System and method for assisting dynamic pricing management of jewelry

Abstract

The invention belongs to the technical field of auxiliary pricing, and provides a system and a method for assisting dynamic pricing management of jewelry. The method comprises the steps of obtaining raw material price data according to a raw material type of target jewelry, obtaining exchange rate data according to a raw material production place, and performing analysis to obtain a first target price of the target jewelry; crawling customer discussion big data related to the target jewelry and merchant promotion big data related to the target jewelry; obtaining the forward popularity and a first price adjustment value of the target jewelry according to the customer network discussion big data; obtaining promotion popularity and a second price adjustment value of the target jewelry according to the merchant promotion big data; and obtaining a price adjustment coefficient according to the first price adjustment value and the second price adjustment value, correcting the first target price by using the price adjustment coefficient to obtain a second target price, and outputting the second target price to assist jewelry pricing personnel in updating the price of the target jewelry. According to the invention, accurate dynamic pricing of jewelry is realized, and the economic benefits of merchants can be improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of auxiliary pricing, and in particular, relates to a system and method for assisting dynamic pricing management of jewelry. Background Art

[0002] Jewelry, also known as jewellery or jewellery, refers to items with decorative, artistic and collection value that are made from precious metals such as gold, silver, platinum and their alloys, as well as diamonds, gems, jade, jadeite, pearls, etc., which are designed and processed.

[0003] In order to obtain the maximum economic benefits, merchants need to pay real-time attention to the price of jewelry and dynamically adjust the price of jewelry for sale. However, the existing methods for dynamically adjusting jewelry prices are basically based on the real-time fluctuations in raw material prices. For example, if the price of raw materials rises, the price of jewelry will be increased accordingly, and if the price of raw materials falls, the price of jewelry will be decreased accordingly. However, this adjustment method lacks comprehensive consideration of many other factors, resulting in the inability of the adjusted jewelry price to promote jewelry sales to the greatest extent, resulting in the inability to obtain the best economic benefits. This technical problem urgently needs to be improved. Summary of the invention

[0004] In order to solve the technical problems existing in the above-mentioned background technology, the present invention provides a method, system, electronic device, computer storage medium and computer program product for assisting dynamic pricing management of jewelry.

[0005] The present invention provides a method for assisting dynamic pricing management of jewelry, the method comprising the following steps: Determine the raw material type and raw material origin of the target jewelry, obtain raw material price data according to the raw material type, and obtain exchange rate data according to the raw material origin; deriving a first target price of the target jewelry according to the raw material price data and the exchange rate data; Using web crawler technology to crawl customer discussion big data related to the target jewelry, analyzing the customer network discussion big data to obtain the positive popularity of the target jewelry, and determining a first price adjustment value based on the positive popularity; Using web crawler technology to crawl merchant promotion big data related to the target jewelry, analyzing the merchant promotion big data to obtain the promotion popularity of the target jewelry, and determining the second price adjustment value based on the promotion popularity; A price adjustment coefficient is calculated based on the first price adjustment value and the second price adjustment value, the first target price is corrected using the price adjustment coefficient to obtain a second target price, and the second target price is output to assist jewelry pricing personnel in updating the price of the target jewelry.

[0006] Optionally, obtaining raw material price data according to the raw material type includes: Acquire a plurality of first raw material price data from different quotation channels according to the raw material type; The first raw material price data and the corresponding quotation channel information are input into an artificial intelligence prediction model, and the artificial intelligence prediction model predicts the second raw material price data corresponding to the quotation channel information, calculates the average value of each second raw material price data, and determines the average value as the raw material price data.

[0007] Optionally, the training method of the artificial intelligence prediction model is as follows: Analyze the price stability of the raw material type to obtain a price stability value, and determine a specified crawling time according to the price stability value; Acquire a historical quotation data set of each quotation path according to the specified crawling time, wherein the historical quotation data set includes a plurality of historical quotation data; Acquire merchant purchase price data corresponding to each of the historical quotation data, wherein the merchant purchase price data and the historical quotation data are in the same time period; The historical quotation data and the corresponding merchant purchase price data are constructed as a piece of training data, thereby constructing a training data set; The artificial intelligence prediction model is trained using the training data set with an unsupervised training method until a training termination condition is met.

[0008] Optionally, determining a specified crawling duration according to the price stability value includes: ; In the above formula, To specify the crawling time; Is the basic crawling time; is the adjustment coefficient, and ; is the price stability value, is a constant, and .

[0009] Optionally, the step of obtaining the positive popularity of the target jewelry according to the customer network discussion big data analysis and determining the first price adjustment value according to the positive popularity includes: Perform semantic analysis on the customer network discussion big data to screen out customer network discussion data that contains positive evaluation information, perform statistical analysis on a first quantity of the customer network discussion data, calculate the positive heat based on the first quantity, and determine the first price adjustment value based on the positive heat and a corresponding first comparison relationship.

[0010] Optionally, the step of analyzing the promotional big data of the merchant to obtain the promotional popularity of the target jewelry and determining the second price adjustment value according to the promotional popularity includes: Perform statistical analysis on the merchant promotion big data to obtain a second quantity of promotion information, calculate the time concentration of each promotion information, calculate the promotion heat based on the second quantity and the time concentration, and determine the second price adjustment value based on the promotion heat and the corresponding second comparison relationship.

[0011] The present invention also provides a system for assisting dynamic pricing management of jewelry, the system comprising a processing module and a storage module, the processing module calls and executes a computer program stored in the storage module to perform the following method steps: Determine the raw material type and raw material origin of the target jewelry, obtain raw material price data according to the raw material type, and obtain exchange rate data according to the raw material origin; deriving a first target price of the target jewelry according to the raw material price data and the exchange rate data; Using web crawler technology to crawl customer discussion big data related to the target jewelry, analyzing the customer network discussion big data to obtain the positive popularity of the target jewelry, and determining a first price adjustment value based on the positive popularity; Using web crawler technology to crawl merchant promotion big data related to the target jewelry, analyzing the merchant promotion big data to obtain the promotion popularity of the target jewelry, and determining the second price adjustment value based on the promotion popularity; A price adjustment coefficient is calculated based on the first price adjustment value and the second price adjustment value, the first target price is corrected using the price adjustment coefficient to obtain a second target price, and the second target price is output to assist jewelry pricing personnel in updating the price of the target jewelry.

[0012] The present invention also provides an electronic device, comprising: at least one processor, a memory, and a computer program stored in the memory and executable on the at least one processor, wherein the processor executes the computer program to implement any of the methods described above.

[0013] The present invention also provides a computer storage medium, wherein the computer readable storage medium stores a computer program, and the computer program is executed by a processor to implement any of the above methods.

[0014] The present invention also provides a computer program product, wherein the computer program includes a number of computer codes, and when the computer codes are executed by a processor, any of the above methods is implemented.

[0015] After determining the first target price of the target jewelry using a conventional pricing management method, the solution of the present invention further analyzes the real-time supply and demand relationship of the target jewelry, that is, the first price adjustment value on the customer side and the second price adjustment value on the merchant side, and obtains a more reasonable and accurate second target price based on the comprehensive analysis, which is beneficial to improving the probability of merchants obtaining higher economic benefits. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the embodiments are briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without creative work.

[0017] Figure 1 It is a flow chart of a method for assisting dynamic pricing management of jewelry disclosed in an embodiment of the present invention; Figure 2 The invention discloses a structural diagram of a system for assisting dynamic pricing management of jewelry. DETAILED DESCRIPTION

[0018] The exemplary embodiments of the present invention will now be described more fully with reference to the accompanying drawings. Although each exemplary embodiment can be implemented in a variety of specific ways, it should not be understood that the present invention is limited to the embodiments set forth herein. On the contrary, these exemplary embodiments are provided to make the content of the present invention more complete and to facilitate the comprehensive communication of the inventive concept to those skilled in the art.

[0019] Under the premise of conforming to the technical concept of the present invention, the structure, performance, effect or other characteristics described in a specific embodiment may be combined with one or more other embodiments in any appropriate manner.

[0020] In the process of introducing specific embodiments, the detailed description of the structure, performance, effect or other characteristics is to enable those skilled in the art to fully understand the embodiments. However, it does not exclude that those skilled in the art can implement the present invention with a technical solution that does not contain the above-mentioned structure, performance, effect or other characteristics under certain circumstances.

[0021] The flowchart in the accompanying drawings is only an exemplary process demonstration, and does not mean that the solution of the present invention must include all the contents, operations and steps in the flowchart, nor does it mean that it must be executed in the order shown in the figure. For example, some operations / steps in the flowchart can be decomposed, some operations / steps can be combined or partially combined, etc. The execution order shown in the flowchart can be changed according to actual conditions without departing from the main purpose of the present invention.

[0022] Frames in the attached figure Figure 1 Generally, it refers to functional entities, which do not necessarily correspond to physically independent entities. That is, these functional entities can be implemented in software form, or in one or more hardware modules or integrated circuits, or in different networks and / or processor devices and / or microcontroller devices.

[0023] The same reference numerals in the drawings represent the same or similar elements, components or parts, and thus the repeated description of the same or similar elements, components or parts may be omitted below. It should also be understood that although the first, second, third and other attributives representing the numbers may be used herein to describe various devices, elements, components or parts, these devices, elements, components or parts should not be limited by these attributives. In other words, these attributives are only used to distinguish one from another. For example, the first device may also be referred to as the second device, but it does not deviate from the essential technical solution of the present invention. In addition, the terms "and / or" and "and / or" refer to all combinations including any one or more of the listed items.

[0024] like Figure 1 As shown, a method for assisting dynamic pricing management of jewelry according to an embodiment of the present invention comprises the following steps: Determine the raw material type and raw material origin of the target jewelry, obtain raw material price data according to the raw material type, and obtain exchange rate data according to the raw material origin; deriving a first target price of the target jewelry according to the raw material price data and the exchange rate data; Using web crawler technology to crawl customer discussion big data related to the target jewelry, analyzing the customer network discussion big data to obtain the positive popularity of the target jewelry, and determining a first price adjustment value based on the positive popularity; Using web crawler technology to crawl merchant promotion big data related to the target jewelry, analyzing the merchant promotion big data to obtain the promotion popularity of the target jewelry, and determining the second price adjustment value based on the promotion popularity; A price adjustment coefficient is calculated based on the first price adjustment value and the second price adjustment value, the first target price is corrected using the price adjustment coefficient to obtain a second target price, and the second target price is output to assist jewelry pricing personnel in updating the price of the target jewelry.

[0025] In the scheme of the present invention, for the target jewelry to be price managed, the raw material types (gold, platinum, silver, platinum, diamond, ruby, sapphire, emerald, pearl, jade, etc.) and raw material origins of the various raw materials needed to make the jewelry are first determined, and the raw material price data of the raw material type can be directly obtained in the corresponding raw material quotation channel. In addition, when some raw materials are imported raw materials, the corresponding exchange rate data is further obtained according to the raw material origin, and the raw material price data and exchange rate data are analyzed and calculated according to the merchant's own pricing system to obtain a preliminary first target price. Then, the web crawler technology is used to crawl the Internet to obtain customer discussion big data related to the target jewelry. Customer discussion big data refers to information related to the target jewelry posted by consumers on some websites, APPs (such as Douyin, Weibo, Xiaohongshu, etc.), and forums. Based on these network discussion big data, the positive heat that is conducive to promoting the sales of the target jewelry can be analyzed, and then the first price adjustment value is determined based on the positive heat. In addition, web crawler technology is used to crawl the merchant promotion big data related to the target jewelry on the Internet. Merchant promotion big data mainly refers to the relevant promotion information about the target jewelry released by merchants (which can be all merchants and manufacturers selling the jewelry) on the Internet, such as advertising information, relevant promotion and introduction information released by the merchant official account on APPs such as Douyin, Weibo, and Xiaohongshu, etc. The promotion heat of the target jewelry can be analyzed based on the merchant promotion big data, and the second price adjustment value can be determined based on the promotion heat. Finally, the price adjustment coefficient is determined by combining the first price adjustment value and the second price adjustment value, and the price adjustment coefficient is used to correct the first target price obtained by the conventional pricing method to obtain the second target price, and the second target price is output to the merchant's pricing personnel for reference, thereby assisting the jewelry pricing personnel to update the price of the target jewelry in a timely manner.

[0026] Therefore, after the solution of the present invention determines the first target price of the target jewelry using the conventional pricing management method, it further analyzes the real-time supply and demand relationship of the target jewelry, that is, the first price adjustment value on the customer side and the second price adjustment value on the merchant side. Based on this comprehensive analysis, a more reasonable and accurate second target price is obtained, which is conducive to improving the probability of merchants obtaining higher economic benefits.

[0027] It should be noted that each merchant will build its own pricing system. These pricing systems mainly formulate pricing strategies for different types of jewelry based on factors such as raw material prices, design and craftsmanship, brand value, risk management, and market strategies, so that pricing prices for various types of jewelry can be automatically or semi-automatically generated.

[0028] Optionally, obtaining raw material price data according to the raw material type includes: Acquire a plurality of first raw material price data from different quotation channels according to the raw material type; The first raw material price data and the corresponding quotation channel information are input into an artificial intelligence prediction model, and the artificial intelligence prediction model predicts the second raw material price data corresponding to the quotation channel information, calculates the average value of each second raw material price data, and determines the average value as the raw material price data.

[0029] In the embodiment of the present invention, the quotations of jewelry raw materials can be obtained through multiple quotation channels, but there are some differences in the quotations of different quotation channels. Based on a single quotation, it is easy to cause deviations in the determination of the quotation, so it is necessary to conduct a comprehensive analysis of the raw material quotations of each quotation channel to determine the relatively true quotation of the jewelry raw material. In this regard, the present invention constructs an artificial intelligence prediction model, which can conduct a comprehensive and in-depth analysis of each first raw material price data and its corresponding quotation channel information, and predict the second raw material price data corresponding to each quotation channel information. The second raw material price data is used to characterize the acceptable "selling price" predicted by the quotation channel, and the average value of the second raw material price data of each quotation channel is used as the above raw material price data, so that the raw material price data is closer to the current real quotation.

[0030] The artificial intelligence prediction model of the present invention is preferably constructed based on a hybrid model, which is a combination of a traditional machine learning model and a deep learning model. The traditional machine learning model can be a model based on machine learning algorithms such as support vector machine (SVM), random forest, gradient boosting machine (GBM), XGBoost, etc. The deep learning algorithm can be a model based on deep learning algorithms such as long short-term memory network (LSTM), convolutional neural network (CNN) and deep belief network (DBN).

[0031] Optionally, the training method of the artificial intelligence prediction model is as follows: Analyze the price stability of the raw material type to obtain a price stability value, and determine a specified crawling time according to the price stability value; Acquire a historical quotation data set of each quotation path according to the specified crawling time, wherein the historical quotation data set includes a plurality of historical quotation data; Acquire merchant purchase price data corresponding to each of the historical quotation data, wherein the merchant purchase price data and the historical quotation data are in the same time period; The historical quotation data and the corresponding merchant purchase price data are constructed as a piece of training data, thereby constructing a training data set; The artificial intelligence prediction model is trained using the training data set with an unsupervised training method until a training termination condition is met.

[0032] In an embodiment of the present invention, after constructing the above-mentioned artificial intelligence prediction model based on the hybrid model, it is necessary to collect and construct a training data set to train the artificial intelligence prediction model so that it has the ability to accurately predict the above-mentioned second raw material price data. Each piece of training data includes historical quotation data and merchant purchase price data, and the historical quotation data and merchant purchase price data are in the same time period, for example, within a week. Finally, the artificial intelligence prediction model is trained using an unsupervised training method until the training termination condition is met, and the training of the artificial intelligence prediction model is completed.

[0033] In addition, historical quotation data and merchant purchase price data are also crawled out through network crawling technology. However, it is necessary to limit the time attributes of the two data targeted during crawling, that is, to analyze the price stability according to the type of raw materials, such as analyzing its overall price stability in the past two years. When the price stability is higher, the raw material seller's quotation is closer to the merchant purchase price, because the price transparency at this time is already high, and the raw material seller's quotation is closer to the real selling price; and when the price stability is lower, that is, the price has a large fluctuation, the raw material seller is usually more difficult to grasp the best quotation, so the raw material seller's quotation will have a greater deviation from the merchant purchase price, that is, the real selling price. Therefore, based on the above characteristics, the present invention is set to determine the specified crawling time according to the price stability value, and the specified crawling time refers to the span time from the current moment to the direction of historical data.

[0034] Optionally, determining a specified crawling duration according to the price stability value includes: ; In the above formula, To specify the crawling time; Is the basic crawling time; is the adjustment coefficient, and ; is the price stability value, is a constant, and .

[0035] In an embodiment of the present invention, based on the above analysis, the present invention sets a positive correlation between the specified crawling time and the price stability value, that is, when the price stability value is higher, more training data can be constructed based on a longer time span, more historical quotation data, and merchant purchase price data, which is conducive to more in-depth training of the artificial intelligence prediction model. When the price stability value is lower, the quotation rules of sellers in each quotation channel are more affected by recent price fluctuations, so it is set to construct training data based on more recent and less historical quotation data and merchant purchase price data to improve the representativeness and effectiveness of the training data, thereby improving the artificial intelligence prediction model's ability to accurately predict the second raw material price data.

[0036] Optionally, the step of obtaining the positive popularity of the target jewelry according to the customer network discussion big data analysis and determining the first price adjustment value according to the positive popularity includes: Perform semantic analysis on the customer network discussion big data to screen out customer network discussion data that contains positive evaluation information, perform statistical analysis on a first quantity of the customer network discussion data, calculate the positive heat based on the first quantity, and determine the first price adjustment value based on the positive heat and a corresponding first comparison relationship.

[0037] In the embodiment of the present invention, semantic analysis technology can be used to filter out customer network discussion data belonging to positive evaluation information from the crawled customer network discussion big data. Positive evaluation information refers to the customer's favorable or neutral content about the target jewelry. The positive heat is calculated based on the first quantity of the customer network discussion data. The conversion function between the positive heat and the first quantity is a positive correlation function, but the present invention does not limit the specific expression of the conversion function. Finally, the first price adjustment value is determined based on the positive heat and the corresponding first comparison relationship.

[0038] The first comparison relationship is preset, which includes a plurality of corresponding relationship data between positive heat and first price adjustment value, and the first price adjustment value can be determined by looking up the table. The first price adjustment value is, for example, a value between 0.6-1.5, and of course, it can also be other value ranges.

[0039] Optionally, the step of analyzing the promotional big data of the merchant to obtain the promotional popularity of the target jewelry and determining the second price adjustment value according to the promotional popularity includes: Perform statistical analysis on the merchant promotion big data to obtain a second quantity of promotion information, calculate the time concentration of each promotion information, calculate the promotion heat based on the second quantity and the time concentration, and determine the second price adjustment value based on the promotion heat and the corresponding second comparison relationship.

[0040] In an embodiment of the present invention, the crawled merchant promotion big data is directly statistically analyzed to obtain the second quantity of promotion information, and the time concentration of each promotion information can also be calculated. The more concentrated the release time of each promotion information is, the higher the time concentration is. The conversion function between the promotion heat and the second quantity and time concentration is also a positive correlation function, but the present invention does not limit the specific expression of the conversion function. Similar to the above, the second price adjustment value is determined based on the promotion heat and the corresponding second comparison relationship.

[0041] The second comparison relationship is also preset, which includes multiple corresponding relationship data between promotion heat and second price adjustment value, and the second price adjustment value can be determined by looking up the table. The second price adjustment value is also a value between 0.6-1.5, and of course it can also be other value ranges.

[0042] In addition, the price adjustment coefficient can be equal to the first price adjustment value multiplied by the second price adjustment value. Of course, other weight coefficients can be added to keep it within a reasonable range, for example, the price adjustment coefficient is in the interval [0.8, 1.2]. The price adjustment coefficient is then multiplied by the first price adjustment value to obtain the second price adjustment value.

[0043] like Figure 2 As shown, an embodiment of the present invention further discloses a system for assisting dynamic pricing management of jewelry, the system comprising a processing module and a storage module, the processing module calls and executes a computer program stored in the storage module to perform the following method steps: Determine the raw material type and raw material origin of the target jewelry, obtain raw material price data according to the raw material type, and obtain exchange rate data according to the raw material origin; deriving a first target price of the target jewelry according to the raw material price data and the exchange rate data; Using web crawler technology to crawl customer discussion big data related to the target jewelry, analyzing the customer network discussion big data to obtain the positive popularity of the target jewelry, and determining a first price adjustment value based on the positive popularity; Using web crawler technology to crawl merchant promotion big data related to the target jewelry, analyzing the merchant promotion big data to obtain the promotion popularity of the target jewelry, and determining the second price adjustment value based on the promotion popularity; A price adjustment coefficient is calculated based on the first price adjustment value and the second price adjustment value, the first target price is corrected using the price adjustment coefficient to obtain a second target price, and the second target price is output to assist jewelry pricing personnel in updating the price of the target jewelry.

[0044] An embodiment of the present invention further discloses an electronic device, comprising: at least one processor, a memory, and a computer program stored in the memory and executable on the at least one processor, wherein the processor executes the computer program to implement the method described in the above embodiment.

[0045] An embodiment of the present invention further discloses a computer storage medium, wherein the computer storage medium stores a computer program, and the computer program is executed by a processor to implement the method described in the above embodiment.

[0046] The embodiment of the present invention further discloses a computer program product. The computer program includes a number of computer codes. When the computer codes are executed by a processor, any of the above methods is implemented.

[0047] The computer readable medium may be a readable signal medium or a readable storage medium. The readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination thereof. More specific examples (non-exhaustive list) of readable storage media include: an electrical connection with one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. When the computer program is executed by one or more data processing devices, the computer readable medium is enabled to implement the above-mentioned scheme of the present invention.

[0048] Through the description of the above implementation modes, it is easy for those skilled in the art to understand that the exemplary embodiments described in the present invention can be implemented by software, or by software combined with necessary hardware. Therefore, the technical solution according to the implementation mode of the present invention can be embodied in the form of a software product, which can be stored in a computer-readable storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, and includes several instructions to enable a data processing device (which can be a personal computer, a server, or a network device, etc.) to execute the above solution according to the present invention.

[0049] The computer readable medium may include a data signal propagated in a baseband or as part of a carrier wave, wherein a readable program code is carried. The data signal of this propagation may take a variety of forms, including but not limited to an electromagnetic signal, an optical signal, or any suitable combination of the above. The readable storage medium may also be any readable medium other than a readable storage medium, which may send, propagate, or transmit a program for use by an instruction execution system, an apparatus, or a device or used in combination with it. The program code contained on the readable storage medium may be transmitted with any appropriate medium, including but not limited to wireless, wired, optical cable, RF, etc., or any suitable combination of the above.

[0050] In summary, the present invention can be implemented by executing a computer program solution, system, electronic device or computer readable medium. In practice, a general data processing device such as a microprocessor or a digital signal processor (DSP) can be used to implement some or all functions of the present invention.

[0051] The specific embodiments described above further describe the purpose, technical solutions and beneficial effects of the present invention in detail. It should be understood that the present invention is not inherently related to any specific computer, virtual device or electronic device, and various general devices can also implement the present invention. The above description is only a specific embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for assisting dynamic pricing management of jewelry, characterized in that: The method comprises the following steps: Determine the raw material type and raw material origin of the target jewelry, obtain raw material price data according to the raw material type, and obtain exchange rate data according to the raw material origin; deriving a first target price of the target jewelry according to the raw material price data and the exchange rate data; Using web crawler technology to crawl customer discussion big data related to the target jewelry, analyzing the customer network discussion big data to obtain the positive popularity of the target jewelry, and determining a first price adjustment value based on the positive popularity; Using web crawler technology to crawl merchant promotion big data related to the target jewelry, analyzing the merchant promotion big data to obtain the promotion popularity of the target jewelry, and determining the second price adjustment value based on the promotion popularity; A price adjustment coefficient is calculated based on the first price adjustment value and the second price adjustment value, the first target price is corrected using the price adjustment coefficient to obtain a second target price, and the second target price is output to assist jewelry pricing personnel in updating the price of the target jewelry.

2. A method for assisting dynamic pricing management of jewelry according to claim 1, characterized in that: The raw material price data is obtained according to the raw material type, including: Acquire a plurality of first raw material price data from different quotation channels according to the raw material type; The first raw material price data and the corresponding quotation channel information are input into an artificial intelligence prediction model, and the artificial intelligence prediction model predicts the second raw material price data corresponding to the quotation channel information, calculates the average value of each second raw material price data, and determines the average value as the raw material price data.

3. A method for assisting dynamic pricing management of jewelry according to claim 2, characterized in that: The training method of the artificial intelligence prediction model is as follows: Analyze the price stability of the raw material type to obtain a price stability value, and determine a specified crawling time according to the price stability value; Acquire a historical quotation data set of each quotation path according to the specified crawling time, wherein the historical quotation data set includes a plurality of historical quotation data; Acquire merchant purchase price data corresponding to each of the historical quotation data, wherein the merchant purchase price data and the historical quotation data are in the same time period; The historical quotation data and the corresponding merchant purchase price data are constructed as a piece of training data, thereby constructing a training data set; The artificial intelligence prediction model is trained using the training data set with an unsupervised training method until a training termination condition is met.

4. A method for assisting dynamic pricing management of jewelry according to claim 3, characterized in that: The specified crawling duration is determined according to the price stability value, including: ; In the above formula, To specify the crawling time; Is the basic crawling time; is the adjustment coefficient, and ; is the price stability value, is a constant, and .

5. A method for assisting dynamic pricing management of jewelry according to claim 4, characterized in that: The method of obtaining the positive popularity of the target jewelry according to the customer network discussion big data analysis and determining the first price adjustment value according to the positive popularity includes: Perform semantic analysis on the customer network discussion big data to screen out customer network discussion data that contains positive evaluation information, perform statistical analysis on a first quantity of the customer network discussion data, calculate the positive heat based on the first quantity, and determine the first price adjustment value based on the positive heat and a corresponding first comparison relationship.

6. A method for assisting dynamic pricing management of jewelry according to claim 5, characterized in that: Analyzing the promotional big data of the merchant to obtain the promotional popularity of the target jewelry, and determining the second price adjustment value according to the promotional popularity, including: Perform statistical analysis on the merchant promotion big data to obtain a second quantity of promotion information, calculate the time concentration of each promotion information, calculate the promotion heat based on the second quantity and the time concentration, and determine the second price adjustment value based on the promotion heat and the corresponding second comparison relationship.

7. A system for assisting dynamic pricing management of jewelry, the system comprising a processing module and a storage module, characterized in that: The processing module calls and executes the computer program stored in the storage module to perform the following method steps: Determine the raw material type and raw material origin of the target jewelry, obtain raw material price data according to the raw material type, and obtain exchange rate data according to the raw material origin; deriving a first target price of the target jewelry according to the raw material price data and the exchange rate data; Using web crawler technology to crawl customer discussion big data related to the target jewelry, analyzing the customer network discussion big data to obtain the positive popularity of the target jewelry, and determining a first price adjustment value based on the positive popularity; Using web crawler technology to crawl merchant promotion big data related to the target jewelry, analyzing the merchant promotion big data to obtain the promotion popularity of the target jewelry, and determining the second price adjustment value based on the promotion popularity; A price adjustment coefficient is calculated based on the first price adjustment value and the second price adjustment value, the first target price is corrected using the price adjustment coefficient to obtain a second target price, and the second target price is output to assist jewelry pricing personnel in updating the price of the target jewelry.

8. An electronic device comprising: At least one processor, a memory, and a computer program stored in the memory and executable on the at least one processor, wherein the processor executes the computer program to implement the method according to any one of claims 1 to 6.

9. A computer storage medium storing a computer program, characterized in that: The computer program is executed by a processor to implement the method according to any one of claims 1 to 6.

10. A computer program product, characterized in that: The computer program includes a number of computer codes, and when the computer codes are executed by a processor, the method according to any one of claims 1 to 6 is implemented.

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