An economic data analysis method and system

Abstract

This invention relates to the field of economic data analysis and discloses an economic data analysis method and system, including an economic data collection module, a cycle delineation module, an economic data analysis module, and a benefit model establishment module. Through the coordinated setup of related functional modules, the system enables the classification and analysis of economic data. This data can be used in shops and other venues, especially for cooked food businesses that require timely sales, to analyze the sales of their own products. It allows for the allocation of product output based on sales volume and production capacity, thereby optimizing business operations and maximizing economic benefits.

Description

Technical Field

[0001] This invention relates to the field of economic data analysis, specifically an economic data analysis method and system. Background Technology

[0002] Economic data analysis involves using economic theories and methods to study and analyze various current economic data, identify patterns, determine the degree of economic benefits that different models and content bring to users, and thus provide users with a range of development paths.

[0003] Existing economic data analysis methods are mostly used for analyzing economic models of large enterprises and making decisions on the direction of enterprise planning. However, there is a lack of economic data analysis methods for small, time-sensitive individual business owners, and a lack of effective ways to guide the development of such individuals. Summary of the Invention

[0004] The purpose of this invention is to provide an economic data analysis method and system to solve the problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention provides the following technical solution:

[0006] An economic data analysis system, comprising:

[0007] An economic data collection module is used to collect data to be analyzed through IoT settlement devices, and to preprocess the data to be analyzed to obtain multiple datasets corresponding to different recording objects. The data to be analyzed includes the recording object and the recording time.

[0008] The cycle period delineation module is used to analyze several data points to be analyzed in each dataset based on a preset cycle period, and to divide the dataset into multiple cycle periods according to the recording time. The number of data points to be analyzed in each cycle period is variable, and the cycle period represents a sales cycle time period.

[0009] The economic data analysis module is used to perform economic benefit analysis on the data to be analyzed in the cycle, and obtain the sales status model of the record object corresponding to each dataset. The sales status model represents the distribution of the sales of the record object over time in the cycle.

[0010] The benefit model establishment module is used to obtain the single-item benefit and maximum inventory quantity of the recorded object, and establish a periodic benefit model. The periodic benefit model represents the maximum inventory quantity allocation method that maximizes the total benefit within the cycle and conforms to the sales status model.

[0011] As a further aspect of the present invention: the cycle benefit model includes multiple sub-stocking cycles, each sub-stocking cycle being a sub-cycle of the cycle, and each sub-stocking cycle corresponding to a maximum stocking quantity. Correspondingly, the cycle benefit model includes a sub-cycle benefit model corresponding to the sub-stocking cycle, and the benefit model establishment module includes:

[0012] The product restriction unit is used to obtain the periodic output of the recorded object, and to judge and restrict the periodic benefit model based on the periodic output. The maximum inventory allocation ratio of the corresponding recorded object in the periodic benefit model within the cycle should not be greater than the periodic output.

[0013] As a further embodiment of the present invention, it also includes a production allocation module:

[0014] The output allocation module is used to allocate the output capacity of the sub-cycle based on the cycle benefit model to complete the allocation of the recorded objects in the sub-cycle and the historical sub-cycles within the current cycle. If the allocation of the corresponding multiple sub-cycles has been completed, the allocation of the recorded objects with the lowest output in the next sub-cycle is obtained and allocated for pre-production. The recorded objects also include a freshness requirement marker, which is used to characterize the longest production time when the recorded object is sold. When the pre-production of the recorded objects in the next sub-cycle is carried out, if the pre-production time exceeds the freshness requirement marker, the pre-production recorded objects are replaced.

[0015] As a further embodiment of the present invention, it also includes an online assistance module, the online assistance module comprising:

[0016] The online sales unit is used to obtain online users' product reservation information through a cloud server. The product reservation information includes a pickup time period, which corresponds to a sub-period.

[0017] The online sampling unit is used to push new product information through a cloud server and obtain pre-order feedback information from users. The pre-order feedback information includes the recording time of the pickup period for establishing a sales status model and the user's historical purchase information. The user's historical purchase information is used to generate user purchase preferences. The pre-production of new products is referenced by the cycle benefit model corresponding to the user purchase preferences of several pre-order users.

[0018] As a further aspect of the present invention: the data to be analyzed further includes special markers, which are used to characterize the recorded objects sold during a specific time period. The recorded objects with the feature markers are not subject to the analysis and generation of a periodic benefit model.

[0019] This invention aims to provide an economic data analysis method, including the following steps:

[0020] Data to be analyzed is collected through IoT settlement devices, and the data to be analyzed is preprocessed to obtain multiple datasets corresponding to different recording objects. The data to be analyzed includes the recording object and the recording time.

[0021] Based on a preset cycle, several data points to be analyzed in each dataset are analyzed. The dataset is divided into multiple cycles according to the recording time. The number of data points to be analyzed in each cycle is variable. The cycle represents a sales cycle time period.

[0022] Economic benefit analysis is performed on the data to be analyzed using the cycle period to obtain the sales status model of the record object corresponding to each dataset. The sales status model represents the distribution of the sales of the record object over time within the cycle period.

[0023] Obtain the individual product benefits and maximum inventory quantity of the recorded object, and establish a cycle benefit model. The cycle benefit model represents the maximum inventory quantity allocation method that maximizes the total benefit within the cycle and conforms to the sales status model.

[0024] As a further aspect of the present invention: the cycle benefit model includes multiple sub-stocking cycles, each sub-stocking cycle being a sub-cycle of the cycle, and each sub-stocking cycle corresponding to a maximum stocking quantity. Correspondingly, the cycle benefit model includes a sub-cycle benefit model corresponding to the sub-stocking cycle. The step of establishing the cycle benefit model further includes:

[0025] Obtain the periodic output of the recorded object, and judge and limit the periodic benefit model based on the periodic output. The maximum inventory allocation ratio of the corresponding recorded object in the periodic benefit model within the cycle should not be greater than the periodic output.

[0026] As a further aspect of the present invention, it also includes the following steps:

[0027] Based on the cycle efficiency model, the output capacity of the sub-cycle is allocated to complete the allocation of goods to the recorded objects in the sub-cycle and the historical sub-cycles within the current cycle. If the allocation of goods to the corresponding multiple sub-cycles has been completed, the allocation of goods to the recorded objects with the lowest output in the next sub-cycle is obtained and allocated for pre-production. The recorded objects also include a freshness requirement marker, which is used to characterize the longest production time when the recorded object is sold. When the pre-production of the recorded object in the next sub-cycle is carried out, if the pre-production time exceeds the freshness requirement marker, the pre-production recorded object is replaced.

[0028] As a further aspect of the present invention, it also includes the following steps:

[0029] The product reservation information of online users is obtained through a cloud server. The product reservation information includes a pickup time period, which corresponds to a sub-period.

[0030] New product information is pushed through a cloud server, and reservation feedback information from users is obtained. The reservation feedback information includes the recording time of the pickup period for establishing a sales status model and the user's historical purchase information. The user's historical purchase information is used to generate user purchase preferences. The pre-production of new products is referenced by the cycle benefit model corresponding to the user purchase preferences of several reservation users.

[0031] As a further aspect of the present invention: the data to be analyzed further includes special markers, which are used to characterize the recorded objects sold during a specific time period. The recorded objects with the feature markers are not subject to the analysis and generation of a periodic benefit model.

[0032] Compared with the prior art, the beneficial effects of the present invention are: through the coordinated setting of relevant functional modules, the classification and analysis of economic data are realized, which can be used for the sales analysis of their own products in places such as shops, especially for merchants of cooked food that need to be sold in a timely manner. Based on the sales popularity of the products and the production capacity, the distribution of product output is realized, thereby optimizing the business allocation of merchants and maximizing economic benefits. Attached Figure Description

[0033] Figure 1 This is a block diagram of an economic data analysis system.

[0034] Figure 2 This is a block diagram of the components of an online auxiliary module in an economic data analysis system.

[0035] Figure 3 This is a flowchart of an economic data analysis method. Detailed Implementation

[0036] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0037] The specific implementation of the present invention will be described in detail below with reference to specific embodiments.

[0038] like Figure 1 The economic data analysis system provided in one embodiment of the present invention includes:

[0039] The economic data collection module 100 is used to collect data to be analyzed through IoT settlement devices, and to preprocess the data to be analyzed to obtain multiple datasets corresponding to different recording objects. The data to be analyzed includes the recording object and the recording time.

[0040] The cycle period delineation module 300 is used to analyze several data points to be analyzed in each dataset based on a preset cycle period, and to divide the dataset into multiple cycle periods according to the recording time. The number of data points to be analyzed in each cycle period is variable, and the cycle period represents a sales cycle time period.

[0041] The economic data analysis module 500 is used to perform economic benefit analysis on the data to be analyzed in the cycle, and obtain the sales status model of the record object corresponding to each dataset. The sales status model represents the distribution of the sales of the record object over time in the cycle.

[0042] The benefit model establishment module 700 is used to obtain the single-item benefit and maximum inventory quantity of the recorded object, and establish a periodic benefit model. The periodic benefit model represents the maximum inventory quantity allocation method that maximizes the total benefit within the cycle and conforms to the sales status model.

[0043] This embodiment presents an economic data analysis system that, through the coordinated setup of relevant functional modules, enables the classification and analysis of economic data. This system can be used by shops and other establishments, especially for cooked food businesses requiring timely sales, to analyze the sales of their own goods. It allows for the allocation of product output based on sales volume and production capacity, thereby optimizing business operations and maximizing economic benefits. In practical use, businesses generate a large number of customer payment records during their historical operations. These records typically include the type of goods, price, and time of purchase. Therefore, they can be used for economic analysis of product sales. The cycle period can be understood as a sales cycle time period, and the cycle period can be set to multiple times based on the analysis requirements (this application). A cycle can be understood as a natural day, and a second cycle can be set up on top of a natural day cycle, with a week as the second cycle. Here, the sub-cycle of a natural day shows the sales situation of goods in a single day, while the cycle of a week shows the overall sales situation of goods on different workdays and rest days. Both can be used to optimize the sales of merchants' products. Furthermore, a quarterly cycle can be set up based on demand, and the acceptance and popularity of different products in different seasons can be used to optimize production and sales plans. By analyzing the sales distribution of a certain product within a cycle, and then conducting an economic benefit analysis based on the sales distribution of multiple products, the total production of different products within the cycle can be allocated. This optimizes production allocation while meeting demand as much as possible, thereby increasing economic benefits.

[0044] In another preferred embodiment of the present invention, the cycle benefit model includes multiple sub-stocking cycles, each sub-stocking cycle being a sub-cycle of the cycle, and each sub-stocking cycle corresponding to a maximum stocking quantity. Correspondingly, the cycle benefit model includes a sub-cycle benefit model corresponding to the sub-stocking cycle, and the benefit model establishment module 700 includes:

[0045] The product restriction unit is used to obtain the periodic output of the recorded object, and to judge and restrict the periodic benefit model based on the periodic output. The maximum inventory allocation ratio of the corresponding recorded object in the periodic benefit model within the cycle should not be greater than the periodic output.

[0046] Furthermore, it also includes an output allocation module;

[0047] The output allocation module is used to allocate the output capacity of the sub-cycle based on the cycle benefit model to complete the allocation of the recorded objects in the sub-cycle and the historical sub-cycles within the current cycle. If the allocation of the corresponding multiple sub-cycles has been completed, the allocation of the recorded objects with the lowest output in the next sub-cycle is obtained and allocated for pre-production. The recorded objects also include a freshness requirement marker, which is used to characterize the longest production time when the recorded object is sold. When the pre-production of the recorded objects in the next sub-cycle is carried out, if the pre-production time exceeds the freshness requirement marker, the pre-production recorded objects are replaced.

[0048] In this embodiment, the content of the benefit model establishment module 700 has been restricted and supplemented to make its implementation of required functions more reasonable and reliable. One aspect is the sub-cycle. The setting of sub-cycles can further optimize the production allocation of products. Because the sales performance of different products varies at different times within a cycle (day), and product production output is continuous, subdividing the cycle allows for more refined production allocation of different products. Furthermore, during the progress of multiple sub-cycles within a cycle, merchants can also judge the actual compliance of the allocated cycle based on the actual situation, and then optimize and adjust the actual production, which can effectively... To avoid the problem of large deviations in actual sales caused by excessively long cycles; the output allocation module can be used to optimize the production process. In multiple sub-cycles within a cycle, there will inevitably be situations where the overall sales efficiency of different sub-cycles is low. Therefore, the inventory allocated to the corresponding sub-cycles will also be different, and the output capacity may be surplus. At this time, it can be used to allocate products with slower production speed in the next sub-cycle, which can effectively prevent the problem of sub-cycles arriving early in actual sales (the peak of buyers arriving early); the freshness mark here is used to limit the storage time of goods produced, such as fresh bread. If it is stored for too long, it will cause changes in taste, flavor, etc., affecting the product reputation.

[0049] like Figure 2 As shown, in another preferred embodiment of the present invention, an online assistance module 900 is further included, the online assistance module 900 comprising:

[0050] The online sales unit 901 is used to obtain online users' product reservation information through a cloud server. The product reservation information includes a pickup time period, which corresponds to a sub-period.

[0051] The online sampling unit 902 is used to push new product information through a cloud server and obtain pre-order feedback information from users. The pre-order feedback information includes the recording time of the pickup period for establishing a sales status model and the user's historical purchase information. The user's historical purchase information is used to generate user purchase preferences. The pre-production of new products is referenced by the cycle benefit model corresponding to the user purchase preferences of several pre-order users.

[0052] In this embodiment, the online auxiliary module is used to conduct surveys on the corresponding groups for new products based on the online part, and to match the periodic characteristics of the recorded data, so as to facilitate the initial production planning of new products.

[0053] In another preferred embodiment of the present invention, the data to be analyzed further includes special markers, which are used to characterize the recorded objects sold during a specific time period. The recorded objects with the feature markers are not subjected to the analysis and generation of a periodic benefit model.

[0054] In this embodiment, the function of special marking can be understood as marking special products so that they can be skipped when conducting periodic benefit analysis. This is because these products are sold at specific times and do not have long-term benefit references. They are more for brand effect enhancement, such as different types of products launched for different holidays.

[0055] like Figure 3 As shown, the present invention also provides an economic data analysis method, including the following steps:

[0056] S200: Collect data to be analyzed through IoT settlement device, and preprocess the data to be analyzed to obtain multiple datasets corresponding to different recording objects. The data to be analyzed includes the recording object and the recording time.

[0057] S400, based on a preset cycle, analyze several data points to be analyzed in each dataset, divide the dataset into multiple cycles according to the recording time, the number of data points to be analyzed in each cycle is variable, and the cycle represents a sales cycle time period.

[0058] S600, perform economic benefit analysis on the data to be analyzed using the cycle period, and obtain the sales status model of the record object corresponding to each dataset. The sales status model represents the distribution of the sales of the record object over time within the cycle period.

[0059] S800: Obtain the single-item benefit and maximum inventory quantity of the recorded object, and establish a cycle benefit model. The cycle benefit model represents the maximum inventory quantity allocation method that maximizes the total benefit within the cycle and conforms to the sales status model.

[0060] In another preferred embodiment of the present invention, the cycle benefit model includes multiple sub-stocking cycles, each sub-stocking cycle being a sub-cycle of the cycle, and each sub-stocking cycle corresponding to a maximum stocking quantity. Correspondingly, the cycle benefit model includes a sub-cycle benefit model corresponding to the sub-stocking cycle. The step of establishing the cycle benefit model further includes:

[0061] Obtain the periodic output of the recorded object, and judge and limit the periodic benefit model based on the periodic output. The maximum inventory allocation ratio of the corresponding recorded object in the periodic benefit model within the cycle should not be greater than the periodic output.

[0062] As another preferred embodiment of the present invention, the method further includes the following steps:

[0063] Based on the cycle efficiency model, the output capacity of the sub-cycle is allocated to complete the allocation of goods to the recorded objects in the sub-cycle and the historical sub-cycles within the current cycle. If the allocation of goods to the corresponding multiple sub-cycles has been completed, the allocation of goods to the recorded objects with the lowest output in the next sub-cycle is obtained and allocated for pre-production. The recorded objects also include a freshness requirement marker, which is used to characterize the longest production time when the recorded object is sold. When the pre-production of the recorded object in the next sub-cycle is carried out, if the pre-production time exceeds the freshness requirement marker, the pre-production recorded object is replaced.

[0064] As another preferred embodiment of the present invention, the method further includes the following steps:

[0065] The product reservation information of online users is obtained through a cloud server. The product reservation information includes a pickup time period, which corresponds to a sub-period.

[0066] New product information is pushed through a cloud server, and reservation feedback information from users is obtained. The reservation feedback information includes the recording time of the pickup period for establishing a sales status model and the user's historical purchase information. The user's historical purchase information is used to generate user purchase preferences. The pre-production of new products is referenced by the cycle benefit model corresponding to the user purchase preferences of several reservation users.

[0067] In another preferred embodiment of the present invention, the data to be analyzed further includes special markers, which are used to characterize the recorded objects sold during a specific time period. The recorded objects with the feature markers are not subjected to the analysis and generation of a periodic benefit model.

[0068] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments described above. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and RAMbus dynamic RAM (RDRAM), etc.

[0069] Other embodiments of this disclosure will readily occur to those skilled in the art upon consideration of the disclosure in the specification and embodiments. This application is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not disclosed herein. The specification and embodiments are to be considered exemplary only, and the true scope and spirit of this disclosure are indicated by the claims.

[0070] It should be understood that this disclosure is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this disclosure is limited only by the appended claims.

Claims

1. An economic data analysis system, characterized by, Include: An economic data collection module is used to collect data to be analyzed through IoT settlement devices, and to preprocess the data to be analyzed to obtain multiple datasets corresponding to different recording objects. The data to be analyzed includes the recording object and the recording time. The cycle period delineation module is used to analyze several data points to be analyzed in each dataset based on a preset cycle period, and to divide the dataset into multiple cycle periods according to the recording time. The number of data points to be analyzed in each cycle period is variable, and the cycle period represents a sales cycle time period. The economic data analysis module is used to perform economic benefit analysis on the data to be analyzed in the cycle, and obtain the sales status model of the record object corresponding to each dataset. The sales status model represents the distribution of the sales of the record object over time in the cycle. The benefit model establishment module is used to obtain the single-item benefit and maximum inventory quantity of the recorded object, and to establish a periodic benefit model. The periodic benefit model represents the maximum inventory quantity allocation method that maximizes the total benefit within the cycle and conforms to the sales status model. The cycle efficiency model includes multiple sub-stocking cycles, each of which is a sub-cycle of the cycle. Each sub-stocking cycle has a corresponding maximum stocking quantity. Correspondingly, the cycle efficiency model includes a sub-cycle efficiency model corresponding to each sub-stocking cycle. The efficiency model establishment module includes: The product restriction unit is used to obtain the periodic output of the recorded object, and to judge and restrict the periodic benefit model based on the periodic output. The maximum inventory ratio allocated to the corresponding recorded object in the periodic benefit model within the cycle should not be greater than the periodic output. It also includes an output allocation module: The output allocation module is used to allocate the output capacity of the sub-cycle based on the cycle benefit model to complete the allocation of the recorded objects in the sub-cycle and the historical sub-cycles within the current cycle. If the allocation of the corresponding multiple sub-cycles has been completed, the allocation of the recorded objects with the lowest output in the next sub-cycle is obtained and allocated for pre-production. The recorded objects also include a freshness requirement marker, which is used to characterize the longest production time when the recorded object is sold. When the pre-production of the recorded objects in the next sub-cycle is carried out, if the pre-production time exceeds the freshness requirement marker, the pre-production recorded objects are replaced.

2. The economic data analysis system according to claim 1, characterized in that, It also includes an online support module, which includes: The online sales unit is used to obtain online users' product reservation information through a cloud server. The product reservation information includes a pickup time period, which corresponds to a sub-period. The online sampling unit is used to push new product information through a cloud server and obtain pre-order feedback information from users. The pre-order feedback information includes the recording time and the user's historical purchase information. The recording time is the pickup period used to build a sales status model. The user's historical purchase information is used to generate user purchase preferences. The pre-production of the new product is referenced by the cycle benefit model corresponding to the user purchase preferences of several pre-order users.

3. The economic data analysis system according to claim 1, characterized in that, The data to be analyzed also includes special markers, which are used to characterize the recorded objects sold during a specific time period. The recorded objects with the special markers are not subject to the generation of a periodic benefit model.

4. An economic data analysis method, characterized in that, Including the following steps: Data to be analyzed is collected through IoT settlement devices, and the data to be analyzed is preprocessed to obtain multiple datasets corresponding to different recording objects. The data to be analyzed includes the recording object and the recording time. Based on a preset cycle, several data points to be analyzed in each dataset are analyzed. The dataset is divided into multiple cycles according to the recording time. The number of data points to be analyzed in each cycle is variable. The cycle represents a sales cycle time period. Economic benefit analysis is performed on the data to be analyzed using the cycle period to obtain the sales status model of the record object corresponding to each dataset. The sales status model represents the distribution of the sales of the record object over time within the cycle period. Obtain the single-item benefits and maximum inventory quantity of the recorded object, and establish a periodic benefit model. The periodic benefit model represents the maximum inventory quantity allocation method that maximizes the total benefits and conforms to the sales status model within the cycle. The cycle efficiency model includes multiple sub-stocking cycles, each of which is a sub-cycle of the cycle. Each sub-stocking cycle has a corresponding maximum stocking quantity. Correspondingly, the cycle efficiency model includes a sub-cycle efficiency model corresponding to each sub-stocking cycle. The step of establishing the cycle efficiency model further includes: Obtain the periodic output of the recorded object, and judge and limit the periodic benefit model based on the periodic output. The maximum inventory allocation ratio of the corresponding recorded object in the periodic benefit model within the cycle should not be greater than the periodic output. It also includes the following steps: Based on the cycle efficiency model, the output capacity of the sub-cycle is allocated to complete the allocation of goods to the recorded objects in the sub-cycle and the historical sub-cycles within the current cycle. If the allocation of goods to the corresponding multiple sub-cycles has been completed, the allocation of goods to the recorded objects with the lowest output in the next sub-cycle is obtained and allocated for pre-production. The recorded objects also include a freshness requirement marker, which is used to characterize the longest production time when the recorded object is sold. When the pre-production of the recorded object in the next sub-cycle is carried out, if the pre-production time exceeds the freshness requirement marker, the pre-production recorded object is replaced.

5. The economic data analysis method according to claim 4, characterized in that, It also includes the following steps: The product reservation information of online users is obtained through a cloud server. The product reservation information includes a pickup time period, which corresponds to a sub-period. New product information is pushed through a cloud server, and reservation feedback information from users is obtained. The reservation feedback information includes the recording time and the user's historical purchase information. The recording time is the pickup period used to build a sales status model. The user's historical purchase information is used to generate user purchase preferences. The pre-production of new products is referenced by the cycle benefit model of the recorded objects corresponding to the user purchase preferences of several reservation users.

6. The economic data analysis method according to claim 4, characterized in that, The data to be analyzed also includes special markers, which are used to characterize the recorded objects sold during a specific time period. The recorded objects with the special markers are not subject to the generation of a periodic benefit model.

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