Recycling method, device and equipment applied to precious metal recycling system and medium

Abstract

The invention relates to a recovery method, device and equipment applied to a precious metal recovery system and a medium, and the method comprises the steps: screening out alternative recovery stores in a selection range according to a precious metal recovery request and a user address reported by a user equipment end; the optimal target recovery store is determined by using a k sigma algorithm by acquiring the historical passenger flow volume of the store to be selected and the recovery unit price of the corresponding noble metal type and color. And generating and returning precious metal recovery information to the user equipment side. And after the user agrees, the recovery submission mode and the collection information are extracted, and precious metal transaction information is generated and sent to the target store. And after confirmation of the store, agreement information is sent to the user, and intelligent and efficient processing of precious metal recovery is achieved. According to the method, the precious metal recovery convenience and the user experience are improved. And the precious metal recovery efficiency and the user experience are improved.

Description

Technical Field

[0001] The present disclosure relates to the field of recycling technology, and in particular to a recycling method, device, equipment and medium applied to a precious metal recycling system. Background Art

[0002] In the field of precious metal recycling, traditional recycling methods often rely on users to find recycling stores on their own, or contact recyclers through telephone, Internet, etc. This process is not only cumbersome but also inefficient. In addition, due to the large price fluctuations in the precious metal market, the recycling unit price of the same precious metal type at different recycling stores at the same time may be significantly different. In addition, users consider factors such as the reputation, service quality and geographical location of the recycling store, which makes users face many difficulties in choosing a recycling store.

[0003] In order to overcome the above problems, some precious metal recycling systems based on Internet technology have emerged. These systems usually receive precious metal recycling requests from users through user devices (such as smartphones, tablets, etc.), and recommend nearby recycling stores to users based on their geographic location information. However, when recommending recycling stores, these systems often only consider the geographical location of the store, while ignoring key information such as the store's customer flow and recycling unit price, resulting in users not being able to obtain the best recycling options. Summary of the invention

[0004] The purpose of the present invention is to provide a recovery method, device, equipment and medium applied to a precious metal recovery system, aiming to improve the convenience and efficiency of precious metal recovery and enhance the user experience of precious metal recovery.

[0005] In order to achieve the above-mentioned purpose, a first aspect of the embodiments of the present disclosure provides a recovery method applied to a precious metal recovery system, the method comprising: Receive a precious metal recycling request reported by a user device, and determine, according to the store address of the recycling store and the user address carried in the precious metal recycling request, the recycling store whose store address is within the selection range of the user address as an alternative recycling store for recycling the precious metal type to be recycled corresponding to the precious metal recycling request; Obtain the historical customer flow of the candidate recycling stores on each day within the historical preset time period, as well as the recycling unit price of the precious metal types and the precious metal purity corresponding to the precious metals to be recycled, and determine the target recycling store from the plurality of candidate recycling stores based on the historical customer flow of the candidate recycling stores on each day within the historical preset time period and the recycling unit price of the precious metal types corresponding to the precious metals to be recycled based on the k sigma algorithm; Generate precious metal recycling information for the precious metal to be recycled according to the store address of the target recycling store and the corresponding recycling unit price, and return the precious metal recycling information to the user device; receiving feedback information from the user device regarding the precious metal recycling information, and extracting the precious metal recycling submission method and recycling payment information carried in the feedback information when the feedback information indicates that the user device agrees to recycle the precious metals to be recycled through the target recycling store; Precious metal transaction information is generated based on the type and weight of the precious metals to be recycled, the method of submitting precious metal recycling, and the recycling collection information, and the precious metal transaction information is sent to the store device end corresponding to the target recycling store, and upon receiving the consent information of the store device end regarding the precious metal transaction information, the consent information is sent to the user device end.

[0006] In a possible implementation, the k sigma algorithm is based on the historical customer flow of the candidate recycling stores on each day within a preset historical period and the recycling unit price of the precious metal type corresponding to the precious metal to be recycled, and the target recycling store is determined from a plurality of the candidate recycling stores, including: According to the store historical customer flow of each candidate recycling store on each day within the historical preset time period, calculate the store customer flow mean of each candidate recycling store in the time interval corresponding to the historical moments from the current moment to different preset time lengths, and calculate the customer flow standard deviation of each candidate recycling store in each time interval; According to the store customer flow mean and the customer flow standard deviation corresponding to each time interval of each of the candidate recycling stores, and the preset value of k in the k sigma algorithm, the Laida confidence interval of the store customer flow corresponding to each of the time intervals corresponding to each of the candidate recycling stores is calculated; Matching the Laida confidence interval of the store customer flow corresponding to each of the time intervals corresponding to each of the candidate recycling stores, the recycling unit price of the type of precious metal corresponding to the precious metal to be recycled by each of the candidate recycling stores; Determine an average of the first recycling unit prices of all the candidate recycling stores according to the recycling unit prices corresponding to each of the candidate recycling stores in each of the time intervals; The target recycling store is determined from a plurality of the candidate recycling stores according to the recycling prices of the candidate recycling stores within the historical preset period of time and the average of the first recycling unit prices.

[0007] In a possible implementation, the method of calculating the store customer flow average of each candidate recycling store in a time interval corresponding to a historical moment from the current moment to different preset time lengths according to the store historical customer flow of each candidate recycling store on each day within the historical preset time length, and calculating the customer flow standard deviation of each candidate recycling store in each time interval includes: According to the store historical customer flow of each candidate recycling store on each day within the historical preset time period, determine the store historical customer flow of each candidate recycling store on each day within the time interval corresponding to the historical moment of different preset time lengths from the current moment, wherein different preset time lengths are determined in units of weeks; Determine whether the weekly historical total customer flow of each candidate recycling store in each of the time intervals is greater than a preset total customer flow threshold; When the weekly historical total customer flow of the store in each week within each of the time intervals corresponding to the candidate recycling store is greater than the preset total customer flow threshold, the time interval is used as the target time interval of the corresponding candidate recycling store; According to the historical customer flow of the store on each day within the target time interval corresponding to the candidate recycling store, the store customer flow mean and the customer flow standard deviation within the target time interval corresponding to the candidate recycling store are calculated.

[0008] In a possible implementation, the method further includes: When the weekly historical total customer flow of the candidate recycling store in each week within each of the time intervals corresponding to the candidate recycling store is less than the preset total customer flow threshold, the candidate recycling store is used as the target recycling store.

[0009] In a possible implementation, determining the target recycling store from a plurality of candidate recycling stores according to the recycling prices of the candidate recycling stores within the historical preset period and the average of the first recycling unit prices includes: According to the recycling prices of the candidate recycling stores within the historical preset time period, determine the average second recycling unit price of the precious metal type recycled by each of the candidate recycling stores within the historical preset time period; From the multiple candidate recycling stores, the candidate recycling stores whose second average recycling unit price is greater than the first average recycling unit price are determined as the target recycling stores.

[0010] In a possible implementation, the Laida confidence interval of the store customer flow corresponding to each of the time intervals corresponding to each of the candidate recycling stores is calculated according to the store customer flow mean and the customer flow standard deviation corresponding to each of the time intervals of each of the candidate recycling stores, and the preset value of k in the k sigma algorithm, including: According to the customer flow standard deviation corresponding to each time interval of each candidate recycling store and the preset value of k in the k sigma algorithm, the product of the preset value of k in the k sigma algorithm and the customer flow standard deviation corresponding to each time interval of each candidate recycling store is calculated to obtain a first calculated value; The Laida confidence interval of the store customer flow corresponding to each time interval of each of the alternative recycling stores is determined according to the difference and sum of the mean value of the store customer flow corresponding to each of the time intervals of each of the alternative recycling stores and the corresponding first calculated value.

[0011] In a possible implementation, the method further includes: In the case where the feedback information indicates that the user device refuses to recycle the precious metal to be recycled through the target recycling store, the following steps are executed in a loop: Expanding the selection range according to the distance difference between the maximum distance and the minimum distance between the store address of the currently selected candidate recycling store and the user address; According to the expanded selection range, the steps of determining, based on the store address of the recycling store and the user address carried in the precious metal recycling request, the recycling store whose store address is within the selection range of the user address as an alternative recycling store for recycling the precious metal type to be recycled corresponding to the precious metal recycling request, to returning the precious metal recycling information to the user device end are executed; Until the number of times the precious metal recycling information is returned to the user device reaches a preset number, and / or the feedback information returned by the user device indicates that the user device agrees to recycle the precious metals to be recycled through the target recycling store sent for the last time.

[0012] A second aspect of the embodiments of the present disclosure provides a recovery device applied to a precious metal recovery system, comprising: A receiving and determining module is configured to receive a precious metal recycling request reported by a user device, and determine, according to the store address of the recycling store and the user address carried in the precious metal recycling request, the recycling store whose store address is within the selection range of the user address as an alternative recycling store for recycling the precious metal type to be recycled corresponding to the precious metal recycling request; The acquisition and determination module is configured to acquire the store customer flow of the candidate recycling store within a preset historical period of time and the recycling unit price of the precious metal type and the precious metal purity corresponding to the precious metal to be recycled, and determine the target recycling store from the plurality of candidate recycling stores based on the store customer flow corresponding to the candidate recycling store and the recycling unit price of the precious metal type corresponding to the precious metal to be recycled based on the k sigma algorithm; A first generating and sending module is configured to generate precious metal recycling information for the precious metal to be recycled according to the store address of the target recycling store and the corresponding recycling unit price, and return the precious metal recycling information to the user device; A receiving and extracting module is configured to receive feedback information from the user device regarding the precious metal recycling information, and extract the precious metal recycling submission method and recycling payment information carried in the feedback information when the feedback information indicates that the user device agrees to recycle the precious metals to be recycled through the target recycling store; The second generation and sending module is configured to generate precious metal transaction information based on the type and weight of the precious metals to be recycled, the precious metal recycling submission method and the recycling collection information, and send the precious metal transaction information to the store device end corresponding to the target recycling store, and upon receiving the consent information of the store device end regarding the precious metal transaction information, send the consent information to the user device end.

[0013] According to a third aspect of the present disclosure, an electronic device is provided, including: a memory having a computer program stored thereon; A processor is used to execute the computer program in the memory to implement the steps of any one of the methods in the first aspect.

[0014] According to a fourth aspect of an embodiment of the present disclosure, a computer-readable storage medium is provided, on which a computer program is stored, and when the program is executed by a processor, the steps of any one of the methods described in the first aspect are implemented.

[0015] The present invention provides a recovery method, device, equipment and medium applied to a precious metal recovery system. Compared with the prior art, it has the following beneficial effects: By receiving precious metal recycling requests reported by the user's device, and according to the store address of the recycling store and the user's address, the recycling store within the user's address selection range is determined as an alternative recycling store. This can effectively narrow the user's search range and improve the accuracy of the recommendation. By obtaining the store traffic of the alternative recycling store within the historical preset time period and the recycling unit price of the corresponding precious metal type and precious metal quality, and based on the k sigma algorithm, the target recycling store is determined from multiple alternative recycling stores, and the stability and reliability of the recycling store for precious metal recycling can be evaluated. It can screen out convenient and economical recycling stores for users.

[0016] Furthermore, after the target recycling store is determined, precious metal recycling information is generated based on the store address and recycling unit price, and returned to the user's device. The user can decide whether to agree to recycling through the target recycling store based on the recycling information. If the user agrees, the precious metal recycling submission method and recycling collection information fed back by the user's device will be further extracted to generate precious metal transaction information and send it to the store device corresponding to the target recycling store. After confirming that the transaction information is correct, the store device sends a consent message to the user's device to complete the entire recycling process.

[0017] To sum up, the precious metal recycling method of the present invention provides users with a more convenient, efficient and economical precious metal recycling service by comprehensively considering multiple factors such as store address, customer flow, recycling unit price, etc., effectively solving the problems existing in traditional recycling methods, thereby improving the convenience and efficiency of precious metal recycling, and improving the user experience of precious metal recycling.

[0018] Other features and advantages of the present disclosure will be described in detail in the following detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] The accompanying drawings are used to provide a further understanding of the present disclosure and constitute a part of the specification. Together with the following specific embodiments, they are used to explain the present disclosure but do not constitute a limitation of the present disclosure. In the accompanying drawings: Figure 1 It is a flow chart of a recovery method applied to a precious metal recovery system according to an embodiment of the specification.

[0020] Figure 2 It is a block diagram of a recovery device applied to a precious metal recovery system according to an embodiment of the specification.

[0021] Figure 3 It is a block diagram of another recovery device applied to a precious metal recovery system according to an embodiment of the specification. DETAILED DESCRIPTION

[0022] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0023] The specific implementation of the present disclosure is described in detail below in conjunction with the accompanying drawings. It should be understood that the specific implementation described herein is only used to illustrate and explain the present disclosure, and is not used to limit the present disclosure.

[0024] The present invention provides a recovery method applied to a precious metal recovery system. Figure 1 The flowchart of a recovery method applied to a precious metal recovery system according to an embodiment is shown. The method can be applied to a precious metal recovery platform system, and specifically the method includes: In step S11, a precious metal recycling request reported by a user device is received, and according to the store address of the recycling store and the user address carried in the precious metal recycling request, the recycling store whose store address is within the selection range of the user address is determined as an alternative recycling store for recycling the precious metal type to be recycled corresponding to the precious metal recycling request; Among them, the precious metal recycling request can be a request for precious metals (such as gold, silver, platinum, etc.) that the user wishes to recycle through a device (such as a mobile phone, computer, etc.). For example, when a user decides to recycle the precious metals he holds, he can submit a recycling request including information such as the type, weight, fineness, and user's own address through the device application or webpage. After the platform receives this request, it can conduct, for example, manual review or machine review to determine whether the information such as the type of precious metal is true. For example, a user can upload a certificate corresponding to gold (such as an electronic signature) to the precious metal recycling application, and then submit a request for recycling gold with the certificate, which includes the weight, fineness, and home address of the gold.

[0025] The store address is the geographic location information of the precious metal recycling store, which is pre-stored on the precious metal recycling platform. The user address is the user's own geographic location information provided when submitting a recycling request. The selection range is a geographical area set based on the user's address, which is used to filter recycling stores that are close to the user. Usually, it can be set based on the density of precious metal recycling stores in the area where the user's address is located.

[0026] In the disclosed embodiment, based on the address provided by the user, a recycling store that is close to the user and can handle the type of precious metals that the user wishes to recycle is screened out. The system first receives a precious metal recycling request reported by the user's device, which includes the user's address and detailed information (such as type, fineness, etc.) of the precious metals to be recycled. Then, based on the store address of the recycling store and the user's address, the system calculates the distance between each store and the user, and determines the stores within the selection range set by the user as candidate recycling stores.

[0027] For example: suppose a user submits a gold recycling request through the APP, and his residential address is "XX Street, Chaoyang District, Beijing". After receiving the request, the system queries the address information of all gold recycling stores, calculates the distance between these stores and the user's address, and selects stores within the 5-kilometer selection range set by the user as alternative recycling stores.

[0028] In step S12, the store customer flow of the candidate recycling store within a preset historical period and the type of precious metal corresponding to the precious metal to be recycled and the recycling unit price of the precious metal purity are obtained, and based on the k sigma algorithm, according to the store customer flow corresponding to the candidate recycling store and the recycling unit price of the type of precious metal corresponding to the precious metal to be recycled, a target recycling store is determined from a plurality of the candidate recycling stores; Among them, store traffic refers to the number of customers received by the recycling store in a certain period of time, reflecting the store's busyness and attractiveness. The recycling unit price is the price quoted by the recycling store for a specific type and fineness of precious metals.

[0029] In the disclosed embodiment, the passenger flow data within the corresponding time period can be extracted from the integrated data according to the historical preset time period set by the system (such as the past month, three months, etc.). The extracted historical passenger flow data is statistically analyzed, such as calculating the average daily passenger flow.

[0030] At the same time, the system regularly (such as daily and weekly) obtains the latest precious metal recycling prices released by recycling stores, including the recycling unit prices of different types (such as gold, silver, platinum, etc.) and different fineness (such as pure gold, 999 gold, etc.). The system captures the latest recycling unit price data from the price publishing system through the API interface or web crawler technology. The captured data is parsed and processed to extract the recycling unit price of specific precious metal types and fineness.

[0031] In the disclosed embodiment, one or more optimal stores are selected as target recycling stores from multiple candidate recycling stores based on historical customer flow and recycling unit price. The system first obtains the store customer flow of each candidate recycling store within a historical preset period of time (such as the past month), as well as the recycling unit price for the type and quality of precious metals that the user wants to recycle. Then, the k sigma algorithm is used to process these data, and based on the comprehensive performance of store customer flow and recycling unit price, the store with the best performance (such as moderate customer flow and high recycling unit price) is selected from the candidate stores as the target recycling store.

[0032] Continuing with the above example, the system obtains the customer traffic of all candidate recycling stores in the past month and the recycling unit price for gold (the type of precious metal that users want to recycle). Suppose there are two candidate stores A and B. Store A has moderate customer traffic and a higher recycling unit price; although store B has a large customer traffic, its recycling unit price is lower. The system uses the k sigma algorithm to process these data and finds that the comprehensive performance of store A's customer traffic and recycling unit price is better than that of store B, so store A is determined as the target recycling store.

[0033] In this way, it is possible to intelligently push traffic to the recycling store based on the customer flow of the alternative recycling store and the recycling unit price of the type of precious metal corresponding to the precious metal to be recycled.

[0034] In step S13, precious metal recycling information for the precious metal to be recycled is generated according to the store address of the target recycling store and the corresponding recycling unit price, and the precious metal recycling information is returned to the user device; The precious metal recycling information may include a data packet of detailed information such as the target recycling store address, recycling unit price, type and quality of the precious metals to be recycled. The user device is the same as the above, which is the device used by the user to submit the precious metal recycling request, such as a smartphone, tablet computer, etc.

[0035] In the disclosed embodiment, after the target recycling store is determined, the system will generate a detailed precious metal recycling information based on the store's address and recycling unit price, combined with the precious metal information to be recycled (such as type, quality, etc.) submitted by the user. This information is intended to clearly show the user the specific details of the recycling, including key elements such as recycling location and price. Subsequently, the system will send this information back to the user's device via the network for the user to review and confirm.

[0036] For example: Assume that the target recycling store is "Jindian Recycling Store", located at "No. XX, XX Road, Chaoyang District, Beijing", and the recycling price of gold (the type of precious metal to be recycled by the user) is 300 yuan per gram. The system will generate a precious metal recycling information based on this information, combined with the gold fineness and other details submitted by the user, and push it to the user's mobile phone through the APP. The information content may include: "The recycling store you can choose is Jindian Recycling Store, located at No. XX, XX Road, Chaoyang District, and the recycling price of gold is 300 yuan per gram. After confirming that it is correct, select the recycling method and submit it." In step S14, feedback information from the user device regarding the precious metal recycling information is received, and when the feedback information indicates that the user device agrees to recycle the precious metals to be recycled through the target recycling store, the precious metal recycling submission method and recycling payment information carried in the feedback information are extracted; Among them, feedback information is the response made by the user's device to the precious metal recycling information, which may include the decision to agree or refuse recycling, as well as the recycling submission method and payment information. The precious metal recycling submission method is the method selected by the user to send the precious metals to be recycled to the recycling store, such as self-delivery, express mail, etc. The recycling payment information is the financial information such as the bank account and Alipay account into which the user hopes to deposit the recycling funds.

[0037] In the disclosed embodiment, the system will continuously monitor the feedback from the user's device. Once the user reads and makes a decision (agree or refuse to recycle), the system will immediately receive the corresponding feedback information. If the user agrees to recycle, the feedback information will also include the recycling submission method and payment information selected by the user. The system will parse and extract this information to prepare for subsequent transaction processing.

[0038] For example: after reading the precious metal recycling information, the user decides to agree to the recycling, and selects "self-delivery" as the recycling submission method, and provides his own Alipay account as the payment information. After receiving the user's feedback information, the system will extract the two key information of "self-delivery" and Alipay account from it, and prepare for the next step of transaction processing.

[0039] In step S15, precious metal transaction information is generated based on the type and weight of the precious metals to be recycled, the precious metal recycling submission method and the recycling collection information, and the precious metal transaction information is sent to the store device end corresponding to the target recycling store, and upon receiving the consent information of the store device end for the precious metal transaction information, the consent information is sent to the user device end.

[0040] Among them, precious metal transaction information may include data packets of transaction details such as the type, weight, recycling unit price, submission method, and payment information of the precious metals to be recycled. The store equipment end is the equipment used by the recycling store to receive and process precious metal transaction information, such as computers, tablet computers, cash registers, etc. The consent information is the affirmative response made by the store equipment end to the precious metal transaction information, indicating consent to recycling according to the transaction information.

[0041] In the disclosed embodiment, after extracting the user's recycling submission method and payment information, the system will generate a complete precious metal transaction information based on the type and weight of the precious metals to be recycled and the previously determined recycling unit price. Then, the system will send this information to the device end of the target recycling store through the network for the store staff to review and confirm. Once the store staff agrees to recycle according to the transaction information, the system will receive the consent information sent by the store device end and immediately forward this information to the user's device end to inform the user that the transaction has been successfully confirmed.

[0042] For example: Based on the user's selection and the detailed information of the gold to be recycled, the system generates a precious metal transaction information and sends it to the computer terminal of the "Jindian Recycling Store" through the network. After checking the information, the store staff confirms that it is correct and agrees to recycle according to the transaction information. After receiving the store's consent information, the system immediately sends a notification to the user through the APP: "Your gold recycling transaction has been successfully confirmed. Please deliver the gold to the Jindian Recycling Store at the agreed time." The above technical solution receives the precious metal recycling request reported by the user device, and determines the recycling store within the user address selection range as the alternative recycling store according to the store address of the recycling store and the user address. It can effectively narrow the user's search range and improve the accuracy of the recommendation. By obtaining the store traffic of the alternative recycling store within the historical preset time period and the recycling unit price of the corresponding precious metal type and precious metal quality, and based on the k sigma algorithm, the target recycling store is determined from multiple alternative recycling stores, and the stability and reliability of the recycling store for precious metal recycling can be evaluated. It can screen out convenient and economical recycling stores for users.

[0043] Furthermore, after the target recycling store is determined, precious metal recycling information is generated based on the store address and recycling unit price, and returned to the user's device. The user can decide whether to agree to recycling through the target recycling store based on the recycling information. If the user agrees, the precious metal recycling submission method and recycling collection information fed back by the user's device will be further extracted to generate precious metal transaction information and send it to the store device corresponding to the target recycling store. After confirming that the transaction information is correct, the store device sends a consent message to the user's device to complete the entire recycling process.

[0044] To sum up, the precious metal recycling method of the present invention provides users with a more convenient, efficient and economical precious metal recycling service by comprehensively considering multiple factors such as store address, customer flow, recycling unit price, etc., effectively solving the problems existing in traditional recycling methods, thereby improving the convenience and efficiency of precious metal recycling, and improving the user experience of precious metal recycling.

[0045] In a possible implementation, in step S12, the k sigma algorithm is used to determine a target recycling store from a plurality of candidate recycling stores according to the store historical customer flow of the candidate recycling store on each day within a historical preset period of time and the recycling unit price of the precious metal type corresponding to the precious metal to be recycled, including: In step S121, based on the store historical customer flow of each candidate recycling store on each day within the historical preset time period, the store customer flow mean of each candidate recycling store in the time interval corresponding to the historical moments from the current moment to different preset time lengths is calculated, and the customer flow standard deviation of each time interval corresponding to each candidate recycling store is calculated; The mean customer flow is the average customer flow of the store in each time interval. Similarly, the standard deviation of customer flow is the square root of the average of the sum of the squares of the deviations between the store customer flow and the mean customer flow in each time interval, which is used to measure the degree of dispersion of customer flow.

[0046] Among them, the time interval corresponding to the historical moments from the current moment to different preset time lengths can be a time interval obtained by tracing back from the current moment to multiple different preset time lengths, wherein the preset time length is related to the historical preset time length. For example, if the historical preset time length is, for example, two months, then every ten days is a preset time length, then you can get the current moment to the past ten days, the current moment to the past twenty days, the current moment to the past thirty days, ..., and so on, until all moments within the historical preset time length are divided into the time interval. For another example, if the historical preset time length is, for example, six months, and each month is a preset time length, then you can get the current moment to the past month, the current moment to the past two months, the current moment to the past three months, ..., and so on, until all moments within the historical preset time length are divided into the time interval.

[0047] In the embodiment of the present disclosure, in order to evaluate the stability and volatility of customer flow of the candidate recycling stores in different time periods, the average customer flow level of the store in a certain time interval can be understood by calculating the average customer flow in different time intervals; and the standard deviation of customer flow reflects the fluctuation of customer flow.

[0048] For example, assume that the customer flow data of each day of the selected recycling store A has been obtained within a preset historical period (such as the past month). The customer flow mean and standard deviation of store A in different preset time intervals (such as 7 days, 14 days, and 30 days) from the current time (such as October 1) can be calculated.

[0049] For the 7-day period (September 25 to October 1), we first calculated the sum of the daily passenger flow in these 7 days, and then divided it by 7 to get the mean passenger flow. Next, we calculated the square of the difference between the passenger flow and the mean on each day, summed it up, and then divided it by 7 to get the variance, and finally took the square root of the variance to get the standard deviation of the passenger flow.

[0050] Repeat the above process to calculate the mean and standard deviation of passenger flow in the 14-day (September 18 to October 1) and 30-day (September 2 to October 1) time periods.

[0051] In step S122, according to the store customer flow mean and the customer flow standard deviation corresponding to each time interval of each candidate recycling store, and the preset value of k in the k sigma algorithm, the Laida confidence interval of the store customer flow corresponding to each time interval of each candidate recycling store is calculated; Among them, the Laida confidence interval is an interval calculated by standard deviation and mean based on the normal distribution assumption, which contains the observed values ​​at a certain confidence level.

[0052] In the disclosed embodiment, the Laida confidence interval of the customer flow of each candidate recycling store in different time intervals is calculated using the customer flow mean, standard deviation, and k value in the k sigma algorithm. This interval gives the range that the store customer flow may fall into at a certain confidence level. By comparing the confidence intervals of different stores in the same time interval, their customer flow stability and reliability can be evaluated.

[0053] Continuing with the example of alternative recycling store A, assuming that k=2 is selected (that is, a 95% confidence level, because for a normal distribution, approximately 95% of the data points fall within the range of ±2 standard deviations of the mean), calculate the Laida confidence interval of store A's customer flow in the 7-day, 14-day, and 30-day time periods.

[0054] For the 7-day time interval, the passenger flow mean and standard deviation calculated in step S121 and k=2 are used to calculate the lower limit (mean-2 standard deviation) and upper limit (mean+2 standard deviation) of the confidence interval.

[0055] Repeat the above process to calculate the Laida confidence interval of passenger flow in the 14-day and 30-day time intervals.

[0056] Finally, the mean, standard deviation, and confidence interval of customer flow of alternative recycling store A in different time intervals can be obtained. These data will serve as important inputs for subsequent decision-making algorithms to evaluate the stability and reliability of the store's customer flow.

[0057] In step S123, the recycling unit price of the type of precious metal corresponding to the precious metal to be recycled by each of the candidate recycling stores is matched in the Laida confidence interval of the store customer flow corresponding to each of the time intervals corresponding to each of the candidate recycling stores; In the disclosed embodiment, the Laida confidence interval of the customer flow of each candidate recycling store in different time intervals is matched with the recycling unit price in the corresponding time interval. The purpose of this is to evaluate the recycling unit price level of each store when the customer flow is relatively stable (i.e., falls within the confidence interval).

[0058] For example, suppose we have calculated the Laida confidence intervals of customer traffic of candidate recycling store A in the 7-day, 14-day, and 30-day time intervals, and we have also obtained the recycling unit prices of store A for specific precious metals (such as gold) in these time intervals. Now, we need to match these recycling unit prices with the corresponding customer traffic confidence intervals.

[0059] For the 7-day time interval, if the customer flow of store A falls within the confidence interval, we record the recycling unit price within that time interval.

[0060] Repeat the above process and match the 14-day and 30-day time intervals.

[0061] In step S124, according to the recycling unit price corresponding to each of the candidate recycling stores in each of the time intervals, an average of the first recycling unit prices of all the candidate recycling stores is determined; Among them, the first recycling unit price average is the average unit price of recycling specific precious metal types by all alternative recycling stores in multiple time periods.

[0062] In the disclosed embodiment, the average recycling unit price of all candidate recycling stores for a specific type of precious metal in the same time interval is calculated, and this average value will serve as an important reference indicator for subsequent decision-making. For example, suppose there are three candidate recycling stores A, B, and C, and their recycling unit prices for gold in four time intervals of 7 days, 14 days, 21 days, and 28 days have been obtained (these unit prices have been matched with the customer flow confidence intervals). Now, calculate the average recycling unit price of these three stores in the four time intervals. That is, add the recycling unit prices of stores A, B, and C in the four time intervals, and then divide them by the number of stores 3 and the number of time intervals 12 to obtain the first recycling unit price average.

[0063] In step S125, the target recycling store is determined from a plurality of the candidate recycling stores according to the recycling prices of the candidate recycling stores within the historical preset time period and the average of the first recycling unit prices.

[0064] In the disclosed embodiment, the customer flow stability and recycling unit price of the candidate recycling stores are comprehensively considered to determine an optimal target recycling store from multiple candidate stores. This usually involves weighing and comparing the customer flow confidence interval and the recycling unit price mean.

[0065] For example, it is assumed that the Laida confidence interval of customer flow and the mean recycling unit price of all candidate recycling stores in four time intervals have been calculated, and then the target recycling store is determined based on this information.

[0066] In one implementation, first, the width of the confidence interval of customer flow of each store can be compared, and the store with a narrower confidence interval (i.e., more stable customer flow) can be selected as a candidate. Then, among the candidate stores, their average recycling unit prices are compared, and the store with a higher unit price is selected as the target recycling store.

[0067] By matching the confidence interval of customer flow with the recycling unit price, calculating the mean of the recycling unit price, and comprehensively evaluating multiple factors, the target recycling store was finally determined. This process comprehensively considers the stability of customer flow and the economic efficiency of the recycling unit price, providing users with a more reliable and efficient precious metal recycling service.

[0068] The above technical solution can intuitively understand the stability and volatility of the customer flow of the alternative recycling stores by calculating the mean and standard deviation of the customer flow in different time intervals. This is crucial for store traffic promotion, because stable customer flow means that the store can continue to attract customers, improve service efficiency and customer satisfaction. The Laida confidence interval gives the range that the store customer flow may fall into at a certain confidence level. For store traffic promotion, this helps to predict the changing trend of future customer flow, so as to make more reasonable resource allocation and service arrangements. By comparing the relationship between actual customer flow and confidence interval, abnormal customer flow can be discovered and handled in a timely manner to avoid waste of resources or decline in service quality.

[0069] Furthermore, by matching the recycling unit price with the confidence interval of customer flow, we can analyze the changes in the recycling unit price of stores at different customer flow levels. This helps to formulate a more reasonable pricing strategy for store traffic promotion to attract more customers and improve profitability. By calculating the average recycling unit price of all candidate recycling stores within the time interval, the price competitiveness of each store can be evaluated. This helps to identify stores with obvious price advantages for store traffic promotion, so as to carry out key promotion or cooperation. In this way, considering the stability of customer flow and the recycling unit price factors, the target recycling store is determined from multiple candidate recycling stores.

[0070] This helps optimize store layout and resource allocation, improve overall service quality and profitability. And by selecting stores with stable customer flow and reasonable recycling unit prices as target recycling stores, customers' recycling experience and satisfaction can be improved, thereby enhancing brand loyalty and market competitiveness. This in turn helps optimize store layout and resource allocation, improve service quality and profitability, and enhance customer experience and satisfaction.

[0071] In a possible implementation, in step S121, according to the store historical customer flow of each candidate recycling store on each day within the historical preset time period, the store customer flow mean of each candidate recycling store in the time interval corresponding to the historical moments from the current moment to different preset time lengths is calculated, and the customer flow standard deviation of each candidate recycling store in each time interval is calculated, including: In step S1211, based on the store historical customer flow of each candidate recycling store on each day within the historical preset time period, determine the store historical customer flow of each candidate recycling store on each day within the time interval from the current moment to the historical moment corresponding to the different preset time lengths, wherein the different preset time lengths are determined in units of weeks; Among them, the store’s historical customer flow refers to the customer flow data of the alternative recycling store every day within a historical preset time period.

[0072] In the disclosed embodiment, based on the customer flow data of the candidate recycling store on each day within the historical preset time period, the historical customer flow of the store on each day within the time interval corresponding to the historical moments from the current moment to different preset time lengths (in weeks) is determined.

[0073] For example: Assume that the historical preset time period is the past month (about 4 weeks), and the candidate recycling store A has customer flow data every day in this month. Determine the historical customer flow of store A every day in the 1 week, 2 weeks, 3 weeks, and 4 weeks calculated from the current time.

[0074] For the first time interval, directly obtain the customer flow data of store A for each day in the first week. Similarly, for the second time interval, obtain the customer flow data of store A for each day in these two weeks. For the third time interval, obtain the customer flow data of store A for each day in these three weeks. For the fourth time interval, obtain the customer flow data of store A for each day in these four weeks.

[0075] In step S1212, it is determined whether the weekly historical total customer flow of each candidate recycling store in each of the time intervals is greater than a preset total customer flow threshold; The weekly store historical total customer flow is the sum of the weekly customer flow of the candidate recycling store within a certain time period. The preset total customer flow threshold is a critical value used to determine whether the weekly store historical total customer flow has reached a certain level.

[0076] In the disclosed embodiment, it is checked whether the total weekly customer flow of the candidate recycling store in each time interval is greater than the preset total customer flow threshold. This is helpful to screen out stores with a certain level of customer flow. For example: Taking store A as an example, it is assumed that the preset total customer flow threshold is 1000 people / week. Check whether the total weekly customer flow of store A in the 1st to 4th time interval is greater than 1000 people.

[0077] In step S1213, when the weekly historical total customer flow of the store in each week within each time interval corresponding to the candidate recycling store is greater than the preset total customer flow threshold, the time interval is used as the target time interval of the corresponding candidate recycling store; The target time interval is the time period during which the historical total customer flow of the candidate recycling store in each week is greater than the preset total customer flow threshold.

[0078] In the disclosed embodiment, in each time interval corresponding to the candidate recycling store, the time interval in which the weekly store historical total customer flow is greater than the preset total customer flow threshold is selected as the target time interval of the store. This ensures that the customer flow data for subsequent analysis is representative and stable, and can reflect the customer flow characteristics of the store over a longer period of time.

[0079] In step S1214, based on the historical customer flow of the store on each day within the target time interval corresponding to the candidate recycling store, the store customer flow mean and the customer flow standard deviation within the target time interval corresponding to the candidate recycling store are calculated.

[0080] In the disclosed embodiment, based on the customer flow data of each candidate recycling store on each day within the target time interval, the customer flow mean and standard deviation of each candidate recycling store within all target time intervals are calculated. These two statistics can intuitively reflect the overall level and fluctuation of store customer flow.

[0081] The above technical solution can carefully process the customer flow data of the candidate recycling stores every day within the historical preset time period, and divide these data according to different preset time lengths (in weeks) to form multiple time intervals. This ensures the integrity and consistency of the data. Furthermore, by setting the preset total customer flow threshold, those stores with relatively stable weekly customer flow in each time interval are screened out. This helps to exclude stores with large customer flow fluctuations that may be affected by special events, thereby improving the accuracy and reliability of the analysis.

[0082] Furthermore, based on the previous screening results, the target time interval for each candidate recovery store is determined. The customer flow data in this time interval is representative and stable, and can truly reflect the customer flow characteristics of the store over a longer period of time. By calculating the mean and standard deviation of the store's customer flow in the target time interval, a quantitative description of the store's customer flow characteristics is achieved. These two statistics can not only reflect the overall level of store customer flow, but also reveal the fluctuation of customer flow.

[0083] In this way, by finely processing data, screening stable stores, determining representative time intervals, and quantifying customer flow characteristics, a scientific and accurate method is provided for analyzing customer flow of candidate recycling stores. This helps companies better understand the customer flow of stores, thereby formulating more reasonable recycling and pricing strategies, and improving resource recycling efficiency and user experience.

[0084] In a possible implementation, the method further includes: When the weekly historical total customer flow of the candidate recycling store in each week within each of the time intervals corresponding to the candidate recycling store is less than the preset total customer flow threshold, the candidate recycling store is used as the target recycling store.

[0085] In the disclosed embodiment, the candidate recycling stores are screened for stores whose weekly customer flow is continuously lower than the preset total customer flow threshold in all the checked time intervals. These stores may have a continuous low customer flow due to various factors such as geographical location, market demand, and competitive environment, and are therefore considered as target recycling stores that require special attention. Identifying these stores can help promote traffic to increase the customer flow and recycling efficiency of these stores.

[0086] For example, suppose the weekly total customer flow data of the equipment recycling store B in four time periods is as follows: The first time period (week 1): 800 people; The second time period (week 1-2): 800 people in the first week, 750 people in the second week; The third time period (weeks 1-3): 800 people in the first week, 750 people in the second week, and 950 people in the third week; The fourth time period (weeks 1-4): 800 people in the first week, 750 people in the second week, 950 people in the third week, and 800 people in the fourth week; If the preset total customer flow threshold is 1,000 people / week, then: within the four time periods, the customer flow of store B each week is far lower than the preset total customer flow threshold.

[0087] Therefore, store B's weekly customer flow is continuously lower than the preset threshold in all the time periods checked, so it can be regarded as a target recycling store. This means that store B may need to promote traffic to increase its customer flow and recycling efficiency.

[0088] In this way, it is possible to more accurately identify alternative recycling stores with persistently low customer traffic and increase traffic to these stores, thereby improving overall recycling efficiency and customer satisfaction.

[0089] In a possible implementation, in step S125, determining the target recycling store from a plurality of candidate recycling stores according to the recycling prices of the candidate recycling stores within the historical preset period and the average of the first recycling unit prices includes: In step S1251, according to the recycling prices of the candidate recycling stores within the historical preset time period, the average second recycling unit price of the precious metal type recycled by each of the candidate recycling stores within the historical preset time period is determined; Among them, the second average recycling unit price is the average recycling unit price of precious metals of the same type as the precious metals to be recycled, recycled by the alternative recycling stores within the historical preset time period.

[0090] In the disclosed embodiment, the second average recycling unit price of each candidate recycling store for recycling the same type of precious metal as the precious metal to be recycled within a preset historical period is calculated. For example, this can be achieved by statistically analyzing all recycling records of the candidate recycling store within the time period. Specifically, the system can collect all recycling transaction data of each store within a specified time period, and then calculate the recycling unit price of each precious metal, and take the average value as the second average recycling unit price.

[0091] In step S1252, from the multiple candidate recycling stores, the candidate recycling store whose second average recycling unit price is greater than the first average recycling unit price is determined as the target recycling store.

[0092] In the disclosed embodiment, those stores whose second recycling unit price average is higher than the first recycling unit price average are selected from a plurality of candidate recycling stores as target recycling stores. If the recycling unit price average of a candidate recycling store is higher than the market or industry average, then the store may have stronger competitiveness or higher profitability.

[0093] The above technical solution can identify alternative recycling stores that have relatively high recycling prices and may be more competitive.

[0094] In a possible implementation, in step S122, the Laida confidence interval of the store customer flow corresponding to each of the time intervals corresponding to each of the candidate recycling stores is calculated according to the store customer flow mean and the customer flow standard deviation corresponding to each of the time intervals of each of the candidate recycling stores, and the preset value of k in the k sigma algorithm, including: In step S1221, according to the customer flow standard deviation corresponding to each time interval of each candidate recycling store and the preset value of k in the k sigma algorithm, the product of the preset value of k in the k sigma algorithm and the customer flow standard deviation corresponding to each time interval of each candidate recycling store is calculated to obtain a first calculated value; Among them, the standard deviation of customer flow can be used to reflect the degree of dispersion of store customer flow data, that is, the fluctuation of customer flow around its mean. The preset value of k is in the Laida criterion. Usually k=2 represents a 95% execution level, and k=3 represents a 99.73% confidence level. However, k here is a preset value and can be adjusted according to actual needs to balance the confidence level and the error rate.

[0095] In the embodiment of the present disclosure, for each candidate recycling store in each time interval, the customer flow standard deviation is multiplied by the preset value of k, and the result obtained is the product (first calculated value) of the store in the time interval. This value represents the range of customer flow fluctuations above and below the mean.

[0096] In step S1222, the Laida confidence interval of the store customer flow corresponding to each time interval of each of the alternative recycling stores is determined based on the difference and sum of the mean store customer flow corresponding to each time interval of each of the alternative recycling stores and the corresponding first calculated value.

[0097] In the embodiment of the present disclosure, for each candidate recycling store in each time interval, the first calculated value is added and subtracted upward and downward respectively with the average customer flow as the center, and the two obtained values ​​constitute the upper and lower limits of the Laida confidence interval of the store in the time interval. This interval gives the range of possible fluctuations in store customer flow at a given confidence level.

[0098] Through the above steps, the Laida confidence interval of the customer flow of each candidate recycling store can be calculated in each time interval, and then the stability and reliability of the customer flow of each store can be analyzed.

[0099] In a possible implementation, the method further includes: In the case where the feedback information indicates that the user device refuses to recycle the precious metal to be recycled through the target recycling store, the following steps are executed in a loop: Expanding the selection range according to the distance difference between the maximum distance and the minimum distance between the store address of the currently selected candidate recycling store and the user address; According to the expanded selection range, the steps of determining, based on the store address of the recycling store and the user address carried in the precious metal recycling request, the recycling store whose store address is within the selection range of the user address as an alternative recycling store for recycling the precious metal type to be recycled corresponding to the precious metal recycling request, to returning the precious metal recycling information to the user device end are executed; Until the number of times the precious metal recycling information is returned to the user device reaches a preset number, and / or the feedback information returned by the user device indicates that the user device agrees to recycle the precious metals to be recycled through the target recycling store sent for the last time.

[0100] Among them, when the user's device receives the target recycling store information recommended by the system, the user may refuse to recycle the precious metals to be recycled through the store for various reasons (such as too far away, poor service evaluation, etc.). At this time, the system needs to handle this rejection and take corresponding measures to continue to try to provide users with satisfactory recycling services.

[0101] In the disclosed embodiment, after the user rejects the currently recommended target recycling store, the system needs to adjust the strategy to find a more suitable store. This step is achieved by expanding the selection range. Specifically, the difference between the maximum distance and the minimum distance between the currently selected candidate recycling store and the user's address (i.e., the distance difference) is calculated, and then the selection range is expanded based on this difference. The degree of expansion can be flexibly adjusted according to the actual situation to ensure that more possible candidate stores can be covered.

[0102] For example, suppose the system currently recommends three alternative recycling stores for the user, and the distances between their store addresses and the user's address are 5 kilometers, 8 kilometers, and 12 kilometers, respectively. It can be seen that among these three stores, the one farthest from the user's address is 12 kilometers (maximum distance), and the one closest is 5 kilometers (minimum distance). Then the difference between the maximum distance and the minimum distance is calculated, that is, 12 kilometers - 5 kilometers = 7 kilometers. You can choose to expand the selection range by a certain proportion of the distance difference (such as 50%, 100%, etc.) or a fixed value (such as increasing by 5 kilometers, 10 kilometers, etc.). For example, suppose you choose to expand the selection range by 50% of the distance difference, that is, by 3.5 kilometers (7 kilometers * 50%). Therefore, the new selection range will be an area centered on the user's address with a radius of 5 kilometers (minimum distance) + 3.5 kilometers = 8.5 kilometers.

[0103] In the disclosed embodiment, after the selection range is expanded, the system needs to re-execute the screening process to determine new candidate recycling stores. This process is similar to the initial screening, that is, based on the distance relationship between the store address of the recycling store and the user's address, the stores within the user's address selection range are screened out. Then, the system will further screen and optimize these stores based on their actual conditions (such as recycling price, service quality, etc.), and finally determine one or more target recycling stores.

[0104] Next, the system will send new precious metal recycling information to the user's device, including information about the target recycling store, recycling prices, etc. If the user still refuses, the system will repeat steps S22 and S23 until the preset upper limit of the number of sending times is reached, or the user finally agrees to recycle through the target recycling store sent once.

[0105] In this cycle, the system needs to continuously adjust the selection range, screening conditions and sending strategies to meet the needs and expectations of users to the greatest extent possible. At the same time, the system also needs to record the recycling information of each send and the user's feedback for subsequent analysis and optimization.

[0106] The above technical solution can flexibly deal with the situation where users refuse recycling requests, and provide users with more choices and better service experience by continuously expanding the selection range and re-screening alternative recycling stores. At the same time, the system can also continuously optimize its recommendation algorithm and strategy based on user feedback to improve the overall quality and efficiency of recycling services.

[0107] The present disclosure also provides a recovery device for use in a precious metal recovery system. Figure 2 As shown, including: The receiving and determining module 210 is configured to receive a precious metal recycling request reported by a user device, and determine, according to the store address of the recycling store and the user address carried in the precious metal recycling request, the recycling store whose store address is within the selection range of the user address as an alternative recycling store for recycling the precious metal type to be recycled corresponding to the precious metal recycling request; The acquisition and determination module 220 is configured to acquire the store customer flow of the candidate recycling store within a preset historical period of time and the recycling unit price of the precious metal type and the precious metal purity corresponding to the precious metal to be recycled, and determine the target recycling store from the plurality of the candidate recycling stores based on the store customer flow corresponding to the candidate recycling store and the recycling unit price of the precious metal type corresponding to the precious metal to be recycled based on the k sigma algorithm; The first generating and sending module 230 is configured to generate precious metal recycling information for the precious metal to be recycled according to the store address of the target recycling store and the corresponding recycling unit price, and return the precious metal recycling information to the user device; The receiving and extracting module 240 is configured to receive feedback information from the user device regarding the precious metal recycling information, and extract the precious metal recycling submission method and recycling payment information carried in the feedback information when the feedback information indicates that the user device agrees to recycle the precious metals to be recycled through the target recycling store; The second generation and sending module 250 is configured to generate precious metal transaction information according to the type and weight of the precious metals to be recycled, the method of submitting precious metal recycling and the recycling collection information, and send the precious metal transaction information to the store device end corresponding to the target recycling store, and upon receiving the consent information of the store device end regarding the precious metal transaction information, send the consent information to the user device end.

[0108] In a possible implementation, the acquisition and determination module 220 is configured to: According to the store historical customer flow of each candidate recycling store on each day within the historical preset time period, calculate the store customer flow mean of each candidate recycling store in the time interval corresponding to the historical moments from the current moment to different preset time lengths, and calculate the customer flow standard deviation of each candidate recycling store in each time interval; According to the store customer flow mean and the customer flow standard deviation corresponding to each time interval of each of the candidate recycling stores, and the preset value of k in the k sigma algorithm, the Laida confidence interval of the store customer flow corresponding to each of the time intervals corresponding to each of the candidate recycling stores is calculated; Matching the Laida confidence interval of the store customer flow corresponding to each of the time intervals corresponding to each of the candidate recycling stores, the recycling unit price of the type of precious metal corresponding to the precious metal to be recycled by each of the candidate recycling stores; Determine an average of the first recycling unit prices of all the candidate recycling stores according to the recycling unit prices corresponding to each of the candidate recycling stores in each of the time intervals; The target recycling store is determined from a plurality of the candidate recycling stores according to the recycling prices of the candidate recycling stores within the historical preset period of time and the average of the first recycling unit prices.

[0109] In a possible implementation, the acquisition and determination module 220 is configured to: According to the store historical customer flow of each candidate recycling store on each day within the historical preset time period, determine the store historical customer flow of each candidate recycling store on each day within the time interval corresponding to the historical moment of different preset time lengths from the current moment, wherein different preset time lengths are determined in units of weeks; Determine whether the weekly historical total customer flow of each candidate recycling store in each of the time intervals is greater than a preset total customer flow threshold; When the weekly historical total customer flow of the store in each week within each of the time intervals corresponding to the candidate recycling store is greater than the preset total customer flow threshold, the time interval is used as the target time interval of the corresponding candidate recycling store; According to the historical customer flow of the store on each day within the target time interval corresponding to the candidate recycling store, the store customer flow mean and the customer flow standard deviation within the target time interval corresponding to the candidate recycling store are calculated.

[0110] In a possible implementation, the acquisition and determination module 220 is configured to: When the weekly historical total customer flow of the candidate recycling store in each week within each of the time intervals corresponding to the candidate recycling store is less than the preset total customer flow threshold, the candidate recycling store is used as the target recycling store.

[0111] In a possible implementation, the acquisition and determination module 220 is configured to: According to the recycling prices of the candidate recycling stores within the historical preset time period, determine the average second recycling unit price of the precious metal type recycled by each of the candidate recycling stores within the historical preset time period; From the multiple candidate recycling stores, the candidate recycling stores whose second average recycling unit price is greater than the first average recycling unit price are determined as the target recycling stores.

[0112] In a possible implementation, the acquisition and determination module 220 is configured to: According to the customer flow standard deviation corresponding to each time interval of each candidate recycling store and the preset value of k in the k sigma algorithm, the product of the preset value of k in the k sigma algorithm and the customer flow standard deviation corresponding to each time interval of each candidate recycling store is calculated to obtain a first calculated value; The Laida confidence interval of the store customer flow corresponding to each time interval of each of the alternative recycling stores is determined according to the difference and sum of the mean value of the store customer flow corresponding to each of the time intervals of each of the alternative recycling stores and the corresponding first calculated value.

[0113] In a possible implementation, the recycling device applied to the precious metal recycling system further includes: a circulation calculation module configured to: In the case where the feedback information indicates that the user device refuses to recycle the precious metal to be recycled through the target recycling store, the following steps are executed in a loop: Expanding the selection range according to the distance difference between the maximum distance and the minimum distance between the store address of the currently selected candidate recycling store and the user address; According to the expanded selection range, the steps of determining, based on the store address of the recycling store and the user address carried in the precious metal recycling request, the recycling store whose store address is within the selection range of the user address as an alternative recycling store for recycling the precious metal type to be recycled corresponding to the precious metal recycling request, to returning the precious metal recycling information to the user device end are executed; Until the number of times the precious metal recycling information is returned to the user device reaches a preset number, and / or the feedback information returned by the user device indicates that the user device agrees to recycle the precious metals to be recycled through the target recycling store sent for the last time.

[0114] The present disclosure also provides a computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, the steps of the method described in any one of the aforementioned embodiments are implemented.

[0115] The present disclosure also provides an electronic device, including: a memory having a computer program stored thereon; A processor is used to execute the computer program in the memory to implement the steps of the method in any one of the aforementioned embodiments.

[0116] Figure 3The recovery device 100 shown for use in a precious metal recovery system includes: a processor 1001 and a memory 1003. The processor 1001 and the memory 1003 are connected, such as through a bus 1002. Optionally, the recovery device 100 for use in a precious metal recovery system may also include a communication component 1004, which may be used for data interaction between the device 100 and other devices, such as data transmission and / or data reception. It should be noted that in actual scheduling, the communication component 1004 is not limited to one, and the structure of the recovery device 100 for use in a precious metal recovery system does not constitute a limitation on the embodiments of the present application.

[0117] Processor 1001 may be a CPU (Central Processing Unit), a general-purpose processor, a DSP (Digital Signal Processor), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array) or other programmable logic devices, transistor logic devices, hardware components or any combination thereof. It may implement or execute various exemplary logic blocks, modules and circuits described in conjunction with the disclosure of this application. Processor 1001 may also be a combination that implements computing functions, such as a combination of one or more microprocessors, a combination of a DSP and a microprocessor, etc.

[0118] The bus 1002 may include a path to transmit information between the above components. The bus 1002 may be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus. The bus 1002 may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 3 Only one thick line is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0119] The memory 1003 may be a ROM (Read Only Memory) or other types of static storage devices that can store static information and instructions, a RAM (Random Access Memory) or other types of dynamic storage devices that can store information and instructions, or an EEPROM (Electrically Erasable Programmable Read Only Memory), a CD-ROM (Compact Disc Read Only Memory) or other optical disk storage, optical disk storage (including compressed optical disk, laser disk, optical disk, digital versatile disk, Blu-ray disk, etc.), magnetic disk storage medium, other magnetic storage devices, or any other medium that can be used to carry or store program code and can be read by a computer, without limitation herein.

[0120] The memory 1003 is used to store program codes for executing the embodiments of the present disclosure, and the execution is controlled by the processor 1001. The processor 1001 is used to execute the program codes stored in the memory 1003 to implement the steps shown in the above-mentioned recovery method embodiment applied to the precious metal recovery system.

[0121] The embodiment of the present disclosure also provides a computer-readable storage medium having program code stored thereon. When the program code is executed by a processor, the steps and corresponding contents of the aforementioned recovery method embodiment applied to a precious metal recovery system can be implemented.

[0122] The preferred embodiments of the present disclosure are described in detail above in conjunction with the accompanying drawings; however, the present disclosure is not limited to the specific details in the above embodiments; within the technical concept of the present disclosure, various changes, modifications, substitutions and variations may be made to these embodiments, and these changes, modifications, substitutions and variations all fall within the protection scope of the present disclosure.

[0123] It should also be noted that the various specific technical features described in the above specific embodiments can be combined in any appropriate manner without contradiction, and they should also be regarded as the contents disclosed in this disclosure. In order to avoid unnecessary repetition, this disclosure will not further describe various possible combinations. The technical scope of this application is not limited to the contents in the specification, and its technical scope must be determined according to the scope of the claims.

Claims

1. A recovery method applied to a precious metal recovery system, characterized in that: The method comprises: Receive a precious metal recycling request reported by a user device, and determine, according to the store address of the recycling store and the user address carried in the precious metal recycling request, the recycling store whose store address is within the selection range of the user address as an alternative recycling store for recycling the precious metal type to be recycled corresponding to the precious metal recycling request; Obtain the historical customer flow of the candidate recycling stores on each day within the historical preset time period, as well as the recycling unit price of the precious metal types and the precious metal purity corresponding to the precious metals to be recycled, and determine the target recycling store from the plurality of candidate recycling stores based on the historical customer flow of the candidate recycling stores on each day within the historical preset time period and the recycling unit price of the precious metal types corresponding to the precious metals to be recycled based on the k sigma algorithm; Generate precious metal recycling information for the precious metal to be recycled according to the store address of the target recycling store and the corresponding recycling unit price, and return the precious metal recycling information to the user device; receiving feedback information from the user device regarding the precious metal recycling information, and extracting the precious metal recycling submission method and recycling payment information carried in the feedback information when the feedback information indicates that the user device agrees to recycle the precious metals to be recycled through the target recycling store; Based on the type and weight of the precious metals to be recycled, the method of submitting the precious metal recycling, and the recycling collection information, precious metal transaction information is generated, and the precious metal transaction information is sent to the store device end corresponding to the target recycling store. Upon receiving the consent information of the store device end for the precious metal transaction information, the consent information is sent to the user device end.

2. The recovery method applied to the precious metal recovery system according to claim 1, characterized in that: The method based on the Ksigma algorithm, according to the historical customer flow of the candidate recycling stores every day within the historical preset time period and the recycling unit price of the precious metal type corresponding to the precious metal to be recycled, determines the target recycling store from the multiple candidate recycling stores, including: According to the store historical customer flow of each candidate recycling store on each day within the historical preset time period, calculate the store customer flow mean of each candidate recycling store in the time interval corresponding to the historical moments from the current moment to different preset time lengths, and calculate the customer flow standard deviation of each candidate recycling store in each time interval; According to the store customer flow mean and the customer flow standard deviation corresponding to each time interval of each of the candidate recycling stores, and the preset value of k in the k sigma algorithm, the Laida confidence interval of the store customer flow corresponding to each of the time intervals corresponding to each of the candidate recycling stores is calculated; Matching the Laida confidence interval of the store customer flow corresponding to each of the time intervals corresponding to each of the candidate recycling stores, the recycling unit price of the type of precious metal corresponding to the precious metal to be recycled by each of the candidate recycling stores; Determine an average of the first recycling unit prices of all the candidate recycling stores according to the recycling unit prices corresponding to each of the candidate recycling stores in each of the time intervals; The target recycling store is determined from a plurality of the candidate recycling stores according to the recycling prices of the candidate recycling stores within the historical preset period of time and the average of the first recycling unit prices.

3. The recovery method applied to the precious metal recovery system according to claim 2, characterized in that: The method of calculating the store customer flow average of each candidate recycling store in a time interval corresponding to a historical moment from the current moment to different preset time lengths according to the store historical customer flow of each candidate recycling store on each day within the historical preset time length, and calculating the customer flow standard deviation of each candidate recycling store in each time interval, includes: According to the store historical customer flow of each candidate recycling store on each day within the historical preset time period, determine the store historical customer flow of each candidate recycling store on each day within the time interval corresponding to the historical moment of different preset time lengths from the current moment, wherein different preset time lengths are determined in units of weeks; Determine whether the weekly historical total customer flow of each candidate recycling store in each of the time intervals is greater than a preset total customer flow threshold; When the weekly historical total customer flow of the store in each week within each of the time intervals corresponding to the candidate recycling store is greater than the preset total customer flow threshold, the time interval is used as the target time interval of the corresponding candidate recycling store; According to the historical customer flow of the store on each day within the target time interval corresponding to the candidate recycling store, the store customer flow mean and the customer flow standard deviation within the target time interval corresponding to the candidate recycling store are calculated.

4. The recovery method applied to the precious metal recovery system according to claim 3, characterized in that: The method further comprises: When the weekly historical total customer flow of the candidate recycling store in each week within each of the time intervals corresponding to the candidate recycling store is less than the preset total customer flow threshold, the candidate recycling store is used as the target recycling store.

5. The recovery method applied to the precious metal recovery system according to claim 2, characterized in that: The step of determining the target recycling store from a plurality of candidate recycling stores according to the recycling prices of the candidate recycling stores within the historical preset period of time and the average of the first recycling unit prices includes: According to the recycling prices of the candidate recycling stores within the historical preset time period, determine the average second recycling unit price of the precious metal type recycled by each of the candidate recycling stores within the historical preset time period; From the multiple candidate recycling stores, the candidate recycling stores whose second average recycling unit price is greater than the first average recycling unit price are determined as the target recycling stores.

6. The recovery method applied to the precious metal recovery system according to claim 2, characterized in that: The Laida confidence interval of the store customer flow corresponding to each time interval corresponding to each of the candidate recycling stores is calculated according to the store customer flow mean and the customer flow standard deviation corresponding to each of the time intervals of each of the candidate recycling stores, and the preset value of k in the k sigma algorithm, including: According to the customer flow standard deviation corresponding to each time interval of each candidate recycling store and the preset value of k in the k sigma algorithm, the product of the preset value of k in the k sigma algorithm and the customer flow standard deviation corresponding to each time interval of each candidate recycling store is calculated to obtain a first calculated value; The Laida confidence interval of the store customer flow corresponding to each time interval of each of the alternative recycling stores is determined according to the difference and sum of the mean value of the store customer flow corresponding to each of the time intervals of each of the alternative recycling stores and the corresponding first calculated value.

7. The recovery method applied to a precious metal recovery system according to any one of claims 1 to 6, characterized in that: The method further comprises: In the case where the feedback information indicates that the user device refuses to recycle the precious metal to be recycled through the target recycling store, the following steps are executed in a loop: Expanding the selection range according to the distance difference between the maximum distance and the minimum distance between the store address of the currently selected candidate recycling store and the user address; According to the expanded selection range, the steps of determining, based on the store address of the recycling store and the user address carried in the precious metal recycling request, the recycling store whose store address is within the selection range of the user address as an alternative recycling store for recycling the precious metal type to be recycled corresponding to the precious metal recycling request, to returning the precious metal recycling information to the user device end are executed; Until the number of times the precious metal recycling information is returned to the user device reaches a preset number, and / or the feedback information returned by the user device indicates that the user device agrees to recycle the precious metals to be recycled through the target recycling store sent for the last time.

8. A recovery device applied to a precious metal recovery system, characterized in that: include: A receiving and determining module is configured to receive a precious metal recycling request reported by a user device, and determine, according to the store address of the recycling store and the user address carried in the precious metal recycling request, the recycling store whose store address is within the selection range of the user address as an alternative recycling store for recycling the precious metal type to be recycled corresponding to the precious metal recycling request; The acquisition and determination module is configured to acquire the store customer flow of the candidate recycling store within a preset historical period of time and the recycling unit price of the precious metal type and the precious metal purity corresponding to the precious metal to be recycled, and determine the target recycling store from a plurality of the candidate recycling stores based on the store customer flow corresponding to the candidate recycling store and the recycling unit price of the precious metal type corresponding to the precious metal to be recycled based on the Ksigma algorithm; A first generating and sending module is configured to generate precious metal recycling information for the precious metal to be recycled according to the store address of the target recycling store and the corresponding recycling unit price, and return the precious metal recycling information to the user device; A receiving and extracting module is configured to receive feedback information from the user device regarding the precious metal recycling information, and extract the precious metal recycling submission method and recycling payment information carried in the feedback information when the feedback information indicates that the user device agrees to recycle the precious metals to be recycled through the target recycling store; The second generation and sending module is configured to generate precious metal transaction information based on the type and weight of the precious metals to be recycled, the precious metal recycling submission method and the recycling collection information, and send the precious metal transaction information to the store device end corresponding to the target recycling store, and upon receiving the consent information of the store device end regarding the precious metal transaction information, send the consent information to the user device end.

9. An electronic device, characterized in that: include: a memory having a computer program stored thereon; A processor, configured to execute the computer program in the memory to implement the steps of the method according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps of the method described in any one of claims 1 to 7 are implemented.

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