A real-time resource valuation data processing method, device, equipment and medium

Abstract

The present application relates to the field of resource data processing technology, and discloses a real-time resource valuation data processing method, device, equipment and medium, including: receiving a resource valuation request for a resource input by a user, the resource valuation request including resource original valuation net value data and resource initial share data; determining first resource change data corresponding to the resource original valuation net value data; performing resource conversion on the first resource change data to obtain second resource change data; receiving resource real-time share data, and determining target resource valuation data corresponding to the resource valuation request according to the second resource change data and the resource initial share data. Its beneficial effect is to improve the accuracy of real-time evaluation value prediction of data resources.

Description

Technical Field

[0001] The present application relates to the field of resource data processing technology, and in particular to a real-time resource valuation data processing method, device, equipment and medium. Background Art

[0002] With the development of Internet technology, a large amount of data resources have been accumulated on the Internet. The value of these data resources is usually measured by evaluation value. In some application scenarios, the value of data resources changes in real time, and its real-time value is often not directly known.

[0003] Therefore, it is necessary to predict the real-time evaluation value of data resources so as to determine their actual value based on the prediction results. Therefore, improving the accuracy of the prediction of the real-time evaluation value of data resources has become a key issue that technicians in this field need to continue to explore. Summary of the invention

[0004] The present application provides a real-time resource valuation data processing method, device, equipment and medium, which solves the technical problem of how to improve the accuracy of real-time evaluation value prediction of data resources, and achieves the technical effect of improving the accuracy of real-time evaluation value prediction of data resources.

[0005] In order to achieve the above objectives, the main technical solutions adopted in this application include: In a first aspect, an embodiment of the present application provides a real-time resource valuation data processing method, the method comprising: Receiving a resource valuation request for a resource input by a user, and acquiring original resource valuation net value data and resource initial share data of the resource according to the resource valuation request; Determine first resource change data corresponding to the original estimated net value data of the resource; Performing resource conversion on the first resource change data to obtain second resource change data; The real-time resource share data is received, and the target resource valuation data corresponding to the resource valuation request is determined according to the second resource change data and the initial resource share data.

[0006] The present embodiment provides a real-time resource valuation data processing method, which first receives a resource valuation request provided by a user. Then, the change data of the resource is determined based on the original net value data of the resource valuation, and is transformed to adapt to different market conditions. Next, the real-time share data of the resource is received, and the target resource valuation data is calculated by combining the transformed change data and the initial share data of the resource. Through this process, it is possible to respond to market changes in real time and dynamically adjust the valuation results, thereby providing users with high-precision and reliable resource valuations. Overall, through data conversion and standardization, the accuracy and timeliness of the valuation are improved, while allowing users to easily obtain valuation results, meeting the needs of different market environments.

[0007] Optionally, the determining the first resource change data corresponding to the original resource valuation net value data includes: Determine the real-time compensated floating profit data corresponding to the resource; Determine the total amount of data to be supplemented corresponding to the resource; Determine the daily floating profit data corresponding to the resource; The original estimated net value data of the resource is adjusted based on the real-time compensated floating profit data corresponding to the resource, the total compensated data and the compensated floating profit data on the day to obtain the adjusted first resource change data.

[0008] This embodiment can accurately calculate the real-time replenished floating profit data by real-time monitoring of market price changes and resource replenishment costs, thereby reflecting the value-added or loss of resources. Based on market demand, inventory status and historical data, the total replenishment data required is further predicted to ensure the accuracy of resource replenishment. On this basis, the replenished floating profit data of the day can also be calculated in real time to evaluate the effect of the replenishment operation on the day and make dynamic adjustments based on real-time feedback. Finally, by comprehensively considering the real-time replenished floating profit data, the total replenishment data and the replenished floating profit data of the day, the original resource valuation net value data is adjusted to obtain more accurate resource change data. This series of steps enables the valuation and replenishment strategies of resources to be closely integrated with market changes, thereby optimizing resource allocation, improving management accuracy, and effectively controlling financial and market risks, and improving the flexibility and response speed of decision-making.

[0009] Optionally, the determining the real-time compensated floating profit data corresponding to the resource includes: Obtaining the real-time net replenished product quantity, the latest product attribute value and the real-time net replenished cost data corresponding to the resource; Determine the real-time net replenished gain data corresponding to the resource according to the product of the real-time net replenished product quantity and the latest product attribute value; The real-time compensated floating profit data is determined according to the difference between the real-time net compensated gain data and the real-time net compensated cost data.

[0010] This embodiment ensures dynamic tracking of resource status by acquiring the real-time net replenished product quantity, the latest product attribute value, and the real-time net replenished cost data of the resources in real time. Then, by multiplying the real-time net replenished product quantity with the latest product attribute value, the real-time net replenished gain data is accurately calculated to reflect the potential benefits under the current market environment. Subsequently, by comparing the real-time net replenished gain data with the real-time net replenished cost data, the real-time replenished floating profit data is calculated in real time to help users accurately understand the profit and loss of the current investment. The entire process ensures the timeliness and efficiency of the data, especially in the case of large market fluctuations, it can provide timely decision-making basis, optimize risk management and resource allocation, and improve the level of intelligent decision-making.

[0011] Optionally, the determining the total supplementary data corresponding to the resource includes: Obtain the latest product attribute value, yesterday's product closing attribute value, and the net product quantity to be replenished on the day corresponding to the resource; Determine the net value attribute value between the latest product attribute value and yesterday's product closing attribute value; The total data to be replenished is determined based on the product of the net quantity of products to be replenished on that day and the net value attribute value.

[0012] This embodiment fully reflects the market dynamics by acquiring the latest product attribute value of the product, yesterday's product closing attribute value, and the net number of products to be replenished on the day in real time. Based on this data, the net value attribute value between the latest product attribute value and yesterday's product closing attribute value is calculated to provide a quantitative basis for analyzing market fluctuations. Subsequently, the total replenishment data is accurately calculated based on the product of the net number of products to be replenished on the day and the net value attribute value, thereby quantifying the replenishment demand. This process ensures the real-time and accuracy of the decision-making basis, helps users flexibly adjust resource allocation according to market fluctuations, optimize fund management, and effectively control risks. In general, this technical process improves the scientific nature of investment decisions and risk response capabilities through precise market analysis and dynamic adjustments.

[0013] Optionally, the determining the daily compensated floating profit data corresponding to the resource includes: Obtain the current day's non-real-time net replenished product quantity, the latest product attribute value, and the current day's non-real-time net replenished cost data corresponding to the resource; Determine the non-real-time net replenished gain data for the resource on the same day according to the product of the non-real-time net replenished product quantity on the same day and the latest product attribute value; The compensated floating profit data for the day is determined according to the difference between the non-real-time net compensated gain data for the day and the non-real-time net compensated cost data for the day.

[0014] This embodiment can calculate the non-real-time net replenished gain data for the day by obtaining the non-real-time net replenished product quantity and the latest product attribute value for the day; then, the non-real-time net replenished gain data for the day is compared with the non-real-time net replenished cost data for the day to obtain the non-real-time net replenished gain data for the day, and provide the profitability of the product replenishment operation. Although there is a certain delay in data updates, dynamic market analysis and decision support can still be provided to users based on these non-real-time data. Ultimately, through these gain and loss calculations, users can not only evaluate the financial effects of product replenishment, but also effectively identify risks and optimize decisions to ensure fund security and value-added.

[0015] Optionally, performing resource conversion on the first resource change data to obtain the second resource change data includes: for a differential product affected by a resource conversion difference, obtaining the same-day purchase conversion ratio data, the same-day sale conversion ratio data, the same-day purchase resource data, and the same-day sale resource data corresponding to the differential product; Determine the same-day purchase adjustment data corresponding to the differential product according to the same-day purchase conversion ratio data, the same-day sale conversion ratio data and the same-day purchase resource data; Determine the same-day selling adjustment data corresponding to the differential product according to the same-day buying conversion ratio data, the same-day selling conversion ratio data and the same-day selling resource data; Determine the net adjustment data corresponding to the difference product according to the difference between the purchase adjustment data of the day and the sale adjustment data of the day; Perform resource conversion on the first resource change data according to the net adjustment data to obtain the second resource change data.

[0016] This embodiment provides accurate basic data for subsequent adjustments by obtaining the purchase conversion ratio data of the day, the sale conversion ratio data of the day, the purchase resource data of the day, and the sale resource data of the day. On this basis, the adjustment data of buying and selling are calculated, and the resource flow in the market is optimized by analyzing the conversion efficiency of resources. Then, by comparing the purchase and sale adjustment data, the net adjustment data is determined to reflect the balance of capital flow. Finally, the first resource change data is converted according to the net adjustment data to obtain the final resource change data. The whole process not only improves the efficiency of resource conversion and enhances market liquidity and stability, but also provides strong support for users, helping them to accurately control market dynamics, optimize strategy adjustments, and ensure the efficient allocation of market resources and the rationality of capital flow.

[0017] Optionally, the receiving the real-time resource share data, and determining the target resource valuation data corresponding to the resource valuation request according to the second resource change data and the resource initial share data, includes: The adjusted unit net value data is determined according to the ratio of the second resource change data to the resource initial share data; and the target resource valuation data is determined according to the product of the unit net value data and the resource real-time share data.

[0018] This embodiment dynamically adjusts the unit net value by calculating the ratio of the second resource change data to the initial resource share data to accurately reflect the actual value changes of resources in the market. The target resource valuation data is calculated using the adjusted unit net value data and the real-time resource share data, thereby ensuring that the market value of the resource can immediately reflect the flow of funds and market fluctuations. This method effectively improves the real-time and flexibility of valuation, helps investors or managers make timely decisions, optimizes capital liquidity management, and avoids risks caused by valuation lags. In addition, for cross-market valuation, this method can eliminate interference from factors such as exchange rate fluctuations, ensure valuation consistency and stability between multiple markets, and enhance the accuracy and stability of valuation. Therefore, this technology provides strong support for real-time tracking and estimation of the market value of resources, optimization of capital flow management, and cross-market resource valuation.

[0019] In a second aspect, an embodiment of the present application provides a real-time resource valuation data processing device, the device comprising: a valuation request receiving unit, configured to receive a resource valuation request for a resource input by a user, and obtain resource original valuation net value data and resource initial share data of the resource according to the resource valuation request; A first data determination unit, configured to determine first resource change data corresponding to the original estimated net value data of the resource; The second data determination unit is used to perform resource conversion on the first resource change data to obtain second resource change data; the target data determination unit is used to receive real-time resource share data and determine the target resource valuation data corresponding to the resource valuation request based on the second resource change data and the resource initial share data.

[0020] In a third aspect, an embodiment of the present application provides a computer device, including: A memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the above-mentioned real-time resource valuation data processing method by executing the computer instructions.

[0021] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, on which computer instructions are stored, and the computer instructions are used to enable a computer to execute the real-time resource valuation data processing method described above. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the specific implementation methods of the present application or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0023] Figure 1 A flowchart of a real-time resource valuation data processing method provided in an embodiment of the present application; Figure 2 A flowchart of step S3 provided in an embodiment of the present application; Figure 3 A flowchart of step S31 provided in an embodiment of the present application; Figure 4 A flowchart of step S33 provided in an embodiment of the present application; Figure 5 A flowchart of step S35 provided in an embodiment of the present application; Figure 6 A flowchart of step S5 provided in an embodiment of the present application; Figure 7 A flowchart of step S7 provided in an embodiment of the present application; Figure 8 A block diagram of a real-time resource valuation data processing device provided in an embodiment of the present application; Fig. 9 A schematic diagram of the structure of a computer device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0024] In order to make the purpose, technical solution and advantages of the embodiments of the present application clearer, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present application.

[0025] In the field of real-time investment transactions, especially in the management of ETF (Exchange Traded Fund) products, traditional trading systems (such as Hang Seng O32, etc.) have multiple defects, resulting in a significant gap between the real-time valuation of ETF products and their actual valuation. These problems are particularly prominent in ETF products that include on-site subscription and redemption, which makes it difficult for ETF investment managers to accurately adjust their portfolios, and also exacerbates the tracking error of ETF products. The following are the main problems with traditional ETF real-time valuation methods: 1. Lack of real-time calculation of on-site subscription and redemption shares: The real-time net value of ETFs calculated by traditional trading systems fails to reflect the subscription and redemption operations on the exchange in a timely manner, especially when the subscription and redemption volume is large on the same day. Since the subscription and redemption shares are not included in the valuation calculation, the real-time net value of ETF products will be greatly different from the actual valuation at the end of the day. This makes it impossible for investment managers to accurately grasp the real-time value of ETF products, thus affecting real-time decision-making and position adjustment strategies.

[0026] 2. Real-time valuation deviation of cross-market ETF products: For cross-market ETFs (such as Hong Kong Stock Connect ETFs), since cross-market securities are required when ETFs are subscribed and redeemed, the traditional valuation method includes the subscribed securities in the fund's net value, and this part of the subscribed securities may cause large market fluctuations during the trading session, resulting in a large deviation between the valuation on the day and the actual valuation. When the ETF subscription and redemption volume is large, the fund manager will face uncertainty in adjusting the portfolio, which will affect the rebalancing operation.

[0027] 3. Ignore the rebalancing impact of the outstanding bond assets: When ETF products are rebalanced, the stocks to be covered are theoretically already assets of the fund and should be considered in real-time valuation. However, traditional valuation schemes do not fully consider these assets to be covered, resulting in the trading system adjusting positions based on actual positions during the rebalancing process, which may lead to over-buying, thus affecting investment decisions and the efficiency of fund allocation.

[0028] 4. Valuation differences of Hong Kong Stock Connect ETF products: For ETF products investing in Hong Kong Stock Connect, the traditional valuation scheme uses the exchange rate issued by the exchange, which often includes a 3% discount, mainly used to reduce settlement risk. If a large number of Hong Kong Stock Connect transactions occur on the same day, the traditional valuation method may not be able to adjust the exchange rate difference in time, resulting in a large deviation between the real-time net value of the ETF product and the end-of-day valuation, affecting the fund manager's effective management of the fund portfolio.

[0029] 5. Distortion and misinterception of risk control indicators: Traditional ETF real-time valuation methods ignore the impact of on-site subscription and redemption of shares and margin trading. Since the stock assets of margin trading should be included in the equity position, but are not considered in the traditional valuation system, this may lead to distortion in the calculation of risk control indicators. For example, when calculating the risk exposure of the fund, the risk control system fails to accurately reflect the actual holdings of the ETF product, which may cause the risk control system to mistakenly intercept or fail to accurately identify instructions with compliance risks, affecting the fund's risk management and operational compliance.

[0030] In order to solve the above technical problems, according to an embodiment of the present application, a real-time resource valuation data processing method embodiment is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0031] In this embodiment, a real-time resource valuation data processing method is provided. Figure 1 A flowchart of a real-time resource valuation data processing method provided in an embodiment of the present application, such as Figure 1 As shown, the process includes the following steps: Step S1, receiving a resource valuation request for a resource input by a user, and obtaining original resource valuation net value data and resource initial share data of the resource according to the resource valuation request.

[0032] Specifically, a resource valuation request is a valuation requirement proposed by a user for a certain resource (such as a fund, security, commodity, etc.). Then, based on the resource valuation request, the original valuation net value data and initial share data of the resource are obtained from the database to determine the value of the resource in the current market.

[0033] The original valuation net value data of resources can be calculated based on the net value of fund accounting assets. The resources in this application refer to ETF products. Therefore, the original valuation net value data of resources for ETF products = cash assets + receivable assets - payable assets + stock assets + bond assets + fund assets + reverse repurchase assets - positive repurchase liabilities + gold assets + warrant assets + futures assets + option assets + other assets. The initial share data of resources refers to the ETF product share at the beginning of the day, and does not include real-time subscription and redemption data.

[0034] Step S3, determining the first resource change data corresponding to the original resource valuation net value data.

[0035] Specifically, the first resource change data refers to the valuation data of the ETF product recalculated after the original resource valuation net value data is adjusted for the profit and loss of the replenishment. It reflects the value change of the ETF due to market fluctuations or capital flow after the replenishment operation. For example, if the ETF generates profits and losses due to market price fluctuations during the replenishment process, then such profits and losses will affect the net value of the ETF, and then adjust its valuation data.

[0036] Step S5: performing resource conversion on the first resource change data to obtain second resource change data.

[0037] Specifically, the second resource change data refers to the valuation of ETF products adjusted by the Hong Kong-Shenzhen Stock Connect exchange rate. The valuation of ETFs is converted from one resource (such as RMB) to another resource (such as Hong Kong dollars or US dollars) to reflect the actual value of ETFs in different currency environments. This adjustment helps users understand the real performance of ETFs in cross-border transactions or cross-currency environments.

[0038] Step S7: receiving the real-time resource share data, and determining the target resource valuation data corresponding to the resource valuation request according to the second resource change data and the resource initial share data.

[0039] Specifically, the second resource change data and the initial resource share data after eliminating the impact of the Hong Kong Stock Connect exchange rate on the valuation can be used to calculate the adjusted unit net value data, which does not include real-time subscription and redemption. Then, the final target resource valuation data including real-time subscription and redemption can be obtained based on the product of the adjusted unit net value data and the real-time share data of the resource, thereby ensuring that the market value of the resources can reflect capital flows and market fluctuations in real time.

[0040] The present embodiment provides a real-time resource valuation data processing method, which first receives a resource valuation request provided by a user. Then, the change data of the resource is determined based on the original net value data of the resource valuation, and is transformed to adapt to different market conditions. Next, the real-time share data of the resource is received, and the target resource valuation data is calculated by combining the transformed change data and the initial share data of the resource. Through this process, it is possible to respond to market changes in real time and dynamically adjust the valuation results, thereby providing users with high-precision and reliable resource valuations. Overall, through data conversion and standardization, the accuracy and timeliness of the valuation are improved, while allowing users to easily obtain valuation results, meeting the needs of different market environments.

[0041] Figure 2 The flowchart of step S3 provided in the embodiment of the present application may include the following steps: Step S31, determining the real-time compensated floating profit data corresponding to the resources.

[0042] Step S33, determining the total data to be supplemented corresponding to the resource.

[0043] Step S35, determining the daily floating profit data corresponding to the resource.

[0044] Step S37, adjusting the original estimated net value data of the resources based on the real-time compensated floating profit data, the total compensated data and the compensated floating profit data of the day corresponding to the resources to obtain the adjusted first resource change data.

[0045] The real-time covered floating profit data represents the floating profit of the real-time covered products. If the floating profit is positive, it means that the latest transaction price can be sold at a profit; if it is negative, it means there is a loss. The total data to be covered refers to the data obtained by multiplying the number of net covered products on the day by the net value attribute value, which indicates the total amount of resources or funds required to be replenished under the current market conditions based on the price difference between yesterday and today. It reflects the impact of resource changes on replenishment or replenishment needs in a specific period. The covered floating profit data on the day is the actual floating profit and loss generated by replenishing products, i.e. stocks, and takes into account the difference between the non-real-time net covered gain data on the day and the non-real-time net covered cost data actually paid on the day. If the result is positive, it means profit, and if it is negative, it means loss.

[0046] Specifically, the original valuation net value data is calculated based on the initially set resource value, but with changes in market prices and adjustments to replenishment volume, the valuation needs to be updated in real time. By integrating the real-time replenished floating profit data, total replenishment data, and replenished floating profit data for the day, the original valuation net value data of resources is adjusted to obtain a more accurate resource valuation. This is to reflect the impact of market fluctuations, changes in replenishment demand, and other factors on the net value of resources during the resource replenishment process. First, resource change data = original valuation net value data of resources – real-time replenished floating profit data + total replenishment data – replenished floating profit data for the day.

[0047] and Figure 1 Compared with the embodiment shown, this embodiment can accurately calculate the real-time replenished floating profit data by real-time monitoring of market price changes and resource replenishment costs, thereby reflecting the value-added or loss of resources. Based on market demand, inventory status and historical data, the total replenishment data required to be replenished is further predicted to ensure the accuracy of resource replenishment. On this basis, the replenished floating profit data of the day can also be calculated in real time to evaluate the effect of the replenishment operation on the day and dynamically adjust according to real-time feedback. Finally, by comprehensively considering the real-time replenished floating profit data, the total replenishment data and the replenished floating profit data of the day, the original resource valuation net value data is adjusted to obtain more accurate resource change data. This series of steps enables the valuation and replenishment strategies of resources to be closely integrated with market changes, thereby optimizing resource allocation, improving management accuracy, and effectively controlling financial and market risks, and improving the flexibility and response speed of decision-making.

[0048] Figure 3 The flowchart of step S31 provided in the embodiment of the present application may include the following steps: Step S311, obtaining the real-time net replenished product quantity, the latest product attribute value and the real-time net replenished cost data corresponding to the resource.

[0049] Specifically, the real-time net replenished product quantity represents the real-time net increase in the number of products, which is used here to represent the real-time replenished stock shares. The latest product attribute value represents the latest market price, evaluation value, etc. of the product. If the product is a stock, it refers to the latest trading price of the stock, which reflects the current market evaluation of the product (stock). The real-time net replenished cost data represents the cost of replenishing the product, which can also be understood as the total amount of money spent on purchasing these stocks.

[0050] Step S313, determining the real-time net replenished gain data corresponding to the resource according to the product of the real-time net replenished product quantity and the latest product attribute value.

[0051] Specifically, the real-time net replenished gain data represents the total income that can be theoretically obtained by selling the replenished products at the current latest transaction price. Real-time net replenished gain data = real-time net replenished product quantity × latest product attribute value Step S315, determine the real-time replenished floating profit data according to the difference between the real-time net replenished gain data and the real-time net replenished cost data.

[0052] Specifically, the real-time covered floating profit data represents the floating profit of the real-time covered products. If the floating profit is positive, it means that the product can be sold at the latest transaction price at a profit; if it is negative, it means that there is a loss. Real-time covered floating profit data = real-time net covered gain data - real-time net covered cost data.

[0053] and Figure 2 Compared with the embodiment shown, this embodiment ensures dynamic tracking of resource status by acquiring the real-time net replenished product quantity, the latest product attribute value and the real-time net replenished cost data of the resources in real time. Then, by multiplying the real-time net replenished product quantity with the latest product attribute value, the real-time net replenished gain data is accurately calculated to reflect the potential benefits in the current market environment. Subsequently, by comparing the real-time net replenished gain data with the real-time net replenished cost data, the real-time replenished floating profit data is calculated in real time to help users accurately understand the profit and loss of the current investment. The whole process ensures the timeliness and efficiency of the data, especially in the case of large market fluctuations, it can provide timely decision-making basis, optimize risk management and resource allocation, and improve the level of intelligent decision-making.

[0054] Figure 4 The flowchart of step S33 provided in the embodiment of the present application may include the following steps: Step S331, obtaining the latest product attribute value corresponding to the resource, yesterday's product closing attribute value, and the net quantity of products to be replenished on the day.

[0055] Specifically, the latest product attribute value represents the latest market price, assessed value, etc. of the product. If the product is a stock, it refers to the latest trading price of the stock, which reflects the current market evaluation of the product (stock). Yesterday's product closing attribute value represents the attribute value of the resource or product on the previous trading day, usually the closing price of the product, i.e., the stock, at the end of yesterday's trading. This value represents the state of the market yesterday and serves as a benchmark for comparison. The net product quantity to be replenished on the day represents the amount of resources that need to be replenished on the current trading day, usually the amount of replenishment or liquidation in the market.

[0056] Step S333, determining the net value attribute value between the latest product attribute value and yesterday's product closing attribute value.

[0057] Specifically, the net value attribute value is calculated by subtracting the product closing attribute value of yesterday from the latest product attribute value, indicating the change (increase or decrease) of the product attribute value within one day. The net value attribute value can reflect the increase or decrease of the product, i.e., the stock. Net value attribute value = latest product attribute value - yesterday's product closing attribute value.

[0058] Step S335, determine the total replenishment data based on the product of the net replenishment quantity of the day and the net value attribute value.

[0059] Specifically, the total replenishment data refers to the total amount of resources or funds required to be replenished based on the price difference between yesterday and today under the current market conditions, which is obtained by multiplying the number of net replenishment products on the day by the net value attribute value. It reflects the impact of resource changes on replenishment or replenishment demand in a specific period. Total replenishment data = the number of net replenishment products on the day × net value attribute value.

[0060] and Figure 2 Compared with the embodiment shown, this embodiment fully reflects the market dynamics by acquiring the latest product attribute value of the product, yesterday's product closing attribute value, and the net number of products to be replenished on the day in real time. Based on this data, the net value attribute value between the latest product attribute value and yesterday's product closing attribute value is calculated to provide a quantitative basis for analyzing market fluctuations. Subsequently, the total replenishment data is accurately calculated based on the product of the net number of products to be replenished on the day and the net value attribute value, thereby quantifying the replenishment demand. This process ensures the real-time and accuracy of the decision-making basis, helps users flexibly adjust resource allocation according to market fluctuations, optimize fund management, and effectively control risks. In general, this technical process improves the scientific nature of investment decisions and risk response capabilities through precise market analysis and dynamic adjustments.

[0061] Figure 5The flowchart of step S35 provided in the embodiment of the present application may include the following steps: Step S351, obtaining the non-real-time net replenished product quantity, the latest product attribute value and the non-real-time net replenished cost data of the day corresponding to the resource.

[0062] Specifically, the non-real-time net replenished product quantity for the day refers to the number of products that have been replenished (or delivered) on the day, but this part of the data is not reflected immediately, but delayed for a certain period of time. This may mean that there is a time lag in data update, and the quantity represents the non-real-time replenishment quantity. The latest product attribute value represents the latest market price, appraisal value, etc. of the product. If the product is a stock, it refers to the latest trading price of the stock, which reflects the current evaluation of the product (stock) in the market. The non-real-time net replenished cost data for the day refers to the cost data corresponding to the products replenished on the day. This is also non-real-time, which means that these data are reflected after a delay, and this part of the data represents the funds or costs required for replenishment. The non-real-time net replenished cost data for the day refers to the gains brought by replenishing products.

[0063] Step S353, determining the non-real-time net replenished gain data for the day corresponding to the resource according to the product of the non-real-time net replenished product quantity for the day and the latest product attribute value.

[0064] Specifically, the non-real-time net replenished product quantity of the day is multiplied by the latest product attribute value to obtain the added value brought by the current replenished product. The non-real-time net replenished gain data of the day can reflect the current market value of the replenished product and compare it with the actual purchase cost to provide a basis for profit and loss analysis. The non-real-time net replenished gain data of the day = the non-real-time net replenished product quantity of the day × the latest product attribute value.

[0065] Step S355, determining the compensated floating profit data for the day according to the difference between the non-real-time net compensated gain data for the day and the non-real-time net compensated cost data for the day.

[0066] Specifically, the day's covered floating profit data is the actual floating profit and loss generated by the supplementary product, i.e., the stock, taking into account the difference between the day's non-real-time net covered gain data and the day's non-real-time net covered cost data actually paid. If the result is positive, it means profit, and if it is negative, it means loss. The day's covered floating profit data = the day's non-real-time net covered gain data - the day's non-real-time net covered cost data.

[0067] and Figure 2Compared with the embodiment shown in the figure, this embodiment can calculate the non-real-time net replenished gain data of the day by obtaining the non-real-time net replenished product quantity and the latest product attribute value of the day; then, the non-real-time net replenished gain data of the day is compared with the non-real-time net replenished cost data of the day to obtain the non-real-time net replenished gain data of the day, and provide the profitability of the product replenishment operation. Although there is a certain delay in data update, dynamic market analysis and decision support can still be provided to users based on these non-real-time data. Ultimately, through these gain and loss calculations, users can not only evaluate the financial effects of product replenishment, but also effectively identify risks and optimize decisions to ensure fund security and value-added.

[0068] Figure 6 The flowchart of step S5 provided in the embodiment of the present application may include the following steps: Step S51, for the difference products affected by the resource conversion difference, obtain the daily purchase conversion ratio data, the daily sale conversion ratio data, the daily purchase resource data and the daily sale resource data corresponding to the difference products.

[0069] Differentiated products refer to financial products or assets that have different impacts during the resource conversion process. For example, in ETF products, due to differences in transactions and exchange rates between different markets, different products will have different resource conversion impacts. The differentiated products here refer to the Hong Kong Stock Connect, which will have exchange rate differences. For example, there may be a 3% difference between the reference exchange rate and the actual exchange rate published by the exchange. Therefore, it is necessary to eliminate the exchange rate impact for ETF products that have Hong Kong Stock Connect transactions on the same day.

[0070] Specifically, the daily purchase conversion ratio data is used to measure the ratio of resources from the buyer to the conversion of ETF products. In the process of buying ETFs, it involves converting funds into assets, which is usually completed by purchasing Hong Kong stocks. That is, the daily purchase conversion ratio data refers to the exchange rate between RMB and Hong Kong dollars when buying operations are carried out in Hong Kong Stock Connect transactions. This exchange rate reflects the market's exchange rate for RMB and Hong Kong dollars. Similarly, the daily sale conversion ratio data refers to the exchange rate between RMB and Hong Kong dollars when selling transactions are carried out through Hong Kong Stock Connect. When selling ETFs, the income is Hong Kong dollars, and then the Hong Kong dollars need to be converted into RMB through the exchange rate. The daily purchase resource data refers to the total amount of funds used to buy Hong Kong market stocks or ETFs through the Hong Kong Stock Connect channel on the same day. In other words, it reflects the RMB amount of stocks or securities bought by investors through the Hong Kong Stock Connect on the same day. This data is counted by the Hong Kong Stock Connect trading system and is usually calculated based on the purchase transaction amount and the current exchange rate. The daily sale resource data refers to the total amount of funds used to sell Hong Kong market stocks or ETFs through the Hong Kong Stock Connect channel on the same day. It represents the RMB amount involved after investors sell assets through the Hong Kong Stock Connect. This data is calculated using the selling transaction amount and the current exchange rate.

[0071] Step S53, determining the current day purchase adjustment data corresponding to the differential product according to the current day purchase conversion ratio data, the current day sale conversion ratio data and the current day purchase resource data.

[0072] Specifically, Based on the relationship between the purchased resources and the conversion ratio, the resources can be reasonably adjusted to ensure that the data of the purchased resources can reflect the real market conditions. The adjustment formula takes into account the difference between the purchase conversion ratio data and the sales conversion ratio data of the same day, so that the purchased resource data is more in line with market demand.

[0073] Step S55, determining the current day selling adjustment data corresponding to the differential product according to the current day buying conversion ratio data, the current day selling conversion ratio data and the current day selling resource data.

[0074] Specifically, Similar to the daily buying adjustment data, the selling adjustment is also intended to correct the valuation of sold resources so that it can more accurately reflect market trading conditions and avoid misleading resource sales due to differences in conversion ratios.

[0075] Step S57, determining the net adjustment data corresponding to the difference product according to the difference between the purchase adjustment data and the sale adjustment data of the day.

[0076] Specifically, net adjustment data = daily purchase adjustment data - daily sales adjustment data. Through the difference between purchase and sales adjustments, net adjustment data can reflect the final impact of product net conversion. This difference can positively or negatively adjust resource data.

[0077] Step S59: performing resource conversion on the first resource change data according to the net adjustment data to obtain second resource change data.

[0078] Specifically, the second resource change data = the first resource change data + the net adjustment data. The first resource change data is corrected by the net adjustment data in order to eliminate the errors in product valuation due to factors such as conversion ratio differences and market fluctuations. The converted resource change data can better reflect the real situation of the market.

[0079] and Figure 1Compared with the embodiment shown in the figure, this embodiment provides accurate basic data for subsequent adjustments by obtaining the purchase conversion ratio data of the day, the sale conversion ratio data of the day, the purchase resource data of the day, and the sale resource data of the day. On this basis, the adjustment data of buying and selling are calculated, and the resource flow in the market is optimized by analyzing the conversion efficiency of resources. Then, by comparing the purchase and sale adjustment data, the net adjustment data is determined to reflect the balance of capital flow. Finally, the first resource change data is converted according to the net adjustment data to obtain the final resource change data. The whole process not only improves the efficiency of resource conversion and enhances market liquidity and stability, but also provides strong support for users, helping them to accurately control market dynamics, optimize strategy adjustments, and ensure the efficient allocation of market resources and the rationality of capital flow.

[0080] Figure 7 The flowchart of step S7 provided in the embodiment of the present application may include the following steps: Step S71, determining the adjusted unit net value data according to the ratio of the second resource change data to the resource initial share data.

[0081] Specifically, in order to reflect the actual value change of each unit of resources (such as each ETF fund share) during the process of resource flow and change, the adjusted unit net value data is calculated by dividing the second resource change data by the resource initial share data. The resource initial share data here refers to the ETF product share at the beginning of the day, and does not include real-time subscription and redemption data, that is, without considering subscription and redemption factors, the adjusted value of the resource is calculated only by the ratio of resource changes to initial shares. Adjusted unit net value data = second resource change data / resource initial share data.

[0082] Step S73, determining the target resource valuation data based on the product of the unit net value data and the resource real-time share data.

[0083] Specifically, based on the unit net value data calculated in the previous step, the target resource valuation data is calculated in combination with the real-time resource share data in the current market. The real-time share data reflects the current total share of the resource in the market, so multiplying it with the unit net value can get the actual market value, that is, the target resource valuation. The real-time resource share data here refers to the real-time ETF product share, including real-time subscription and redemption data. Target resource valuation data = adjusted unit net value data × real-time resource share data.

[0084] Therefore, in the adjustment of the valuation of ETF products for resources, the impact of the Hong Kong Stock Connect exchange rate on the valuation is eliminated, making the target resource valuation data of the ETF product more accurate. This ensures that the valuation of ETF products is not affected by exchange rate fluctuations when converting between different markets, and maintains the stability of the valuation. By calculating the valuation of ETF products including real-time subscription and redemption, the real-time status of ETF products on the market can be reflected. This makes the embodiment of the present application more efficient and timely, and can quickly respond to the flow of funds and subscription and redemption behavior in the market, thereby providing users with accurate market data support.

[0085] It should also be noted here that the target resource valuation data obtained every day and every hour can be stored in a database, and machine learning technology can be used to train and predict resource valuation data for future times.

[0086] First, the target resource valuation data obtained in real time is stored in the database, and then the data is preprocessed. The preprocessing steps include data cleaning, missing value processing and feature engineering to ensure the quality of the data.

[0087] Next, the model is trained using machine learning algorithms such as linear regression, decision trees, random forests, support vector machines, neural networks, etc., and the accuracy of the model is evaluated using a test set. During model training, machine learning algorithms analyze patterns and trends in historical data to generate a model that can make predictions about future data.

[0088] Finally, once the model is trained and validated, it can be used to predict resource valuation data at future points in time.

[0089] The embodiment of the present application is optimized at multiple stages of data processing. First, through precise data acquisition and processing processes, the valuation data of the target resources is ensured to be highly accurate. Furthermore, advanced machine learning algorithms are combined to predict the value of data resources at future moments. Through this method, the future valuation trend of data resources can be tracked and predicted in real time, significantly improving the response speed and prediction ability to market fluctuations.

[0090] Specifically, based on the prediction of future resource valuation, users can identify potential risks and opportunities in advance, thereby obtaining more accurate decision-making support in risk management. This technology can not only help users understand the current status of data resources in real time, but also help users to make asset allocation and risk prevention through forward-looking predictions, thereby minimizing the impact of uncertainty. The application of this technology will provide users with more scientific and accurate risk management strategies, improve decision-making efficiency, and optimize resource allocation and investment returns.

[0091] and Figure 1Compared with the embodiment shown in the figure, this embodiment dynamically adjusts the unit net value by calculating the ratio of the second resource change data to the resource initial share data to accurately reflect the actual value change of resources in the market. The target resource valuation data is calculated using the adjusted unit net value data and the real-time resource share data, thereby ensuring that the market value of the resource can immediately reflect the flow of funds and market fluctuations. This method effectively improves the real-time and flexibility of valuation, helps investors or managers make timely decisions, optimizes the management of capital liquidity, and avoids the risks caused by valuation lags. In addition, for cross-market valuation, this method can eliminate the interference of factors such as exchange rate fluctuations, ensure the consistency and stability of valuations between multiple markets, and enhance the accuracy and stability of valuations. Therefore, this technology provides strong support for real-time tracking and estimation of the market value of resources, optimization of capital flow management, and cross-market resource valuation. At the same time, it simplifies the logic, improves the execution efficiency of the algorithm, and can obtain valuation data faster and more accurately.

[0092] Accordingly, please refer to Figure 8 A block diagram of a real-time resource valuation data processing device provided in an embodiment of the present application, the device comprising: The valuation request receiving unit 101 is used to receive a resource valuation request for a resource input by a user, and obtain the resource original valuation net value data and resource initial share data of the resource according to the resource valuation request; A first data determination unit 103, configured to determine first resource change data corresponding to the original estimated net value data of the resource; The second data determination unit 105 is used to perform resource transformation on the first resource change data to obtain second resource change data; the target data determination unit 107 is used to receive real-time resource share data and determine the target resource valuation data corresponding to the resource valuation request based on the second resource change data and the resource initial share data.

[0093] In some optional implementations, the first data determining unit 103 includes: Determine the real-time compensated floating profit data corresponding to the resources; Determine the total supplementary data corresponding to the resources; Determine the daily floating profit data corresponding to the resource; Based on the real-time compensated floating profit data, the total compensated data and the compensated floating profit data of the day corresponding to the resources, the original estimated net value data of the resources is adjusted to obtain the adjusted first resource change data.

[0094] In some optional implementations, determining the real-time compensated floating profit data corresponding to the resource includes: Obtain the real-time net replenished product quantity, the latest product attribute value and the real-time net replenished cost data corresponding to the resource; The real-time net replenished gain data corresponding to the resource is determined based on the product of the real-time net replenished product quantity and the latest product attribute value; the real-time replenished floating profit data is determined based on the difference between the real-time net replenished gain data and the real-time net replenished cost data.

[0095] In some optional implementations, determining the total supplementary data corresponding to the resource includes: Get the latest product attribute value corresponding to the resource, yesterday's product closing attribute value, and the net number of products to be replenished on the day; Determine the net value attribute value between the latest product attribute value and yesterday's product closing attribute value; The total data to be compensated is determined based on the product of the net quantity of products to be compensated on that day and the net value attribute value.

[0096] In some optional implementations, determining the daily compensated floating profit data corresponding to the resource includes: Obtain the current day’s non-real-time net replenished product quantity, the latest product attribute value, and the current day’s non-real-time net replenished cost data corresponding to the resource; Determine the non-real-time net replenished gain data for the day corresponding to the resource based on the product of the non-real-time net replenished product quantity and the latest product attribute value on the day; The day's compensated floating profit data is determined based on the difference between the day's non-real-time net compensated gain data and the day's non-real-time net compensated cost data.

[0097] In some optional implementations, the second data determination unit 105 includes: For differentiated products affected by resource conversion differences, obtain the daily purchase conversion ratio data, daily sale conversion ratio data, daily purchase resource data, and daily sale resource data corresponding to the differentiated products; Determine the same-day purchase adjustment data corresponding to the difference product based on the same-day purchase conversion ratio data, the same-day sale conversion ratio data and the same-day purchase resource data; Determine the same-day selling adjustment data corresponding to the difference product based on the same-day buying conversion ratio data, the same-day selling conversion ratio data and the same-day selling resource data; Determine the net adjustment data corresponding to the difference product based on the difference between the purchase adjustment data and the sales adjustment data of the day; The first resource change data is resource-converted according to the net adjustment data to obtain second resource change data.

[0098] In some optional implementations, the target data determination unit 107 includes: Determine the adjusted unit net value data according to the ratio of the second resource change data to the initial resource share data; The target resource valuation data is determined based on the product of the unit net value data and the real-time resource share data.

[0099] The further functional description of each of the above modules and units is the same as that of the above corresponding embodiments and will not be repeated here.

[0100] A real-time resource valuation data processing device in this embodiment is presented in the form of a functional unit, where the unit refers to an ASIC (Application Specific Integrated Circuit) circuit, a processor and memory that executes one or more software or fixed programs, and / or other devices that can provide the above-mentioned functions.

[0101] See also Fig. 9 , Fig. 9 A schematic diagram of the structure of a computer device provided in an embodiment of the present application is shown in FIG. Fig. 9 As shown, the computer device includes: one or more processors 10, a memory 20, and interfaces for connecting various components, including high-speed interfaces and low-speed interfaces. Various components are connected to each other using different buses for communication, and can be installed on a common mainboard or installed in other ways as needed. The processor can process the instructions executed in the computer device, including instructions stored in or on the memory to display the graphical information of the GUI on an external input / output device (such as, a display device coupled to the interface). In some optional embodiments, if necessary, multiple processors and / or multiple buses can be used together with multiple memories and multiple memories. Similarly, multiple computer devices can be connected, and each device provides some necessary operations (for example, as a server array, a group of blade servers, or a multi-processor system). Fig. 9 A processor 10 is taken as an example.

[0102] The processor 10 may be a central processing unit, a network processor or a combination thereof. The processor 10 may further include a hardware chip. The hardware chip may be a dedicated integrated circuit, a programmable logic device or a combination thereof. The programmable logic device may be a complex programmable logic device, a field programmable gate array, a general purpose array logic or any combination thereof.

[0103] The memory 20 stores instructions executable by at least one processor 10, so that the at least one processor 10 executes the method shown in the above embodiment.

[0104] The memory 20 may include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function; the data storage area may store data created according to the use of the computer device, etc. In addition, the memory 20 may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some optional embodiments, the memory 20 may optionally include a memory remotely arranged relative to the processor 10, and these remote memories may be connected to the computer device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0105] The memory 20 may include a volatile memory, such as a random access memory; the memory may also include a non-volatile memory, such as a flash memory, a hard disk or a solid state drive; the memory 20 may also include a combination of the above types of memory.

[0106] The computer device further comprises a communication interface 30 for the computer device to communicate with other devices or a communication network.

[0107] The embodiment of the present application also provides a computer-readable storage medium. The above method according to the embodiment of the present application can be implemented in hardware, firmware, or can be implemented as a computer code that can be recorded in a storage medium, or can be implemented as a computer code that is originally stored in a remote storage medium or a non-temporary machine-readable storage medium and will be stored in a local storage medium through a network download, so that the method described herein can be stored in such software processing on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. Among them, the storage medium can be a magnetic disk, an optical disk, a read-only storage memory, a random access memory, a flash memory, a hard disk or a solid-state hard disk, etc.; further, the storage medium can also include a combination of the above types of memories. It can be understood that a computer, a processor, a microprocessor controller, or programmable hardware includes a storage component that can store or receive software or computer code. When the software or computer code is accessed and executed by a computer, a processor, or hardware, the method shown in the above embodiment is implemented.

[0108] The systems, devices, and units described in the above embodiments may be implemented by computer chips or entities, or by products with certain functions. A typical implementation device is a computer. Specifically, the computer may be, for example, a personal computer, a laptop computer, a cellular phone, a camera phone, a smart phone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.

[0109] For the convenience of description, the above device is described in various units according to their functions. Of course, when implementing the present application, the functions of each unit can be implemented in the same or multiple software and / or hardware.

[0110] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods and systems. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0111] The present application is described with reference to the flowcharts and / or block diagrams of the methods and systems according to the embodiments of the present application. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of the processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0112] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0113] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0114] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0115] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0116] The above is only an embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.

[0117] Although the embodiments of the present application have been described in conjunction with the accompanying drawings, those skilled in the art may make various modifications and variations without departing from the spirit and scope of the present application, and such modifications and variations are all within the scope defined by the appended claims.

Claims

1. A real-time resource valuation data processing method, characterized in that: The method comprises: Receiving a resource valuation request for a resource input by a user, and acquiring original resource valuation net value data and resource initial share data of the resource according to the resource valuation request; Determine first resource change data corresponding to the original estimated net value data of the resource; Performing resource conversion on the first resource change data to obtain second resource change data; The real-time resource share data is received, and the target resource valuation data corresponding to the resource valuation request is determined according to the second resource change data and the initial resource share data.

2. The method according to claim 1, characterized in that: The determining of the first resource change data corresponding to the original resource valuation net value data includes: Determine the real-time compensated floating profit data corresponding to the resource; Determine the total amount of data to be supplemented corresponding to the resource; Determine the daily floating profit data corresponding to the resource; The original estimated net value data of the resource is adjusted based on the real-time compensated floating profit data corresponding to the resource, the total compensated data and the compensated floating profit data on the day to obtain the adjusted first resource change data.

3. The method according to claim 2, characterized in that The determining of the real-time compensated floating profit data corresponding to the resource includes: Obtaining the real-time net replenished product quantity, the latest product attribute value and the real-time net replenished cost data corresponding to the resource; Determine the real-time net replenished gain data corresponding to the resource according to the product of the real-time net replenished product quantity and the latest product attribute value; The real-time compensated floating profit data is determined according to the difference between the real-time net compensated gain data and the real-time net compensated cost data.

4. The method according to claim 2, characterized in that: Determining the total replenishment data corresponding to the resource includes: obtaining the latest product attribute value corresponding to the resource, yesterday's product closing attribute value, and the net replenishment product quantity on the day; Determine the net value attribute value between the latest product attribute value and yesterday's product closing attribute value; The total data to be replenished is determined based on the product of the net quantity of products to be replenished on that day and the net value attribute value.

5. The method according to claim 2, characterized in that: The determining of the daily compensated floating profit data corresponding to the resource includes: Obtain the current day's non-real-time net replenished product quantity, the latest product attribute value, and the current day's non-real-time net replenished cost data corresponding to the resource; Determine the non-real-time net replenished gain data for the resource on the same day according to the product of the non-real-time net replenished product quantity on the same day and the latest product attribute value; The compensated floating profit data for the day is determined according to the difference between the non-real-time net compensated gain data for the day and the non-real-time net compensated cost data for the day.

6. The method according to claim 1, characterized in that The performing resource conversion on the first resource change data to obtain second resource change data includes: For the difference products affected by the resource conversion difference, obtain the same-day purchase conversion ratio data, the same-day sale conversion ratio data, the same-day purchase resource data, and the same-day sale resource data corresponding to the difference products; Determine the same-day purchase adjustment data corresponding to the differential product according to the same-day purchase conversion ratio data, the same-day sale conversion ratio data and the same-day purchase resource data; Determine the same-day selling adjustment data corresponding to the differential product according to the same-day buying conversion ratio data, the same-day selling conversion ratio data and the same-day selling resource data; Determine the net adjustment data corresponding to the difference product according to the difference between the purchase adjustment data of the day and the sale adjustment data of the day; Perform resource conversion on the first resource change data according to the net adjustment data to obtain the second resource change data.

7. The method according to claim 1, characterized in that The receiving of the real-time resource share data, and determining the target resource valuation data corresponding to the resource valuation request according to the second resource change data and the resource initial share data, includes: The adjusted unit net value data is determined according to the ratio of the second resource change data to the resource initial share data; and the target resource valuation data is determined according to the product of the unit net value data and the resource real-time share data.

8. A real-time resource valuation data processing device, characterized in that: The device comprises: A valuation request receiving unit, configured to receive a resource valuation request for a resource input by a user, and obtain resource original valuation net value data and resource initial share data of the resource according to the resource valuation request; A first data determination unit, configured to determine first resource change data corresponding to the original estimated net value data of the resource; The second data determination unit is used to perform resource conversion on the first resource change data to obtain second resource change data; the target data determination unit is used to receive real-time resource share data and determine the target resource valuation data corresponding to the resource valuation request based on the second resource change data and the resource initial share data.

9. A computer device, characterized in that: include: A memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the real-time resource valuation data processing method described in any one of claims 1 to 7 by executing the computer instructions.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a computer to execute the real-time resource valuation data processing method according to any one of claims 1 to 7.

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