Real-time valuation result generation method and system, electronic device

Abstract

The application discloses a kind of real-time valuation result generation method and system, electronic equipment, wherein, the method comprises: generating the parameter estimation event of target object, wherein, parameter estimation event is used to request the real-time valuation result of target object;In response to parameter estimation event, target computing reference data of target object is obtained from pre-stored data cache, wherein, target computing reference data is used to indicate the valuation reference parameter and risk sensitivity parameter determined in historical calculation period of target object;According to the event type of parameter estimation event, vectorization calculation operation is performed on target computing reference data, to generate real-time valuation result.Through the present application, the technical problem of poor timeliness in valuation calculation in the related art is solved, and the technical effect of ensuring high timeliness of valuation calculation while ensuring valuation accuracy is achieved.

Description

Technical Field

[0001] This application relates to the field of computers, and more specifically, to a method and system for generating real-time valuation results, and an electronic device. Background Technology

[0002] In related technologies, the valuation of objects usually adopts the micro-batch full calculation mode. However, the micro-batch full calculation mode has a fixed calculation cycle and a long chain. The valuation results cannot reflect the instantaneous changes in the value of the object in a timely manner, making it difficult to meet the real-time valuation needs of the object.

[0003] No effective solution has yet been proposed to address the issue of poor timeliness in valuation calculations within the relevant technologies. Summary of the Invention

[0004] This application provides a method, system, and electronic device for generating real-time valuation results, in order to at least solve the technical problem of poor timeliness in valuation calculations in related technologies.

[0005] According to one aspect of the embodiments of this application, a method for generating real-time valuation results is provided, comprising: generating a parameter estimation event for a target object, wherein the parameter estimation event is used to request the generation of a real-time valuation result for the target object; responding to the parameter estimation event, obtaining target calculation benchmark data of the target object from a pre-stored data cache, wherein the target calculation benchmark data is used to represent valuation benchmark parameters and risk sensitivity parameters of the target object determined in a historical calculation period; and performing a vectorized calculation operation on the target calculation benchmark data according to the event type of the parameter estimation event to generate the real-time valuation result.

[0006] According to another aspect of the embodiments of this application, a real-time valuation result generation system is also provided, including a computing component and an estimation component interconnected. The computing component is configured to perform full valuation calculations on all objects in an object set according to a preset time period to generate a calculation benchmark data set. The objects in the object set are all objects to be valued, and the target object is any object in the object set. The calculation benchmark data set includes target calculation benchmark data. The estimation component is configured to generate parameter estimation events for the target object, wherein the parameter estimation events are used to request the generation of a real-time valuation result for the target object. In response to the parameter estimation events, the component retrieves the target calculation benchmark data for the target object from a pre-stored data cache. The target calculation benchmark data represents the valuation benchmark parameters and risk sensitivity parameters determined for the target object within a historical calculation period. The component performs vectorized calculation operations on the target calculation benchmark data according to the event type of the parameter estimation events to generate the real-time valuation result.

[0007] According to another aspect of the embodiments of this application, a computer-readable storage medium is also provided, wherein a computer program is stored therein, wherein the computer program is configured to perform the steps in any of the above method embodiments when executed by a processor.

[0008] According to another aspect of the embodiments of this application, a computer program product or computer program is provided, the computer program product or computer program including computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, causing the computer device to perform the steps in any of the method embodiments described above.

[0009] According to another aspect of the embodiments of this application, an electronic device is also provided, including a memory and a processor, wherein the memory stores a computer program, and the processor is configured to perform the steps of any of the above method embodiments through the computer program.

[0010] By using this application to trigger valuation requests through event-driven mechanisms and utilizing pre-stored target calculation benchmark data containing valuation benchmark parameters and risk sensitivity parameters, combined with event types, efficient vectorized calculation operations are performed, thereby avoiding the high latency of traditional micro-batch full calculations. Therefore, the technical problem of poor timeliness in valuation calculations in related technologies can be solved, achieving the technical effect of ensuring both valuation accuracy and high timeliness in valuation calculations. Attached Figure Description

[0011] Figure 1 This is a schematic diagram illustrating an application scenario of a method for generating real-time valuation results according to an embodiment of this application.

[0012] Figure 2 This is a flowchart illustrating an optional method for generating real-time valuation results according to an embodiment of this application.

[0013] Figure 3 This is a schematic diagram of the structure of the system for generating real-time valuation results in this optional example;

[0014] Figure 4 This is a schematic diagram of the structure of a real-time bond valuation system based on event-driven and vectorized approximation in this optional example;

[0015] Figure 5 This is a flowchart of the event-driven and vectorized approximation-based real-time bond valuation method in this optional example;

[0016] Figure 6This is a flowchart of an estimation method according to a specific embodiment of the present application;

[0017] Figure 7 This is a structural block diagram of a device for generating real-time valuation results according to an embodiment of this application;

[0018] Figure 8 A schematic block diagram of a computer system architecture for implementing an electronic device according to embodiments of the present application is shown. Detailed Implementation

[0019] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort should fall within the scope of protection of the present application.

[0020] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0021] According to one aspect of the embodiments of this application, a method for generating real-time valuation results is provided. Optionally, in this embodiment, the above-described method for generating real-time valuation results may be applied, but is not limited to, to applications such as... Figure 1 The hardware environment shown includes terminal device 102 and server 104. Server 104 can be connected to terminal device 102 via a network and can be used to provide services (e.g., application services, etc.) to terminal device 102 or clients installed on terminal device 102. A database can be set up on server 104 or independently of server 104 to provide data storage services for server 104.

[0022] The aforementioned network may include, but is not limited to, at least one of the following: wired network and wireless network. The aforementioned wired network may include, but is not limited to, at least one of the following: wide area network (WAN), metropolitan area network (MAN), and local area network (LAN). The aforementioned wireless network may include, but is not limited to, at least one of the following: Wireless Fidelity (WIFI) and Bluetooth. Terminal device 102 may be, but is not limited to, a personal computer (PC), mobile phone, tablet computer, etc. Server 104 may be, but is not limited to, a cloud server, server cluster, or other server types.

[0023] The method for constructing a data lineage graph according to embodiments of this application can be executed by server 104, by terminal device 102, or by both server 104 and terminal device 102. The data query method of this application according to embodiments of this application can also be executed by a client installed on the terminal device 102.

[0024] Taking the method of generating real-time valuation results in this embodiment, executed by server 104, as an example, Figure 2 This is a flowchart illustrating an optional method for generating real-time valuation results according to an embodiment of this application, as shown below. Figure 2 As shown, the process of this method may include the following steps:

[0025] Step S202: Generate a parameter estimation event for the target object, wherein the parameter estimation event is used to request the generation of a real-time estimation result for the target object;

[0026] This embodiment can be applied to the field of object data computation, and is particularly suitable for scenarios involving high-frequency trading, real-time risk monitoring, and automated investment decision-making. For example, in the trading halls of stock exchanges or large commercial banks, when the market experiences sharp fluctuations or a large transaction occurs in a particular bond, the real-time valuation of the bond and its associated portfolio can be updated instantly within milliseconds using a cached key interest rate duration and a vectorized approximation algorithm, based on an event-driven mechanism. This helps traders and risk control systems quickly make stop-loss, hedging, or portfolio adjustment decisions without waiting for traditional hourly or half-hourly full batch processing results. It should be noted that this embodiment can also be applied to any scenario requiring high-frequency, low-latency state updates and where the computational model has linear approximation characteristics. For example, in autonomous driving fleet collaboration, it can be used for real-time position and speed prediction based on vehicle dynamics sensitivity vectors; in cloud computing resource scheduling, it can be used for real-time resource demand estimation based on service sensitivity weights; and in industrial IoT monitoring, it can be used for real-time equipment health status assessment based on sensor sensitivity vectors.

[0027] Optionally, the target object in this embodiment can be an independent entity unit in the system that requires real-time state updates, valuation, or performance evaluation. This object has a quantifiable baseline state, and its current state changes are primarily influenced by a small number of key external factors (i.e., parameters). In the financial field, the target object can be bonds, stocks, derivative contracts, or investment portfolios. For example, in a bond valuation scenario, the target object is a specific government bond or corporate bond. The core indicator the system focuses on is the bond's real-time price or yield. Besides a single bond, the target object can also be an index constituent stock of a certain industry, or a fund containing multiple assets. In these scenarios, the value fluctuation of the target object depends on key financial parameters such as market interest rates and credit spreads. In the field of autonomous driving and intelligent transportation, the target object is a connected vehicle. For example, in a vehicle-road cooperative system, the target object is a specific vehicle traveling on a road. The core indicator the system focuses on is the vehicle's real-time location, speed, or estimated arrival time. The target object can also be a traffic light controller at a traffic intersection, whose state is the current traffic phase and remaining countdown. In the field of cloud computing, the target object is a microservice instance or container. For example, in a Kubernetes cluster, the target object is a running container Pod. The core metrics the system focuses on are its resource load or service response latency. The target object can also be a database table, its status being the current level of contention or query latency. In the Industrial Internet of Things (IIoT), the target object can be industrial equipment or sensor nodes. For example, in smart manufacturing, the target object is a CNC machine tool or a wind turbine blade. The core metrics the system focuses on are its health index, remaining lifespan, or vibration spectrum characteristics. The target object can also be a smart meter, its status being the current real-time power consumption or voltage fluctuations.

[0028] Optionally, in this embodiment, the parameter estimation event refers to a signal triggered by an external monitoring mechanism or internal logic, indicating that the environment or input parameters of the target object have undergone significant changes sufficient to affect its current state value. This event carries key change data, aiming to request the system to quickly estimate the latest state of the target object based on these changes using cached benchmark data, rather than re-executing the complete, precise calculation process. For example, the parameter estimation event could be a key maturity point yield jump event, a latest bond transaction event, or a significant credit spread fluctuation event. For instance, when the data warehouse detects that the 5-year Treasury bond yield instantly changes from 2.50% to 2.52%, exceeding a preset threshold, the system generates a yield change event. This event contains two key pieces of information: the affected bond; and the amount of yield change. Upon receiving this event, the system triggers estimation logic, using the cached 5-year key interest rate duration and yield change of the bond to instantly estimate the new price.

[0029] Events can also be time windows such as market opening or closing, or the last trading session before holidays, used to trigger batch valuation updates. For example, in autonomous driving and intelligent transportation, parameter estimation events refer to events such as emergency braking of a vehicle ahead, traffic light status changes, and sudden changes in wind speed. When onboard sensors detect that the brake lights of a vehicle ahead are illuminated and its deceleration exceeds 1.5 m / s², a forward braking event is generated. This event carries information: the distance to the vehicle ahead; the deceleration of the vehicle ahead. After receiving this event, the system triggers the estimation logic, using cached vehicle dynamics sensitivity coefficients (such as the delay time of braking response and friction coefficient sensitivity) to instantly estimate the vehicle's expected braking distance and safe following speed under the current road conditions, without having to rerun a complex physical collision simulation engine. Events can also be high-precision map data update events, triggered when local map features (such as construction areas) change.

[0030] In cloud computing, parameter estimation events can be sudden traffic surges, dependent service timeouts, or scheduled task startup events. For example, a monitoring system might detect a sudden spike in Queries Per Second (QPS) for an e-commerce microservice, jumping from 1000 to 10000, generating a traffic surge event. This event carries the service ID and the current QPS value. Upon receiving this event, the system triggers estimation logic, using a cached service resource sensitivity model (e.g., 0.5 additional Central Processing Units (CPUs) are needed for every 1000 QPS increase) to instantly estimate the total CPU resources required and determine whether to immediately trigger a scaling instruction without waiting for a full performance analysis in the next monitoring cycle (e.g., every minute). Events can also be code release events, triggering resource estimation for new versions.

[0031] Step S204: In response to the parameter estimation event, retrieve the target calculation benchmark data of the target object from the pre-stored data cache, wherein the target calculation benchmark data is used to represent the valuation benchmark parameters and risk sensitivity parameters of the target object determined in the historical calculation period;

[0032] Optionally, in this embodiment, the target calculation benchmark data refers to a set of static or semi-static parameters pre-calculated and stored for a specific target object during historical calculation cycles (such as the previous batch full calculation or offline modeling phase). This data contains two core dimensions: valuation benchmark parameters, used to describe the basic attribute values ​​of the target object in the reference state (such as benchmark price, benchmark location, benchmark load, etc.); and risk sensitivity parameters, used to quantify the degree or weight of the target object's response to changes in specific external variables (such as duration, sensitivity coefficient, elasticity coefficient, etc.). This data can be stored in a cache so that it can be read in milliseconds when an event is triggered, thereby avoiding the repeated execution of costly and precise calculations.

[0033] For example, in the financial field, the target calculation benchmark data specifically refers to the benchmark price and the key interest rate duration vector. The valuation benchmark parameter refers to the fair market value of the bond determined at the end of the previous micro-batch (e.g., hourly) precise calculation. For example, the price of a 10-year Treasury bond at the benchmark time is 98.50 yuan. The risk sensitivity parameter refers to the key interest rate duration (KRD). It represents the sensitivity of the bond price to changes in yield when the yield curve undergoes a small parallel shift at a specific maturity point (e.g., 2 years, 5 years, 10 years, 30 years). For example, the key interest rate duration of this bond at the 10-year point is 7.5 years. When an event is triggered, the system directly uses the benchmark price = 98.50 and KRD1 = 7.5 as the starting point for calculation.

[0034] For example, in autonomous driving and intelligent transportation scenarios, the target calculation baseline data specifically refers to the baseline motion state and dynamic sensitivity matrix. The valuation baseline parameters are the target vehicle's baseline position coordinates, baseline velocity vector, and baseline heading angle within the previous high-precision positioning cycle. Risk sensitivity parameters are sensitivity coefficients in the vehicle dynamics model. For example, the sensitivity coefficient of the steering angle to changes in lateral acceleration, and the sensitivity of the tire friction coefficient to changes in braking distance. When an event (such as sudden braking ahead) is triggered, the system uses the baseline position and dynamic sensitivity to quickly calculate the vehicle's predicted trajectory under the current disturbance without having to resolve complex differential equations.

[0035] For example, in cloud computing resource scheduling scenarios, the target computing baseline data specifically refers to the baseline resource quota and load sensitivity weight. The valuation baseline parameters are the baseline CPU utilization, baseline memory usage, or baseline number of microservice instances in the previous monitoring period. The risk sensitivity parameter is the resource consumption sensitivity. For example, a coefficient of 5% increase in CPU utilization for every 1000 additional QPS requests; a coefficient of 2ms increase in automatic memory management (Garbage Collection, or GC) pause time for every 1GB additional memory. When an event (such as a traffic surge) is triggered, the system uses the baseline resources and sensitivity weights to instantly estimate the total amount of resources currently required to decide whether to scale up.

[0036] Step S206: Perform vectorized calculation operation on the target calculation benchmark data according to the event type of the estimated event based on the parameters to generate the real-time estimation result.

[0037] Optionally, the vectorized computation operation in this embodiment refers to utilizing vector processing techniques from computer science (such as Single Instruction Multiple Data (SIMD) instruction sets and matrix operation libraries) to transform the linear approximate computation process for a single target object into batch parallel computation of arrays or matrices. This operation is based on the principle of linear superposition, that is, the total change is equal to the linear weighted sum of the changes caused by each independent factor. Its core mathematical form is usually vector dot product or matrix multiplication.

[0038] For example, in the financial field, vectorized computation refers to the vector dot product of key interest rate duration and yield changes. Compared to calling complex analytical pricing models, vector dot products require only a few floating-point multiplication and addition operations, and can process tens of thousands of bonds in parallel on modern CPUs or graphics processing units (GPUs), increasing speed by several orders of magnitude.

[0039] For example, in autonomous driving and intelligent transportation scenarios, vectorized computation specifically refers to the dot product of the sensitivity coefficient vector and the environmental disturbance vector. Compared to running a complete physics engine and performing integration calculations at each step, vector dot product can instantly complete trajectory prediction for multi-vehicle collaboration, meeting the requirements of real-time control loops.

[0040] For example, in cloud computing resource scheduling scenarios, vectorized computation operations specifically refer to the dot product of the resource sensitivity weight vector and the load change vector. This avoids the need to launch a full performance profiler to analyze the current request stack; resource gaps can be estimated in seconds through simple weighted summation, enabling rapid automatic scaling up and down.

[0041] Through the above steps, by triggering valuation requests through event-driven mechanisms and utilizing pre-stored target calculation benchmark data containing valuation benchmark parameters and risk sensitivity parameters, combined with event types, efficient vectorized calculation operations are performed, thereby avoiding the high latency of traditional micro-batch full calculations. Therefore, the technical problem of poor timeliness in valuation calculations in related technologies can be solved, achieving the technical effect of ensuring both valuation accuracy and high timeliness in valuation calculations.

[0042] In an exemplary embodiment, generating a parameter estimation event for a target object includes: acquiring market data of the target object, wherein the market data includes: multiple profit parameters of the target object at multiple preset time points, transaction parameters of the target object, and real-time profit parameters of the target object; generating the parameter estimation event when the difference between the target profit parameter and the benchmark profit parameter is greater than a preset threshold, wherein the target profit parameter is any one of the multiple profit parameters; or, generating the parameter estimation event when it is determined from the transaction parameters and the real-time profit parameters that the target object has generated transaction record data.

[0043] Optionally, in this embodiment, market data refers to a set of external input information reflecting the current state or recent historical state of the target object. It is not limited to financial market transaction data, but refers to all dynamic data streams that can characterize the current environmental state, input variables, or market activity of the target object. This data typically has high frequency and high dimensionality characteristics.

[0044] For example, in the financial field, this refers to real-time market quotes and transaction records. Multiple yield parameters for various preset maturity points refer to the spot or forward yields at specific key maturity points on the yield curve (such as 1 year, 5 years, 10 years, and 30 years). For example, the 10-year Treasury bond yield. Transaction parameters refer to the actual transaction information of the target asset (e.g., bonds) in the secondary market, including the latest transaction price, transaction volume, and best bid or ask price. Real-time yield parameters refer to the bond yield to maturity derived from the latest transaction price, or the market's implied immediate yield. This data is the raw material for the system to determine whether the market is experiencing drastic fluctuations.

[0045] Optionally, in this embodiment, the target return parameter refers to a specific variable in the market data that is heavily monitored by the system and has a decisive impact on the core state (valuation / performance) of the target object. At a preset timeframe, it refers to the key dimension variables that constitute the description of the system's state space.

[0046] For example, in the financial field, this specifically refers to the yield at key maturity points. The system predefines several key maturity points (such as 2 years, 5 years, 10 years, and 30 years). The target return parameter is the latest market yield corresponding to these five maturity points. Bond prices are most sensitive to changes in yields at these specific maturities (determined by the KRD vector), therefore monitoring the yields at these specific points is more efficient than monitoring the entire yield curve.

[0047] Optionally, in this embodiment, the transaction parameters refer to data that directly reflects the actual interaction or state transition of the target object in the market. In the physical world, this corresponds to collisions, contact, or energy exchange between objects; in a network scenario, it corresponds to request processing or state changes. The real-time revenue parameters refer to derived indicators that are calculated in real time based on the latest interaction data and reflect the current comprehensive performance or equivalent state of the target object.

[0048] For example, in the financial sector, the transaction parameter is the latest transaction price or the latest transaction volume. Bonds are actually bought and sold, and the price has undergone actual market acceptance. The real-time yield parameter is the immediate yield to maturity, which is the annualized rate of return if held to maturity, derived from the latest transaction price. This is a more valuable equivalent yield than simply referring to the price.

[0049] Optionally, the triggering condition in this embodiment refers to the logical rules by which the system determines whether to generate a parameter estimation event. It typically includes two modes: threshold triggering, which is triggered when the change in a key variable exceeds a preset safety or fluctuation threshold, suitable for continuously changing parameters; and event triggering, which is triggered when a specific discrete event (such as a transaction, error, or state switch) is detected, suitable for discrete states or sudden situations.

[0050] For example, in the financial sector, threshold triggering generates an event when the yield change at a key maturity point (such as 5 years) exceeds 1 basis point. The aim is to filter out minor market noise and only handle significant market fluctuations. An event is also generated when a recent transaction record for the bond is detected. The goal is to use a true market price anchor to correct estimation biases based on curve changes.

[0051] The core logic of this embodiment lies in layered monitoring and hybrid triggering. The monitoring layer monitors continuous variables (such as yield, CPU utilization, and volatility) as well as discrete events (such as transactions, errors, and startups). For continuous variables, the triggering layer uses threshold filtering to avoid invalid calculations (such as minor fluctuations in finance or subtle jitters in autonomous driving). For discrete events, it uses instant triggering to capture sudden changes in state (such as large transactions in finance, sudden traffic spikes in the cloud, and equipment startup and shutdown in industry).

[0052] This embodiment addresses the technical problems in related technologies caused by a single monitoring dimension, such as market noise interference, waste of ineffective computing resources, and delayed response to sudden trading signals. By monitoring the target object's profit parameters, transaction parameters, and real-time profit parameters at multiple preset time points in a hierarchical manner, and by using a hybrid mechanism combining threshold triggering and event triggering to generate parameter estimation events, it effectively filters noise interference caused by minor fluctuations to improve estimation stability. At the same time, by capturing substantial transaction signals in real time to correct estimation deviations, it achieves the technical effect of generating real-time valuation results with high signal-to-noise ratio, low latency, and self-calibration capability.

[0053] In an exemplary embodiment, before generating the parameter estimation event for the target object, the method further includes: obtaining a computational benchmark data set of the object set from a data cache, wherein the objects included in the object set are all objects to be valued, the target object is any object in the object set, and the computational benchmark data set includes the target computational benchmark data.

[0054] Optionally, in this embodiment, the object set refers to a complete list or subset of all target objects that need to be monitored, evaluated, or have their status updated within the current time window. It is a static or quasi-static data structure (such as an array, list, or hash table key set). The object set can define the boundary range of the computation. In a financial scenario, it could be a list of all bonds in the market.

[0055] Optionally, in this embodiment, the computational baseline data set refers to a pre-calculated and cached set of basic parameters that corresponds one-to-one with each object in the object set. This set includes the baseline state value and sensitivity coefficient vector determined for each object in the previous complete computation cycle (or offline modeling phase).

[0056] Optionally, in this embodiment, the target calculation benchmark data refers to the subset of data belonging to a specific target object in the aforementioned calculation benchmark data set.

[0057] For example, in the financial field, the object set is a list of all tradable bonds in the entire market. The benchmark dataset is a large in-memory object pool containing these 2000 bonds generated at the end of the precise calculation in the previous hour.

[0058] Benchmark price P base (Corresponding to the benchmark valuation parameters mentioned above);

[0059] Key Interest Rate Duration Vector (KRD) vec =[KRD1, KRD2, ..., KRD n [This corresponds to a pre-set key timeframe (e.g., 2 years, 5 years, 10 years, 30 years);]

[0060] The benchmark yield curve, including the yields y at the aforementioned key maturity points. base_i ;

[0061] Total duration parameter D total =ΣKRD j .

[0062] The target calculation benchmark data is the data corresponding to a certain government bond that is extracted from the set when a yield change event is triggered.

[0063] This embodiment solves the problems of high network latency and poor concurrent processing capability caused by querying the database or remote service one by one when real-time valuation requests are triggered, by pre-retrieving the full set of calculation benchmark data containing all objects to be valued from the high-speed data cache before generating the parameter estimation event, and accurately extracting the target calculation benchmark data of the target object from it. This achieves the technical effect of high-speed response to parameter estimation events.

[0064] In one exemplary embodiment, in response to the parameter estimation event, obtaining target calculation benchmark data of the target object from a pre-stored data cache includes: in response to the parameter estimation event, reading at least one of the following information of the target object from the data cache to obtain the target calculation benchmark data: a benchmark valuation parameter, representing the base for real-time valuation of the object; a key interest rate duration vector, representing the sensitivity of the object to changes in yield; a benchmark yield curve, including the yield values ​​of the object at multiple key maturity points; and a total duration parameter, representing the sum of multiple elements in the key interest rate duration vector.

[0065] Optionally, in this embodiment, the benchmark valuation parameter refers to the state value of the target object determined in the previous complete calculation cycle (benchmark time), which serves as the starting point for the current real-time estimation. It is the fundamental anchor point for calculating changes. For example, in the financial field, it could be the benchmark price P. base .

[0066] Optionally, the key interest rate duration vector in this embodiment is a multi-dimensional vector representing the sensitivity of the target asset's value to parallel shifts in the yield curve at specific key maturity points. This linearizes the complex nonlinear price-yield relationship. Compared to traditional effective duration (which only considers parallel shifts), KRD can capture non-parallel shifts in the yield curve (such as steepening, flattening, and butterfly effects), thus providing a more granular description of risk exposure. Each element in the vector corresponds to a specific maturity point (e.g., 2 years, 5 years, 10 years).

[0067] Optionally, the benchmark yield curve in this embodiment is a set of risk-free or benchmark yields corresponding to key maturity points in the market at a benchmark time. It is used to construct a complete pricing environment. Although real-time estimation primarily relies on changes in magnitude, the benchmark curve is used to verify the reasonableness of the current market state or when it is necessary to reverse-engineer yields.

[0068] Optionally, in this embodiment, the total duration parameter is the algebraic sum of all elements in the key interest rate duration vector. It represents the sensitivity of the target object to overall parallel shifts in the yield curve. It is a simplified indicator suitable for rapid estimation or rough calculations when the yield curve undergoes approximately parallel shifts, with minimal computational overhead.

[0069] For example, in the financial sector, a benchmark valuation parameter could be the full price of a 3-year government bond at the end of the previous hour's precise calculation, which would be 100.50 yuan.

[0070] The key interest rate duration vector is [0.05, 1.2, 4.5, 8.2], corresponding to the sensitivity at 1-year, 3-year, 5-year, and 10-year maturity points, respectively. This means that if the 3-year yield rises by 1%, the bond price will fall by approximately 4.5%.

[0071] The benchmark yield curve is 2.0% for 1 year, 2.5% for 3 years, 3.0% for 5 years, and 3.5% for 10 years.

[0072] The total duration parameter is 0.05 + 1.2 + 4.5 + 8.2 = 13.95. This means that if the entire yield curve rises by 1% in parallel, the bond price will decrease by approximately 13.95%.

[0073] This embodiment accurately obtains benchmark valuation parameters, key interest rate duration vectors, benchmark yield curves, and total duration parameters from the data cache. Therefore, it can use the key interest rate duration vector to capture subtle changes in non-parallel shifts in the yield curve, and combine it with the benchmark valuation base to achieve high-precision real-time price estimation. At the same time, it uses the total duration parameter to simplify the calculation complexity to deal with parallel shift scenarios, thus significantly improving the calculation efficiency while ensuring the accuracy of the valuation.

[0074] In an exemplary embodiment, performing vectorized calculation operations on the target calculation benchmark data according to the event type of the parameter estimation event to generate the real-time valuation result includes: when the event type of the parameter estimation event is a yield curve change event, obtaining the yield change vector of the target object from the benchmark yield curve; performing a vector dot product operation on the key interest rate duration vector and the yield change vector to obtain a first change amount, wherein the first change amount is a linear combination of the key interest rate duration vector and the yield change vector, used to represent the relative change ratio of the target object due to the yield; calculating the product between the benchmark valuation parameter and the first change amount to obtain an absolute change value; and superimposing the benchmark valuation parameter and the absolute change value to obtain the real-time valuation result.

[0075] Optionally, in this embodiment, yield curve change events refer to events where non-uniform or non-parallel changes are detected in the market benchmark interest rate curve at multiple key maturity points. Such changes may manifest as curve shifts (parallel shifts), distortions (steepening or flattening), or a butterfly effect (changes at the short and long ends, while the middle end remains unchanged).

[0076] Optionally, the yield change vector in this embodiment is a multi-dimensional vector, representing the change in yield at each key maturity point based on the benchmark yield curve. It is a digital representation of external market disturbances. The dimension of the vector corresponds to the preset number of key maturity points, and each element represents the specific magnitude of the yield change at that maturity point (which can be expressed in basis points (bp) or decimals).

[0077] Optionally, the vector dot product operation in this embodiment is a linear algebraic operation of summing the corresponding elements of two equal-dimensional vectors. In financial valuation, sensitivity-weighted summation can be implemented. That is, total percentage change = ∑(sensitivity at each maturity point × change in yield at each maturity point).

[0078] Optionally, in this embodiment, the first change is a scalar value obtained through vector dot product, representing the percentage change in the target price relative to the benchmark price. is a dimensionless coefficient reflecting price elasticity due to changes in the yield curve. For example, -0.05 indicates an expected price decline of 5%.

[0079] Optionally, in this embodiment, the absolute change value is the result of multiplying the first change (percentage change) by the benchmark valuation parameter (absolute price), representing the specific amount of change in the price of the target object.

[0080] This embodiment ensures that, even under complex market environment changes, it can still provide fast, accurate and interpretable real-time valuation results through vectorized calculation operations based on vector dot products.

[0081] In an exemplary embodiment, performing vectorized calculation operations on the target calculation benchmark data according to the event type of the parameter estimation event to generate the real-time valuation result includes: when the event type of the parameter estimation event is an object transaction event, determining the yield difference of the target object from the transaction data of the object transaction event, wherein the yield difference is the difference between the yield calculated based on the real-time transaction parameters of the target object and the yield value of the key maturity point corresponding to the target object in the benchmark yield curve; calculating the product between the total duration parameter and the yield difference to obtain the duration effect value; calculating the difference between the preset parameter and the duration effect value to obtain the correction coefficient; and calculating the product between the benchmark valuation parameter and the correction coefficient to obtain the real-time valuation result.

[0082] Optionally, in this embodiment, an object transaction event refers to the detection that a target object (such as bonds, stocks, or assets) has undergone actual trading in the market, generating a transaction record that includes elements such as price, trading volume, and time.

[0083] Optionally, in this embodiment, the yield difference refers to the difference between the implied yield derived from the latest transaction data and the benchmark yield. It quantifies the required excess return of the specific asset relative to the benchmark asset. Positive values ​​indicate risk premiums (such as credit risk and liquidity risk), while negative values ​​indicate a premium advantage.

[0084] Optionally, in this embodiment, the duration effect value is the product of the total duration parameter of the target asset and the yield difference. Duration measures the sensitivity of asset prices to changes in yield. This product approximately represents the magnitude of the relative price change caused by yield deviation.

[0085] Optionally, the correction coefficient in this embodiment is obtained by subtracting the duration effect value from a preset parameter (e.g., 1, representing the price multiple under the baseline state). This is a scaling factor. If the yield increases, the duration effect value becomes positive, the correction coefficient is less than 1, leading to a decline in valuation.

[0086] Optionally, the preset parameter in this embodiment is a constant used to balance dimensions or as a benchmark point in the basic valuation model. It can be set to 1, representing a multiple of the benchmark price.

[0087] This embodiment uses real-time transaction data of the target object to back-calculate the implied yield and compare it with the benchmark yield curve to obtain the yield difference. Then, it combines the total duration parameter to calculate the duration effect value and generate a correction coefficient. Therefore, it can accurately capture the market pricing deviation of a single object caused by individual factors such as credit and liquidity. It overcomes the shortcomings of traditional methods that rely solely on changes in the macro curve and cannot reflect the specific risks of individual assets. It achieves second-level, high-precision real-time valuation based on real transaction signals without calling complex pricing models.

[0088] In an exemplary embodiment, after performing vectorized calculation operations on the target computational benchmark data according to the event type of the parameter estimation event to generate the real-time estimation result, the method further includes: sending the real-time estimation result to an application service to instruct the application service to adjust the target computational benchmark data based on a preset adjustment strategy and the real-time estimation result; wherein, if the preset adjustment strategy indicates that adjustment of the target computational benchmark data is permitted, the application service updates the benchmark estimation parameters in the target computational benchmark data to the real-time estimation result; if the preset adjustment strategy indicates that adjustment of the target computational benchmark data is not permitted, the application service adjusts the benchmark estimation parameters according to the time period for generating the real-time estimation result.

[0089] Optionally, in this embodiment, the application service refers to a software module or microservice that receives real-time valuation results and executes subsequent business logic or data maintenance. This can decouple the computation layer (valuation generation) from the business layer (data maintenance). The application service can dynamically determine the data update strategy based on business rules (such as risk control requirements and performance load).

[0090] Optionally, the preset adjustment strategy in this embodiment is a set of predefined rules or configurations used to indicate whether, after receiving the real-time valuation result, it is allowed to directly overwrite the old benchmark data, or whether it is necessary to make gradual adjustments over a specific time period. Assuming the estimation is sufficiently accurate, it is permissible to directly use the estimated value as the new benchmark. Situations where adjustment is not allowed include pursuing long-term stability, preventing the accumulation of estimation errors from causing benchmark drift, and forcing the benchmark to be corrected through periodic, full, and accurate calculations.

[0091] Optionally, in this embodiment, the benchmark valuation parameter update is performed in an adjustable mode, where the old benchmark price (or related parameters) in the cache is directly replaced with the currently generated real-time valuation result.

[0092] Optionally, in this embodiment, the time period adjustment is performed in a mode where direct adjustment is not allowed. Instead of updating the benchmark immediately, the current real-time estimation result is recorded, and the benchmark is updated by full-quantity accurate calculation or a specific correction algorithm when the next preset time window (such as the end of a micro-batch cycle or a specific calibration interval) arrives.

[0093] This embodiment flexibly selects to directly update the real-time valuation result to the benchmark value or to calibrate it according to the time period based on the preset adjustment strategy. Therefore, it can eliminate state lag through real-time benchmark reset to achieve extreme timeliness in high-liquidity scenarios, while preventing the accumulation of estimation errors through periodic precise calibration in low-precision or risk-sensitive scenarios. Thus, it achieves the adaptation of system resource consumption and business needs while dynamically balancing the timeliness and long-term accuracy of valuation.

[0094] In an exemplary embodiment, after performing vectorized computation operations on the target computational benchmark data according to the event type of the parameter estimation event to generate the real-time estimation result, the method further includes: responding to a periodic scheduling instruction to trigger a recalculation of the estimation of the target object, wherein the periodic scheduling instruction is determined based on the object type of the target object or the event type of the parameter estimation event.

[0095] Optionally, in this embodiment, the periodic scheduling instruction is a control signal issued by the system scheduler to trigger a full and accurate estimation calculation for a specific target object. It includes the triggering condition, the target object identifier, and the execution time window.

[0096] Optionally, the object type in this embodiment is a classification label for the target asset or entity, determined based on attributes such as liquidity, risk level, structural complexity, or market activity.

[0097] Optionally, in this embodiment, the event type of the parameter estimation event refers to the specific market behavior or data change type that triggers the real-time estimation. For example, parallel shift of the yield curve, non-parallel shift of key maturity points, large single transaction, sharp price fluctuations (gaps), and credit events (such as rating downgrades).

[0098] Optionally, in this embodiment, re-valuation calculation involves starting a complete, high-precision valuation engine, reloading all market data, yield curves, cash flow models, etc., performing full pricing on the target object, and generating new benchmark data.

[0099] This embodiment dynamically generates periodic scheduling instructions based on the attribute characteristics of the target object and the type of recent market events, thereby triggering full and accurate valuation calculations in a differentiated manner. Therefore, it can quickly calibrate the benchmark to eliminate estimation errors in high volatility or high-risk scenarios, while avoiding invalid calculations in stable scenarios.

[0100] According to another aspect of the embodiments of this application, a system for generating real-time valuation results is provided, such as... Figure 3 The diagram shown is a structural schematic of the real-time estimation result generation system of this embodiment, including a calculation component 32 and an estimation component 34 that are interconnected.

[0101] The computing component 32 is used to perform full valuation calculations on all objects in the object set according to a preset time period to generate a calculation benchmark data set. The objects included in the object set are all objects to be valued, the target object is any object in the object set, and the calculation benchmark data set includes target calculation benchmark data.

[0102] The estimation component 34 is configured to generate a parameter estimation event for a target object, wherein the parameter estimation event is used to request the generation of a real-time valuation result for the target object; in response to the parameter estimation event, to retrieve the target calculation benchmark data of the target object from a pre-stored data cache, wherein the target calculation benchmark data is used to represent the valuation benchmark parameters and risk sensitivity parameters of the target object determined in a historical calculation period; and to perform a vectorized calculation operation on the target calculation benchmark data according to the event type of the parameter estimation event to generate the real-time valuation result.

[0103] This system, through which the calculation component pre-generates a calculation benchmark data set, and the valuation component triggers valuation requests via event-driven mechanisms, utilizes the target calculation benchmark data containing valuation benchmark parameters and risk sensitivity parameters in the benchmark data set, and performs efficient vectorized calculation operations in combination with event types. This avoids the high latency of traditional micro-batch full-volume calculations, thus solving the technical problem of poor timeliness in valuation calculations in related technologies, achieving the technical effect of ensuring both valuation accuracy and high timeliness in valuation calculations.

[0104] The method for generating real-time valuation results in this application embodiment is explained below with reference to optional examples. In this optional example, bonds are used as an example. The method for generating real-time valuation results in this optional example is a bond real-time valuation method based on event-driven and vectorized approximation. The system for generating real-time valuation results is a bond real-time valuation system based on event-driven and vectorized approximation. This method and system, while ensuring valuation accuracy, overcome the timeliness limitations of traditional batch processing, constructing a real-time bond valuation method that can quickly respond to market changes and possesses high stability and low coupling.

[0105] Figure 4 This is a schematic diagram of the structure of a real-time bond valuation system based on event-driven and vectorized approximation in this optional example, such as... Figure 4 As shown, the system includes:

[0106] The micro-batch precision calculation module (corresponding to the calculation components mentioned above) is the foundation of the system. It utilizes the existing system architecture and performs a full, precise bond valuation calculation once per hour at the original frequency. This module generates benchmark valuations for all bonds, key interest rate duration (KRD), and other important parameters, storing them in a cache or database. This approach ensures the absolute accuracy and completeness of the valuation results.

[0107] The event-driven real-time estimation module (corresponding to the estimation component mentioned above) is used to continuously monitor real-time market data in the data warehouse (such as bond transaction prices, changes in yields at key maturity points, etc.). When a change in the market data of a specific bond or related market indicator is detected, this module is triggered by the event and initiates a rapid incremental valuation estimation only for the affected bonds, thereby achieving a response time within seconds.

[0108] This system abandons real-time calls to complex external formula libraries, instead employing an approximate estimation method based on vectorized computation. Its core is to use key interest rate duration indicators and yield curve changes generated by the micro-batch calculation module to estimate real-time changes in bond prices. Key interest rate duration decomposes the risk of non-parallel shifts in bond prices to several key maturity points, simplifying price estimation into an efficient vector dot product operation.

[0109] Figure 5 This is a flowchart of a real-time bond valuation method based on event-driven and vectorized approximation, as shown in this optional example. Figure 5 As shown, the process of this event-driven and vectorized approximation-based real-time bond valuation method may include the following steps:

[0110] S501, Initialization and Baseline Data Preparation, specifically includes:

[0111] After the system starts, the micro-batch precision calculation module is run first to perform a precise valuation of all bonds. The following benchmark data for each bond is calculated and stored:

[0112] Benchmark price (corresponding to the benchmark valuation parameter mentioned above) P base ;

[0113] Key Interest Rate Duration Vector (KRD) vec =[KRD1, KRD2, ..., KRD n [This corresponds to a pre-set key timeframe (e.g., 2 years, 5 years, 10 years, 30 years);]

[0114] The benchmark yield curve includes the yields y at the aforementioned key maturity points. base_i ;

[0115] Total duration parameter D total =ΣKRD j ;

[0116] The benchmark data is stored in a high-speed cache (such as Redis) or an in-memory database for the real-time estimation module to access quickly.

[0117] S502, real-time market data monitoring and event triggering, specifically includes:

[0118] After the event-driven real-time estimation module starts, it acts as a consumer subscribing to real-time market data streams from the data warehouse (such as Kafka message queues). Real-time market data includes, but is not limited to, the latest yield y at various key time points. current_i The latest transaction price or latest yield of a specific bond.

[0119] When monitoring the yield y at any key maturity point current_i Compared with the benchmark price y base_i The difference Δy between i A valuation estimation event is triggered when the preset threshold is exceeded or when a new transaction record for a bond is detected.

[0120] S503, Vectorized Approximate Estimation Calculation, specifically includes:

[0121] Once the valuation estimation event is triggered, the following steps are executed:

[0122] S5031, Obtain Calculation Parameters: Quickly retrieve the key interest rate duration vector (KRD) of the affected bonds from the cache. vec Benchmark price P base And total duration parameter D total For key maturity point changes, it is also necessary to calculate Δy for all bonds. i , where Δy i This is used to represent the real-time change in yield at the i-th key rate. It is the difference between the market yield at that key rate at the current moment and the yield at the base time (referring to the state at the end of the previous round of precise micro-batch calculation).

[0123] S5032 performs vectorized computation, specifically including:

[0124] Scenario 1: Estimation based on curve changes (no transaction records):

[0125] The system uses a vectorized computation library (such as NumPy) to calculate the percentage change in price for all affected bonds in one go: ΔP percent =-(KRD vec ·Δy vec ), where · represents the vector dot product operation. Calculate the real-time estimated price: P estimate=P base (1+ΔP percent ), where Δy vec The yield change vector is a vector that contains the real-time changes in yields at all key maturity points. This vector represents the overall change shape of the yield curve relative to the benchmark yield curve at the current moment.

[0126] The specific formula for estimating price changes is as follows: based on the linear relationship between the duration of key interest rates and the changes in yields at each key maturity point, the percentage change in bond prices is estimated. Where n is the number of critical deadlines, KRД j Let Δy be the key interest rate duration of the bond at the j-th key maturity point. j P represents the real-time change in yield at this key maturity point, and is used to represent the bond price.

[0127] When there is no immediate trading of the bond itself, the new price is estimated by using changes in its key interest rate duration and yield curve. , where P i-1 It is the benchmark price calculated from the previous batch, P i This is the current real-time estimated price.

[0128] Scenario 2: Estimation based on single-voucher transactions (with transaction records):

[0129] Calculate the yield difference Δy of the bond based on the transaction data. bond The real-time estimated price is calculated as: P estimate =P base (1-Δy bond D total ).

[0130] When bonds are traded in real time, the trading information can be used directly. If the difference between the traded yield and the benchmark yield Δy is known, it can be used based on the total duration D. total =ΣKRД j Perform a quick estimate. .

[0131] Yield estimates can be derived by inferring changes in the equivalent yield of bonds based on price changes. .

[0132] S5033, Output and Update of Results: Calculate P... estimate The real-time valuation result of the bond is pushed to downstream applications (such as trading terminals and risk monitoring systems). Simultaneously, the benchmark price P in the cache can be selectively updated. base For P estimateThis provides a new benchmark for the next estimation, but a trade-off must be made between computational accuracy and cumulative error.

[0133] S504, periodic synchronization and calibration, specifically includes: when the next scheduling cycle of the micro-batch accurate calculation module arrives, the system performs a full round of accurate estimation. The newly generated accurate estimation results (new P...) are then used... base KRD vec (etc.) Overwrite old data in the cache.

[0134] This embodiment can effectively correct the cumulative errors that may occur during real-time estimation, ensure the accuracy of the system in long-term operation, and form a closed loop of accurate calculation, real-time estimation, and periodic calibration.

[0135] For example, in a specific financial scenario, suppose at a certain moment, news in the market causes the 5-year Treasury yield to jump by 2 basis points (0.02%) instantaneously. The event-driven module detects this change and immediately starts. The system retrieves all bonds holding a 5-year key interest rate duration from the cache and assigns their KRDs... 5Y with Δy 5Y It performs vectorized dot product operations, instantly calculating the price changes of all affected bonds and outputting new valuations. The entire process is completed within milliseconds, without needing to restart any complex formula libraries or perform full calculations. Figure 6 As shown, the specific steps include:

[0136] S601, Event Capture and Target Screening, specifically includes: when the market detects a change in yield at a key 5-year maturity point (e.g., a jump in Δy). 5Y When the key interest rate duration (KRD) is 0.02%, the event-driven module receives the message as a consumer. The system then quickly retrieves all 5-year key interest rate durations (KRDs) from the cache according to preset rules. 5Y List of target bonds with a value greater than zero.

[0137] S602, Batch Parameter Retrieval, specifically includes: the system retrieves the benchmark valuation parameters (including the benchmark price P) of the aforementioned target bonds from an in-memory database (such as Redis Cluster) through a single batch read operation. base And the complete key interest rate duration vector (KRD).

[0138] S603, Vectorized Approximation Estimation, specifically includes:

[0139] Using a vectorization engine, parallel computation is performed on the selected target bonds, including: constructing a yield change vector Δ, where only the 5th element (corresponding to the 5-year maturity) is 0.02%, and the remaining elements are 0; and performing batch vector dot product operations: ΔP. percent =-(KRDaffected· Δy). Calculate the real-time estimate: P realtime =P base (1+ΔP percent ).

[0140] S604, result publishing and decoupling, specifically includes: generating and distributing computation results within milliseconds. This process relies solely on static parameters in memory (KRD, P...). base This module utilizes lightweight linear operations, completely decoupled from the complex downstream pricing formula library. Even when the downstream formula library is under maintenance, upgrades, or high load, this module can still independently and continuously provide real-time valuation services with sub-second response times, ensuring high business availability.

[0141] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, as some steps may be performed in other orders or simultaneously according to this application. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions and modules involved are not necessarily essential to this application.

[0142] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods according to the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as read-only memory (ROM) / random access memory (RAM), magnetic disk, optical disk), and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, or network device, etc.) to execute the methods described in the various embodiments of this application.

[0143] It should be noted that, through the above description of the embodiments, those skilled in the art can clearly understand that the methods according to the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, or network device, etc.) to execute the methods described in the various embodiments of this application.

[0144] This embodiment also provides an apparatus for constructing a data kinship map, which is used to implement the above embodiments and preferred embodiments; details already described will not be repeated. As used below, the term "module" can refer to a combination of software and / or hardware that performs a predetermined function. Although the apparatus described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0145] Figure 7 This is a structural block diagram of a real-time estimation result generation device according to an embodiment of this application, such as... Figure 7 As shown, the device includes:

[0146] The generation module 72 is used to generate a parameter estimation event for the target object, wherein the parameter estimation event is used to request the generation of a real-time estimation result for the target object;

[0147] The response module 74 is configured to respond to the parameter estimation event by retrieving the target calculation benchmark data of the target object from a pre-stored data cache, wherein the target calculation benchmark data is used to represent the valuation benchmark parameters and risk sensitivity parameters of the target object determined in the historical calculation period;

[0148] The calculation module 76 is used to perform vectorized calculation operations on the target calculation benchmark data according to the event type of the estimated event based on the parameters, so as to generate the real-time estimation result.

[0149] It should be noted that the generation module 72 in this embodiment can be used to execute the above step S202, the response module 74 in this embodiment can be used to execute the above step S204, and the calculation module 76 in this embodiment can be used to execute the above step S206.

[0150] The embodiments provided in this application, by triggering valuation requests through event-driven mechanisms and utilizing pre-stored target calculation benchmark data containing valuation benchmark parameters and risk sensitivity parameters, combined with event types, perform efficient vectorized calculation operations, thereby avoiding the high latency of traditional micro-batch full calculations. Therefore, the technical problem of poor timeliness in valuation calculations in related technologies can be solved, achieving the technical effect of ensuring both valuation accuracy and high timeliness in valuation calculations.

[0151] In an exemplary embodiment, the generation module 72 is configured to generate a parameter estimation event for a target object by: acquiring market data of the target object, wherein the market data includes: multiple profit parameters of the target object at multiple preset time points, transaction parameters of the target object, and real-time profit parameters of the target object; generating the parameter estimation event when the difference between the target profit parameter and the benchmark profit parameter is greater than a preset threshold, wherein the target profit parameter is any one of the multiple profit parameters; or, generating the parameter estimation event when it is determined from the transaction parameters and the real-time profit parameters that the target object has generated transaction record data.

[0152] In an exemplary embodiment, the above-described apparatus is further configured to obtain a set of computational benchmark data for an object set from a data cache before generating a parameter estimation event for a target object, wherein the objects included in the object set are all objects to be valued, the target object is any object in the object set, and the set of computational benchmark data includes the target computational benchmark data.

[0153] In an exemplary embodiment, the response module 74 is configured to respond to the parameter estimation event by obtaining target calculation benchmark data of the target object from a pre-stored data cache in the following manner: in response to the parameter estimation event, reading at least one of the following information of the target object from the data cache to obtain the target calculation benchmark data: benchmark valuation parameters, used to represent the base for real-time valuation of the object; key interest rate duration vector, used to represent the sensitivity of the object to changes in yield; benchmark yield curve, including the yield values ​​of the object at multiple key maturity points; total duration parameter, used to represent the sum of multiple elements in the key interest rate duration vector.

[0154] In an exemplary embodiment, the calculation module 76 is further configured to perform vectorized calculation operations on the target calculation benchmark data according to the event type of the parameter estimation event in the following manner to generate the real-time valuation result: when the event type of the parameter estimation event is a yield curve change event, obtain the yield change vector of the target object from the benchmark yield curve; perform a vector dot product operation on the key interest rate duration vector and the yield change vector to obtain a first change amount, wherein the first change amount is a linear combination of the key interest rate duration vector and the yield change vector, used to represent the relative change ratio of the target object due to the yield; calculate the product between the benchmark valuation parameter and the first change amount to obtain an absolute change value; and perform a superposition operation on the benchmark valuation parameter and the absolute change value to obtain the real-time valuation result.

[0155] In an exemplary embodiment, the calculation module 76 is further configured to perform vectorized calculation operations on the target calculation benchmark data according to the event type of the parameter estimation event to generate the real-time valuation result: when the event type of the parameter estimation event is an object transaction event, determine the yield difference of the target object from the transaction data of the object transaction event, wherein the yield difference is the difference between the yield calculated based on the real-time transaction parameters of the target object and the yield value of the key maturity point corresponding to the target object in the benchmark yield curve; calculate the product between the total duration parameter and the yield difference to obtain the duration effect value; calculate the difference between the preset parameter and the duration effect value to obtain the correction coefficient; calculate the product between the benchmark valuation parameter and the correction coefficient to obtain the real-time valuation result.

[0156] In an exemplary embodiment, the above-described apparatus is further configured to perform vectorized computation operations on the target computational benchmark data according to the event type of the parameter estimation event to generate the real-time estimation result, and then send the real-time estimation result to an application service to instruct the application service to adjust the target computational benchmark data based on a preset adjustment strategy and the real-time estimation result; wherein, if the preset adjustment strategy indicates that adjustment of the target computational benchmark data is permitted, the application service updates the benchmark estimation parameters in the target computational benchmark data to the real-time estimation result; if the preset adjustment strategy indicates that adjustment of the target computational benchmark data is not permitted, the application service adjusts the benchmark estimation parameters according to the time period for generating the real-time estimation result.

[0157] In an exemplary embodiment, the above-described apparatus is further configured to perform vectorized computation operations on the target computational benchmark data according to the event type of the parameter estimation event to generate the real-time estimation result, and then, in response to a periodic scheduling instruction, trigger a recalculation of the estimation of the target object, wherein the periodic scheduling instruction is determined based on the object type of the target object or the event type of the parameter estimation event.

[0158] It should be noted that the above modules can be implemented by software or hardware. For the latter, they can be implemented in the following ways, but are not limited to: all the above modules are located in the same processor; or, the above modules are located in different processors in any combination.

[0159] According to another aspect of the embodiments of this application, a computer-readable storage medium is provided, the computer-readable storage medium including a stored program, wherein the program executes the steps in any of the above method embodiments when it is run.

[0160] In one exemplary embodiment, the aforementioned computer-readable storage medium may include, but is not limited to, various media capable of storing computer programs, such as USB flash drives, ROMs, RAMs, portable hard drives, magnetic disks, or optical disks.

[0161] According to another aspect of the embodiments of this application, an electronic device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor. The processor is configured to perform the steps of any of the method embodiments described above via the computer program. In an exemplary embodiment, the electronic device may further include a transmission device and an input / output device, wherein the transmission device is connected to the processor, and the input / output device is connected to the processor.

[0162] Specific examples in this embodiment can be found in the examples described in the above embodiments and exemplary implementations, and will not be repeated here.

[0163] According to another aspect of the embodiments of this application, a computer program product is also provided, which includes a computer program / instructions containing program code for performing the methods shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network via a communication component, and / or installed from a removable medium. When the computer program is executed by a central processing unit, it performs various functions provided in the embodiments of this application. The sequence numbers of the embodiments of this application above are merely descriptive and do not represent the superiority or inferiority of the embodiments.

[0164] Figure 8 A schematic block diagram of a computer system architecture for implementing embodiments of the present application is shown. Figure 8 As shown, the computer system includes a Central Processing Unit (CPU) 801, which performs various appropriate actions and processes based on programs stored in ROM 802 or loaded into RAM 803 from storage section 808. Random Access Memory 803 also stores various programs and data required for system operation. The CPU 801, ROM 802, and RAM 803 are interconnected via bus 804. Input / Output (I / O) interface 805 is also connected to bus 804.

[0165] The following components are connected to I / O interface 805: an input section 806 including a keyboard, mouse, etc.; an output section 807 including a cathode ray tube (CRT), liquid crystal display (LCD), and speakers, etc.; a storage section 808 including a hard disk, etc.; and a communication section 809 including a network interface card, such as a local area network card or modem, etc. The communication section 809 performs communication processing via a network such as the Internet. A drive 810 is also connected to I / O interface 805 as needed. Removable media 811, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., are installed on drive 810 as needed so that computer programs read from them can be installed into storage section 808 as needed.

[0166] Specifically, according to embodiments of this application, the processes described in the various method flowcharts can be implemented as computer software programs. For example, embodiments of this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication component, and / or installed from a removable medium. When the computer program is executed by a central processing unit, it performs various functions defined in the system of this application.

[0167] It should be noted that, Figure 8 The computer system of the electronic device shown is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments of this application.

[0168] Obviously, those skilled in the art should understand that the modules or steps of this application described above can be implemented using general-purpose computing devices. They can be centralized on a single computing device or distributed across a network of multiple computing devices. They can be implemented using computer-executable program code, and thus can be stored in a storage device for execution by a computing device. In some cases, the steps shown or described can be performed in a different order than those described herein, or they can be fabricated as separate integrated circuit modules, or multiple modules or steps can be fabricated as a single integrated circuit module. Thus, this application is not limited to any particular combination of hardware and software.

[0169] The above are merely preferred embodiments of this application and are not intended to limit this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the principles of this application should be included within the protection scope of this application.

Claims

1. A method for generating real-time valuation results, characterized in that, include: A parameter estimation event for a target object is generated, wherein the parameter estimation event is used to request the generation of a real-time estimation result for the target object; In response to the parameter estimation event, target calculation benchmark data of the target object is obtained from a pre-stored data cache, wherein the target calculation benchmark data is used to represent the valuation benchmark parameters and risk sensitivity parameters of the target object determined in the historical calculation period; Based on the event type estimated by the parameters, a vectorized calculation operation is performed on the target calculation benchmark data to generate the real-time estimation result.

2. The method according to claim 1, characterized in that, The event that generates parameter estimation for the target object includes: Obtain market data of the target object, wherein the market data includes: multiple profit parameters of the target object at multiple preset time points, transaction parameters of the target object, and real-time profit parameters of the target object; If the difference between the target return parameter and the benchmark return parameter is greater than a preset threshold, the parameter estimation event is generated, wherein the target return parameter is any one of the plurality of return parameters; or... The parameter estimation event is generated when the target object generates transaction record data based on the transaction parameters and the real-time revenue parameters.

3. The method according to claim 1, characterized in that, Before generating the parameter estimation event for the target object, the method further includes: The calculation benchmark data set of the object set is obtained from the data cache, wherein the objects included in the object set are all objects to be valued, the target object is any object in the object set, and the calculation benchmark data set includes the target calculation benchmark data.

4. The method according to claim 1, characterized in that, In response to the parameter estimation event, retrieving target computational baseline data for the target object from a pre-stored data cache includes: In response to the parameter estimation event, at least one of the following information about the target object is read from the data cache to obtain the target computational baseline data: The baseline valuation parameter is used to represent the base for real-time valuation of the object. The key interest rate duration vector is used to represent the sensitivity of the object to changes in its yield. A benchmark yield curve, including the yield values ​​of the object at multiple key maturity points; Total duration parameter, used to represent the sum of multiple elements in the key interest rate duration vector.

5. The method according to claim 4, characterized in that, Based on the event type estimated by the parameters, a vectorized calculation operation is performed on the target calculation benchmark data to generate the real-time estimation result, including: If the event type of the parameter estimation event is a yield curve change event, the yield change vector of the target object is obtained from the benchmark yield curve. Perform a vector dot product operation on the key interest rate duration vector and the yield change vector to obtain a first change, wherein the first change is a linear combination of the key interest rate duration vector and the yield change vector, used to represent the relative change ratio of the target object due to the yield. Calculate the product between the benchmark valuation parameter and the first change to obtain the absolute change value; The real-time valuation result is obtained by superimposing the benchmark valuation parameter with the absolute change value.

6. The method according to claim 4, characterized in that, Based on the event type estimated by the parameters, a vectorized calculation operation is performed on the target calculation benchmark data to generate the real-time estimation result, including: When the event type of the parameter estimation event is an object transaction event, the yield difference of the target object is determined from the transaction data of the object transaction event, wherein the yield difference is the difference between the yield calculated based on the real-time transaction parameters of the target object and the yield value of the key term point corresponding to the target object in the benchmark yield curve; The duration effect value is obtained by multiplying the total duration parameter by the yield difference. The difference between the preset parameter and the duration effect value is calculated to obtain the correction coefficient; The real-time valuation result is obtained by calculating the product between the benchmark valuation parameter and the correction coefficient.

7. The method according to claim 1, characterized in that, After performing vectorized calculation operations on the target computational benchmark data based on the event type estimated according to the parameters to generate the real-time estimation result, the method further includes: The real-time valuation result is sent to the application service to instruct the application service to adjust the target calculation benchmark data based on the preset adjustment strategy and the real-time valuation result; Wherein, when the preset adjustment strategy indicates that the target calculation benchmark data can be adjusted, the benchmark estimation parameters in the target calculation benchmark data are updated to the real-time estimation results through the application service; If the preset adjustment strategy indicates that the target calculation benchmark data cannot be adjusted, the benchmark valuation parameters are adjusted through the application service according to the time period for generating the real-time valuation results.

8. The method according to claim 1, characterized in that, After performing vectorized calculation operations on the target computational benchmark data based on the event type estimated according to the parameters to generate the real-time estimation result, the method further includes: In response to a periodic scheduling instruction, a recalculation of the valuation of the target object is triggered, wherein the periodic scheduling instruction is determined based on the object type of the target object or the event type of the parameter estimation event.

9. A system for generating real-time valuation results, characterized in that, This includes interconnected computational and estimation components, among which, The computing component is used to perform full valuation calculations on all objects in the object set according to a preset time period to generate a calculation benchmark data set. The objects included in the object set are all objects to be valued, the target object is any object in the object set, and the calculation benchmark data set includes target calculation benchmark data. The estimation component is configured to generate a parameter estimation event for a target object, wherein the parameter estimation event is used to request the generation of a real-time valuation result for the target object; in response to the parameter estimation event, to retrieve the target calculation benchmark data of the target object from a pre-stored data cache, wherein the target calculation benchmark data is used to represent the valuation benchmark parameters and risk sensitivity parameters of the target object determined within a historical calculation period; and to perform a vectorized calculation operation on the target calculation benchmark data according to the event type of the parameter estimation event to generate the real-time valuation result.

10. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the method described in any one of claims 1 to 8.

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