CN116703453A - Demand data processing system and demand data processing method - Google Patents
Demand data processing system and demand data processing method Download PDFInfo
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Abstract
The invention provides a demand data processing system and a demand data processing method. The demand data processing system includes a data calculation engine and a data adjustment engine. The data computing engine is coupled to the data receiving engine. The data receiving engine receives a plurality of initial data. The data receiving engine performs splicing operation on the plurality of initial data, and then splices the data in different date intervals into a plurality of spliced data. The data calculation engine performs a data offset operation on the plurality of spliced data according to the demand parameters to generate a plurality of demand data. The data calculation engine outputs a plurality of demand data to an external business process system.
Description
Technical Field
The present invention relates to data processing technology, and more particularly, to a demand data processing system and a demand data processing method.
Background
Generally, when a matching enterprise receives a customer's demand, the existing practice will manually perform data differences of different versions and the required product quantity. However, the number data provided by the clients has not only version differences but also time interval differences, so that a lot of time is consumed for manual processing, and there is a problem of high error rate. Meanwhile, after the number required by the customer is calculated, the number must be calculated with the current number of a plurality of stock and the number of safety stock, and the situation of data calculation errors or missing can also happen. Therefore, in order to avoid that the supply of goods cannot meet the requirement of users, the supporting enterprises usually produce more parts, which results in high stock pressure and high production funds and space occupation cost.
Disclosure of Invention
The invention aims at a demand data processing system and a demand data processing method, which can accurately and automatically generate demand data and stock data.
According to an embodiment of the present invention, a demand data processing system of the present invention includes a data receiving engine and a data computing engine. The data computing engine is coupled to the data receiving engine. The data receiving engine receives a plurality of initial data. The data receiving engine performs splicing operation on the plurality of initial data, and then splices the data in different date intervals into a plurality of spliced data. The data calculation engine performs a data offset operation on the plurality of spliced data according to the demand parameters to generate a plurality of demand data. The data calculation engine outputs a plurality of demand data to an external business process system.
According to an embodiment of the present invention, the demand data processing method of the present invention includes the steps of: receiving, by a data receiving engine, a plurality of initial data; performing splicing operation on the plurality of initial data through the data receiving engine, and splicing the data in different date intervals into a plurality of spliced data; executing data offset operation on the plurality of spliced data according to the demand parameters by a data calculation engine so as to generate a plurality of demand data; and outputting the plurality of demand data to an external business process system through a data calculation engine.
Based on the above, the demand data processing system and the demand data processing method of the present invention can accurately generate data meeting the demand of the grabbing clients, so as to effectively and automatically initiate to the business process system.
In order to make the above features and advantages of the present invention more comprehensible, embodiments accompanied with figures are described in detail below.
Drawings
FIG. 1 is a schematic diagram of a demand data processing system in accordance with one embodiment of the present invention;
FIG. 2 is a flow chart of a method of processing demand data according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of a plurality of engines implemented by a demand data processing system in accordance with one embodiment of the present invention;
FIG. 4 is a schematic diagram of an embodiment of a demand data processing system;
FIG. 5 is a schematic diagram of a stitching operation according to an exemplary embodiment of the present invention;
FIG. 6 is a schematic diagram of an interval reassembly operation according to an example embodiment of the present invention;
FIG. 7 is a schematic diagram of a data cancellation operation according to an example embodiment of the invention.
Description of the reference numerals
100: a demand data processing system;
101: initial data;
102: demand data;
110: a data receiving engine;
120: a data calculation engine;
130: a data adjustment engine;
131: a predictive plan adjustment module;
132: a to-be-delivered plan adjustment module;
133: a delivery plan adjustment module;
300: an enterprise resource planning system;
510: n th edition of original data;
520: n+1st edition of raw data;
530: splicing data;
610: predicting splicing data;
620: splicing data to be checked;
630: reorganizing data;
710: predicting the reorganized data;
720: to-be-restocked data;
730: demand data;
s210 to S240, S401 to S408: and (3) step (c).
Detailed Description
Reference will now be made in detail to the exemplary embodiments of the present invention, examples of which are illustrated in the accompanying drawings. Wherever possible, the same reference numbers are used in the drawings and the description to refer to the same or like parts.
FIG. 1 is a schematic diagram of a demand data processing system in accordance with one embodiment of the present invention. Referring to FIG. 1, a demand data processing system 100 includes a data receiving engine 110 and a data computing engine 120. The data computing engine 120 is coupled to the data receiving engine 110. The demand data processing system 100 may also include a communication interface or data transfer interface with the actual circuit components to enable the data receiving engine 110, the data computing engine 120, and the data conditioning engine 130 to communicate with or transfer data to external business process systems or enterprise resource planning (Enterprise Resource Planning, ERP) systems. In this embodiment, the requirement data processing system 100 and the business process system may be implemented in a cloud server or a private server within an enterprise, respectively.
FIG. 2 is a flow chart of a method for processing demand data according to an embodiment of the present invention. Reference is made to fig. 1 and 2. The data calculation engine 120 may also detect time window information cached in memory and/or databases and data received and generated by the data reception engine 110. The demand data processing system 100 executes steps S210 to S240 as follows. In step S210, the data reception engine 110 receives a plurality of initial data 101. Specifically, the data receiving engine 110 may receive a plurality of initial data 101 (i.e., raw data) through a data transmission interface. In one embodiment, the plurality of initial data 101 is customer provided product/part demand data. Specifically, the plurality of initial data 101 includes a plurality of versions of recent product demand data and a plurality of versions of long-term product demand data.
In one embodiment, the data processing system 100 may receive the initial data 101 through a hypertext transfer protocol by implementing a caching function through a cloud service. Alternatively, the initial data 101 may be stored directly in the memory and/or database of the demand data processing system 100, even in the form of file data, and the data interaction may be performed through inter-proximity communication or an operating file interface in the operating system.
In step S220, the data receiving engine 110 performs a splicing operation on the plurality of initial data 101, thereby splicing data of different date intervals into a plurality of spliced data. Specifically, the data receiving engine 110 first selects the latest version of the initial data 101 from the plurality of different versions of the initial data 101, and splices the latest version of the initial data with other versions of the initial data based on the latest version of the initial data, thereby generating data covering the widest date interval.
For example, the date range of the latest version of the initial data 101 is 3 months 5 days to 5 months 5 days, and the date range of the other version of the initial data 101 is 2 months 5 days to 4 months 5 days, then the data receiving engine 110 splices the data of the other version of the initial data 101 from 2 months 5 days to 3 months 4 days to the latest version of the initial data 101, and forms spliced data of which the data is latest and the date range is 2 months 5 days to 5 months 5 days. In other words, the data receiving engine 110 concatenates the most recent data content (e.g., value, number) and the most widely-date concatenated data from the plurality of initial data. In one embodiment, the data receiving engine 110 will reject data that has expired, i.e., the splice data includes only data after the current date. It should be noted that the spliced data generated by the data receiving engine 110 belongs to rolling spliced data. That is, each time a new version of the initial data 101 is received, the data receiving engine 110 adjusts the plurality of spliced data accordingly.
In step S230, the data calculation engine 120 performs a data cancellation operation on the plurality of spliced data according to the requirement parameters to generate a plurality of requirement data 102. Specifically, the data calculation engine 120 integrates the plurality of spliced data into the plurality of demand data 102 according to the demand parameter (i.e., the demand preset value). In one embodiment, the demand parameter relates to how far from the current date (i.e., the days of the gap) the data demand date is. For example, the demand data 102 uses the quantity in the to-be-spliced data when the date in the spliced data is less than the threshold number of days from the current date. When the date and current date difference days in the spliced data are between two thresholds, the required data 102 adopts the to-be-spliced data and the data indicating that the numerical value between the spliced data is large. When the date in the splice data is greater than another threshold by a number of days, the demand data 102 indicates that the quantity in the splice data or the date data is not part of the manufacture (i.e., is not stock).
In step S240, the data calculation engine 120 outputs the plurality of demand data 102 to the external business process system. In this embodiment, the demand data 102 is the stock parameters to be outputted to the external business process system. In another embodiment, before outputting the demand data 102, the data calculation engine 120 adjusts the values in the demand data according to the stock quantity, and then initiates the adjusted demand data 102 to the external business process system. Thus, the demand data processing system 100 of the present embodiment can automatically calculate demand data 102 conforming to the data having the latest version and the widest date range from the initial data 101 of the plurality of versions and the plurality of parts. In addition, according to the setting of the demand parameters, the demand data processing system 100 accurately integrates the forecast data (i.e. the demand data 102) and the demand data (i.e. the long-term demand forecast data) into the demand data 102 of each product (e.g. the parts and the car parts) for the customer, thereby improving the forecast accuracy of the data, reducing the labor cost and the stock pressure and the cost.
FIG. 3 is a schematic diagram of a plurality of engines executed by a demand data processing system in accordance with one embodiment of the present invention. FIG. 4 is a schematic diagram illustrating the execution of a demand data processing system according to an embodiment of the present invention. Referring to fig. 3 and 4, the demand data processing system 100 executes steps S401 to S408 as follows. In step S401, the data reception engine 110 generates a data import template, and the data reception engine 110 receives a plurality of initial data 101 according to the data import template (step S402). In one embodiment, the data receiving engine 110 generates the data import template according to a predetermined parameter or a data template parameter inputted by a user. Specifically, the data receiving engine 110 receives a plurality of data and aggregates (gathers) the data through a plurality of data import templates to generate a plurality of initial data 101. It should be noted that, the data collection refers to classification and collection of data according to a preset category (such as products, work items, departments, clients, and workshops).
Fig. 5 is a schematic diagram of a splicing operation according to an exemplary embodiment of the present invention. Please refer to fig. 4 and fig. 5. In step S403, the step of the data receiving engine performing a splicing operation on the plurality of initial data, and further splicing the data of different date intervals into a plurality of spliced data 530 further includes: the data receiving engine performs a splicing operation on a plurality of initial data having the same data category according to the data version and the date section to generate a plurality of spliced data 530. In one embodiment, the data categories include a category of pickup data and a category of forecast data, that is, the initial data includes the pickup data and the forecast data. And, splice data 530 includes order splice data and forecast splice data. The splicing operation splices date intervals of other versions of data based on the date interval of the latest version of data.
Specifically, splicing data is predicted, and splicing operations are performed on the splicing data to be shipped, respectively. For example, of the n-th version of original data 510 (i.e., initial data) and the n+1th version of original data 520, the latest version of data is the n+1th version of original data 520. Therefore, the data receiving engine 110 takes the n+1st edition of original data 520 as a basis, and simultaneously splices data of a date zone which is not covered by the n+1st edition of original data 520 from other new edition of data into the n+1st edition of original data 520. As shown in fig. 5, the n+1th edition of original data 520 is the latest edition of data, so the data receiving engine 110 splices the data of the date zone {01-02} in the n-th edition of original data 510 into the n+1th edition of original data 520 to form spliced data 530. In one embodiment, whenever the data receiving engine 110 receives new initial data (e.g., updated version of data), the data receiving engine 110 re-generates the splice data 530 based on the updated version of data, in other words, the splice data 530 is rolling splice data.
FIG. 6 is a schematic diagram of an interval reassembly operation according to an example embodiment of the present invention. Please refer to fig. 4 and fig. 6. Before the step of performing the data cancellation operation on the plurality of spliced data by the data calculation engine according to the requirement parameter in step S404 and between the steps S403, the data calculation engine further includes a step S404 and a step S405. The data calculation engine 120 builds an interval reassembly model according to the reassembly settings (step S404). In one embodiment, the data calculation engine 120 builds an interval reassembly model according to the data reassembly rules, the data reassembly parameters, and the interval parameters. The data calculation engine 120 performs an interval reassembly operation on the plurality of spliced data through the interval reassembly model to generate reassembled data 630 (step S405). In another embodiment, the data calculation engine 120 may reassemble the spliced data into the reassembled data 630 according to the reassembly rules.
In one embodiment, the data calculation engine 120 performs an interval reorganization operation, so as to cut the time interval (i.e., the day interval, for example, at the day interval, at Zhou Jiange) of the spliced data corresponding to the same part in the plurality of spliced data into the minimum time units (e.g., day, week, month) of the data of the same part. As shown in FIG. 6, the time intervals of the predicted splice data 610 are { 01-02-01-08,01-09-01-15,01-16-01-22,01-23-01-29,01-30-02-05 }, respectively. The time intervals for the to-be-spliced data 620 are {01-02,01-03,01-04,01-05,01-06,01-07,01-08,01-09,01-10,01-11}, respectively. Since the time interval of the pickup signature data 620 is smaller than the time interval of the predicted signature data 610, the data calculation engine 120 uses the minimum time unit (i.e. the time interval (in days) of the pickup signature data 620) for the time range { 01-02-01-11 } where the predicted signature data 610 overlaps with the pickup signature data 620, thereby forming the reorganized data 630 (i.e. the pickup reorganized data). In this embodiment, the reorganization data 630 includes the to-be-shipped reorganization data and the forecast reorganization data.
FIG. 7 is a schematic diagram of a data cancellation operation according to an example embodiment of the invention. In one embodiment, the demand parameters include a first preset parameter and a second preset parameter. Referring to fig. 4 and 7, in step S406, the data computing engine 120 performs a data cancellation operation on the reconfiguration data according to the requirement parameters, so as to generate a plurality of requirement data 730. Specifically, the data calculation engine 120 determines the time parameters in the plurality of reorganized data, and fills the to-be-restocked data 720 or the forecast reorganized data 710 according to the time parameters of each cell in the reorganized data. In this embodiment, when the number of days (days of separation) between the time parameter and the current date is smaller than the first preset parameter, the data calculation engine 120 correspondingly uses the corresponding to-be-purchased reconfiguration data 720 as the demand parameter (i.e. the number/value/field of the corresponding time parameter).
It should be noted that the reorganization data in the data offset operation is the to-be-shipped reorganization data 720 and the forecast reorganization data 710 corresponding to the same product. In other words, the to-be-shipped reorganization data 720 and the predictive reorganization data 710 corresponding to the same product are judged simultaneously. Then, when the number of days of difference between the time parameter and the current date in the reorganized data is between the first preset parameter and the second preset parameter, the data calculation engine 120 selects a large-value parameter (e.g. data) from the corresponding to-be-restocked data 720 and the forecast reorganized data 710 as the demand data 730. In this embodiment, the second preset parameter is greater than the first preset parameter.
In one embodiment, when the number of days of the difference between the time parameter and the current date is greater than the second preset parameter, the data calculation engine 120 uses the corresponding predicted reorganization data 710 as the corresponding parameter in the demand data 730. In another embodiment, the data calculation engine 120 eliminates the demand data 730 corresponding to the time parameter when the number of days of the difference between the time parameter and the current date is greater than the second preset parameter. In other words, when the removed demand data 730 is sent to an external enterprise resource planning system, no product (i.e., no stock) is generated on behalf of the corresponding time parameter.
As shown in FIG. 7, predictive reorganization data (i.e., predictive reorganization data group) 710 includes predictive reorganization data for product A and product B. The to-be-restocked data (i.e., to-be-restocked data set) 720 includes to-be-restocked data of the product a and the product B. In this embodiment, the first preset parameter is a demand time grid, and the second preset parameter is a planning time grid. When the data calculation engine 120 performs the data offset operation on the data having the time parameter of "01-02 to 01-08", the data calculation engine 120 uses the number of corresponding time parameters "01-02 to 01-08" in the to-be-restocked data (i.e., to-be-restocked data group) 720 as the parameter having the corresponding time parameter of "01-02 to 01-08" in the demand data 730 in response to the number of days of the difference of "01-02 to 01-08" from the current date (e.g., 01-01) being smaller than the demand time grid (i.e., the first preset parameter).
Next, when the data calculation engine 120 performs the data offset operation on the data having the time parameter of "01-09" to "01-22", since the number of days of difference between "01-09" to "01-22" and the current date is between the demand time grid and the planning time grid, the data calculation engine 120 selects a large number of pieces of the data as the reorganized data (for example, 450, 600, 480, 500 in fig. 7) from the corresponding to-be-stocked reorganized data 720 and the predictive reorganized data 710. In another case, when the time parameter in the reorganization data (i.e., the forecast reorganization data set 710 and the to-be-restocked data set 720) is greater than the schedule grid, the data calculation engine 120 selects the reorganization data (i.e., the restocking) forecast the reorganization data 710 or the reorganization data not generating the time parameter.
As shown in fig. 4, in step S407, the data calculation engine 120 generates a demand association model from the inventory association data. The inventory related data relates to the already produced quantity of the product. In this embodiment, the inventory related data includes the inventory level of the customer corresponding to each product, the customer corresponding to each warehouse, the warehouse (e.g., three-party warehouse, exotic warehouse) corresponding to each product, and the secure inventory level. In other words, the number of parts/products and the safe stock amount to be stored in the warehouse (e.g., the three-party warehouse and the outside-market warehouse) by the data calculation engine 120 are also considered in the calculation of the actual demand data 730 (i.e., the stock data). So configured, the data calculation engine 120 may generate a demand correlation model from the inventory correlation data.
Specifically, the demand association model establishes association between the product, the customer and the warehouse to analyze the warehouse corresponding to each demand data 730, thereby obtaining the existing stock quantity and the security stock quantity. So configured, the data calculation engine 120 may convert the demand data 730 into stock data via the demand correlation model. In step S408, the data calculation engine 120 generates a plurality of stock data according to the plurality of demand data 730 and the demand association model. In this embodiment, data calculation engine 120 outputs the plurality of stock data to an external business process system (e.g., enterprise resource planning system 300).
The demand data processing system 100 also includes a data adjustment engine 130. The data adjustment engine 130 is coupled to the data receiving engine 110 and the data computing engine 120. The data adjustment engine 130 includes a forecast plan adjustment module 131, a delivery plan adjustment module 132, and a delivery plan adjustment module 133. The forecast plan adjustment module 131, the order plan adjustment module 132, and the delivery plan adjustment module 133 may be implemented in, for example, a program language such as JSON (JavaScript Object Notation), extensible markup language (Extensible Markup Language, XML), or YAML, but the present invention is not limited thereto.
The delivery plan adjustment module 133 adjusts the plurality of stock data according to the adjustment instruction. The delivery plan adjustment module 133 receives the adjustment data and adjusts the plurality of demand data 730 accordingly. The plurality of initial data includes a plurality of order data and a plurality of forecast data. The delivery plan adjustment module 133 receives the adjustment data and adjusts the plurality of demand data 730. The order plan adjustment module 132 correspondingly adjusts the plurality of order data according to the order data adjustment instructions. The predictive plan adjusting module 131 correspondingly adjusts the plurality of predictive data according to the predictive data adjusting instruction.
In summary, the demand data processing system and the demand data processing method of the present invention can accurately generate the spliced data by splicing the data of multiple versions based on the latest version data and updating the spliced data in a rolling manner, so as to effectively grasp the product demand number provided by the customer. And the time intervals of the data are recombined to avoid inconsistent data intervals when the data offset operation is executed, so that the accuracy of the stock data is improved, and the stock data can be automatically sent to an enterprise resource planning system, so that the purposes of reducing labor cost, improving calculation efficiency and avoiding artificial misunderstanding are achieved.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present invention, and not for limiting the same; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some or all of the technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit of the invention.
Claims (20)
1. A demand data processing system, comprising:
a data receiving engine; and
a data computation engine coupled to the data reception engine;
wherein the data receiving engine receives a plurality of initial data,
wherein the data receiving engine performs a splicing operation on the plurality of initial data, thereby splicing the data of different date intervals into a plurality of spliced data,
wherein the data calculation engine performs a data cancellation operation on the plurality of spliced data according to the demand parameters to generate a plurality of demand data,
the data calculation engine outputs the plurality of requirement data to an external business process system.
2. The demand data processing system of claim 1, wherein the data receiving engine generates a data importation template and the data receiving engine receives the plurality of initial data in accordance with the data importation template.
3. The demand data processing system of claim 1, wherein the data receiving engine performs the splicing operation on the plurality of initial data having the same data category to generate the plurality of spliced data, wherein the splicing operation is to splice date intervals of other versions of data based on date intervals of the latest version of data.
4. The demand data processing system of claim 1, wherein the data calculation engine performs an interval reassembly operation on the plurality of spliced data to produce a plurality of reassembled data,
wherein the interval reorganization operation is to generate time intervals corresponding to a plurality of products respectively based on the plurality of spliced data in a minimum time unit,
wherein the data calculation engine performs a data cancellation operation on the plurality of reorganized data according to the demand parameters to generate the plurality of demanded data, wherein the plurality of reorganized data comprises a plurality of to-be-restocked data and a plurality of predictive reorganized data,
the data cancellation operation combines the plurality of to-be-freighted reorganized data and the plurality of predicted reorganized data according to the demand parameters to generate the plurality of demand data.
5. The demand data processing system of claim 4, wherein the demand parameters include a first predetermined parameter and a second predetermined parameter, wherein the data calculation engine determines a time parameter of the plurality of reorganized data, respectively,
in response to the number of days of difference of the time parameter from the current date being less than the first preset parameter, the data calculation engine correspondingly treats the plurality of to-be-freighted reconfiguration data as the plurality of demand data,
and responding to the difference days between the time parameter and the current date between the first preset parameter and the second preset parameter, wherein the data calculation engine correspondingly takes the data with large values from the plurality of to-be-purchased reorganization data and the plurality of predictive reorganization data as the plurality of requirement data.
6. The demand data processing system of claim 5, wherein the data calculation engine correspondingly treats the plurality of predictive reorganization data as the plurality of demand data in response to the time parameter differing from a current date by a number of days greater than the second preset parameter.
7. The demand data processing system of claim 5, wherein the data calculation engine eliminates the plurality of demand data corresponding to the time parameter in response to the time parameter differing from the current date by a number of days greater than the second preset parameter.
8. The demand data processing system of claim 1, wherein the data calculation engine generates a demand correlation model from inventory correlation data, wherein the data calculation engine generates a plurality of stock data from the plurality of demand data and the demand correlation model, wherein the data calculation engine outputs the plurality of stock data to the external business process system.
9. The demand data processing system of claim 8, further comprising:
a data adjustment engine coupled to the data receiving engine and the data computing engine, wherein the data adjustment engine includes a delivery plan adjustment module,
the delivery plan adjusting module adjusts the plurality of stock data according to the adjusting instruction.
10. The demand data processing system of claim 9, wherein the plurality of initial data comprises a plurality of pickup data and a plurality of forecast data, wherein the data adjustment engine further comprises a pickup plan adjustment module and a forecast plan adjustment module,
wherein the order plan adjustment module correspondingly adjusts the plurality of order data according to order data adjustment instructions,
the predictive plan adjusting module correspondingly adjusts the plurality of predictive data according to the predictive data adjusting instruction.
11. A demand data processing method, characterized by comprising:
receiving, by a data receiving engine, a plurality of initial data;
performing splicing operation on the plurality of initial data through the data receiving engine, and splicing the data in different date intervals into a plurality of spliced data;
executing data offset operation on the plurality of spliced data according to the demand parameters by a data calculation engine so as to generate a plurality of demand data; and
and outputting the plurality of requirement data to an external business process system through the data calculation engine.
12. The method of claim 11, wherein the step of receiving, by the data receiving engine, the plurality of initial data comprises:
a data import template is generated by the data receiving engine, and the data receiving engine receives the plurality of initial data according to the data import template.
13. The method of claim 11, wherein performing, by the data receiving engine, a stitching operation on the plurality of initial data comprises:
and performing, by the data receiving engine, the splicing operation on the plurality of initial data having the same data category to generate the plurality of spliced data, wherein the splicing operation is to splice date intervals of other versions of data based on date intervals of the latest version of data.
14. The method of claim 11, further comprising, prior to the step of performing, by the data calculation engine, a data cancellation operation on the plurality of spliced data according to the demand parameters:
performing interval reorganization operation on the spliced data by the data calculation engine to generate recombined data, wherein the interval reorganization operation is to generate time intervals corresponding to a plurality of products respectively by the spliced data based on a minimum time unit;
performing, by the data calculation engine, a data cancellation operation on the plurality of reorganized data according to the demand parameter to generate the plurality of demanded data, wherein the plurality of reorganized data includes a plurality of to-be-restocked data and a plurality of predictive reorganized data; and
the data cancellation operation combines the plurality of to-be-freighted reorganized data and the plurality of predicted reorganized data according to the demand parameters to generate the plurality of demand data.
15. The method of claim 14, wherein the demand parameters include a first preset parameter and a second preset parameter, and wherein the step of performing, by the data calculation engine, the data cancellation operation on the plurality of reorganized data in accordance with the demand parameters to generate the plurality of demand data comprises:
respectively judging time parameters in the plurality of recombined data by the data computing engine;
correspondingly taking the plurality of to-be-freighted reconfiguration data as the plurality of requirement data through the data calculation engine in response to the number of days of difference between the time parameter and the current date is smaller than the first preset parameter; and
responding to the difference days between the time parameter and the current date between the first preset parameter and the second preset parameter, and correspondingly taking the data with large values from the plurality of to-be-freighted reorganization data and the plurality of predictive reorganization data as the plurality of requirement data through the data calculation engine.
16. The method of claim 15, wherein the step of performing, by the data calculation engine, the data cancellation operation on the plurality of reorganized data in accordance with the demand parameters to generate the plurality of demand data, further comprises:
and correspondingly taking the plurality of predictive reorganization data as the plurality of requirement data by the data calculation engine in response to the number of days of difference between the time parameter and the current date being greater than the second preset parameter.
17. The method of claim 15, wherein the step of performing, by the data calculation engine, the data cancellation operation on the plurality of reorganized data in accordance with the demand parameters to generate the plurality of demand data, further comprises:
and in response to the number of days of the difference between the time parameter and the current date being greater than the second preset parameter, eliminating the plurality of requirement data corresponding to the time parameter through the data calculation engine.
18. The method of claim 11, wherein outputting, by the data calculation engine, the plurality of demand data to an external through-business process system comprises:
generating a demand association model from inventory association data by the data calculation engine;
generating a plurality of stock data according to the plurality of demand data and the demand association model by the data calculation engine; and
and outputting the plurality of stock data to the external business process system through the data calculation engine.
19. The method of claim 18, wherein prior to the step of outputting, by the data calculation engine, the plurality of stock data to the external business process system, comprising:
and adjusting the plurality of stock data according to the adjustment instruction by a delivery plan adjustment module.
20. The method of claim 19, wherein the plurality of initial data includes a plurality of order data and a plurality of forecast data, wherein outputting the plurality of order data to the external business process system by the data calculation engine further comprises:
correspondingly adjusting the plurality of the data to be freighted according to the data to be freighted adjusting instruction through a freighting plan adjusting module; and
and correspondingly adjusting the plurality of predictive data according to the predictive data adjusting instruction by the predictive plan adjusting module.
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