Data processing method and device, computer equipment and readable storage medium

Abstract

The invention relates to the technical field of big data processing, and discloses a data processing method and device, computer equipment, a computer readable storage medium and a computer program product. The method comprises the following steps: receiving a data processing request for a target product, and extracting product data of the target product from the data processing request; under the condition that the product data represents that the target product belongs to the preset product, contract data matched with the preset product is extracted from the product data, and a resource gain result of the target product is determined based on the contract data; according to the contract data and the resource gain result, obtaining a touch probability of the contract touch event of the target product; and determining a product attribute value of the target product according to the touch probability. By adopting the method provided by the invention, the data processing efficiency can be improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of big data processing, and in particular to a data processing method and device, computer equipment, a computer readable storage medium and a computer program product. BACKGROUND

[0002] With the rapid development of social economy and the continuous accumulation of residents' wealth, computer technology is increasingly widely used in the financial field. For example, product data of a financial product can be processed based on computer technology to determine product attribute values of the financial product.

[0003] In the traditional technology, product attribute values can be determined by relying on Monte Carlo simulation. However, the problem with using Monte Carlo simulation is that the data processing speed is relatively slow, and a large number of sample simulations are needed to improve accuracy. Generally, at least 10,000 simulations are needed to achieve good convergence results. Therefore, how to improve the data processing efficiency while improving the accuracy of the processing results has become a problem to be solved. SUMMARY

[0004] Therefore, it is necessary to provide a data processing method, device, computer equipment, computer readable storage medium and computer program product that can improve the processing efficiency of product data and improve the accuracy of processing results.

[0005] In a first aspect, the present application provides a data processing method, comprising:

[0006] receiving a data processing request for a target product, and extracting product data of the target product from the data processing request;

[0007] if the product data indicates that the target product belongs to a preset product, extracting contract data matched with the preset product from the product data, and determining a resource gain result of the target product based on the contract data;

[0008] obtaining a touch probability of a contract touch event of the target product according to the contract data and the resource gain result;

[0009] determining a product attribute value of the target product according to the touch probability.

[0010] In one embodiment, the determination of the product attribute value of the target product according to the touch probability comprises:

[0011] parsing the contract data to obtain a resource gain rate upper limit, a resource gain rate lower limit, an expiration time and an observation start time corresponding to the target product;

[0012] fusing the resource gain rate upper limit and the resource gain rate lower limit based on the touch probability, to obtain an expected gain rate of the target product;

[0013] determining an expected gain of the target product corresponding to a first time difference between the expiration time and the observation start time based on the expected gain rate and the first time difference;

[0014] superimposing an expected attribute value corresponding to the expected gain on the initial attribute value of the target product, to obtain a product attribute value of the target product at a current time.

[0015] In one of the embodiments, the initial attribute value of the target product is obtained in the following manner:

[0016] determining an expected attribute value represented by the touch probability and a non-touch probability corresponding to the touch probability;

[0017] obtaining the initial attribute value of the target product according to the expected attribute value and a discount factor of a preset currency corresponding to the expiration time.

[0018] In one of the embodiments, the contract data includes an observation end time and a resource attribute threshold value; and the determination of the resource gain result of the target product based on the contract data includes:

[0019] determining a resource gain type to which the target product belongs; the resource gain type includes a first type and a second type of contract touch event;

[0020] determining a first gain result corresponding to the first type of the target product based on the resource attribute threshold value, a resource attribute value corresponding to the target product at a current time, the observation end time, and a target adjustment coefficient corresponding to the first type;

[0021] determining a second gain result corresponding to the second type of the target product based on the first gain result;

[0022] determining a resource gain result including the first gain result and the second gain result.

[0023] In one of the embodiments, the target adjustment coefficient includes a first coefficient and a second coefficient which are opposite to each other; and the method further includes:

[0024] in a case where the first type represents that the resource attribute value of the target product passes through the resource attribute threshold value by decreasing, setting the first coefficient as negative and setting the second coefficient as positive;

[0025] In a case where the resource attribute value of the first type representing the target product touches the resource attribute threshold by growth, the first coefficient is set as positive, and the second coefficient is set as negative.

[0026] In one of the embodiments, the determining the second gain result of the target product corresponding to the second type based on the first gain result comprises:

[0027] determining a continuous gain result of the target product under a continuous gain attribute value based on a second time difference between the observation end time and a current time;

[0028] determining a second gain result of the target product for the second type based on a difference between the continuous gain result and the first gain result.

[0029] In one of the embodiments, the contract data comprises: an observation end time; and the determining the touch probability of the target product corresponding to the contract touch event based on the contract data and the resource gain result comprises:

[0030] determining a growth factor of the target product corresponding to the second time difference based on a second time difference between the observation end time and a current time;

[0031] determining a touch probability of the target product corresponding to the contract touch event based on a product of the growth factor and the resource gain result.

[0032] In a second aspect, the present application provides a data processing device, which comprises:

[0033] an extraction module configured to receive a data processing request for a target product, and extract product data of the target product from the data processing request;

[0034] an analysis module configured to, in a case where the product data represents that the target product belongs to a preset product, extract contract data matched with the preset product from the product data, and determine a resource gain result of the target product based on the contract data;

[0035] a processing module configured to determine a touch probability of the target product corresponding to a contract touch event based on the contract data and the resource gain result;

[0036] a determination module configured to determine a product attribute value of the target product based on the touch probability.

[0037] In one of the embodiments, the determination module comprises:

[0038] The analysis unit is configured to analyze the contract data to obtain an upper limit of a resource gain rate, a lower limit of the resource gain rate, an expiration time, and an observation start time corresponding to the target product;

[0039] The fusion unit is configured to fuse the upper limit of the resource gain rate and the lower limit of the resource gain rate based on the touch probability to obtain an expected gain rate of the target product;

[0040] The time unit is configured to determine an expected gain of the target product corresponding to a first time difference between the expiration time and the observation start time based on the expected gain rate and the first time difference.

[0041] The superposition unit is configured to superimpose an expected attribute value corresponding to the expected gain on an initial attribute value of the target product to obtain a product attribute value of the target product at a current time.

[0042] In one of the embodiments, the determination module is further configured to determine an expected attribute value represented by the touch probability and a non-touch probability corresponding to the touch probability.

[0043] The analysis module is further configured to obtain an initial attribute value of the target product according to the expected attribute value and a discount factor of a preset currency corresponding to the expiration time.

[0044] In one of the embodiments, the contract data includes an observation end time and a resource attribute threshold value; and the analysis module includes:

[0045] The type analysis unit is further configured to determine a resource gain type to which the target product belongs; the resource gain type includes a first type and a second type of contract touch event.

[0046] The gain analysis unit is further configured to determine a first gain result corresponding to the first type of the target product based on the resource attribute threshold value, a resource attribute value corresponding to the target product at a current time, the observation end time, and a target adjustment coefficient corresponding to the first type.

[0047] The result analysis unit is further configured to determine a second gain result corresponding to the second type of the target product based on the first gain result.

[0048] The obtaining unit is further configured to determine a resource gain result including the first gain result and the second gain result.

[0049] In one of the embodiments, the target adjustment coefficient includes a first coefficient and a second coefficient which are reciprocal of each other; and the analysis module further includes:

[0050] The setting unit is configured to set the first coefficient as negative and the second coefficient as positive in a case where the first type of resource attribute value of the target product touches the resource attribute threshold by decreasing; and set the first coefficient as positive and the second coefficient as negative in a case where the first type of resource attribute value of the target product touches the resource attribute threshold by increasing.

[0051] In one of the embodiments, the gain analysis unit is further configured to determine a continuous gain result of the target product at a continuous gain attribute value based on a second time difference between the observation end time and a current time.

[0052] The result analysis unit is further configured to determine a second gain result of the target product for the second type based on a difference between the continuous gain result and the first gain result.

[0053] In one of the embodiments, the contract data includes an observation end time, and the processing module includes:

[0054] The factor processing unit is configured to determine a growth factor of the target product corresponding to a second time difference between the observation end time and a current time based on the second time difference.

[0055] The probability processing unit is configured to obtain a touch probability of the target product in a contract touch event based on a product of the growth factor and the resource gain result. In one of the embodiments, step A includes:

[0056] In a third aspect, the application further provides a computer device including a memory and a processor, the memory storing a computer program, and the processor implementing the following steps when executing the computer program:

[0057] receiving a data processing request for a target product, and extracting product data of the target product from the data processing request;

[0058] extracting contract data matched with a preset product from the product data in a case where the product data represents that the target product belongs to the preset product, and determining a resource gain result of the target product based on the contract data;

[0059] obtaining a touch probability of the target product in a contract touch event based on the contract data and the resource gain result;

[0060] determining a product attribute value of the target product according to the touch probability.

[0061] In a fourth aspect, the present application provides a computer readable storage medium, having stored thereon a computer program, which, when executed by a processor, implements the following steps:

[0062] receiving a data processing request for a target product, and extracting product data of the target product from the data processing request;

[0063] in a case where the product data indicates that the target product belongs to a preset product, extracting contract data matched with the preset product from the product data, and determining a resource gain result of the target product based on the contract data;

[0064] obtaining a touch probability of a contract touch event of the target product according to the contract data and the resource gain result;

[0065] determining a product attribute value of the target product according to the touch probability.

[0066] In a fifth aspect, the present application provides a computer program product, comprising a computer program, which, when executed by a processor, implements the following steps:

[0067] receiving a data processing request for a target product, and extracting product data of the target product from the data processing request;

[0068] in a case where the product data indicates that the target product belongs to a preset product, extracting contract data matched with the preset product from the product data, and determining a resource gain result of the target product based on the contract data;

[0069] obtaining a touch probability of a contract touch event of the target product according to the contract data and the resource gain result;

[0070] determining a product attribute value of the target product according to the touch probability.

[0071] The aforementioned data processing method, apparatus, computer equipment, computer-readable storage medium, and computer program product receive a data processing request for a target product, extract product data of the target product from the data processing request, extract contract data matching the preset product from the product data if the product data indicates that the target product belongs to a preset product, determine the resource gain result of the target product based on the contract data, obtain the touch probability of the target product experiencing a contract touch event based on the contract data and the resource gain result, and then determine the product attribute value of the target product based on the touch probability. Therefore, the method provided in this application simplifies the data processing method for the target product's product data from Monte Carlo simulation to a probability solution process by introducing the resource gain result of the target product and the touch probability of the target product experiencing a contract touch event. This eliminates the need for a certain amount of data accumulation, improving the accuracy of the processing results and accelerating data processing efficiency. It overcomes the problem of low data processing efficiency caused by the need for simulating a large number of samples in existing Monte Carlo simulation methods. Thus, by accurately and promptly obtaining the product attribute value of the target product, the scientific validity and effectiveness of decision-making regarding the target product can be improved. Attached Figure Description

[0072] To more clearly illustrate the technical solutions in the embodiments of this application or related technologies, the drawings used in the description of the embodiments of this application or related technologies will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0073] Figure 1 This is a diagram illustrating the application environment of a data processing method in one embodiment.

[0074] Figure 2 This is a flowchart illustrating a data processing method in one embodiment;

[0075] Figure 3 This is a flowchart illustrating the process of determining the product attribute value of a target product based on the touch probability in one embodiment.

[0076] Figure 4 This is a flowchart illustrating the process of determining the resource gain result of a target product based on contract data in one embodiment.

[0077] Figure 5 This is a schematic diagram of data flow in one embodiment;

[0078] Figure 6 This is a schematic diagram of a data processing method in one embodiment;

[0079] Figure 7Fig. 1 is a flowchart of a data processing method according to an embodiment of the present application;

[0080] Figure 8 Fig. 2 is a block diagram of a data processing apparatus according to an embodiment of the present application;

[0081] Figure 9 Fig. 3 is a diagram of an internal structure of a computer device according to an embodiment of the present application. DETAILED DESCRIPTION

[0082] In order to make the objects, technical solutions and advantages of the present application clearer, the present application will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and should not be used to limit the present application. It should be noted that in the embodiments of the present application, some software, groups, models and other industry existing solutions may be mentioned, which should be considered as exemplary, and the purpose is only to illustrate the feasibility of the technical solution implementation of the present application, but does not mean that the applicant has or will necessarily use the solution.

[0083] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, displayed data, etc.) involved in the present application are all information and data authorized by the user or authorized by all parties, and the collection, use and processing of related data need to comply with relevant regulations. The acquisition, storage, use, processing and other data in the technical solution of the present application comply with the relevant regulations of national laws and regulations.

[0084] The terms "include" and "have" and any variations thereof used in the present application are intended to cover non-exclusive inclusion. The term "a plurality of" used in the present application means two or more. The term "and / or" used in the present application means one of the solutions or any combination of a plurality of solutions.

[0085] Taking the target product as a structural deposit as an example, the structural deposit refers to that an investor deposits the funds legally held in a bank, and the bank embeds financial derivative tools (including but not limited to forward, swap, option or futures, etc.) on the basis of ordinary deposit, so as to link the investor's income with interest rate, exchange rate, stock price, commodity price, credit, index and other financial or non-financial targets to obtain a certain risk financial product.

[0086] Generally, the structured deposit can be divided into touch barrier option structured deposit and non-touch barrier option structured deposit according to whether the payment is subject to the barrier. The touch barrier option structured deposit refers to that a cash is paid to the option holder if the price of the underlying asset touches the barrier (i.e. the barrier price) at any time before the expiration of the option, and the payment is 0 if the barrier is not touched. On the contrary, the non-touch barrier option structured deposit refers to that a cash is paid to the option holder if the price of the underlying asset does not touch the barrier at any time before the expiration of the option, and the payment is 0 if the barrier is touched.

[0087] Taking the product attribute value as the product valuation as an example, the commonly used valuation method for the touch barrier option structured deposit is the Monte Carlo simulation. The basic steps of the Monte Carlo simulation are as follows: it is assumed that the price S of the underlying asset is subject to a certain stochastic process, and the stochastic path of S is sampled in the risk-neutral world; whether the barrier is touched is determined according to a series of discrete observation days of the touch barrier option structured deposit, and the yield is calculated; the above two steps are repeated, so as to obtain a plurality of samples of the yield of the product in the risk-neutral world and a plurality of yields; the yield average of the plurality of yields is calculated; the yield average is discounted at the risk-free rate, and the valuation result of the touch barrier option structured deposit can be obtained.

[0088] As can be seen from the above, the problem of using the Monte Carlo simulation method to process product data is that the processing speed is relatively slow, and a large number of sample simulations are required to improve the accuracy of the processing result. Generally, at least 20,000 times of simulation are required to obtain a good convergence effect. Therefore, how to improve the data processing efficiency while improving the accuracy of the processing result has become a problem to be solved.

[0089] Therefore, the present application provides a data processing method, which can be applied to an application environment as shown in Figure 1 The terminal 102 communicates with the server 104 through the network. The data storage system can store the data required to be processed by the server 104. The data storage system can be integrated on the server 104, or placed on the cloud or other network servers.

[0090] For example, the server 104 obtains a data processing request for a target product from the terminal 102, and extracts product data of the target product from the data processing request. When the product data represents that the target product is subject to a contract, the server 104 determines a resource gain result corresponding to the contract of the target product based on contract data of the contract in the product data, obtains a touch probability of a contract touch event of the target product according to the contract data and the resource gain result, and further determines a product attribute value corresponding to the contract of the target product according to the touch probability.

[0091] The terminal 102 can be, but is not limited to, various personal computers, notebook computers, smart phones, tablet computers, and the like. The server 104 can be a standalone physical server, a server cluster or a distributed system composed of multiple physical servers, or a cloud server providing cloud computing services.

[0092] In an exemplary embodiment, as shown in Figure 2 , a data processing method is provided, which is applied to the server 104 in Figure 1 for example, and includes the following steps:

[0093] S202, receiving a data processing request for a target product, and extracting product data of the target product from the data processing request.

[0094] The data processing request refers to a request for processing data generated by the target product, and the data processing request can carry product data of the target product. Specifically, the product data carried in the data processing request is extracted and processed to obtain the product data of the target product.

[0095] The product data refers to a set of information describing the product characteristics of the target product. For example, the product data includes, but is not limited to, product identification, product name, product type, product issuer, product holder, product risk level, etc. The product issuer can refer to a financial institution, and the product holder refers to a user who purchases the target product.

[0096] S204, when the product data indicates that the target product belongs to a preset product, extracting contract data matched with the preset product from the product data, and determining a resource gain result of the target product based on the contract data.

[0097] The preset product represents a structured deposit. When the product data indicates that the target product belongs to the preset product, it means that the target product refers to a product representing a structured deposit, that is, the target product can be a touch option structured deposit or a non-touch option structured deposit.

[0098] For example, the product data includes a product name. When the product name of the target product matches the product name of the preset product, it is determined that the target product belongs to the preset product. Alternatively, the product data includes a product identification. When the product identification of the target product matches the product identification of the preset product, it is determined that the target product belongs to the preset product. Alternatively, the product data includes a product type. When the product type of the target product matches the preset product, it is determined that the target product belongs to the preset product.

[0099] In a case where the product data characterizes the target product as a preset product, product clause data and product structure data can also be extracted from the product data of the target product. The product clause data includes but is not limited to a currency, an interest start time, an expiration time, an observation start time, and an observation end time, etc. The observation start time refers to a time at which the target product is monitored for whether a contract touch event occurs. In embodiments related to the present application, the time can represent a specific time or a date. The observation end time can be the same as the expiration time or later than the expiration time.

[0100] The product structure data includes but is not limited to an upper limit of a resource gain rate, a lower limit of the resource gain rate, and a resource attribute threshold, etc. The lower limit of the resource gain rate refers to a guaranteed yield, the upper limit of the resource gain rate refers to a maximum yield, and the resource attribute threshold refers to a barrier price. The product clause data and the product structure data can be collectively referred to as contract data of the target product.

[0101] The resource gain result is used to represent a value of an Option-Adjusted Spread (OTE) of the target product. By determining the product attribute value based on the value of the OTE, the determination accuracy of the product attribute value can be improved.

[0102] In S206, a touch probability of the target product for a contract touch event is obtained according to the contract data and the resource gain result.

[0103] The contract touch event is used to represent that a product holder of the target product obtains a gain resource. That is, for a touch option structured deposit, the product holder can obtain the gain resource when a target asset of the target product touches a barrier price. Or, for a non-touch option structured deposit, the product holder can also obtain the gain resource when the target asset of the target product does not touch the barrier price.

[0104] Specifically, the contract data includes the resource attribute threshold. In a case where the target asset of the target product touches the resource attribute threshold, that is, the target asset of the target product is consistent with the resource attribute threshold, it can be determined that the target product has the contract touch event.

[0105] The touch probability of the target product for the contract touch event can be determined according to the contract data and the resource gain result in the following manner. Specifically,

[0106] In an embodiment, the contract data comprises an observation end time. Specifically, according to the contract data and the resource gain result, the touch probability of the target product to touch the contract touch event is obtained, comprising: determining a growth factor of the target product corresponding to a second time difference between the observation end time and a current time based on the second time difference; obtaining the touch probability of the target product to touch the contract touch event based on a product of the growth factor and the resource gain result.

[0107] In some embodiments, the resource gain result comprises a first gain result and a second gain result. Specifically, in a case that the target product matches a first type of the contract touch event, a first probability of the target product corresponding to the first type is obtained according to the contract data and the first gain result of the target product corresponding to the first type. In a case that the target product matches a second type of the contract touch event, a second probability of the target product corresponding to the second type is obtained according to the contract data and the second gain result of the target product corresponding to the second type. That is, the touch probability of the target product to touch the contract touch event comprises the first probability of the target product corresponding to the first type and the second probability of the target product corresponding to the second type.

[0108] The implementation manners of obtaining the first probability of the target product corresponding to the first type according to the contract data and the first gain result of the target product corresponding to the first type, and obtaining the second probability of the target product corresponding to the second type according to the contract data and the second gain result of the target product corresponding to the second type can be described in the foregoing manner, or can be implemented in other manners.

[0109] The first type and the second type of the contract touch event both belong to a specific sub-type of a preset product. For example, the first type represents that the resource attribute value of the target product is reduced by a touch resource attribute threshold, or the first type represents that the resource attribute value of the target product is increased by a touch resource attribute threshold. For example, the second type represents that the resource attribute value of the target product is less than a resource attribute threshold, or the second type represents that the resource attribute value of the target product is greater than the resource attribute threshold.

[0110] It can be understood that, assuming that the target asset corresponding to the target product is represented as S, The risk-neutral probability (i.e., the first probability) of the target asset S touching the barrier price H before the observation end time T can be represented as:

[0111]

[0112] wherein, is a preset value, in the embodiments related to the present application, may be set to 1. represents the target product in the time corresponding target asset. It can be seen from the above formula that the essence of the touch probability is the non-discounted price of the single-touch option that pays 1 unit of currency at maturity. Based on this, the above formula can be equivalent to:

[0113]

[0114] wherein T represents an observation end time; t represents a current time, i.e., a data processing time, taking a product attribute value as a product valuation as an example, t can be represented as a valuation time; represents a continuous gain attribute value, used to represent a continuous compound interest risk-free rate; represents an expected attribute value, i.e., a risk-neutral expected value. represents a first probability of a contract touch event of the target product occurring before the observation end time T, i.e., a first probability of the target product relative to the first type. represents a growth factor of the target product corresponding to a time difference (T-t), represents a discount factor of a preset currency corresponding to the observation end time, represents a first gain result of the target product relative to the first type, and the resource gain result includes the first gain result. Thus, by multiplying the first gain result and the growth factor, the first probability of the target product relative to the first type can be obtained.

[0115] In an embodiment, the method further includes determining, as a second probability of the target product relative to a second type, a difference between 1 and the first probability of the target product relative to the first type.

[0116] For example, the second probability satisfies:

[0117]

[0118] By using the method of the above embodiment, by considering a second time difference between the observation end time and the current time, a corresponding growth factor is determined, and then based on a product of the growth factor and the resource gain result, a touch probability of the target product occurring a contract touch event is determined, which can improve processing efficiency and processing accuracy.

[0119] In an embodiment, the contract data includes an expiration time, and then a preset prediction model can be searched according to the expiration time; by inputting the resource gain result into the prediction model, a touch probability of the target product occurring a contract touch event can be obtained. Thus, by introducing the preset prediction model, the analysis efficiency can be improved.

[0120] S208, determining a product attribute value of the target product according to the touch probability.

[0121] The product attribute value is used to represent the option value of the target product after the contract touch event occurs, so that the performance of the target product can be understood in real time through the product attribute value of the target product, and the decision on the target product can be adjusted in time.

[0122] In one embodiment, the touch probability of the target product occurring the contract touch event includes a first probability of the target product corresponding to the first type and a second probability of the target product corresponding to the second type.

[0123] Specifically, according to the touch probability, the product attribute value of the target product is determined, including: according to the first probability of the target product corresponding to the first type, determining the product attribute value of the target product corresponding to the first type; and according to the second probability of the target product corresponding to the second type, determining the product attribute value of the target product corresponding to the second type. That is, the product attribute value of the target product includes the product attribute value of the target product corresponding to the first type and the product attribute value of the target product corresponding to the second type.

[0124] Based on Figure 2 As shown in the content, by receiving a data processing request for the target product, extracting product data of the target product from the data processing request, in the case that the product data represents that the target product belongs to a preset product, extracting contract data matched with the preset product from the product data, and based on the contract data, determining a resource gain result of the target product, and according to the contract data and the resource gain result, obtaining a touch probability of the target product occurring a contract touch event, and then according to the touch probability, determining a product attribute value of the target product. It can be seen that the method provided by the present application can simplify the data processing method of the product data of the target product from the Monte Carlo simulation method to the probability solving process by introducing the resource gain result of the target product and the touch probability of the target product occurring the contract touch event, without the need for a certain amount of data accumulation. Not only can the accuracy of the processing result be improved, but also the data processing efficiency can be accelerated, overcoming the problem of low data processing efficiency caused by the need to simulate a large number of samples in the existing Monte Carlo simulation method. Therefore, by accurately and timely obtaining the product attribute value of the target product, the scientificity and effectiveness of the decision on the target product can be improved.

[0125] In one embodiment, as Figure 3 shown, a flowchart for determining a product attribute value of a target product according to a touch probability is provided. Taking the server 104 in Figure 1 as an example, the method comprises the following steps:

[0126] S302, analyzing the contract data to obtain the upper limit of the resource gain rate, the lower limit of the resource gain rate, the expiration time and the observation start time corresponding to the target product.

[0127] Specifically, the contract data includes an upper limit of a resource gain rate, a lower limit of the resource gain rate, an expiration time and an observation start time corresponding to the target product, and the upper limit of the resource gain rate, the lower limit of the resource gain rate, the expiration time and the observation start time corresponding to the target product can be obtained by analyzing the contract data.

[0128] In S304, the upper limit of the resource gain rate and the lower limit of the resource gain rate are fused based on the touch probability to obtain an expected gain rate of the target product.

[0129] For example, a first gain rate is obtained based on a product of the touch probability and the lower limit of the resource gain rate; a second gain rate is obtained based on a product of a non-touch probability corresponding to the touch probability and the upper limit of the resource gain rate; and a sum of the first gain rate and the second gain rate is determined as the expected gain rate of the target product.

[0130] For example, the expected gain rate G satisfies:

[0131]

[0132] Wherein, R1 represents the lower limit of the resource gain rate, and specifically can refer to a guaranteed yield rate; P represents the touch probability; R2 represents the upper limit of the resource gain rate, and specifically can refer to a maximum yield rate.

[0133] In S306, based on the expected gain rate and a first time difference between the expiration time and the observation start time, an expected gain of the target product corresponding to the first time difference is determined.

[0134] For example, based on the first time difference between the expiration time and the observation start time, an annualization time of an interest interval corresponding to the target product is determined; and a product of the annualization time of the interest interval and the expected gain rate is determined as the expected gain of the target product corresponding to the first time difference.

[0135] For example, the expected gain G satisfies:

[0136]

[0137] Wherein, T0 represents the observation start time, T m represents the expiration time, represents the annualization time of the interest interval corresponding to the target product. represents the expected gain rate. Thus, by multiplying the expected gain rate and the annualization time of the interest interval, the expected gain of the target product corresponding to the first time difference can be obtained.

[0138] In S308, based on the initial attribute value of the target product, an expected attribute value corresponding to the expected gain is superimposed to obtain a product attribute value of the target product at a current time.

[0139] ​In one embodiment, the manner of obtaining the expected attribute value corresponding to the expected gain comprises: determining an expected attribute value represented by the touch probability and the non-touch probability corresponding to the touch probability; and determining a product of the expected attribute value, the expected gain, and a discount factor of a preset currency corresponding to the expiration time as the expected attribute value corresponding to the expected gain.

[0140] In one embodiment, the product attribute value of the target product at the current time is determined as a sum of the initial attribute value and the expected attribute value corresponding to the expected gain.

[0141] By using the method of the above embodiments, the accuracy of the processing result can be improved by combining the initial attribute value and the expected attribute value corresponding to the expected gain in consideration of the expected gain rate of the target product.

[0142] In one embodiment, the manner of obtaining the initial attribute value of the target product comprises: determining an expected attribute value represented by the touch probability and the non-touch probability corresponding to the touch probability; and obtaining the initial attribute value of the target product according to the expected attribute value and a discount factor of a preset currency corresponding to the expiration time.

[0143] In one embodiment, the product attribute value of the target product at the current time is determined as a sum of the initial attribute value and the expected attribute value corresponding to the expected gain.

[0144] In combination with Figure 2 As shown in the content, the product attribute value V(t) of the target product at the current time satisfies:

[0145]

[0146] wherein, represents the expected attribute value, represents the discount factor of the preset currency corresponding to the expiration time, represents the expected attribute value corresponding to the expected gain, represents the initial attribute value.

[0147] In combination with the above content, it can be understood that the touch probability includes a first probability corresponding to the first type of the target product and a second probability corresponding to the second type of the target product, and then the relevant parameters corresponding thereto can be obtained by the first probability and the second probability, respectively, and then the product attribute value corresponding to the first type of the target product and the product attribute value corresponding to the second type of the target product can be obtained.

[0148] In one embodiment, asFigure 4 As shown, a flowchart of a process for determining a resource gain result of a target product based on contract data is provided. The method is applied to Figure 1 The server 104 in the contract system 100 is taken as an example, and includes the following steps:

[0149] S402, determining a resource gain type to which the target product belongs; the resource gain type includes a first type and a second type of contract touch event.

[0150] The resource gain type is used to represent a specific sub-type under a preset product, i.e., the first type and the second type of contract touch event both belong to the specific sub-type under the preset product.

[0151] For example, the first type represents that the resource attribute value of the target product is reduced by lowering the touch resource attribute threshold, or the first type represents that the resource attribute value of the target product is increased by increasing the touch resource attribute threshold.

[0152] For example, the second type represents that the resource attribute value of the target product is less than the resource attribute threshold, or the second type represents that the resource attribute value of the target product is greater than the resource attribute threshold.

[0153] For example, for the first type representing that the resource attribute value of the target product is reduced by lowering the touch resource attribute threshold, it can be described as One Touch Up, i.e., the resource attribute value is greater than or equal to the resource attribute threshold; for the first type representing that the resource attribute value of the target product is increased by increasing the touch resource attribute threshold, it can be described as One Touch Down, i.e., the resource attribute value is less than or equal to the resource attribute threshold.

[0154] For example, for the second type representing that the resource attribute value of the target product is less than the resource attribute threshold, it can be described as No Touch Up, i.e., the resource attribute value is less than the resource attribute threshold; for the second type representing that the resource attribute value of the target product is greater than the resource attribute threshold, it can be described as No Touch Down, i.e., the resource attribute value is greater than the resource attribute threshold.

[0155] The resource attribute value is used to represent a target asset corresponding to the target product, and the resource attribute threshold is used to represent an obstacle price corresponding to the target product.

[0156] S404, determining a first gain result corresponding to the first type of the target product based on the resource attribute threshold, the resource attribute value of the target product at the current time, the observation end time, and a target adjustment coefficient corresponding to the first type.

[0157] In one embodiment, the target adjustment coefficient includes a first coefficient and a second coefficient which are opposite numbers of each other; the method further includes: in the case that the resource attribute value of the target product of the first type touches the resource attribute threshold by decreasing, setting the first coefficient to be negative and setting the second coefficient to be positive; in the case that the resource attribute value of the target product of the first type touches the resource attribute threshold by increasing, setting the first coefficient to be positive and setting the second coefficient to be negative.

[0158] Specifically, the first gain result satisfies:

[0159]

[0160]

[0161]

[0162]

[0163] wherein, represents the first gain result, represents a continuous gain attribute value, i.e., a continuous compound interest risk-free rate; represents a continuous compound yield rate of a target asset corresponding to the target product. T represents an observation end time, and t represents a current time. N(x) represents a cumulative probability distribution function of a standard normal distribution. represents an implied volatility, represents a target asset corresponding to the target product at the current time t.

[0164] represents a first coefficient, represents a second coefficient. For example, in the case that the resource attribute value of the target product of the first type touches the resource attribute threshold by decreasing, the first coefficient is set to be -1, and the second coefficient is set to be 1. In the case that the resource attribute value of the target product of the first type touches the resource attribute threshold by increasing, the first coefficient is set to be 1, and the second coefficient is set to be -1.

[0165] In one embodiment, the method further includes: obtaining a first probability of the target product corresponding to the first type according to the first gain result of the target product corresponding to the first type and the contract data; and obtaining a product attribute value of the target product corresponding to the first type according to the first probability. The specific implementation manner can be referred to the foregoing description and is not described herein again.

[0166] S406, determining a second gain result of the target product for the second type based on the first gain result.​​​​

[0167] wherein, the sum of the option value corresponding to the contract touch event of the target product and the option value corresponding to the non-contract touch event of the target product represents the zero-coupon bond. Based on this, the second gain result of the target product for the second type can be determined based on the first gain result.

[0168] In one embodiment, determining the second gain result of the target product for the second type based on the first gain result comprises: determining a continuous gain result of the target product under a continuous gain attribute value based on a second time difference between the observation end time and the current time; determining the second gain result of the target product for the second type based on a difference between the continuous gain result and the first gain result.

[0169] For example, the second gain result satisfies:

[0170]

[0171] wherein, denotes the second gain result, denotes the continuous gain attribute value, i.e. the continuous compounding risk-free rate; denotes the continuous gain result of the target product under the continuous gain attribute value.

[0172] In one embodiment, the method further comprises: obtaining a second probability of the target product corresponding to the second type according to the second gain result of the target product corresponding to the second type and the contract data; and obtaining a product attribute value of the target product corresponding to the second type according to the second probability. The specific implementation can be referred to the foregoing description, and will not be repeated here.

[0173] S408, determining a resource gain result containing the first gain result and the second gain result.

[0174] By considering the gain results corresponding to the first type and the second type of the contract touch event, the comprehensiveness of the analysis of the target product can be improved by using the method of the above embodiment.

[0175] In combination with the above, as Figure 5As shown, a data flow diagram is provided, in which: in the case of receiving a data processing request, the server can assemble transaction data and market data, and call a computing engine open interface for corresponding data processing. Specifically, the computing engine obtains product data of a target product from the transaction data, and discount factors of a preset currency corresponding to the expiration time, implied volatility and other parameters of the target product from the market data. The computing engine combines the call option pricing model (i.e. the method provided in the present application) to obtain the product attribute value of the target product. Alternatively, in the case of receiving a data processing request, the server can extract product data of a target product from the data processing request, and discount factors of a preset currency corresponding to the expiration time, implied volatility and other parameters of the target product from the market data, and send the extracted data to the computing engine as assembled data.

[0176] Based on Figure 5 As shown, the content is as follows: Figure 6 As shown, a data processing method flow diagram is provided, which is applied to the server in

[0177] In combination with the above content, as shown, a data processing method flow diagram is provided, which is applied to the server in Figure 7 As shown, a data processing method flow diagram is provided, which is applied to the server in Figure 1 may include the following steps:

[0178] S702, receiving a data processing request for a target product, and extracting product data of the target product from the data processing request.

[0179] S704, in the case of product data representing that the target product belongs to a preset product, extracting contract data matched with the preset product from the product data, and determining a resource gain type to which the target product belongs; the resource gain type includes a first type and a second type of contract touch event.

[0180] S706, determining a first gain result of the target product corresponding to the first type based on the resource attribute threshold in the contract data, a resource attribute value of the target product corresponding to the current time, an observation end time, and a target adjustment coefficient corresponding to the first type.

[0181] S708, determining a second gain result of the target product corresponding to the second type based on the first gain result.

[0182] S710, obtaining a first probability of the target product relative to the first type having a contract touch event according to the contract data and the first gain result.

[0183] Specifically, the contract data is parsed to obtain a resource gain rate upper limit, a resource gain rate lower limit, an expiration time, and an observation start time corresponding to the target product; the resource gain rate upper limit and the resource gain rate lower limit are fused based on the first probability to obtain an expected gain rate of the target product corresponding to the first type; an expected gain of the target product corresponding to the first type under a first time difference between the expiration time and the observation start time is determined based on the expected gain rate and the first time difference; an expected attribute value corresponding to the expected gain of the target product corresponding to the first type is superimposed on an initial attribute value of the target product corresponding to the first type to obtain a product attribute value of the target product corresponding to the first type at the current time.

[0184] S712, obtaining a second probability of the target product relative to the second type having a contract touch event according to the contract data and the second gain result.

[0185] S714, determining a product attribute value of the target product relative to the first type according to the first probability, and determining a product attribute value of the target product relative to the second type according to the second probability.

[0186] The specific content of S702-S714 can be referred to the foregoing content.

[0187] As can be seen from the above, taking the target product as a structural deposit and the product attribute value as the product valuation as an example, the model derivation method is used to express the valuation of the single-touch structural deposit in the form of an analytical solution, which can improve the solving efficiency, thereby overcoming the problem of low valuation efficiency caused by the need to simulate a large number of samples in the existing Monte Carlo simulation method, so that the valuation result of the target product can be obtained in a timely manner, thereby improving the scientificity and effectiveness of decision-making for the target product. Moreover, it can also be widely used in the process of financial valuation, market risk sensitivity index calculation, etc.

[0188] It should be understood that although the steps in the flowcharts involved in the above embodiments are shown in sequence according to the arrows, the steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified herein, the execution of the steps is not strictly limited in sequence, and the steps can be executed in other orders. Moreover, at least some of the steps in the flowcharts involved in the above embodiments can include multiple steps or multiple stages, which are not necessarily executed at the same time, but can be executed at different times, and the execution order of the steps or stages is not necessarily sequential, but can be alternately or alternately executed with at least some of the other steps or steps or stages in the other steps. It can be understood that the steps in different embodiments can be freely combined as needed, and various non-contradictory schemes formed by the combination are within the scope of protection of the present application.

[0189] Based on the same inventive concept, the embodiments of the present application also provide a data processing apparatus for implementing the above-mentioned data processing method. The implementation scheme of the problem solving provided by the apparatus is similar to the implementation scheme described in the above method, so the specific limitations in one or more data processing apparatus embodiments provided below can refer to the limitations of the data processing method described above, and will not be repeated here.

[0190] In one exemplary embodiment, as shown in Figure 8 a data processing apparatus is provided, comprising: an extraction module 802, an analysis module 804, a processing module 806, and a determination module 808, wherein:

[0191] The extraction module 802 is configured to receive a data processing request for a target product, and extract product data of the target product from the data processing request; the analysis module 804 is configured to extract contract data matched with a preset product from the product data in a case that the product data represents that the target product belongs to the preset product, and determine a resource gain result of the target product based on the contract data; the processing module 806 is configured to obtain a touch probability of a contract touch event of the target product according to the contract data and the resource gain result; and the determination module 808 is configured to determine a product attribute value of the target product according to the touch probability.

[0192] In one of the embodiments, the determining module comprises: a parsing unit configured to parse the contract data to obtain the upper limit of the resource gain rate, the lower limit of the resource gain rate, the expiration time and the observation start time corresponding to the target product; a fusion unit configured to fuse the upper limit of the resource gain rate and the lower limit of the resource gain rate based on the touch probability to obtain the expected gain rate of the target product; a time unit configured to determine the expected gain of the target product corresponding to a first time difference between the expected gain rate and a second time difference between the expiration time and the observation start time; and a superposition unit configured to superimpose an expected attribute value corresponding to the expected gain on a basis of an initial attribute value of the target product to obtain a product attribute value of the target product at a current time.

[0193] In one of the embodiments, the determining module is further configured to determine an expected attribute value represented by the touch probability and a non-touch probability corresponding to the touch probability; and the analyzing module is further configured to obtain the initial attribute value of the target product according to the expected attribute value and a discount factor of a preset currency corresponding to the expiration time.

[0194] In one of the embodiments, the contract data comprises an observation end time and a resource attribute threshold value; the analyzing module comprises: a type analyzing unit configured to determine a resource gain type to which the target product belongs; the resource gain type comprises a first type and a second type of contract touch event; a gain analyzing unit configured to determine a first gain result of the target product corresponding to the first type based on the resource attribute threshold value, a resource attribute value of the target product at a current time, the observation end time and a target adjustment coefficient corresponding to the first type; a result analyzing unit configured to determine a second gain result of the target product corresponding to the second type based on the first gain result; and an obtaining unit configured to determine a resource gain result comprising the first gain result and the second gain result.

[0195] In one of the embodiments, the target adjustment coefficient comprises a first coefficient and a second coefficient which are reciprocal of each other; the analyzing module further comprises: a setting unit configured to set the first coefficient to be negative and the second coefficient to be positive in a case where the first type represents that the resource attribute value of the target product touches the resource attribute threshold value by decreasing; and set the first coefficient to be positive and the second coefficient to be negative in a case where the first type represents that the resource attribute value of the target product touches the resource attribute threshold value by increasing.

[0196] In one of the embodiments, the gain analysis unit is further configured to determine a continuous gain result of the target product at a continuous gain attribute value based on a second time difference between the observation end time and a current time; and the result analysis unit is further configured to determine a second gain result of the target product for the second type based on a difference between the continuous gain result and the first gain result.

[0197] In one of the embodiments, the contract data includes an observation end time; and the processing module includes a factor processing unit configured to determine a growth factor of the target product corresponding to a second time difference between the observation end time and a current time based on the second time difference; and a probability processing unit configured to obtain a touch probability of the target product in a contract touch event based on a product of the growth factor and the resource gain result.

[0198] The modules in the data processing apparatus described above can be implemented wholly or partially by software, hardware, and combinations thereof. The modules described above can be embedded in a processor in the computer device in hardware form or independent of the processor in the computer device, or stored in a memory in the computer device in software form, so as to be called and executed by the processor to perform the operations corresponding to the modules.

[0199] In one of the exemplary embodiments, a computer device is provided, which can be a server, and an internal structure diagram of the computer device can be as shown in Figure 9 The computer device includes a processor, a memory, an input / output interface (I / O), and a communication interface. The processor, the memory, and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. The processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operating system and the computer program in the non-volatile storage medium to run. The database of the computer device is configured to store data in a data processing process. The input / output interface of the computer device is configured to exchange information between the processor and external devices. The communication interface of the computer device is configured to communicate with terminals outside through a network connection. The computer program is executed by the processor to implement a data processing method.

[0200] Those skilled in the art can understand that Figure 8 The structure shown in the above-mentioned embodiments is only a block diagram of part of the structure related to the scheme of the present application, and does not constitute a limitation on the computer device to which the scheme of the present application is applied. The specific computer device can include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0201] In an embodiment, a computer device is also provided, comprising a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above method embodiments.

[0202] In an embodiment, a computer readable storage medium is provided, which stores a computer program, and the computer program is executed by a processor to implement the steps in the above method embodiments.

[0203] In an embodiment, a computer program product is provided, which comprises a computer program, and the computer program is executed by a processor to implement the steps in the above method embodiments.

[0204] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by a computer program instructing relevant hardware. The computer program can be stored in a non-volatile computer readable storage medium. When the computer program is executed, the processes of the above-mentioned embodiment methods can be included. Any reference to memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile memory and volatile memory. The non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical storage, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. The volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration but not limitation, the RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc. The database involved in the embodiments provided in the present application can include at least one of a relational database and a non-relational database. The non-relational database can include a distributed database based on a block chain, etc., and is not limited thereto. The processor involved in the embodiments provided in the present application can be a general processor, a central processing unit, a graphics processing unit, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, an artificial intelligence (AI) processor, etc., and is not limited thereto.

[0205] The technical features of the above embodiments can be combined arbitrarily. In order to make the description simple, all possible combinations of the technical features in the above embodiments are not described, however, as long as the combinations of the technical features do not exist contradictory, it should be considered as the scope of the present application.

[0206] The above embodiments only express several implementation ways of the present application, and the description is specific and detailed, but it should not be understood as a limitation to the patent scope of the present application. It should be pointed out that for ordinary skilled in the art, without departing from the concept of the present application, several modifications and improvements can be made, which all belong to the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the appended claims.

Claims

1. A data processing method, characterized in that, The method includes: Receive a data processing request for a target product, and extract product data of the target product from the data processing request; If the product data indicates that the target product belongs to a preset product, extract the contract data that matches the preset product from the product data, and determine the resource gain result of the target product based on the contract data. Based on the contract data and the resource gain results, the probability of the target product triggering a contract trigger event is obtained. The product attribute value of the target product is determined based on the touch probability.

2. The method according to claim 1, characterized in that, Determining the product attribute value of the target product based on the touch probability includes: Analyze the contract data to obtain the upper limit of resource gain rate, the lower limit of resource gain rate, the expiration time, and the observation start time for the target product; Based on the touch probability, the upper limit of the resource gain rate and the lower limit of the resource gain rate are fused together to obtain the expected gain rate of the target product; Based on the expected gain rate and the first time difference between the expiration time and the observation start time, the expected gain of the target product corresponding to the first time difference is determined; Based on the initial attribute values ​​of the target product, the expected attribute values ​​corresponding to the expected gain are superimposed to obtain the product attribute values ​​of the target product at the current time.

3. The method according to claim 2, characterized in that, The methods for obtaining the initial attribute values ​​of the target product include: Determine the expected attribute value represented by the touch probability and the non-touch probability corresponding to the touch probability; The initial attribute values ​​of the target product are obtained based on the expected attribute values ​​and the discount factor of the preset currency corresponding to the maturity time.

4. The method according to claim 1, characterized in that, The contract data includes the observation end time and resource attribute thresholds; determining the resource gain result of the target product based on the contract data includes: Determine the resource gain type to which the target product belongs; the resource gain type includes a first type and a second type of contract triggering event; Based on the resource attribute threshold, the resource attribute value of the target product at the current time, the observation end time, and the target adjustment coefficient corresponding to the first type, the first gain result of the target product corresponding to the first type is determined; Based on the first gain result, it is determined that the target product corresponds to the second gain result of the second type; Determine the resource gain result that includes the first gain result and the second gain result.

5. The method according to claim 4, characterized in that, The target adjustment coefficient includes a first coefficient and a second coefficient that are opposites of each other; the method further includes: When the resource attribute value of the target product in the first type is reduced by touching the resource attribute threshold, the first coefficient is set to negative and the second coefficient is set to positive. When the resource attribute value of the target product in the first type reaches the resource attribute threshold through growth, the first coefficient is set to positive, and the second coefficient is set to negative.

6. The method according to claim 4, characterized in that, The step of determining, based on the first gain result, that the target product corresponds to the second type of second gain result includes: Based on the second time difference between the observation end time and the current time, the continuous gain result of the target product under the continuous gain attribute value is determined; Based on the difference between the continuous gain result and the first gain result, the second gain result of the target product for the second type is determined.

7. The method according to any one of claims 1 to 6, characterized in that, The contract data includes: the observation end time; the step of obtaining the touch probability of the target product corresponding to the contract touch event based on the contract data and the resource gain result includes: Based on the second time difference between the observation end time and the current time, the growth factor of the target product corresponding to the second time difference is determined; The probability of a contract triggering event for the target product is obtained by multiplying the growth factor and the resource gain result.

8. A data processing apparatus, characterized in that, The device includes: The extraction module is used to receive a data processing request for a target product and extract product data of the target product from the data processing request. The analysis module is used to extract contract data matching the preset product from the product data when the product data indicates that the target product belongs to the preset product, and to determine the resource gain result of the target product based on the contract data. The processing module is used to obtain the probability of a contract touch event occurring for the target product based on the contract data and the resource gain result. The determination module is used to determine the product attribute value of the target product based on the touch probability.

9. The apparatus according to claim 8, characterized in that, The determining module includes: The parsing unit is used to parse the contract data to obtain the upper limit of the resource gain rate, the lower limit of the resource gain rate, the expiration time, and the observation start time corresponding to the target product. The fusion unit is used to fuse the upper limit of the resource gain rate and the lower limit of the resource gain rate based on the touch probability to obtain the expected gain rate of the target product; A time unit is used to determine the expected gain of the target product corresponding to the first time difference based on the expected gain rate and the first time difference between the expiration time and the observation start time; The superposition unit is used to superimpose the expected attribute value corresponding to the expected gain on the initial attribute value of the target product to obtain the product attribute value of the target product at the current time.

10. The apparatus according to claim 9, characterized in that: The determining module is further configured to determine the expected attribute value represented by the touch probability and the non-touch probability corresponding to the touch probability; The analysis module is also used to obtain the initial attribute value of the target product based on the expected attribute value and the discount factor of the preset currency corresponding to the maturity time.

11. The apparatus according to claim 8, characterized in that, The contract data includes the observation end time and resource attribute thresholds; the analysis module includes: The type analysis unit is also used to determine the resource gain type to which the target product belongs; the resource gain type includes a first type and a second type of contract triggering event; The gain analysis unit is also used to determine the first gain result of the target product corresponding to the first type based on the resource attribute threshold, the resource attribute value of the target product at the current time, the observation end time, and the target adjustment coefficient corresponding to the first type; The result analysis unit is further configured to determine, based on the first gain result, that the target product corresponds to a second gain result of the second type; The obtaining unit is further configured to determine a resource gain result that includes the first gain result and the second gain result.

12. The apparatus according to claim 11, characterized in that, The target adjustment coefficient includes a first coefficient and a second coefficient that are opposites of each other; the analysis module also includes: The setting unit is configured to set the first coefficient to negative and the second coefficient to positive when the resource attribute value of the first type representing the target product touches the resource attribute threshold by decreasing; and to set the first coefficient to positive and the second coefficient to negative when the resource attribute value of the first type representing the target product touches the resource attribute threshold by increasing.

13. The apparatus according to claim 11, characterized in that: The gain analysis unit is also used to determine the continuous gain result of the target product under the continuous gain attribute value based on the second time difference between the observation end time and the current time; The result analysis unit is further configured to determine, based on the difference between the continuous gain result and the first gain result, the second gain result of the target product for the second type.

14. The apparatus according to any one of claims 8 to 13, characterized in that, The contract data includes: observation end time; the processing module includes: A factor processing unit is used to determine the growth factor of the target product corresponding to the second time difference based on the second time difference between the observation end time and the current time; The probability processing unit is used to obtain the probability of the target product triggering a contract trigger event based on the product of the growth factor and the resource gain result.

15. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 7.

16. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 7.

17. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 7.

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