Decision information generation method and device, equipment and storage medium

Abstract

The invention provides a decision information generation method and device, equipment and a storage medium, and can be applied to the technical field of data processing and the technical field of finance. The decision information generation method comprises the following steps: in response to a received transaction decision service request for a transaction object in a target transaction period, obtaining an identifier of the transaction object, first transaction information of the transaction object at a target moment and second transaction information of the transaction object at a current moment; generating a first transaction fluctuation curve according to the first transaction information, the second transaction information, the current moment and the target moment; querying transaction fluctuation amplitude information and transaction influence factors according to the identifier of the transaction object; and generating a second transaction fluctuation curve according to the second transaction information, the transaction fluctuation amplitude information and the transaction influence factor, and generating transaction decision information according to the first transaction fluctuation curve and the second transaction fluctuation curve.

Description

Technical Field

[0001] The present disclosure relates to the fields of data processing technology and financial technology, and more specifically to a decision information generation method, device, equipment and storage medium. Background Art

[0002] With the rapid development of economic globalization, the business of financial institutions is constantly expanding, and the demand for foreign exchange transactions is also gradually increasing. When it comes to foreign exchange transactions, the relevant technology generally makes trading decisions based on the transaction information provided by financial institutions.

[0003] In the process of realizing the above-mentioned inventive concept, the inventors found that the relevant transaction information has large time series fluctuations, resulting in low accuracy and timeliness of traditional information decision-making based only on transaction information provided by financial institutions. Summary of the invention

[0004] In view of the above problems, the present disclosure provides a decision information generation method, apparatus, device and storage medium.

[0005] According to a first aspect of the present disclosure, a decision information generating method is provided, comprising: in response to a received transaction decision service request for a transaction object within a target transaction period, obtaining an identifier of the transaction object, first transaction information of the transaction object at a target moment, and second transaction information of the transaction object at a current moment; generating a first transaction volatility curve according to the first transaction information, the second transaction information, the current moment, and the target moment; querying transaction volatility range information and transaction impact factor according to the identifier of the transaction object; generating a second transaction volatility curve according to the second transaction information, the transaction volatility range information, and the transaction impact factor, and generating transaction decision information according to the first transaction volatility curve and the second transaction volatility curve.

[0006] According to an embodiment of the present disclosure, generating a second transaction volatility curve based on second transaction information, transaction volatility information and transaction impact factors includes: constructing a transaction volatility probability distribution model based on the transaction volatility information and the transaction impact factors; and generating a second transaction volatility curve based on the second transaction information based on the transaction volatility distribution model.

[0007] According to an embodiment of the present disclosure, based on a transaction volatility distribution model, a second transaction volatility curve is generated according to second transaction information, including: inputting the second transaction information as an initial value into the transaction volatility distribution model to obtain a distribution probability of multiple transaction information corresponding to n transaction moments, wherein n is an integer greater than 2; for the nth transaction moment, based on the distribution probability, obtaining target transaction information from multiple transaction information corresponding to the nth transaction moment; and generating a second transaction volatility curve according to n target transaction information corresponding to the n transaction moments.

[0008] According to an embodiment of the present disclosure, the first curve includes t transaction information corresponding to t moments, wherein t is an integer greater than or equal to 1; generating a first transaction fluctuation curve according to the first transaction information, the second transaction information, the current moment and the target moment, including: obtaining a first duration according to the tth moment and the current moment; obtaining a second duration according to the target moment and the current moment; generating third transaction information corresponding to the tth moment according to the first duration, the second duration, the first transaction information and the second transaction information; in the case of determining that t is less than the target moment, returning to execute the operation of generating the third transaction information and incrementing t; and in the case of determining that t is equal to the target moment, generating the first transaction fluctuation curve according to the first transaction information, the second transaction information, the current moment, the target moment and multiple third transaction information corresponding to t-2 moments.

[0009] According to an embodiment of the present disclosure, transaction fluctuation range information and transaction impact factor are queried based on the identification of the transaction object, including: based on the identification of the transaction object, the first transaction object and the second transaction object used for the transaction and the transaction fluctuation service information are queried; based on the first transaction object, the impact factor of the first institution is queried; based on the second transaction object, the impact factor of the second institution is queried; and based on the impact factor of the first institution and the impact factor of the second institution, the transaction impact factor is obtained.

[0010] According to an embodiment of the present disclosure, generating transaction decision information based on a first transaction volatility curve and a second transaction volatility curve includes: when it is determined that the first transaction volatility curve and the second transaction volatility curve have no intersection, obtaining a plurality of candidate transaction difference information corresponding to a plurality of candidate transaction moments based on the first transaction volatility curve and the second transaction volatility curve; and producing transaction decision information based on the plurality of candidate transaction difference information corresponding to the plurality of candidate transaction moments.

[0011] According to an embodiment of the present disclosure, it also includes: when it is determined that the first transaction volatility curve and the second transaction volatility curve have an intersection, multiple trading time periods are obtained according to the transaction time corresponding to the intersection, the current time and the target time; and for each trading time period, multiple candidate transaction difference information corresponding to the multiple candidate transaction times in each trading time period is obtained according to the first transaction volatility curve and the second transaction volatility curve.

[0012] According to an embodiment of the present disclosure, producing transaction decision information based on multiple candidate transaction difference information corresponding to multiple candidate transaction moments includes: sorting the multiple candidate transaction difference information to obtain a sorting result; based on the sorting result, determining an expected transaction moment and expected transaction information corresponding to the expected transaction moment from multiple candidate transaction moments; and generating transaction decision information based on the expected transaction moment and the expected transaction information.

[0013] A second aspect of the present disclosure provides a decision information generating device, including: an acquisition module, used to obtain an identification of a transaction object, first transaction information of the transaction object at a target time, and second transaction information of the transaction object at a current time in response to a received transaction decision service request for the transaction object within a target transaction period; a first generation module, used to generate a first transaction volatility curve according to the first transaction information, the second transaction information, the current time, and the target time; a query module, used to query transaction volatility information and transaction impact factors according to the identification of the transaction object; a second generation module, used to generate a second transaction volatility curve according to the second transaction information, the transaction volatility information, and the transaction impact factors; and a third generation module, used to generate transaction decision information according to the first transaction volatility curve and the second transaction volatility curve.

[0014] A third aspect of the present disclosure provides an electronic device, comprising: one or more processors; and a memory for storing one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors execute the above method.

[0015] The fourth aspect of the present disclosure also provides a computer-readable storage medium having executable instructions stored thereon, which, when executed by a processor, causes the processor to execute the above method.

[0016] The fifth aspect of the present disclosure also provides a computer program product, including a computer program, which implements the above method when executed by a processor.

[0017] According to the decision information generation method, device, equipment and storage medium disclosed in the present invention, upon receiving a transaction decision service request and a target transaction period from a transaction object, the identification of the transaction object, the first transaction information, the second transaction information, the transaction fluctuation range information and the transaction impact factor are obtained from the system, and a first transaction fluctuation curve is generated according to the first transaction information, the second transaction information, the current time and the target time, and a second transaction fluctuation curve is generated according to the second transaction information, the transaction fluctuation range information and the transaction impact factor, and the two curves are compared to obtain transaction decision information, thereby realizing the generation of the optimal decision for the transaction object, improving the accuracy of information decision-making based on transaction information, and at the same time, with the assistance of computer technology, reducing time consumption, saving labor costs and time costs, and improving the timeliness of providing decision information. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The above contents and other purposes, features and advantages of the present disclosure will become more apparent through the following description of the embodiments of the present disclosure with reference to the accompanying drawings, in which:

[0019] Figure 1 The application scenario diagram of the decision information generation method, device, equipment and storage medium according to the embodiment of the present disclosure is schematically shown;

[0020] Figure 2 A flowchart of a method for generating decision information according to an embodiment of the present disclosure is schematically shown;

[0021] Figure 3 Schematically shows a flow chart of generating a second transaction volatility curve according to an embodiment of the present disclosure;

[0022] Figure 4 A diagram schematically illustrates a judgment diagram for generating a first transaction fluctuation curve according to an embodiment of the present disclosure;

[0023] Figure 5 A flowchart schematically shows a transaction influencing factor according to an embodiment of the present disclosure;

[0024] Figure 6 A flowchart schematically shows transaction decision information according to an embodiment of the present disclosure;

[0025] Figure 7 A schematic diagram schematically shows a transaction curve without an intersection according to an embodiment of the present disclosure;

[0026] Figure 8 A schematic diagram schematically shows another transaction curve without intersection points according to an embodiment of the present disclosure;

[0027] Fig. 9 A schematic diagram schematically shows a transaction curve having an intersection according to an embodiment of the present disclosure;

[0028] Fig.10 A schematic diagram schematically shows a decision application scenario according to an embodiment of the present disclosure;

[0029] Fig.11 A structural block diagram of a decision information generating device according to an embodiment of the present disclosure is schematically shown; and

[0030] Fig.12 A block diagram of an electronic device suitable for implementing a decision information generating method according to an embodiment of the present disclosure is schematically shown. DETAILED DESCRIPTION

[0031] Hereinafter, embodiments of the present disclosure will be described with reference to the accompanying drawings. However, it should be understood that these descriptions are exemplary only and are not intended to limit the scope of the present disclosure. In the following detailed description, for ease of explanation, many specific details are set forth to provide a comprehensive understanding of the embodiments of the present disclosure. However, it is apparent that one or more embodiments may also be implemented without these specific details. In addition, in the following description, descriptions of known structures and technologies are omitted to avoid unnecessary confusion of the concepts of the present disclosure.

[0032] The terms used herein are only for describing specific embodiments and are not intended to limit the present disclosure. The terms "comprise", "include", etc. used herein indicate the existence of the features, steps, operations and / or components, but do not exclude the existence or addition of one or more other features, steps, operations or components.

[0033] All terms (including technical and scientific terms) used herein have the meanings commonly understood by those skilled in the art, unless otherwise defined. It should be noted that the terms used herein should be interpreted as having a meaning consistent with the context of this specification, and should not be interpreted in an idealized or overly rigid manner.

[0034] When using expressions such as "at least one of A, B, and C, etc.", they should generally be interpreted according to the meaning of the expression commonly understood by those skilled in the art (for example, "a system having at least one of A, B, and C" should include but is not limited to a system having A alone, B alone, C alone, A and B, A and C, B and C, and / or A, B, C, etc.).

[0035] In the technical solution of the present invention, the user information (including but not limited to user personal information, user image information, user device information, such as location information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved are all information and data authorized by the user or fully authorized by all parties, and the collection, storage, use, processing, transmission, provision, disclosure and application of relevant data comply with the relevant laws, regulations and standards of relevant countries and regions, take necessary confidentiality measures, do not violate public order and good morals, and provide corresponding operation entrances for users to choose to authorize or refuse.

[0036] In the actual application process, as the business of financial institutions is constantly expanding, the demand for foreign exchange transactions is gradually increasing. In order for customers to get the best choice in foreign exchange transactions, R&D personnel need to spend a lot of time collecting information about customer transaction information and predicting the exchange rate based on the information to obtain a decision-making plan that meets customer requirements and has a suitable transaction exchange rate.

[0037] However, since the relevant transaction information is greatly affected by time series volatility, it is difficult to provide customers with the most effective decision-making plan. At the same time, since the evaluation process requires a lot of time to compare the market quotations provided by major financial institutions, it will consume a lot of manpower and time costs. Therefore, the relevant technology has the problem of low accuracy and low timeliness due to information decision-making based only on transaction information.

[0038] In view of this, an embodiment of the present disclosure provides a decision information generating method, comprising: in response to a received transaction decision service request for a transaction object within a target transaction period, obtaining an identification of the transaction object, first transaction information of the transaction object at a target time, and second transaction information of the transaction object at a current time; generating a first transaction volatility curve according to the first transaction information, the second transaction information, the current time, and the target time; querying transaction volatility range information and transaction impact factors according to the identification of the transaction object; generating a second transaction volatility curve according to the second transaction information, the transaction volatility range information, and the transaction impact factors, and generating transaction decision information according to the first transaction volatility curve and the second transaction volatility curve.

[0039] Figure 1 The application scenario diagram of the decision information generation method according to an embodiment of the present disclosure is schematically shown.

[0040] like Figure 1As shown, the application scenario 100 according to this embodiment may include a first terminal device 101, a second terminal device 102, a third terminal device 103, a network 104, and a server 105. The network 104 is used to provide a medium for communication links between the terminal devices 101, 102, 103 and the server 105. The network 104 may include various connection types, such as wired, wireless communication links or optical fiber cables, etc.

[0041] The user may use at least one of the first terminal device 101, the second terminal device 102, and the third terminal device 103 to interact with the server 105 through the network 104 to receive or send messages, etc. Various communication client applications may be installed on the first terminal device 101, the second terminal device 102, and the third terminal device 103, such as shopping applications, web browser applications, search applications, instant messaging tools, email clients, social platform software, etc. (only as examples).

[0042] For example, the user may use the bank client installed in the first terminal device 101 , the second terminal device 102 , and the third terminal device 103 to send a service request to the server 105 equipped with the service system.

[0043] The first terminal device 101, the second terminal device 102, and the third terminal device 103 may be various electronic devices having display screens and supporting web browsing, including but not limited to smart phones, tablet computers, laptop computers, desktop computers, and the like.

[0044] The server 105 may be a server that provides various services, such as a background management server (only as an example) that provides support for websites browsed by users using the first terminal device 101, the second terminal device 102, and the third terminal device 103. The background management server may analyze and process the received data such as user requests, and feed back the processing results (such as web pages, information, or data obtained or generated according to user requests) to the terminal device.

[0045] For example, the server 105 may be a distributed server, a cloud server, or a centralized server. The server 105 may be installed with an old version of the business system and a new version of the business server at the same time, and the storage method of the business data is tested.

[0046] It should be noted that the decision information generation method provided in the embodiment of the present disclosure can generally be executed by the server 105. Accordingly, the decision information generation device provided in the embodiment of the present disclosure can generally be set in the server 105. The decision information generation method provided in the embodiment of the present disclosure can also be executed by a server or server cluster that is different from the server 105 and can communicate with the first terminal device 101, the second terminal device 102, the third terminal device 103 and / or the server 105. Accordingly, the decision information generation device provided in the embodiment of the present disclosure can also be set in a server or server cluster that is different from the server 105 and can communicate with the first terminal device 101, the second terminal device 102, the third terminal device 103 and / or the server 105.

[0047] It should be understood that Figure 1 The number of terminal devices, networks and servers in the embodiment is only for illustration. Any number of terminal devices, networks and servers may be provided according to implementation requirements.

[0048] The following will be based on Figure 1 The scene described by Figure 2 to Figure 10 The page generation method of the disclosed embodiment is described in detail.

[0049] Figure 2 The flowchart of the decision information generating method according to the embodiment of the present disclosure is schematically shown.

[0050] like Figure 2 As shown, the decision information generating method of this embodiment includes operations S210 to S250.

[0051] In operation S210, in response to a received transaction decision service request for a transaction object within a target transaction period, an identifier of the transaction object, first transaction information of the transaction object at a target time, and second transaction information of the transaction object at a current time are obtained.

[0052] According to an embodiment of the present disclosure, a transaction object may be characterized as a customer who conducts transaction business in a financial institution.

[0053] According to an embodiment of the present disclosure, the target trading period may be characterized as a target time period for a client from the start of a transaction to the end of a foreign exchange transaction.

[0054] For example, a customer's trading period is 30 days.

[0055] According to the embodiment of the present disclosure, the identification of the transaction object can be characterized as each transaction object having a unique identification, so that the financial institution can obtain the relevant information of the transaction object according to the identification. The identification can be numbers, letters, symbols, but is not limited to the above three forms.

[0056] For example, the identifier of transaction object A is A@Lina, and the identifier of transaction object B is B@Taler.

[0057] According to an embodiment of the present disclosure, the target time may be characterized as a target transaction time of a transaction object.

[0058] According to an embodiment of the present disclosure, the current moment may be characterized as the current transaction time of the transaction object.

[0059] For example, it is now November ZZ, XXXX, and the target trading period of transaction object A is 30 days. Then the current time is November ZZ, XXXX, and the target time is December ZZ, XXXX.

[0060] According to an embodiment of the present disclosure, the first transaction information may be characterized as all transaction information generated by the transaction object performing the transaction at the target time.

[0061] According to an embodiment of the present disclosure, the second transaction information may be characterized as all transaction information generated by the transaction object at the current moment.

[0062] For example, the target transaction period of transaction object A is 30 days, the current time is November ZZ, XXXX, and the target time is December ZZ, XXXX. The transaction information of transaction object A on November ZZ, XXXX includes currency ABS, transaction time November ZZ, XXXX, XX:YY:ZZ, and transaction amount 10,000 yuan; the transaction information of transaction object A on December ZZ, XXXX includes currency BBS, transaction time December ZZ, XXXX, XX:YY:ZZ, and transaction amount 50,000 yuan.

[0063] According to an embodiment of the present disclosure, after receiving a transaction decision request from a customer and the target transaction period required by the customer, the financial institution retrieves the identification of the transaction object, the first transaction information and the second transaction information from the system, so as to provide the customer with better decision-making services based on the information.

[0064] In operation S220, a first transaction fluctuation curve is generated according to the first transaction information, the second transaction information, the current time, and the target time.

[0065] According to an embodiment of the present disclosure, the first transaction fluctuation curve can be characterized as an actual transaction fluctuation curve generated by a financial institution based on information about the transaction object, where each moment corresponds to a transaction point. Based on the actual information of each financial institution that has been obtained and standardized, the line connecting all transaction points corresponding to all moments within the target time period is the actual transaction fluctuation curve.

[0066] According to an embodiment of the present disclosure, the actual information of each financial institution can be captured by using computing technology to capture the latest information of the financial institutions from the official websites of different financial institutions. The actual information may include term, currency, spot purchase price, spot sale price, cash purchase price, cash sale price, quotation unit, middle price, and announcement time.

[0067] For example, the actual information of financial institution A includes: currency name ABS, spot buying price 515, cash buying price 86.66, spot selling price 859.6, cash selling price 80.31, quotation unit 100, middle price 517.93, release date XXXX YY ZZ, release time XX:YY:ZZ.

[0068] According to an embodiment of the present disclosure, before receiving standardized actual information from other financial institutions, the system of this financial institution will perform standardized conversion on the actual information from other financial institutions, converting it into standardized actual information of each financial institution for system use.

[0069] In operation S230, transaction fluctuation range information and transaction impact factors are queried according to the identifier of the transaction object.

[0070] According to an embodiment of the present disclosure, a table is stored in the system of a financial institution, and the table may include the identification of the transaction object, the currency exchange direction of the transaction object, the target exchange currency of the transaction object, the transaction fluctuation range information and the transaction impact factor. The system can query the transaction fluctuation range information and the transaction impact factor in the table according to the identification of the transaction object. The table can be updated regularly, and the regular update can be set by the system or manually.

[0071] For example, the table includes the identification A@Kler of the transaction object A, the currency exchange direction ABS to BDE, the target exchange currency BDE, the transaction fluctuation range information a1 and the transaction impact factor a2, the identification B@Ling of the transaction object B, the currency exchange direction AUE to UAB, the target exchange currency UAB of the transaction object, the transaction fluctuation range information b1 and the transaction impact factor b2. The system queries the corresponding transaction fluctuation range information a1 and the transaction impact factor a2 in the table according to the identification A@Kler of the transaction object.

[0072] According to an embodiment of the present disclosure, the transaction fluctuation range information may be characterized as exchange rate fluctuation range information of a currency.

[0073] According to an embodiment of the present disclosure, in a financial institution, each currency has a unique exchange rate fluctuation range, so that the system can retrieve exchange rate fluctuation range information of different currencies according to the types of different currencies.

[0074] For example, the exchange rate fluctuation range of currency A is 0.5, and the exchange rate fluctuation range of currency B is 0.36.

[0075] According to an embodiment of the present disclosure, the transaction impact factor may be characterized as a factor affecting the relevant rules on the transaction circulation of the currency pair under the current rules formulated by the issuing institution corresponding to the currency pair.

[0076] In operation S240, a second transaction fluctuation curve is generated according to the second transaction information, the transaction fluctuation range information, and the transaction impact factor.

[0077] According to an embodiment of the present disclosure, the second transaction volatility curve can be characterized as a predicted transaction volatility curve generated by a financial institution based on information about the transaction object. Each moment corresponds to a transaction point. Based on the current actual information obtained from each financial institution and the transaction volatility information and transaction impact factor of the transaction object, the line connecting all predicted transaction points corresponding to all moments in the target time period is the predicted transaction volatility curve.

[0078] In operation S250, transaction decision information is generated according to the first transaction volatility curve and the second transaction volatility curve.

[0079] According to an embodiment of the present disclosure, the transaction decision information may be characterized as an optimal transaction decision for a transaction object generated by comparing the first transaction volatility curve and the second transaction volatility curve.

[0080] For example, for transaction object A, based on the first transaction volatility curve and the second transaction volatility curve of the transaction object, the optimal transaction decision is to exchange ABS currency for BDE currency at financial institution A on ZZ day of YY month of XXXX, which will generate the greatest transaction benefit.

[0081] According to the embodiments of the present disclosure, upon receiving a transaction decision service request and a target transaction period from a transaction object, an identification of the transaction object, first transaction information, second transaction information, transaction fluctuation range information and transaction impact factor are obtained from the system, a first transaction fluctuation curve is generated according to the first transaction information, the second transaction information, the current moment and the target moment, a second transaction fluctuation curve is generated according to the second transaction information, the transaction fluctuation range information and the transaction impact factor, and the two curves are compared to obtain transaction decision information, thereby realizing the generation of the optimal decision for the transaction object and improving the accuracy of information decision-making based on transaction information. At the same time, with the assistance of computer technology, time consumption is reduced, labor costs and time costs are saved, and the timeliness of providing decision information is improved.

[0082] Figure 3 The flowchart of generating the second transaction fluctuation curve according to the embodiment of the present disclosure is schematically shown.

[0083] like Figure 3 As shown, the decision information generating method of this embodiment includes operations S310 to S320.

[0084] According to an embodiment of the present disclosure, generating a second transaction volatility curve according to the second transaction information, the transaction volatility information and the transaction impact factor includes:

[0085] In operation S310, a transaction fluctuation probability distribution model is constructed according to the transaction fluctuation range information and the transaction impact factor.

[0086] According to an embodiment of the present disclosure, the transaction volatility probability distribution model can be characterized as a model constructed by using a computer based on a priori distribution model, according to transaction volatility information and transaction influencing factors.

[0087] According to the embodiments of the present disclosure, when the transaction impact factor changes, it can bring benefit impact to the preferred transaction of the transaction object. Therefore, by constructing a transaction volatility probability distribution model through transaction volatility information and transaction impact factors, the obtained transaction volatility probability distribution model can be further refined, so as to generate a more accurate second transaction volatility curve and improve the accuracy of the second transaction volatility curve.

[0088] In operation S320, a second transaction fluctuation curve is generated according to the second transaction information based on the transaction fluctuation distribution model.

[0089] According to an embodiment of the present disclosure, in the transaction volatility distribution model, the transaction information at the k+1th moment can be calculated from the transaction information at the kth moment, the transaction volatility information and the transaction impact factor according to the formula (1-1) or (1-2) shown below.

[0090]

[0091] Among them, E k+1 is the transaction information at the k+1th moment, E k is the transaction information at the kth moment, β is the transaction impact factor, and γ is the transaction fluctuation information. In the case where the transaction fluctuation is upward, the transaction information at the k+1th moment adopts the formula E k+1 =E k +(1+β)*γ is used for calculation. When the trading fluctuation shows a downward fluctuation, the trading information at the k+1th moment adopts the formula E k+1 =E k -(1-β)*γ is calculated.

[0092] According to an embodiment of the present disclosure, the transaction information at the second transaction moment needs to be calculated using the transaction information at the previous transaction moment, that is, the second transaction information is used for calculation.

[0093] For example, the transaction impact factor β is 0.86, the transaction fluctuation information γ is 0.19, and when the transaction fluctuation is upward, the exchange rate value at the 5th moment is 5.18, and the exchange rate value at the 6th moment is 5.53; when the transaction fluctuation is downward, the exchange rate value at the 8th moment is 3.82, and the exchange rate value at the 9th moment is 3.79.

[0094] According to an embodiment of the present disclosure, based on the transaction fluctuation distribution model, a second transaction fluctuation curve is generated according to the target transaction period of the transaction object and the second transaction information. The second transaction fluctuation curve includes specific transaction information values ​​for each day in the target transaction period.

[0095] According to the embodiments of the present disclosure, a transaction volatility probability distribution model is constructed based on transaction volatility information and transaction impact factors, and a second transaction volatility curve is generated based on the transaction volatility probability distribution model and the second transaction information, thereby predicting the transaction impact of different institutions' rules on the currency, and improving the accuracy of the generated second transaction volatility curve. At the same time, the model is obtained based on a computer, which saves time costs and improves the speed of generating the second transaction volatility curve.

[0096] According to an embodiment of the present disclosure, generating a second transaction fluctuation curve according to the second transaction information based on the transaction fluctuation distribution model includes:

[0097] According to an embodiment of the present disclosure, the second transaction information is input into the transaction fluctuation distribution model as an initial value to obtain the distribution probability of multiple transaction information corresponding to n transaction moments.

[0098] According to an embodiment of the present disclosure, n is an integer greater than 2.

[0099] According to an embodiment of the present disclosure, the transaction fluctuation distribution model can use the Monte Carlo method to predict transaction information, using the exchange rate value in the second transaction information, that is, the actual exchange rate value at the current moment as the initial value, and input it into the model for simulation prediction.

[0100] According to an embodiment of the present disclosure, the n trading moments are all moments within the target trading period.

[0101] For example, the target trading period is 30 days, and there are 30 trading moments in total within the 30 days.

[0102] According to the embodiment of the present disclosure, a complete transaction within a target transaction period includes multiple transaction moments. The transaction fluctuation distribution model is used to simulate the fluctuation path of the exchange rate from the start moment to the end moment of the transaction multiple times, and multiple exchange rate values ​​corresponding to each moment, and the exchange rate value at a single transaction moment is counted, and the number of times the same exchange rate value appears at a certain transaction moment is calculated, if there is no repetition, it is counted as 1 time, and the total number of simulations at a certain transaction moment is calculated, and the distribution probability is calculated to obtain all the distribution probabilities of a certain transaction information at a certain transaction moment. All the distribution probabilities of the transaction information at all transaction moments are the distribution probabilities of multiple transaction information corresponding to n transaction moments.

[0103] According to an embodiment of the present disclosure, the distribution probability can be generated according to formula (2), and the second transaction information, the number of occurrences of the same exchange rate value at the i-th moment, and the total number of simulations are input into formula (2), and the result obtained is the distribution probability, wherein formula (2) is:

[0104]

[0105] Among them, P i is the distribution probability of the i-th trading moment, NUM i is the number of times the same exchange rate value appears at the i-th moment, NUM is the total number of simulations, and the i-th moment is between the current moment and the target moment.

[0106] For example, the target trading period of the transaction object is 5 days, which includes 3 trading moments. Using the trading volatility distribution model, 5 simulations are performed to obtain 3 exchange rate values ​​corresponding to one moment, and a total of 15 exchange rate values ​​for the 3 trading moments. Specifically, the first trading moment is YY month 01, the first simulated exchange rate value is 8.99, the second simulated exchange rate value is 8.89, the third simulated exchange rate value is 9.53, the fourth simulated exchange rate value is 8.99, and the fifth simulated exchange rate value is 8.29. In the first transaction In the second trading moment, there are 5 simulations in total. The exchange rate value is 8.99 twice, and the distribution probability of the exchange rate value is 8.99 is 40%. The exchange rate value is 8.89 once, and the distribution probability of the exchange rate value is 8.89 is 20%. The exchange rate value is 9.53 once, and the distribution probability of the exchange rate value is 9.53 is 20%. The exchange rate value is 8.29 once, and the distribution probability of the exchange rate value is 8.29 is 20%. The second trading moment is YY month 03, the first simulation exchange rate value is 10.99, the second simulation exchange rate value is 10.99, and the second simulation exchange rate value is 10.99. The exchange rate value of the second simulation is 9.89, the exchange rate value of the third simulation is 10.99, the exchange rate value of the fourth simulation is 10.99, and the exchange rate value of the fifth simulation is 8.29. In the first transaction, the total number of simulations is 5 times, the exchange rate value of 10.99 appears 3 times, and the distribution probability of the exchange rate value of 10.99 is 60%. The exchange rate value of 9.89 appears once, and the distribution probability of the exchange rate value of 9.89 is 20%. The exchange rate value of 8.29 appears once, and the distribution probability of the exchange rate value of 8.29 is 20%; the third trading time is YY month 05, the first simulated exchange rate value is 7.99, the second simulated exchange rate value is 8.18, the third simulated exchange rate value is 8.18, the fourth simulated exchange rate value is 8.18, and the fifth simulated exchange rate value is 8.18. In the first trading time, the total number of simulations is 5 times, the exchange rate value of 7.99 appears once, and the distribution probability of the exchange rate value of 7.99 is 20%, the exchange rate value of 8.18 appears 4 times, and the distribution probability of the exchange rate value of 8.18 is 80%.

[0107] According to an embodiment of the present disclosure, for the nth transaction moment, target transaction information is obtained from a plurality of transaction information corresponding to the nth transaction moment based on the distribution probability.

[0108] According to an embodiment of the present disclosure, the target transaction information may be characterized as transaction information with the highest distribution probability.

[0109] For example, at a certain transaction moment, the exchange rate value of 8.99 appears 2 times, and the distribution probability of the exchange rate value of 8.99 is 40%; the exchange rate value of 8.89 appears 1 time, and the distribution probability of the exchange rate value of 8.89 is 20%; the exchange rate value of 9.53 appears 1 time, and the distribution probability of the exchange rate value of 9.53 is 20%; the exchange rate value of 8.29 appears 1 time, and the distribution probability of the exchange rate value of 8.29 is 20%. The maximum distribution probability is 40%, and the transaction information corresponding to the maximum distribution probability is the exchange rate value of 8.99, that is, the target transaction information is the exchange rate value of 8.99.

[0110] It should be noted that when generating the second trading curve, the predicted fluctuation direction can be preliminarily determined based on the historical fluctuation of the currency pair. For example, if the historical fluctuation is upward, the predicted fluctuation direction is upward, and the trading information can be predicted using formula (1-1). It is also possible to consider two fluctuation situations at the same time, that is, generate two fluctuation curves based on formulas (1-1) and (1-2) respectively, and then determine the final exchange rate value at a certain moment in the target second trading curve based on the distribution probability of the fluctuation value at that moment in the two fluctuation curves.

[0111] According to an embodiment of the present disclosure, a second transaction fluctuation curve is generated based on n target transaction information corresponding to n transaction moments.

[0112] According to the embodiments of the present disclosure, each transaction moment corresponds to a target transaction information with the largest distribution probability. By making each target transaction information correspond to each transaction moment, a second transaction fluctuation curve can be generated to observe the transaction information and generate decision information.

[0113] According to the embodiments of the present disclosure, since the second transaction information is input into the transaction fluctuation distribution model as the initial value, multiple distribution probabilities are obtained, and the transaction information corresponding to the largest distribution probability is selected from the multiple distribution probabilities as the target transaction information, the second transaction fluctuation curve is generated with the target transaction information corresponding to each transaction moment, thereby improving the accuracy of the second transaction fluctuation curve, so as to further improve the accuracy of information decision-making.

[0114] Figure 4 A judgment diagram for generating a first transaction fluctuation curve according to an embodiment of the present disclosure is schematically shown.

[0115] like Figure 4 As shown, the decision information generating method of this embodiment includes operations S401 to S405.

[0116] According to an embodiment of the present disclosure, the first curve includes t transaction information corresponding to t moments, wherein t is an integer greater than or equal to 1; according to the first transaction information, the second transaction information, the current moment and the target moment, a first transaction fluctuation curve is generated, including:

[0117] In operation S401, a first duration is obtained according to the tth moment and the current moment.

[0118] According to an embodiment of the present disclosure, the first duration may be characterized as a time period obtained by subtracting the current moment from the tth moment.

[0119] For example, if the t-th moment is the 5th minute and the current moment is the 1st minute, then the first duration is 4 minutes.

[0120] In operation S402, a second duration is obtained according to the target time and the current time.

[0121] According to an embodiment of the present disclosure, the second duration may be characterized as a time period obtained by subtracting the tth moment from the target moment.

[0122] For example, if the tth moment is the 5th minute and the target moment is the 10th minute, then the second duration is 5 minutes.

[0123] In operation S403, third transaction information corresponding to the t-th moment is generated according to the first duration, the second duration, the first transaction information, and the second transaction information.

[0124] According to an embodiment of the present disclosure, the third transaction information may be characterized as all transaction information generated by the transaction object at time t.

[0125] According to an embodiment of the present disclosure, the third transaction information can be generated according to formula (3). The first duration, the second duration, the first transaction information and the second transaction information are input into formula (3), and the result obtained is the third transaction information, wherein formula (3) is:

[0126]

[0127] Among them, R is the result obtained, that is, the third transaction information, a is the first transaction information, b is the second transaction information, t is the tth moment, t0 is the current moment, t1 is the target moment, t-t0 is the first duration, and t1-t0 is the second duration.

[0128] For example, the first transaction information a of transaction object A is an exchange rate value of 5.86, the second transaction information b is an exchange rate value of 6.19, the fifth moment t is 5, the current moment t0 is 1, and the target moment t1 is 10. According to formula (3), the third transaction information R is an exchange rate value of 6.01. In operation S404, it is determined whether t is less than the target moment. If t is less than the target moment, the operation of generating the third transaction information is returned and t is incremented. If t is equal to the target moment, operation S405 is performed.

[0129] According to an embodiment of the present disclosure, if t is less than the target time, t needs to be incremented, and operations S401, S402, and S403 are re-executed for the incremented t to generate third transaction information corresponding to the incremented t, until t is incremented to equal the target time, so that all transaction times within the target transaction period generate corresponding third transaction information. In operation S405, when it is determined that t is equal to the target time, a first transaction fluctuation curve is generated based on the first transaction information, the second transaction information, the current time, the target time, and a plurality of third transaction information corresponding to t-2 times.

[0130] According to an embodiment of the present disclosure, in the first transaction fluctuation curve, transaction information corresponding to different moments can be found according to different moments.

[0131] According to the embodiments of the present disclosure, a first duration is obtained based on the t-th moment and the current moment, a second duration is obtained based on the target moment and the t-th moment, and then the third transaction information corresponding to the t-th moment is obtained based on the first duration, the second duration, the first transaction information and the second transaction information, thereby obtaining the third transaction information at the t-th moment. A first transaction fluctuation curve can be generated based on the above information, thereby improving the convenience of querying information. At the same time, by judging whether t is less than the target moment, if so, t is incremented to obtain the third transaction information of the incremented t, thereby obtaining the transaction information at each moment, avoiding information omissions and ensuring data integrity.

[0132] Figure 5 A flow chart of transaction influencing factors according to an embodiment of the present disclosure is schematically shown.

[0133] like Figure 5 As shown, the decision information generating method of this embodiment includes operations S510 to S540.

[0134] According to an embodiment of the present disclosure, the querying of transaction fluctuation information and transaction impact factors according to the identifier of the transaction object includes:

[0135] In operation S510, according to the identifier of the transaction object, the first transaction object and the second transaction object used for the transaction and the transaction fluctuation service information are queried.

[0136] According to the embodiment of the present disclosure, the table stored in the system of the financial institution includes the first transaction object, the second transaction object and the transaction fluctuation service information. Therefore, the relevant information can be queried according to the identification of the transaction object.

[0137] According to an embodiment of the present disclosure, the first transaction object may be characterized as a currency before the transaction starts in the transaction.

[0138] For example, in this transaction, customer A wants to exchange ASD currency worth 10,000 into USD currency. The currency to be exchanged is ASD, that is, the currency of the first transaction object is ASD.

[0139] According to an embodiment of the present disclosure, the second transaction object may be characterized as the currency after the transaction is completed.

[0140] For example, in this transaction, customer A wants to exchange ASD currency worth 10,000 into USD currency. The currency after exchange is USD, that is, the currency of the second transaction object is USD.

[0141] According to an embodiment of the present disclosure, the transaction fluctuation service information may be characterized as information on the impact of different institutions on the transaction exchange rate.

[0142] In operation S520, the influence factor of the first institution is queried according to the first transaction object.

[0143] According to an embodiment of the present disclosure, the influence factor of the first institution may be characterized as the influence factor of the rules of the institution to which the first transaction object belongs on the currency transaction.

[0144] For example, trading institution A limits the amount of ASD currency exchanged for other institutions' currencies to no more than RMB 1,000,000.

[0145] In operation S530, the influence factor of the second institution is queried according to the second transaction object.

[0146] According to an embodiment of the present disclosure, the influence factor of the second institution may be characterized as the influence factor of the rules of the institution to which the second transaction object belongs on the currency transaction.

[0147] For example, trading institution B restricts that when exchanging BSD currency with the currency of other institutions, a handling fee of 5% of the total amount is required.

[0148] In operation S540, a transaction influence factor is obtained according to the influence factor of the first institution and the influence factor of the second institution.

[0149] According to an embodiment of the present disclosure, the transaction impact factor can be obtained by weighting or averaging the impact factor of the first institution and the impact factor of the second institution, but is not limited to the above two calculation methods.

[0150] According to the embodiments of the present disclosure, since different institutions have different rules and restrictions on currencies under different institutions, the influence factor of the first institution and the influence factor of the second institution are queried respectively according to the first transaction object and the second transaction object, and the two influence factors are calculated to obtain the transaction influence factor, which further improves the accuracy of the generated transaction curve so that the system can provide customers with more scientific and accurate decision-making information.

[0151] Figure 6 A flowchart of transaction decision information according to an embodiment of the present disclosure is schematically shown.

[0152] like Figure 6 As shown, the decision information generating method of this embodiment includes operations S610 to S620.

[0153] According to an embodiment of the present disclosure, generating transaction decision information according to the first transaction volatility curve and the second transaction volatility curve includes:

[0154] In operation S610, when it is determined that the first transaction volatility curve and the second transaction volatility curve have no intersection, a plurality of candidate transaction difference information corresponding to the plurality of candidate transaction moments is obtained according to the first transaction volatility curve and the second transaction volatility curve.

[0155] According to an embodiment of the present disclosure, the candidate transaction time may be characterized as a transaction time that may be selected by the transaction object.

[0156] According to an embodiment of the present disclosure, the candidate transaction difference information may be characterized as the absolute value information of the transaction information on the first transaction fluctuation curve corresponding to the moment minus the transaction information on the second transaction fluctuation curve corresponding to the moment at a certain transaction moment.

[0157] According to an embodiment of the present disclosure, candidate transaction difference information can be generated according to formula (4), and the transaction information on the first transaction fluctuation curve and the transaction information on the second transaction fluctuation curve corresponding to the i-th transaction moment are input into formula (4), and the result obtained is the candidate transaction difference information, wherein formula (4) is:

[0158] GAP=|a i -b i | (4)

[0159] Among them, GAP is the candidate transaction difference information, a i is the transaction information on the first transaction volatility curve corresponding to the i-th transaction moment, bi It is the transaction information on the second transaction fluctuation curve corresponding to the i-th transaction moment.

[0160] For example, at transaction time A, the transaction information on the first transaction fluctuation curve corresponding to time A is an exchange rate value of 15.28, and the transaction information on the second transaction fluctuation curve corresponding to time A is an exchange rate value of 16.46, then the candidate transaction difference information is an exchange rate value of 1.18.

[0161] In operation S620, transaction decision information is generated according to a plurality of candidate transaction difference information corresponding to a plurality of candidate transaction moments.

[0162] According to an embodiment of the present disclosure, transaction decision information is generated according to the transaction requirements of the transaction object among a plurality of candidate transaction difference information.

[0163] For example, transaction object A requires to conduct the transaction at the moment when the exchange rate is the largest. The transaction difference information at moment A is the exchange rate value 16.46, the transaction difference information at moment B is the exchange rate value 18.16, and the transaction difference information at moment C is the exchange rate value 11.87. Based on the maximum transaction difference information being the exchange rate value 18.16, the decision is made that the transaction should be conducted at moment B.

[0164] According to the embodiments of the present disclosure, since the first transaction volatility curve has no intersection with the second transaction volatility curve, a plurality of candidate transaction difference information is obtained according to the two transaction volatility curves, and then appropriate transaction information is selected from the plurality of candidate transaction difference information, and transaction decision information is generated based on the information, so that the decision information provided to the customer has high accuracy, and the advantage of fast computer processing is utilized to save time costs and improve service efficiency.

[0165] According to an embodiment of the present disclosure, when it is determined that the first trading volatility curve and the second trading volatility curve have an intersection, multiple trading time periods are obtained according to the trading time corresponding to the intersection, the current time and the target time.

[0166] According to the embodiment of the present disclosure, since the first trading volatility curve and the second trading volatility curve have an intersection, multiple trading periods are generated, and different trading information is possessed in different trading periods.

[0167] According to an embodiment of the present disclosure, for each trading period, a plurality of candidate transaction difference information corresponding to a plurality of candidate transaction moments in each trading period is obtained based on the first trading volatility curve and the second trading volatility curve.

[0168] According to the embodiments of the present disclosure, since the first transaction volatility curve and the second transaction volatility curve have an intersection, multiple transaction time periods are obtained according to the transaction time, current time, and target time corresponding to the intersection, and multiple corresponding candidate transaction difference information can be obtained in different transaction time periods, which realizes different stages of selection of transactions according to the wishes and needs of customers in different time periods, and automatically pushes decision information that is more in line with the customer's transaction wishes and can meet the transaction needs to users.

[0169] According to an embodiment of the present disclosure, generating transaction decision information according to a plurality of candidate transaction difference information corresponding to a plurality of candidate transaction moments includes:

[0170] According to an embodiment of the present disclosure, a plurality of candidate transaction difference information is sorted to obtain a sorting result.

[0171] According to an embodiment of the present disclosure, since the candidate transaction difference information is the absolute value of the transaction information corresponding to the first transaction fluctuation curve at a certain transaction moment minus the transaction information corresponding to the second transaction fluctuation curve at the same transaction moment, there are differences in size among multiple candidate transaction difference information, and they are sorted to make a judgment.

[0172] According to an embodiment of the present disclosure, based on the sorting result, an expected transaction time and expected transaction information corresponding to the expected transaction time are determined from a plurality of candidate transaction times.

[0173] According to an embodiment of the present disclosure, the estimated transaction time may be characterized as the estimated transaction time of the transaction object.

[0174] For example, within the 30-day target trading period, the expected trading time is the 10th day.

[0175] According to the embodiments of the present disclosure, the estimated transaction time is determined based on the specific circumstances of the transaction object. The time corresponding to the largest difference information can be selected as the estimated transaction time, or the time corresponding to the smallest difference information can be selected as the estimated transaction time.

[0176] According to an embodiment of the present disclosure, transaction decision information is generated based on the expected transaction time and the expected transaction information.

[0177] According to an embodiment of the present disclosure, a plurality of candidate transaction difference information are sorted to obtain a sorting result, and an expected transaction time and expected transaction information corresponding to the expected transaction time are selected according to the sorting result to generate transaction decision information, thereby improving information processing efficiency, providing users with targeted personalized recommendation information, and improving user experience.

[0178] Figure 7The schematic diagram of a transaction curve without an intersection according to an embodiment of the present disclosure is schematically shown.

[0179] like Figure 7 As shown, in the embodiment 700, a page diagram with no intersection of transaction curves may be included. The system interface displays the first transaction volatility curve 701, the second transaction volatility curve 702, the transaction difference information 703, the exchange rate 704, and the time 705, wherein the exchange rate 704 includes multiple exchange rate values, and the time 705 includes multiple transaction times, so as to view the relevant information of the expected transaction time and the expected transaction information, and provide preferred decision information for the transaction object. For example, based on the line chart including the first transaction volatility curve 701 and the second transaction volatility curve 702, according to the transaction requirements of the customer, the system determines that the 9th day is the expected transaction time, and the transaction difference information at this time is the maximum value of 0.14, that is, the actual exchange rate is 0.14 higher than the predicted exchange rate, which is the time when the transaction benefit is maximized. The system summarizes the above information as decision information and sends it to the customer.

[0180] Figure 8 A schematic diagram showing another transaction curve without intersection according to an embodiment of the present disclosure is schematically shown.

[0181] like Figure 8 As shown, in embodiment 800, another page diagram in which the transaction curve has no intersection can be included. The system interface displays the first transaction volatility curve 801, the second transaction volatility curve 802, the transaction difference information 803, the exchange rate 804, and the time 805, wherein the exchange rate 804 includes multiple exchange rate values, and the time 805 includes multiple transaction times, so as to view the relevant information of the expected transaction time and the expected transaction information, and provide preferred decision information to the transaction object. For example, according to the line chart including the first transaction volatility curve 801 and the second transaction volatility curve 802, according to the transaction requirements of the customer, the system determines that the 21st day is the expected transaction time, and the transaction difference information at this time is the maximum value of 0.09, that is, the actual exchange rate is 0.09 lower than the predicted exchange rate, which is the time when the transaction benefit is maximized. The system summarizes the above information as decision information and sends it to the customer.

[0182] Fig. 9 The schematic diagram schematically shows the intersection of transaction curves according to an embodiment of the present disclosure.

[0183] like Fig. 9As shown, in the embodiment 900, a page diagram with intersections of transaction curves may be included. The system interface displays a first transaction fluctuation curve 901, a second transaction fluctuation curve 902, first transaction difference information 903, second transaction difference information 904, third transaction difference information 905, exchange rate 906, and time 907, wherein the exchange rate 906 includes multiple exchange rate values, and the time 907 includes multiple transaction times, so as to view the relevant information of the expected transaction time and the expected transaction information, and provide preferred decision information to the transaction object. For example, the system divides the complete transaction cycle into three sections based on the line graph including the first transaction fluctuation curve 801 and the second transaction fluctuation curve 802. According to the customer's transaction requirements, in the first transaction cycle, the 7th day is determined as the expected transaction time, and the transaction difference information at this time is the maximum value of 0.05, that is, the actual exchange rate is 0.05 higher than the predicted exchange rate, which is the moment when the transaction benefit is maximized; in the second transaction cycle, the 21st day is determined as the expected transaction time, and the transaction difference information at this time is the maximum value of 0.07, that is, the actual exchange rate is 0.07 lower than the predicted exchange rate, which is the moment when the transaction benefit is maximized; in the third transaction cycle, the 45th day is determined as the expected transaction time, and the transaction difference information at this time is the maximum value of 0.1, that is, the actual exchange rate is 0.1 higher than the predicted exchange rate, which is the moment when the transaction benefit is maximized. The system summarizes the above information and sends it to the customer as decision-making information.

[0184] Fig.10 A schematic diagram schematically shows a decision application scenario according to an embodiment of the present disclosure.

[0185] like Fig.10 As shown, in the embodiment 1000, a user interface of a customer may be included. The user interface of the customer displays a mobile client 1001 used by the customer, an input area 1002 on the mobile client, and an output area 1003 on the mobile client, wherein the input area 1002 includes: buying currency, selling currency, minimum term, and maximum term, and the output area 1003 includes: displaying specifically recommended strategies, including: currency exchange method, buying and selling direction, term, and strategy, so that the customer can directly see the strategy information recommended by the financial institution on the mobile client 1001. For example, a user opens an application software on the mobile phone interface, selects the buying currency AAB, the selling currency BAS, the minimum transaction period 7, and the maximum transaction period 30 in the input area of ​​the application software, and clicks the system recommendation button. The output area of ​​the application software displays the recommended strategy given by the financial institution based on the customer's situation: the currency exchange method is AAB to BAS, the buying currency is AAB, and the selling currency is BAS. The transaction period is 7-30 days. Within 7-30 days, the maximum actual difference on the 14th day is 1.82. The buying financial institution is YY, and the selling financial institution is ZZ.

[0186] According to the embodiments of the present disclosure, when the customer gives a transaction time, a first transaction object, and a second transaction object, it is only necessary to provide transaction decision information containing transaction difference information at the specified transaction time.

[0187] For example, customer A requires to exchange currency ABS for currency BDE. At the same time, customer A requires that the transaction must be carried out on YY-13-XXXX. The transaction decision information provided to customer A includes that the transaction difference information on YY-13-XXXX is 5.66.

[0188] Based on the above decision information generation method, the present disclosure also provides a decision information generation device. Fig.11 The device is described in detail.

[0189] Based on the above decision information generation method, the present disclosure also provides a decision information generation device. Fig.11 The device is described in detail.

[0190] Fig.11 The structural block diagram of the decision information generating device according to an embodiment of the present disclosure is schematically shown.

[0191] like Fig.11 As shown, the decision information generating device 1100 of this embodiment includes an acquisition module 1110 , a first generating module 1120 , a query module 1130 , a second generating module 1140 and a third generating module 1150 .

[0192] The acquisition module 1110 is used to respond to the received transaction decision service request for the transaction object in the target transaction period, and acquire the identification of the transaction object, the first transaction information of the transaction object at the target time, and the second transaction information of the transaction object at the current time. In one embodiment, the acquisition module 1110 can be used to perform the operation S210 described above, which will not be repeated here.

[0193] The first generating module 1120 is used to generate a first transaction fluctuation curve according to the first transaction information, the second transaction information, the current time and the target time. In one embodiment, the first generating module 1120 can be used to perform the operation S220 described above, which will not be described in detail here.

[0194] The query module 1130 is used to query the transaction fluctuation range information and the transaction impact factor according to the identifier of the transaction object. In one embodiment, the query module 1130 can be used to perform the operation S230 described above, which will not be repeated here.

[0195] The second generating module 1140 is used to generate a second trading fluctuation curve according to the second trading information, the trading fluctuation range information and the trading impact factor. In one embodiment, the second generating module 1140 can be used to perform the operation S240 described above, which will not be described in detail here.

[0196] The third generating module 1150 is used to generate trading decision information according to the first trading volatility curve and the second trading volatility curve. In one embodiment, the third generating module 1150 can be used to perform the operation S250 described above, which will not be described in detail here.

[0197] According to an embodiment of the present disclosure, any multiple modules in the acquisition module 1110, the first generation module 1120, the query module 1130, the second generation module 1140 and the third generation module 1150 can be combined in one module for implementation, or any one of the modules can be split into multiple modules. Alternatively, at least part of the functions of one or more of these modules can be combined with at least part of the functions of other modules and implemented in one module. According to an embodiment of the present disclosure, at least one of the acquisition module 1110, the first generation module 1120, the query module 1130, the second generation module 1140 and the third generation module 1150 can be at least partially implemented as a hardware circuit, such as a field programmable gate array (FPGA), a programmable logic array (PLA), a system on a chip, a system on a substrate, a system on a package, an application specific integrated circuit (ASIC), or can be implemented by hardware or firmware such as any other reasonable way of integrating or packaging the circuit, or implemented in any one of the three implementation modes of software, hardware and firmware or in a suitable combination of any of them. Alternatively, at least one of the acquisition module 1110, the first generation module 1120, the query module 1130, the second generation module 1140 and the third generation module 1150 may be at least partially implemented as a computer program module, which may perform a corresponding function when executed.

[0198] Fig.12 A block diagram of an electronic device suitable for implementing a page generating method according to an embodiment of the present disclosure is schematically shown.

[0199] like Fig.12As shown, the electronic device 1200 according to an embodiment of the present disclosure includes a processor 1201, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 1202 or a program loaded from a storage part 1208 into a random access memory (RAM) 1203. The processor 1201 may include, for example, a general-purpose microprocessor (e.g., a CPU), an instruction set processor and / or a related chipset and / or a special-purpose microprocessor (e.g., an application-specific integrated circuit (ASIC)), etc. The processor 1201 may also include an onboard memory for caching purposes. The processor 1201 may include a single processing unit or multiple processing units for performing different actions of the method flow according to an embodiment of the present disclosure.

[0200] In RAM 1203, various programs and data required for the operation of electronic device 1200 are stored. Processor 1201, ROM 1202 and RAM 1203 are connected to each other via bus 1204. Processor 1201 performs various operations of the method flow according to the embodiment of the present disclosure by executing the program in ROM 1202 and / or RAM 1203. It should be noted that the program can also be stored in one or more memories other than ROM 1202 and RAM 1203. Processor 1201 can also perform various operations of the method flow according to the embodiment of the present disclosure by executing the program stored in the one or more memories.

[0201] According to an embodiment of the present disclosure, the electronic device 1200 may further include an input / output (I / O) interface 1205, which is also connected to the bus 1204. The electronic device 1200 may further include one or more of the following components connected to the I / O interface 1205: an input portion 1206 including a keyboard, a mouse, etc.; an output portion 1207 including a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker, etc.; a storage portion 1208 including a hard disk, etc.; and a communication portion 1209 including a network interface card such as a LAN card, a modem, etc. The communication portion 1209 performs communication processing via a network such as the Internet. A drive 1210 is also connected to the I / O interface 1205 as needed. A removable medium 1211, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 1210 as needed, so that a computer program read therefrom is installed into the storage portion 1208 as needed.

[0202] The present disclosure also provides a computer-readable storage medium, which may be included in the device / apparatus / system described in the above embodiments; or may exist independently without being assembled into the device / apparatus / system. The above computer-readable storage medium carries one or more programs, and when the above one or more programs are executed, the method according to the embodiment of the present disclosure is implemented.

[0203] According to an embodiment of the present disclosure, a computer-readable storage medium may be a non-volatile computer-readable storage medium, for example, may include but is not limited to: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In the present disclosure, a computer-readable storage medium may be any tangible medium containing or storing a program, which may be used by or in combination with an instruction execution system, an apparatus, or a device. For example, according to an embodiment of the present disclosure, a computer-readable storage medium may include the ROM 1202 and / or RAM 1203 described above and / or one or more memories other than ROM 1202 and RAM 1203.

[0204] The embodiment of the present disclosure also includes a computer program product, which includes a computer program, and the computer program contains program code for executing the method shown in the flowchart. When the computer program product is run in a computer system, the program code is used to enable the computer system to implement the item recommendation method provided by the embodiment of the present disclosure.

[0205] The above functions defined in the system / device of the embodiment of the present disclosure are performed when the computer program is executed by the processor 1201. According to the embodiment of the present disclosure, the system, device, module, unit, etc. described above can be implemented by a computer program module.

[0206] In one embodiment, the computer program may rely on tangible storage media such as optical storage devices, magnetic storage devices, etc. In another embodiment, the computer program may also be transmitted and distributed in the form of signals on a network medium, and downloaded and installed through the communication part 1209, and / or installed from the removable medium 1211. The program code contained in the computer program may be transmitted using any appropriate network medium, including but not limited to: wireless, wired, etc., or any suitable combination of the above.

[0207] In such an embodiment, the computer program can be downloaded and installed from the network through the communication part 1209, and / or installed from the removable medium 1211. When the computer program is executed by the processor 1201, the above functions defined in the system of the embodiment of the present disclosure are performed. According to the embodiment of the present disclosure, the system, device, means, module, unit, etc. described above can be implemented by a computer program module.

[0208] According to an embodiment of the present disclosure, the program code for executing the computer program provided by the embodiment of the present disclosure can be written in any combination of one or more programming languages. Specifically, these computing programs can be implemented using high-level process and / or object-oriented programming languages, and / or assembly / machine languages. Programming languages ​​include, but are not limited to, Java, C++, python, "C" language or similar programming languages. The program code can be executed entirely on the user computing device, partially on the user device, partially on the remote computing device, or entirely on the remote computing device or server. In the case of a remote computing device, the remote computing device can be connected to the user computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computing device (for example, using an Internet service provider to connect through the Internet).

[0209] The flow charts and block diagrams in the accompanying drawings illustrate the possible architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each box in the flow chart or block diagram can represent a module, a program segment, or a part of a code, and the above-mentioned module, program segment, or a part of a code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram or flow chart, and the combination of the boxes in the block diagram or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0210] It will be appreciated by those skilled in the art that the features described in the various embodiments and / or claims of the present disclosure may be combined and / or combined in a variety of ways, even if such combinations and / or combinations are not explicitly described in the present disclosure. In particular, the features described in the various embodiments and / or claims of the present disclosure may be combined and / or combined in a variety of ways without departing from the spirit and teachings of the present disclosure. All of these combinations and / or combinations fall within the scope of the present disclosure.

[0211] The embodiments of the present disclosure are described above. However, these embodiments are only for illustrative purposes and are not intended to limit the scope of the present disclosure. Although the embodiments are described above separately, this does not mean that the measures in the various embodiments cannot be used in combination to advantage. The scope of the present disclosure is defined by the attached claims and their equivalents. Without departing from the scope of the present disclosure, those skilled in the art may make a variety of substitutions and modifications, which should all fall within the scope of the present disclosure.

Claims

1. A method for generating decision information, comprising: In response to a received transaction decision service request for a transaction object within a target transaction period, acquiring an identifier of the transaction object, first transaction information of the transaction object at a target time, and second transaction information of the transaction object at a current time; generating a first transaction fluctuation curve according to the first transaction information, the second transaction information, the current time, and the target time; According to the identifier of the transaction object, query the transaction fluctuation range information and transaction impact factor; generating a second transaction fluctuation curve according to the second transaction information, the transaction fluctuation range information and the transaction impact factor, and Transaction decision information is generated according to the first transaction volatility curve and the second transaction volatility curve.

2. The method according to claim 1, wherein: The generating a second transaction fluctuation curve according to the second transaction information, the transaction fluctuation range information and the transaction impact factor includes: Constructing a transaction fluctuation probability distribution model according to the transaction fluctuation range information and the transaction influencing factors; and Based on the transaction fluctuation distribution model, the second transaction fluctuation curve is generated according to the second transaction information.

3. The method according to claim 2, wherein: The step of generating the second transaction fluctuation curve based on the transaction fluctuation distribution model and according to the second transaction information includes: Inputting the second transaction information as an initial value into the transaction fluctuation distribution model to obtain distribution probabilities of multiple transaction information corresponding to n transaction moments, where n is an integer greater than 2; For the nth transaction moment, based on the distribution probability, obtaining target transaction information from a plurality of transaction information corresponding to the nth transaction moment; and The second transaction fluctuation curve is generated according to the n target transaction information corresponding to the n transaction moments.

4. The method according to claim 1, wherein: The first curve includes t transaction information corresponding to t moments, where t is an integer greater than or equal to 1; generating a first transaction fluctuation curve according to the first transaction information, the second transaction information, the current moment and the target moment, including: According to the t-th moment and the current moment, a first duration is obtained; Obtaining a second duration according to the target time and the current time; generating third transaction information corresponding to the t-th moment according to the first duration, the second duration, the first transaction information, and the second transaction information; If it is determined that t is less than the target time, returning to perform the operation of generating the third transaction information and incrementing t; and When it is determined that t is equal to the target time, the first transaction fluctuation curve is generated according to the first transaction information, the second transaction information, the current time, the target time and a plurality of the third transaction information corresponding to t-2 times.

5. The method according to claim 1, wherein: The querying of the transaction fluctuation range information and the transaction influencing factor according to the identifier of the transaction object includes: According to the identifier of the transaction object, query the first transaction object and the second transaction object used for the transaction and the transaction fluctuation service information; According to the first transaction object, the influence factor of the first institution is queried; According to the second transaction object, query the influence factor of the second institution; and The transaction impact factor is obtained according to the impact factor of the first institution and the impact factor of the second institution.

6. The method according to claim 1, wherein: The generating of transaction decision information according to the first transaction volatility curve and the second transaction volatility curve includes: When it is determined that the first transaction volatility curve and the second transaction volatility curve have no intersection, obtaining a plurality of candidate transaction difference information corresponding to a plurality of candidate transaction moments according to the first transaction volatility curve and the second transaction volatility curve; and The transaction decision information is generated according to the plurality of candidate transaction difference information corresponding to the plurality of candidate transaction moments.

7. The method according to claim 6, wherein: Also includes: When it is determined that the first trading volatility curve and the second trading volatility curve have an intersection, a plurality of trading periods are obtained according to the trading time corresponding to the intersection, the current time and the target time; as well as For each trading period, the plurality of candidate transaction difference information corresponding to the plurality of candidate transaction moments in each trading period is obtained according to the first trading fluctuation curve and the second trading fluctuation curve.

8. The method according to claim 6, wherein: The generating the transaction decision information according to the plurality of candidate transaction difference information corresponding to the plurality of candidate transaction moments comprises: Sorting the plurality of candidate transaction difference information to obtain a sorting result; Based on the ranking result, determining an estimated transaction time and estimated transaction information corresponding to the estimated transaction time from the plurality of candidate transaction times; and The transaction decision information is generated according to the estimated transaction time and the estimated transaction information.

9. A decision information generating device, comprising: an acquisition module, configured to, in response to a received transaction decision service request for a transaction object within a target transaction period, acquire an identifier of the transaction object, first transaction information of the transaction object at a target time, and second transaction information of the transaction object at a current time; A first generating module, configured to generate a first transaction fluctuation curve according to the first transaction information, the second transaction information, the current time and the target time; A query module, used to query the transaction fluctuation range information and transaction impact factor according to the identifier of the transaction object; a second generating module, configured to generate a second transaction fluctuation curve according to the second transaction information, the transaction fluctuation range information and the transaction impact factor, and The third generating module is used to generate trading decision information according to the first trading volatility curve and the second trading volatility curve.

10. An electronic device, comprising: one or more processors; a storage device for storing one or more programs, When the one or more programs are executed by the one or more processors, the one or more processors are enabled to execute the method according to any one of claims 1 to 9.

11. A computer-readable storage medium having executable instructions stored thereon, which, when executed by a processor, causes the processor to execute the method according to any one of claims 1 to 9.

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