Method and apparatus for commodity price strategy matching

Abstract

The application provides a commodity price strategy matching method and device, the method comprises the following steps: collecting historical order information of commodities; performing data cleaning on the historical order information according to a preset rule to obtain a total sample set, and adding a category label to the commodities in the total sample set, wherein the total sample set comprises channel information, order time and price strategy information of the commodities in the historical order information; adding a label to the commodities of each category label in the total sample set according to the combination of the commodities, the channel and a preset time period to form a sub-sample set; for any commodity channel in the sub-sample set, predicting a reference price interval according to the price of the commodities in the historical time interval of the commodity channel and the price of the commodities in the current time interval of the commodity channel; selecting a price strategy matched with the reference price interval to form a price strategy prediction set, and storing the price strategy prediction set in a cache.

Description

Technical Field

[0001] This invention relates to the field of computer technology, and in particular to a method and apparatus for matching commodity pricing strategies. Background Technology

[0002] Currently, a strategy system is commonly found in supply chain system clusters. Its function is to store and apply various business strategies, such as product pricing strategies. Product pricing strategies are crucial for supply chain systems. A product pricing strategy typically consists of the product price and shipping costs. Prices are further categorized into various types, such as distribution prices and settlement prices. Due to the typically large quantity and variety of products, and the numerous dimensions influencing product prices, the number of strategies generated by combining products and prices is enormous, requiring support for numerous business scenarios. Therefore, those skilled in the art are dedicated to developing related technologies to support the rapid and accurate matching, querying, and display of product prices for use by downstream systems. Summary of the Invention

[0003] In view of the above objectives, the present invention provides a method for matching commodity pricing strategies, the method comprising:

[0004] Collect historical order information for the products;

[0005] The historical order information is cleaned according to preset rules to obtain a total sample set, and category tags are added to the products in the total sample set. The total sample set includes the channel information, order time and pricing strategy information of the products in the historical order information.

[0006] For each category of product in the total sample set, tags are added based on a combination of product, channel, and preset time period to form a sub-sample set;

[0007] For any commodity channel in the subsample set, a reference price range is predicted based on the historical price of the commodity in the current time interval and the price of the commodity in the current time interval.

[0008] A price strategy prediction set is formed by selecting a price strategy that matches the reference price range, and the price strategy prediction set is stored in a cache.

[0009] Furthermore, the algorithm for predicting a reference price range based on the historical price of the product in the product channel and the price in the current time interval includes:

[0010] K = H / P

[0011] Where K represents the price fluctuation threshold, H represents the median of the price range in the historical time interval of the commodity, and P represents the median of the price range in the current time interval of the commodity.

[0012]

[0013] Where f(K) represents the reference price range and S represents the current price of the product.

[0014] Furthermore, the current time interval is a preset time period, and the historical time interval is one or more of the preset time periods.

[0015] Furthermore, the reference price range for each of the aforementioned sub-sample sets is predicted in a distributed server environment.

[0016] Furthermore, the price strategies in the price strategy prediction set use the corresponding product codes as keys.

[0017] Furthermore, in response to a product query command input by the user terminal, a product pricing strategy for the corresponding product is selected from the price strategy prediction set.

[0018] Furthermore, in response to the product query instruction, when the price strategy prediction set has multiple selectable product price strategies, the product price strategy with the lowest price is selected.

[0019] Furthermore, the product pricing strategy selected in response to the product query command is displayed in the graphical interface of the user terminal.

[0020] The present invention also provides a device for matching commodity pricing strategies, the device comprising:

[0021] Processor; and

[0022] A memory is configured to store computer-executable instructions, which, when executed, cause the processor to perform the operations described above.

[0023] The present invention also provides a computer-readable medium for storing instructions that, when executed, cause the system to perform the operations described above.

[0024] The product price strategy matching method and device of the present invention obtains the price strategy prediction set in advance through a prediction model, which greatly narrows the range of price strategies matched when users query product prices. Furthermore, the use of caching to store the price strategy prediction set greatly improves the efficiency of secondary precise queries, thereby enabling the price engine to provide users with the function of quickly querying product prices under high concurrency. Attached Figure Description

[0025] Other features, objects, and advantages of this application will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings:

[0026] Figure 1 A flowchart illustrating a method for matching commodity pricing strategies according to an embodiment of the present invention is shown.

[0027] Figure 2 A schematic diagram illustrating a use case of a user-obtained product price strategy based on the present invention is shown.

[0028] Figure 3 This diagram illustrates the process by which the price engine system retrieves pricing strategies from the cache in one embodiment of the present invention.

[0029] Figure 4 The illustration shows a functional module of an exemplary system that can be used in various embodiments of the present invention.

[0030] The same or similar reference numerals in the accompanying drawings represent the same or similar parts. Detailed Implementation

[0031] The present application will now be described in further detail with reference to the accompanying drawings.

[0032] In a typical configuration of the present invention, the terminal, the device of the service network, and the trusted party all include one or more processors (e.g., a central processing unit (CPU)), input / output interfaces, network interfaces, and memory.

[0033] Memory may include non-persistent storage in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash memory. Memory is an example of computer-readable media.

[0034] Computer-readable media, including both permanent and non-permanent, removable and non-removable media, can store information using any method or technology. Information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PCM), programmable random access 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 technologies, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transfer medium that can be used to store information that can be accessed by a computing device.

[0035] The devices referred to in this invention include, but are not limited to, user equipment, network equipment, or devices composed of user equipment and network equipment integrated through a network. The user equipment includes, but is not limited to, any mobile electronic product capable of human-computer interaction (e.g., via a touchpad), such as smartphones and tablets. These mobile electronic products can use any operating system, such as Android or iOS. The network equipment includes an electronic device capable of automatically performing numerical calculations and information processing according to pre-set or stored instructions. Its hardware includes, but is not limited to, microprocessors, application-specific integrated circuits (ASICs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), digital signal processors (DSPs), and embedded devices. The network equipment includes, but is not limited to, computers, network hosts, single network servers, multiple network server clusters, or clouds composed of multiple servers. Here, a cloud consists of a large number of computers or network servers based on cloud computing, where cloud computing is a type of distributed computing, consisting of a virtual supercomputer composed of a group of loosely coupled computer clusters. The network includes, but is not limited to, the Internet, wide area network, metropolitan area network, local area network, VPN network, and wireless ad hoc network. Preferably, the device can also be a program running on the user equipment, network device, or a device formed by integrating user equipment and network device, network device, touch terminal, or network device and touch terminal through a network.

[0036] Of course, those skilled in the art should understand that the above-described devices are merely examples, and other existing or future devices that are applicable to this invention should also be included within the scope of protection of this invention, and are hereby incorporated by reference.

[0037] In the description of embodiments of the present invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0038] Figure 1 A flowchart illustrating a product price strategy matching method according to an embodiment of the present invention is shown, including the following steps:

[0039] Step 1: Collect historical order information for the products.

[0040] The historical order information for each product is usually quite complex, including information such as the shipping factory, address, order time, channel information, shipping cost, order details, total amount, quantity limit, and pricing strategy used.

[0041] Step 2: Clean the historical order information according to preset rules to obtain a total sample set, and add category tags to the products in the total sample set. The total sample set includes the channel information, order time and pricing strategy information of the products in the historical order information.

[0042] Step 3: Add tags to the products of each category in the total sample set according to the combination of product, channel and preset time period to form a sub-sample set.

[0043] Step 4: For any product channel in the subsample set, predict the reference price range based on the historical price of the product in that product channel and the price in the current time interval.

[0044] Specifically, the algorithm for predicting a reference price range based on the historical price range of the product in the product channel and the price in the current time range includes:

[0045] K = H / P

[0046] Where K represents the price fluctuation threshold, H represents the median of the price range in the historical time interval of the commodity, and P represents the median of the price range in the current time interval of the commodity.

[0047]

[0048] Where f(K) represents the reference price range and S represents the current price of the product.

[0049] The current time interval is a preset time period, and the historical time interval is one or more preset time periods.

[0050] Step 5: Select price strategies that match the reference price range to form a price strategy prediction set, and store the price strategy prediction set in the cache.

[0051] The price strategy in the price strategy prediction set can use the code of the corresponding product as the key.

[0052] Step 6: In response to the product query command input by the user terminal, select the product pricing strategy for the corresponding product from the pricing strategy prediction set.

[0053] When there are multiple available commodity pricing strategies in the pricing strategy prediction set, the commodity pricing strategy with the lowest price is selected.

[0054] Step 7: Display the product pricing strategy selected in response to the product query command on the graphical interface of the user terminal.

[0055] Figure 2 This diagram illustrates a user scenario for obtaining product pricing strategies based on the present invention, including a client 10, a pricing engine system 20, and a distributed computing cluster 30. The client 10 accesses the pricing engine system 20 via the internet. The pricing engine system 20 is connected to the distributed computing cluster 30 via an internal network. The user selects a specific product through the client 10 to request the corresponding product's pricing strategy. The following description uses the rapid acquisition of the pricing strategy for {product A} as an example, combined with the operational deployment of the client 10, the pricing engine system 20, and the distributed computing cluster 30, to provide a detailed explanation of the product pricing strategy matching method of the present invention.

[0056] First, collect historical order information containing {Product A}. Since the historical order information is quite complex (including shipping factory, address information, order time, channel information, shipping cost information, order details, total amount, quantity limits, pricing strategies used, etc.), and the required information may only include the channel information, order time, and pricing strategy used for {Product A} from the historical order information, it is necessary to clean the collected historical order information to retain at least the above data (other relevant information, such as shipping factory, can also be included as needed for subsequent queries) as the total sample set, and add the product tag {Product A}.

[0057] Then, the product is further divided and organized according to the product label {Product A}, and the price of {Product A} in different time periods of each channel is obtained. New subsample sets are formed by the same product, different channels, and different time intervals, and corresponding labels are added. In this embodiment, the time interval is set with quarters as the cycle. The relevant information of the subsample set of {Product A} is shown in Table 1.

[0058] Table 1

[0059] child tags channel Time point Time range price Traditional - 2022 - 1 Traditional channels 2022-01-05 16:31:13 First quarter of 2022 220 Traditional - 2022 - 1 Traditional channels 2022-03-15 09:08:24 First quarter of 2022 200 Traditional - 2022 - 2 Traditional channels 2022-04-09 11:18:56 Second quarter of 2022 224 Traditional - 2022 - 2 Traditional channels 2022-06-29 18:21:41 Second quarter of 2022 208

[0060] In the distributed computing cluster 30 environment, for each product, i.e., each subset of samples, a prediction model is trained to obtain the current reference price range for that product. The algorithm for the reference price range is as follows:

[0061] K = H / P

[0062] Where K represents the price fluctuation threshold, H represents the median of the price range in the historical time interval of the commodity, and P represents the median of the price range in the current time interval of the commodity.

[0063]

[0064] Where f(K) represents the reference price range and S represents the current price of the product.

[0065] Taking the data in Table 1 as an example, for traditional channels, the price range for {Product A} in the first quarter of 2022 was 200-220, and the price range in the second quarter of 2022 was 208-224. In this embodiment, the current time is in the third quarter of 2022, meaning the query time for {Product A} is in the third quarter. The historical time interval is set to the two most recent quarters, namely the first and second quarters of 2022. Therefore, the price ranges of the first and second quarters of 2022 are combined, resulting in 200-224, and the midpoint of this price range is taken, which is 212.

[0066] The price fluctuation threshold is obtained by dividing the median of the price range for {Product A} in traditional channels during the first and second quarters of 2022 by the median of the current actual price range. Assuming the price range for the third quarter of 2022 is 204–218, the median is 211. Therefore, the price fluctuation threshold is 212 / 211 = 1.004739. The price fluctuation threshold can be continuously adjusted based on historical order information obtained for different query times within the current quarter (i.e., the third quarter).

[0067] The price volatility threshold reflects the price volatility of the previous two quarters and the current quarter. The higher the value is (the more it exceeds 1), the more the price has fallen; the lower the value is (the more it is less than 1), the more the price has risen. The subsequent calculation uses a price volatility threshold of 1.0047.

[0068] The price fluctuation threshold is set to 1.0047. The current price of {commodity A} is assumed to be 215. Since the price fluctuation threshold is greater than 1, the reference price range is [215-215*(1.0047-1),215], which is [214,215].

[0069] Based on the reference price range of [214, 215] for {Product A} in traditional channels, this reference price range is matched with currently available pricing strategies (i.e., the prices in the pricing strategies are within the reference price range) to obtain a set of price strategy predictions, which is then stored in the cache, using the product code (e.g., the code for {Product A} is 770210982) as the key. The set of price strategy predictions for {Product A} in traditional channels is shown in Table 2.

[0070] Table 2

[0071]

[0072] Figure 3This is a flowchart illustrating the process by which the pricing engine system retrieves pricing strategies from the cache. When the front-end display system calls the pricing engine system via API, the pricing engine system retrieves the corresponding data from the pricing strategy prediction set cache. First, it obtains the pricing strategy prediction set for the product based on its product code. Then, it quickly matches the product's pricing strategy in memory based on two major dimensions: channel and time period, or by combining other minor dimensions (such as the shipping factory, etc., which need to be retained during data cleaning). If there are multiple pricing strategies, the lowest-priced strategy is selected and provided to the front-end display system.

[0073] This embodiment also provides a computer-readable storage medium storing computer code that, when executed, is performed as described in any of the preceding embodiments.

[0074] This embodiment also provides a computer program product that, when executed by a computer device, performs the method described in any of the preceding embodiments.

[0075] This embodiment also provides a computer device, the computer device comprising:

[0076] One or more processors;

[0077] Memory, used to store one or more computer programs;

[0078] When the one or more computer programs are executed by the one or more processors, the one or more processors cause the one or more processors to perform the method as described in any of the preceding methods.

[0079] Figure 4 Exemplary systems that can be used to implement the various embodiments described in this invention are shown.

[0080] like Figure 4 As shown, in some embodiments, system 1000 can function as any of the user terminal devices described in each of the embodiments. In some embodiments, system 1000 may include one or more computer-readable media having instructions (e.g., system memory or NVM / storage device 1020) and one or more processors (e.g., one or more processors 1005) coupled to the one or more computer-readable media and configured to execute the instructions to implement the module and thus perform the actions described in this invention.

[0081] In one embodiment, the system control module 1010 may include any suitable interface controller to provide any suitable interface to at least one of the processors 1005 and / or any suitable device or component communicating with the system control module 1010.

[0082] The system control module 1010 may include a memory controller module 1030 to provide an interface to the system memory 1015. The memory controller module 1030 may be a hardware module, a software module, and / or a firmware module.

[0083] System memory 1015 may be used, for example, to load and store data and / or instructions for system 1000. In one embodiment, system memory 1015 may include any suitable volatile memory, such as suitable DRAM. In some embodiments, system memory 1015 may include double data rate type quad synchronous dynamic random access memory (DDR4 SDRAM).

[0084] In one embodiment, the system control module 1010 may include one or more input / output (I / O) controllers to provide interfaces to the NVM / storage device 1020 and (one or more) communication interfaces 1025.

[0085] For example, the NVM / storage device 1020 may be used to store data and / or instructions. The NVM / storage device 1020 may include any suitable non-volatile memory (e.g., flash memory) and / or may include any suitable (one or more) non-volatile storage devices (e.g., one or more hard disk drives (HDDs), one or more optical disc drives (CDs), and / or one or more digital universal optical disc (DVD) drives).

[0086] NVM / storage device 1020 may include storage resources that are physically part of a device on which system 1000 is mounted, or that can be accessed by the device without necessarily being part of the device. For example, NVM / storage device 1020 may be accessed via a network through one or more communication interfaces 1025.

[0087] One or more communication interfaces 1025 may provide the system 1000 with an interface to communicate over one or more networks and / or with any other suitable device. The system 1000 may wirelessly communicate with one or more components of a wireless network in accordance with any of one or more wireless network standards and / or protocols.

[0088] In one embodiment, at least one of the processors 1005 may be logically packaged with one or more controllers of the system control module 1010 (e.g., memory controller module 1030). In one embodiment, at least one of the processors 1005 may be logically packaged with one or more controllers of the system control module 1010 to form a system-in-package (SiP). In one embodiment, at least one of the processors 1005 may be integrated with the logic of one or more controllers of the system control module 1010 on the same die. In one embodiment, at least one of the processors 1005 may be integrated with the logic of one or more controllers of the system control module 1010 on the same die to form a system-on-a-chip (SoC).

[0089] In various embodiments, system 1000 may be, but is not limited to, a server, workstation, desktop computing device, or mobile computing device (e.g., laptop computing device, handheld computing device, tablet computer, netbook, etc.). In various embodiments, system 1000 may have more or fewer components and / or different architectures. For example, in some embodiments, system 1000 includes one or more cameras, a keyboard, a liquid crystal display (LCD) screen (including a touchscreen display), a non-volatile memory port, multiple antennas, a graphics chip, an application-specific integrated circuit (ASIC), and a speaker.

[0090] It should be noted that the present invention can be implemented in software and / or a combination of software and hardware, for example, using an application-specific integrated circuit (ASIC), a general-purpose computer, or any other similar hardware device. In one embodiment, the software program of the present invention can be executed by a processor to implement the steps or functions described above. Similarly, the software program of the present invention (including associated data structures) can be stored in a computer-readable recording medium, such as RAM memory, a magnetic or optical drive, a floppy disk, or similar devices. Furthermore, some steps or functions of the present invention can be implemented in hardware, for example, as circuitry that works with a processor to perform the various steps or functions.

[0091] Furthermore, a portion of this invention can be applied as a computer program product, such as computer program instructions, which, when executed by a computer, can invoke or provide the methods and / or technical solutions according to the invention through the operation of the computer. Those skilled in the art will understand that the forms in which computer program instructions exist in a computer-readable medium include, but are not limited to, source files, executable files, installation package files, etc. Correspondingly, the ways in which computer program instructions are executed by a computer include, but are not limited to: the computer directly executing the instructions, or the computer compiling the instructions and then executing the corresponding compiled program, or the computer reading and executing the instructions, or the computer reading and installing the instructions and then executing the corresponding installed program. Here, the computer-readable medium can be any available computer-readable storage medium or communication medium accessible to a computer.

[0092] Communication media include media through which communication signals containing, for example, computer-readable instructions, data structures, program modules, or other data are transmitted from one system to another. Communication media can include guided transmission media (such as cables and wires (e.g., optical fibers, coaxial cables, etc.)) and wireless (unguided transmission) media capable of propagating energy waves, such as sound, electromagnetic, RF, microwave, and infrared. Computer-readable instructions, data structures, program modules, or other data can be embodied as modulated data signals in, for example, wireless media (such as carrier waves or similar mechanisms embodied as part of spread spectrum technology). The term "modulated data signal" refers to a signal whose one or more characteristics are altered or set in a manner that encodes information in the signal. Modulation can be analog, digital, or a hybrid modulation technique.

[0093] By way of example and not limitation, computer-readable storage media may include volatile and non-volatile, removable and non-removable media implemented by any method or technique for storing information such as computer-readable instructions, data structures, program modules or other data. For example, computer-readable storage media include, but are not limited to, volatile memories such as random access memory (RAM, DRAM, SRAM); and non-volatile memories such as flash memory, various read-only memories (ROM, PROM, EPROM, EEPROM), magnetic and ferromagnetic / ferroelectric memories (MRAM, FeRAM); and magnetic and optical storage devices (hard disks, magnetic tapes, CDs, DVDs); or other media now known or hereafter developed capable of storing computer-readable information / data for use by a computer system.

[0094] Hereinafter, an embodiment of the present invention includes an apparatus comprising a memory for storing computer program instructions and a processor for executing the program instructions, wherein when the computer program instructions are executed by the processor, the apparatus is triggered to run a method and / or technical solution based on the foregoing embodiments of the present invention.

[0095] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the invention. Therefore, the embodiments should be considered illustrative and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be embraced within the present invention. No reference numerals in the claims should be construed as limiting the scope of the claims. Furthermore, it is clear that the word "comprising" does not exclude other units or steps, and the singular does not exclude the plural. Multiple units or devices recited in the apparatus claims may also be implemented by a single unit or device in software or hardware. The terms "first," "second," etc., are used to indicate names and do not indicate any particular order.

Claims

1. A method for matching commodity pricing strategies, characterized in that, include: Collect historical order information for the products; The historical order information is cleaned according to preset rules to obtain a total sample set, and category tags are added to the products in the total sample set. The total sample set includes the channel information, order time and pricing strategy information of the products in the historical order information. For each category of product in the total sample set, tags are added based on a combination of product, channel, and preset time period to form a sub-sample set; For any product channel in the subsample set, a reference price range is predicted based on the historical price of the product in that product channel and the price in the current time interval. The algorithm for the reference price range includes: in, Indicates the price fluctuation threshold. This represents the midpoint of a price range within a historical timeframe for a product. This represents the midpoint of the price range within the current time interval of the product, where the midpoint of the price range = (upper limit of the price range + lower limit of the price range) / 2; in, This indicates the range of values ​​for the reference price. Indicates the current price of the product; A price strategy prediction set is formed by selecting a price strategy that matches the reference price range, and the price strategy prediction set is stored in a cache.

2. The method according to claim 1, characterized in that, The current time interval is a preset time period, and the historical time interval is one or more of the preset time periods.

3. The method according to claim 1, characterized in that, Predict reference price ranges for each of the aforementioned sub-sample sets in a distributed server environment.

4. The method according to claim 1, characterized in that, The price strategy prediction set uses the code of the corresponding product as the key for each price strategy.

5. The method according to claim 1, characterized in that, In response to a product query command input by a user terminal, a product pricing strategy for the corresponding product is selected from the price strategy prediction set.

6. The method according to claim 5, characterized in that, In response to the product query instruction, when the price strategy prediction set has multiple selectable product price strategies, the product price strategy with the lowest price is selected.

7. The method according to claim 5, characterized in that, The product pricing strategy selected in response to the product query command will be displayed in the graphical interface of the user terminal.

8. A device for matching commodity pricing strategies, wherein, The device includes: Processor; and A memory configured to store computer-executable instructions, which, when executed, cause the processor to perform the operations of the method according to any one of claims 1 to 7.

9. A computer-readable medium storing instructions that, when executed, cause a system to perform operations according to any one of claims 1 to 7.

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