or even means for achieving the various functions may be regarded as either software modules implementing the methods or structures within hardware components.
The system, apparatus, module or unit set forth in the above embodiments may be implemented in particular by a computer chip or entity, or by a product having a certain function. One typical implementation is a computer. In particular, the computer may be, for example, a personal computer, a laptop computer, a cellular telephone, a camera phone, a smart phone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.
For convenience of description, the above devices are described as being functionally divided into various units, respectively. Of course, the functions of each element may be implemented in one or more software and/or hardware elements when implemented in the present specification.
It will be appreciated by those skilled in the art that the present description may be provided as a method, system, or computer program product. Accordingly, the present specification embodiments may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present description embodiments may take the form of a computer program product on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) having computer-usable program code embodied therein.
The present description is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the specification. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations 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 one typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.
The memory may include volatile memory in a computer-readable medium, random Access Memory (RAM) and/or nonvolatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). Memory is an example of computer-readable media.
Computer readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement 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 storage media for a computer 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 Discs (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. Computer-readable media, as defined herein, does not include transitory computer-readable media (transmission media), such as modulated data signals and carrier waves.
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 phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article or apparatus that comprises the element.
The description may be described in the general context of computer-executable instructions, such as program modules, being executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. The specification may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote computer storage media including memory storage devices.
In this specification, each embodiment is described in a progressive manner, and identical and similar parts of each embodiment are all referred to each other, and each embodiment mainly describes differences from other embodiments. In particular, for apparatus, devices, non-volatile computer storage medium embodiments, the description is relatively simple, as it is substantially similar to method embodiments, with reference to the section of the method embodiments being relevant.
The foregoing describes specific embodiments of the present disclosure. Other embodiments are within the scope of the following claims. In some cases, the actions or steps recited in the claims can be performed in a different order than in the embodiments and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.
The foregoing is merely one or more embodiments of the present description and is not intended to limit the present description. Various modifications and alterations to one or more embodiments of this description will be apparent to those skilled in the art. Any modification, equivalent replacement, improvement, or the like, which is within the spirit and principles of one or more embodiments of the present description, is intended to be included within the scope of the claims of the present description.
Claims (10)
1. A data processing method for a pre-dish transaction progress presentation, the method comprising:
acquiring a first classification index set and a second classification index set corresponding to the prefabricated vegetable transaction; wherein the first set of classification indicators corresponds to a first attribute indicator of the prepared vegetable transaction; the second classification index set corresponds to a second attribute index of the prepared vegetable transaction; the first attribute index comprises the second attribute index;
determining a corresponding predetermined sample size based on the number relationship of the classification index elements in the first classification index set and the second classification index set;
determining a plurality of predetermined service data sets corresponding to the predetermined sample size based on real-time service data of the prepared vegetable transaction from a data acquisition module; the real-time service data comprise service data of nodes in each preset period of time of the prefabricated vegetable transaction;
and determining a transaction progress curve corresponding to each preset time period node of the prefabricated vegetable transaction according to each preset service data set and a preset transaction progress calculation model, so as to send the transaction progress curve to a user terminal in real time, and displaying the whole transaction progress information of the prefabricated vegetable transaction.
2. The method of claim 1, wherein obtaining the first classification index set and the second classification index set corresponding to the prefabricated vegetable transaction specifically comprises:
determining the category of the prefabricated dishes corresponding to the prefabricated dish transaction, wherein the category of the prefabricated dishes is the classification index element of the first classification index set; the prepared dish category at least comprises: sichuan pickle, rouge, huaiyang pickle, yue pickle, zhejiang pickle, mincai pickle, hunan pickle and Hui pickle; and
determining the classification of the eating modes corresponding to the prefabricated vegetable categories according to the prefabricated vegetable categories and a preset eating mode list, and taking the classification as the classification index elements of the second classification index set; wherein, the edible way classification at least comprises: instant preparation, instant cooking, instant heating and instant eating.
3. The method according to claim 1, wherein determining the respective predetermined sample size based on the number relation of the classification index elements in the first and second sets of classification indexes, in particular comprises:
taking the number of the classification index elements in the first classification index set as a first number;
taking the number of the classified index elements in the second classified index set as a second number;
And determining the preset sample quantity according to a preset sample selected value and a product value of the first quantity and the second quantity.
4. The method according to claim 1, wherein determining a plurality of predetermined sets of business data corresponding to the predetermined sample size based on real-time business data of the prepared dish transaction from a data acquisition module, in particular comprises:
the data acquisition module is used for crawling real-time service data corresponding to a plurality of prefabricated vegetable transactions from an Internet platform; the prefabricated vegetable transaction at least comprises: counting selling price and selling quantity;
determining attribute index binary groups of each real-time service data; wherein the attribute index doublet comprises: a first attribute index and the second attribute index;
matching each attribute index binary group with the first classification index set and the second classification index set so as to determine a plurality of preset business data groups corresponding to the preset sample size according to a matching result; the plurality of predetermined time period nodes are different.
5. The method of claim 4, wherein matching each of the attribute index tuples with the first set of classification indexes and the second set of classification indexes to determine a plurality of predetermined traffic data sets corresponding to the predetermined sample size based on a result of the matching, specifically comprising:
Matching a first attribute index in the attribute index binary group with the first classification index set;
under the condition of successful matching, determining a multi-index business data list corresponding to the first attribute index which is successfully matched; wherein a column of the multi-index business data list corresponds to the second attribute index; the number of lines of the multi-element index service data list is a preset sample selection value; and
matching a second attribute index in the attribute index binary group successfully matched with the first classification index set with the second classification index set;
under the condition that the matching with the second classification index set is successful, determining whether the element number of a second attribute index column corresponding to the multi-index service data list reaches the sample selected value;
if not, adding the corresponding real-time service data to the corresponding position of the multi-index service data list until the number of the column elements of each multi-index service data list reaches the sample selected value, so as to form each multi-index service data list into the preset service data group corresponding to the preset sample quantity.
6. The method according to claim 1, wherein determining a transaction progress curve corresponding to each of the predetermined time period nodes of the prefabricated dish transaction according to each of the predetermined service data sets and a predetermined transaction progress calculation model, specifically comprises:
determining a calculation base period of the transaction progress curve according to the predetermined period node;
determining the preset service data group corresponding to the calculation base period;
inputting the preset business data set into the transaction progress calculation model, and determining a base period curve value of the calculated base period;
and generating the transaction progress curve based on the base period curve value, the transaction progress calculation model and each preset business data set.
7. The method of claim 6, wherein the transaction progress computation model comprises a curve function value computation formula for a transaction progress curve:
wherein I is p A first transaction index of the prefabricated dish representing a first reporting period corresponding to the node of the preset time period, i is a sample number of a preset sample quantity, and p 1i For the service data corresponding to the first transaction of the prefabricated dish in the first reporting period, q 0i For the business data corresponding to the second transaction of the prefabricated dish of the calculated basic period, p 0i And calculating service data corresponding to the first transaction of the prefabricated dish in the basic period.
8. The method of claim 6, wherein after generating the transaction progression curve, the method further comprises:
inputting each preset service data group into a weighted average calculation formula of the transaction progress calculation model, and determining a comprehensive average data value of real-time service data corresponding to the first transaction of the preset time period node;
and generating a transaction progress node curve according to each comprehensive average data value so as to show the transaction node progress information to a user.
9. A data processing apparatus for use in a pre-prepared dish transaction progression presentation, the apparatus comprising:
at least one processor; the method comprises the steps of,
a memory communicatively coupled to the at least one processor; wherein,,
the memory stores instructions executable by the at least one processor to enable the at least one processor to:
acquiring a first classification index set and a second classification index set corresponding to the prefabricated vegetable transaction; wherein the first set of classification indicators corresponds to a first attribute indicator of the prepared vegetable transaction; the second classification index set corresponds to a second attribute index of the prepared vegetable transaction; the first attribute index comprises the second attribute index;
Determining a corresponding predetermined sample size based on the number relationship of the classification index elements in the first classification index set and the second classification index set;
determining a plurality of predetermined service data sets corresponding to the predetermined sample size based on real-time service data of the prepared vegetable transaction from a data acquisition module; the real-time service data comprise service data of nodes in each preset period of time of the prefabricated vegetable transaction;
and determining a transaction progress curve corresponding to each preset time period node of the prefabricated vegetable transaction according to each preset service data set and a preset transaction progress calculation model, so as to send the transaction progress curve to a user terminal in real time, and displaying the whole transaction progress information of the prefabricated vegetable transaction.
10. A data processing non-volatile computer storage medium for use in a pre-dish transaction progression presentation, storing computer executable instructions, the computer executable instructions being configured to:
acquiring a first classification index set and a second classification index set corresponding to the prefabricated vegetable transaction; wherein the first set of classification indicators corresponds to a first attribute indicator of the prepared vegetable transaction; the second classification index set corresponds to a second attribute index of the prepared vegetable transaction; the first attribute index comprises the second attribute index;
Determining a corresponding predetermined sample size based on the number relationship of the classification index elements in the first classification index set and the second classification index set;
determining a plurality of predetermined service data sets corresponding to the predetermined sample size based on real-time service data of the prepared vegetable transaction from a data acquisition module; the real-time service data comprise service data of nodes in each preset period of time of the prefabricated vegetable transaction;
and determining a transaction progress curve corresponding to each preset time period node of the prefabricated vegetable transaction according to each preset service data set and a preset transaction progress calculation model, so as to send the transaction progress curve to a user terminal in real time, and displaying the whole transaction progress information of the prefabricated vegetable transaction.
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| CN117149820B (en) * | 2023-09-25 | 2024-05-14 | 湖南长银五八消费金融股份有限公司 | Borrowing operation detection method, device, equipment and storage medium |
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