Method, device, storage medium and electronic equipment for processing financial risk data

Abstract

The application discloses a financial risk data processing method and device, a storage medium and an electronic device, and relates to the fields of financial technology and information security. The method comprises the following steps: determining a system risk value and a regulation parameter from financial risk data of a financial transaction system; determining an inertia factor and a learning factor which change with the iteration of a particle swarm in the iteration process of the particle swarm; and determining a target control coefficient for the financial transaction system according to the minimum value of a target function. The application solves the problem that the analysis result of the financial risk data is inaccurate due to the defects that the particle swarm algorithm is prone to fall into local optimization and has extremely slow convergence speed in the later period when the particle swarm algorithm is used to analyze the financial risk data, and further causes that the risk factors of the financial transaction system cannot be timely and accurately checked out, and the transaction risk is uncontrollable.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of financial technology and the field of information security, in particular, to a financial risk data processing method and device, a storage medium and an electronic equipment. BACKGROUND

[0002] Financial risk refers to the possibility that a financial subject suffers economic losses due to its own or external factors, including credit risk, market risk, liquidity risk, operational risk, legal risk, and operational risk. Each of the above risks has complexity, uncertainty and randomness, and has a significant impact on the financial market and financial institutions, so the demand for risk management in the financial industry is increasingly urgent.

[0003] At present, there are three kinds of risk assessment models commonly used in the financial industry: decision tree, support vector machine and deep learning neural network prediction method. And in solving the optimal solution of the model, there are many algorithms in the industry, among which the particle swarm algorithm has the advantages of easy convergence, fewer related parameters, less restriction on target function, etc., and is suitable for use in financial risk system solving. But when using the particle swarm algorithm to analyze the financial risk data in the prior art, due to the defects that the particle swarm algorithm is easy to fall into local optimum and the convergence speed is extremely slow in the later period, the analysis result of the financial risk data is not accurate, which further causes the risk factors of the financial transaction system cannot be timely and accurately checked out, resulting in the situation that the transaction risk is uncontrollable.

[0004] In view of the above problems, no effective solution has been proposed so far. SUMMARY

[0005] The main purpose of the present application is to provide a financial risk data processing method and device, a storage medium and an electronic equipment, to at least solve the technical problem that the analysis result of the financial risk data is not accurate when using the particle swarm algorithm to analyze the financial risk data in the prior art due to the defects that the particle swarm algorithm is easy to fall into local optimum and the convergence speed is extremely slow in the later period.

[0006] To achieve the above object, according to an aspect of the present application, a method for processing financial risk data is provided, comprising: determining a system risk value and a regulation parameter from financial risk data of a financial transaction system, wherein the system risk value represents a risk value of the financial transaction system caused by data disturbance, and the regulation parameter is used to adjust the risk value; determining an inertia factor and a learning factor which change with the iteration of a particle swarm during the iteration process of the particle swarm, wherein the particle swarm is used to determine a minimum value of an objective function according to the inertia factor and the learning factor, the inertia factor is a non-linearly changing value, the learning factor is a value which changes according to the evolution speed and the aggregation degree of the particle swarm, and the objective function is used to determine the volatility of the system risk value; determining a target control coefficient for the financial transaction system according to the minimum value of the objective function, wherein the target control coefficient is used as a reference threshold for the financial institution when regulating the financial transaction system.

[0007] Optionally, the determining of the system risk value from the financial risk data of the financial transaction system comprises: determining a first type risk value and a second type risk value from the financial risk data, wherein the first type risk value represents a transaction risk value caused by data disturbance within the financial transaction system and / or a transaction risk value caused by data disturbance outside the financial transaction system, and the second type risk value represents a transaction risk value derived from the first type risk value; and taking the first type risk value and the second type risk value as the system risk value.

[0008] Optionally, after the determining of the system risk value and the regulation parameter from the financial risk data of the financial transaction system, the method further comprises: setting the number of particles in the particle swarm, the current position of each particle, the target position of each particle, the target position of the particle swarm, the flight speed of each particle in the search space, and the upper limit value and the lower limit value of the flight speed, wherein each particle represents a process for solving the objective function, the current position of each particle represents the solving progress of the particle for the objective function, the target position of each particle represents the minimum value of the particle for the objective function, the target position of the particle swarm represents the minimum value of all particles in the particle swarm for the objective function, and the flight speed of each particle represents the solving speed of the particle for the objective function.

[0009] Optionally, the determining of the inertia factor which changes with the iteration of the particle swarm during the iteration process of the particle swarm comprises: obtaining a maximum iteration number, a maximum inertia factor, a minimum inertia factor and a random speed parameter set for the particle swarm; and determining the inertia factor corresponding to each particle in the particle swarm at the current iteration number according to the current iteration number of the particle swarm, the maximum iteration number, the maximum inertia factor, the minimum inertia factor and the random speed parameter.

[0010] Optionally, during the iteration of the particle swarm, a learning factor varying with the iteration of the particle swarm is determined, comprising: when the particle swarm is in the nth iteration, determining the corresponding aggregation parameter and speed parameter of the particle swarm in the nth iteration, wherein n is an integer greater than 1, the aggregation parameter is used to quantify the aggregation degree of the particle swarm, and the speed parameter is used to quantify the iteration speed of the particle swarm; and determining the corresponding learning factor of the particle swarm in the nth iteration according to the initialized learning factor, the corresponding aggregation parameter and speed parameter of the particle swarm in the nth iteration, the weight value corresponding to the aggregation parameter, and the weight value corresponding to the speed parameter.

[0011] Optionally, when the particle swarm is in the nth iteration, the corresponding speed parameter of the particle swarm in the nth iteration is determined, comprising: when the particle swarm is in the nth iteration, taking the solution value of the target function of the xth particle in the particle swarm as a first value, wherein the xth particle is any particle in the particle swarm; taking the solution value of the target function of the xth particle in the particle swarm when the particle swarm is in the (n-1)th iteration as a second value; determining the minimum value and the maximum value in the first value and the second value; and calculating the ratio of the minimum value and the maximum value in the first value and the second value to obtain the corresponding speed parameter of the particle swarm in the nth iteration.

[0012] Optionally, when the particle swarm is in the nth iteration, the corresponding aggregation parameter of the particle swarm in the nth iteration is determined, comprising: after the particle swarm is in the nth iteration, obtaining the solution value of the target function of each particle in the particle swarm in the iteration process, and calculating the average value of all solution values corresponding to all particles; when the particle swarm is in the nth iteration, taking the solution value of the target function of the xth particle in the particle swarm as a first value, wherein the xth particle is any particle in the particle swarm; determining the maximum value and the minimum value in the average value and the first value; and calculating the ratio of the minimum value and the maximum value in the average value and the first value to obtain the corresponding aggregation parameter of the particle swarm in the nth iteration.

[0013] To achieve the above object, according to another aspect of the present application, there is provided a financial risk data processing device, comprising: a first determining unit configured to determine a system risk value and a regulation parameter from financial risk data of a financial transaction system, wherein the system risk value represents a risk value of the financial transaction system caused by data interference, and the regulation parameter is used to adjust the risk value; a second determining unit configured to determine an inertia factor and a learning factor which change with the iteration of a particle swarm during the iteration process of the particle swarm, wherein the particle swarm is used to determine a minimum value of an objective function according to the inertia factor and the learning factor, the inertia factor is a non-linearly changing value, the learning factor is a value which changes according to the evolution speed and the aggregation degree of the particle swarm, and the objective function is used to determine the volatility of the system risk value; and a third determining unit configured to determine a target control coefficient for the financial transaction system according to the minimum value of the objective function, wherein the target control coefficient is used as a reference threshold for the financial institution when regulating the financial transaction system.

[0014] According to another aspect of the present application, there is also provided an electronic device comprising one or more processors and a memory, the memory being configured to store one or more programs, wherein the one or more programs, when executed by the one or more processors, cause the one or more processors to perform the above-mentioned financial risk data processing method.

[0015] According to another aspect of the present application, there is also provided a computer-readable storage medium comprising a stored executable program, wherein the executable program, when executed, controls a device in which the computer-readable storage medium is located to perform the above-mentioned financial risk data processing method.

[0016] According to another aspect of the present application, there is also provided a computer program product comprising computer instructions which, when executed by a processor, implement the steps of the above-mentioned financial risk data processing method.

[0017] In the present application, first, a system risk value and a regulation parameter are determined from financial risk data of a financial transaction system, wherein the system risk value represents a risk value of the financial transaction system caused by data interference, and the regulation parameter is used to adjust the risk value, then, an inertia factor and a learning factor which change with the iteration of a particle swarm are determined during the iteration process of the particle swarm, wherein the particle swarm is used to determine a minimum value of an objective function according to the inertia factor and the learning factor, the inertia factor is a non-linearly changing value, the learning factor is a value which changes according to the evolution speed and the aggregation degree of the particle swarm, and the objective function is used to determine the volatility of the system risk value, and finally, a target control coefficient for the financial transaction system is determined according to the minimum value of the objective function, wherein the target control coefficient is used as a reference threshold for the financial institution when regulating the financial transaction system.

[0018] From the above, the application firstly extracts key risk data from the financial transaction system, and analyzes the system risk value and the control parameter. The system risk value comprehensively reflects the risk of the financial transaction system caused by internal and external factors, and the control parameter is used as a lever to adjust the risk value. The optimization of the control parameter is the key of the application.

[0019] Subsequently, the application dynamically adjusts the inertia factor and the learning factor in the framework of the particle swarm algorithm to adapt to the changing environment in the iteration process. The inertia factor adopts a nonlinear change strategy, which better balances the global search and the local search, ensures efficient exploration in the early iteration, and fine convergence in the later iteration, avoiding falling into local optimum. The dynamic adjustment of the learning factor is based on the evolution speed and the aggregation degree of the particles. By introducing the speed parameter and the aggregation parameter, the search range and the convergence speed of the particle swarm are accurately controlled, ensuring the flexibility and stability of the algorithm in the iteration process.

[0020] In addition, the particle swarm algorithm is used to calculate the minimum value of the objective function, which is directly related to the risk volatility of the financial system. The process of finding the minimum value is essentially to find the best target control coefficient in the control parameter space to achieve the goal of minimizing financial risk.

[0021] Therefore, by using the technical solution of the application, the nonlinear system model of financial risk is established, and the particle swarm algorithm is optimized, so as to solve the problems of the particle swarm algorithm, such as easy to fall into local optimum and slow convergence speed in the later period. The improved particle swarm algorithm is applied to the financial risk nonlinear system model to improve the prevention and control ability of financial risk, thereby solving the technical problem that the analysis result of financial risk data is inaccurate due to the defects of the particle swarm algorithm, such as easy to fall into local optimum and slow convergence speed in the later period. BRIEF DESCRIPTION OF DRAWINGS

[0022] The drawings described herein are used to provide a further understanding of the application, and constitute a part of the application. The illustrative embodiments of the application and their descriptions are used to explain the application, and do not constitute an improper limitation on the application. In the drawings:

[0023] Figure 1 A hardware structure block diagram of a computer terminal for implementing the processing method of financial risk data is shown;

[0024] Figure 2 is a flowchart of an optional processing method of financial risk data according to an embodiment of the application;

[0025] Figure 3is a flowchart of an optional improved particle swarm algorithm according to an embodiment of the present application applied in a financial risk model;

[0026] Figure 4 is a schematic diagram of a financial risk data processing device according to an embodiment of the present application;

[0027] Figure 5 is a structural block diagram of an electronic device according to an embodiment of the present application. DETAILED DESCRIPTION

[0028] In order 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 described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor should fall within the scope of protection of the present application.

[0029] It should be noted that the terms "first", "second", and the like in the specification and claims of the present application and the above-described drawings are used to distinguish similar objects, and do not necessarily have to describe a specific order or sequence. It should be understood that the data thus used can be interchanged under appropriate circumstances, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion, for example, a process, method, system, product or device including a series of steps or units does not have to be limited to only those steps or units clearly listed, but can include other steps or units not clearly listed or inherent to the process, method, product or device.

[0030] It should also be noted that the information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data for display, analyzed data, etc.) involved in the present application are information and data authorized by the user or authorized by all parties, and the collection, storage, use, processing, transmission, provision, disclosure and application of related data comply with relevant laws, regulations and standards, take necessary security measures, do not violate public order and good customs, and provide corresponding operation portal for user selection authorization or refusal. For example, the system and related users or institutions are provided with an interface to provide the user with a corresponding operation portal for the user to choose to agree or refuse the automatic decision result; if the user chooses to refuse, the expert decision process is entered.

[0031] According to the embodiment of the present application, a method embodiment of a financial risk data processing method is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although the logical order is shown in the flowchart, in some cases, the steps shown or described herein can be executed in an order different from that shown herein.

[0032] It should be noted that a financial risk data processing system can be used as an execution subject of the financial risk data processing method of the embodiment of the present application. It can be understood that the financial risk data processing method provided by the embodiment of the present application can also be used as an execution subject by other systems or devices, which is not limited in the embodiment of the present application.

[0033] The method embodiment provided by the embodiment of the present application can be executed in a mobile terminal, a computer terminal or a similar computing device. Figure 1 A hardware structure block diagram of a computer terminal (or mobile device) for implementing the financial risk data processing method is shown. As shown in Figure 1 The computer terminal 10 (or mobile device) can include one or more processors 102 (the processor 102 can include but is not limited to a microprocessor MCU or a programmable logic device FPGA processing device), a memory 104 for storing data, and a transmission device 106 for communication function. In addition, it can also include a display, an input / output interface (I / O interface), a universal serial bus (USB) port (which can be included as one of the ports of the BUS bus), a network interface, a power supply and / or a camera. Those skilled in the art can understand that Figure 1 The structure shown is only schematic, which does not limit the structure of the above-mentioned electronic device. For example, the computer terminal 10 can include more or less components than those shown in Figure 1 or have a different configuration than that shown in Figure 1 .

[0034] It should be noted that the one or more processors 102 and / or other data processing circuits described above can be referred to as "data processing circuits" herein. The data processing circuit can be embodied in whole or in part as software, hardware, firmware or any combination thereof. In addition, the data processing circuit can be a single independent processing module, or any one of the other elements combined into the computer terminal 10 (or mobile device) in whole or in part. As the financial risk data processing method involved in the embodiment of the present application, the data processing circuit is used as a processor control (for example, the selection of the variable resistance terminal path connected with the interface).

[0035] The memory 104 can be used to store software programs of application software and modules, such as program instructions / data storage means corresponding to the financial risk data processing method in the embodiments of the present application. The processor 102 executes various functional applications and data processing by running the software programs and modules stored in the memory 104, i.e., implements the financial risk data processing method described above. The memory 104 can include a high-speed random access memory, and can also include a non-volatile memory, such as one or more magnetic storage devices, flash memories, or other non-volatile solid-state memories. In some examples, the memory 104 can further include memories remotely arranged with respect to the processor 102, which can be connected to the computer terminal 10 through a network. Examples of the network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0036] The transmission device 106 is used to receive or send data via a network. Specific examples of the network can include a wireless network provided by a communication provider of the computer terminal 10. In one example, the transmission device 106 includes a network adapter (NIC), which can be connected to other network devices through a base station so as to communicate with the Internet. In one example, the transmission device 106 can be a radio frequency (RF) module, which is used to communicate with the Internet in a wireless manner.

[0037] The display can be, for example, a touch screen type liquid crystal display (LCD), which can enable a user to interact with the user interface of the computer terminal 10 (or mobile device).

[0038] Under the above operating environment, the present application provides a financial risk data processing method as shown in Figure 2 Figure 2 is a flowchart of an optional financial risk data processing method according to the embodiments of the present application. As shown in Figure 2 , the method includes the following steps:

[0039] Step S201: determining a system risk value and a regulation parameter from financial risk data of a financial transaction system.

[0040] In step S201, the system risk value represents a risk value of the financial transaction system caused by data disturbance, and the regulation parameter is used to adjust the risk value.

[0041] Optionally, the system risk value maps the risk degree of the financial transaction system in the face of internal and external data disturbance, and is a key indicator for evaluating the robustness of the financial system.

[0042] ​Optionally, the definition of the regulatory parameters aims to provide a flexible adjustment mechanism for the management and control of financial risks, ensuring that the financial system can effectively respond to risks and maintain stable operation in a dynamic environment.

[0043] Optionally, the system risk value mainly includes X and y, wherein X represents the total system risk value under the impact of external factors in the first stage, and y represents the total system risk value of the internal transmission effect of the system in the second stage.

[0044] Optionally, the regulatory parameters mainly include z, which represents the regulation of system risk by each financial institution in the third stage.

[0045] Optionally, the financial risk nonlinear system model used is as follows: X: the total system risk value under the impact of external factors in the first stage; y: the total system risk value of the internal transmission effect of the system in the second stage; z: the regulation of system risk by each financial institution in the third stage; a and b represent the risk degree of the first two stages; c represents the control strength in the third stage, a, b∈R + , c>1.

[0046]

[0047] From formula (1), it can be found that when the regulation strength c of each financial institution on the system risk decreases, the system will gradually lose control and enter a chaotic state, affecting the operation of the system.

[0048] In step S202, the inertia factor and the learning factor changing with the continuous iteration of the particle swarm are determined in the iteration process of the particle swarm.

[0049] In step S202, the particle swarm is used to determine the minimum value of the objective function according to the inertia factor and the learning factor, the inertia factor is a nonlinear changing value, the learning factor is a value changing according to the evolution speed and aggregation degree of the particle swarm, and the objective function is used to determine the volatility of the system risk value.

[0050] Optionally, the financial risk data processing system adjusts the core parameters of the particle swarm algorithm, namely the inertia factor and the learning factor, in a timely manner through a dynamic iteration mechanism, ensuring that the algorithm continuously evolves in the iteration process. In the iteration process of the particle swarm, the nonlinear change characteristic of the inertia factor is combined with the dynamic adjustment strategy of the learning factor to form a unique parameter optimization scheme.

[0051] Optionally, with the deepening of the iteration process, the inertia and learning behavior of particle motion are gradually tuned to optimize the search process, avoid local optimal trap, and promote the discovery of global optimal solution.

[0052] Optionally, the dynamic adjustment strategy of the inertia factor ensures that the movement of the particles in the search space is guided by the historical trajectory and can properly explore new areas, thereby accelerating the exploration speed in the early iterations and fine-tuning the search strategy in the later iterations to improve the convergence quality.

[0053] Optionally, the learning factor changes according to the evolution speed and aggregation degree of the particle swarm, dynamically balances the contributions of individual learning and group experience, and ensures the flexibility and efficiency of the algorithm throughout the iteration process.

[0054] Optionally, the objective function L of the total risk value X takes the root mean square and maximum value of X as the objective function, and the formula is as follows:

[0055] L = p1rms(X) + p2max(X) (2)

[0056] Where rms represents the mean square error of X, max represents the maximum value of X, and p1, p2 are weighting coefficients. That is, the minimum value of the L function is equivalent to the minimum value of the financial risk system. In the case of fixed risk degree parameters a, b, the appropriate c value is solved by the particle swarm algorithm to realize the minimum solution of the L function.

[0057] Step S203, determining the target control coefficient for the financial transaction system according to the minimum value of the objective function.

[0058] In step S203, the target control coefficient is used as a reference threshold for the financial institution when regulating the financial transaction system.

[0059] Optionally, the financial risk data processing system accurately solves the minimum value of the objective function by the particle swarm algorithm. Based on the solution, the target control coefficient for the specific financial transaction system can be scientifically determined.

[0060] Optionally, the target control coefficient is used as a reference threshold for the financial institution when regulating the financial transaction system, and comprehensively reflects the optimal strategy of risk regulation of the financial transaction system.

[0061] As can be seen from the content of steps S201 to S203, in the embodiment of the present application, first, the system risk value and the regulation parameter are determined from the financial risk data of the financial transaction system, wherein the system risk value represents the risk value of the financial transaction system caused by data interference, and the regulation parameter is used to adjust the risk value; then, in the iteration process of the particle swarm, the inertia factor and the learning factor changing with the iteration of the particle swarm are determined, wherein the particle swarm is used to determine the minimum value of the objective function according to the inertia factor and the learning factor, the inertia factor is a non-linearly changing value, the learning factor is a value changing according to the evolution speed and the aggregation degree of the particle swarm, and the objective function is used to determine the volatility of the system risk value; and then, the target control coefficient for the financial transaction system is determined according to the minimum value of the objective function, wherein the target control coefficient is used as a reference threshold for the financial institution when regulating the financial transaction system.

[0062] As can be seen from the above content, the present application first extracts key risk data from the financial transaction system, and analyzes the system risk value and the regulation parameter. The system risk value comprehensively reflects the risk of the financial transaction system caused by internal and external factor interference, and the regulation parameter is used as a lever to adjust the risk value, and the optimal selection thereof is the key of the present application.

[0063] Subsequently, the inertia factor and the learning factor are dynamically adjusted within the framework of the particle swarm algorithm, so as to adapt to the changing environment in the iteration process of the algorithm. The inertia factor adopts a non-linear change strategy, which better balances the global search and the local search, ensures efficient exploration in the early iteration stage, and fine convergence in the later iteration stage, and avoids falling into local optimum. The dynamic adjustment of the learning factor is according to the evolution speed and the aggregation degree of the particle, and by introducing the speed parameter and the aggregation parameter, the search range and the convergence speed of the particle swarm are accurately controlled, and the flexibility and stability of the algorithm in the iteration process are ensured.

[0064] In addition, the particle swarm algorithm is used to calculate the minimum value of the objective function, which is directly related to the risk volatility of the financial system. The process of finding the minimum value is essentially to find the best target control coefficient in the regulation parameter space, so as to achieve the goal of minimizing the financial risk.

[0065] Therefore, by the technical scheme of the present application, the purpose of establishing a financial risk nonlinear system model and optimizing the particle swarm algorithm is achieved, so as to solve the problems that the particle swarm algorithm is easy to fall into local optimum and has extremely slow convergence speed in the later stage, and the improved particle swarm algorithm is applied to the financial risk nonlinear system model to improve the prevention and control ability of the financial risk, thereby solving the technical problem that the analysis result of the financial risk data is inaccurate due to the defects of the particle swarm algorithm being easy to fall into local optimum and having extremely slow convergence speed in the later stage when the particle swarm algorithm is used to analyze the financial risk data in the prior art.

[0066] In an alternative embodiment, the financial risk data processing system first determines a first type of risk value and a second type of risk value from the financial risk data, wherein the first type of risk value represents a transaction risk value caused by internal data disturbance of the financial transaction system and / or a transaction risk value caused by external data disturbance of the financial transaction system, and the second type of risk value represents a transaction risk value derived from the first type of risk value, and then takes the first type of risk value and the second type of risk value as the system risk value.

[0067] Optionally, the financial risk data processing system distinguishes and determines two types of risk values, i.e. the first type of risk value (X) and the second type of risk value (y), from the financial risk data.

[0068] Optionally, the first type of risk value (X) represents the total system risk value under the impact of external factors in the first stage, and the second type of risk value (y) represents the total system risk value under the impact of internal transmission effect in the second stage.

[0069] Optionally, the system risk value is taken as a basic unit for risk assessment of the financial transaction system, covering the risk quantification index caused by internal data disturbance of the system and the influence of external data fluctuation on transaction stability, and the two aspects of risk value together constitute a comprehensive description of the inherent risk of the financial transaction system.

[0070] Optionally, for the financial risk nonlinear system model, for example, when a = 4 and b = 8, the total system risk value x changes with the control effort parameter c, and it can be concluded that when c gradually decreases, the financial system enters an out-of-control state. And for example, when c is in [4.5, 7], the financial risk system enters a relatively stable motion and is in a relatively stable state, so it is particularly important to solve the appropriate value of c for the stable operation of the financial risk system.

[0071] From the above, it can be seen that the financial risk data processing system can obtain a comprehensive and fine risk assessment system by determining the first type of risk value and the second type of risk value, and taking the first type of risk value and the second type of risk value as the system risk value, which reflects the multi-dimensional characteristics of the financial transaction system risk and also provides a solid data foundation for subsequent risk management and control strategies.

[0072] In an alternative embodiment, after determining the system risk value and the regulatory parameter from the financial risk data of the financial transaction system, the financial risk data processing system sets the number of particles in the particle swarm, the current position of each particle, the target position of each particle, the target position of the particle swarm, the flight speed of each particle in the search space, and the upper and lower limits of the flight speed, wherein each particle represents a process for solving the objective function, the current position of each particle represents the progress of the particle in solving the objective function, the target position of each particle represents the minimum value of the particle in solving the objective function, and the target position of the particle swarm represents the minimum value of all particles in the particle swarm in solving the objective function.

[0073] Optionally, the financial risk data processing system sets the size of the particle swarm, i.e. the number of particles. The determination of the number of particles needs to be based on a deep understanding of the financial risk data and its characteristics, to ensure the efficiency and effectiveness of the algorithm.

[0074] Optionally, the financial risk data processing system initializes the position of each particle in the high-dimensional search space, which represents the initial exploration state of the particle in the search process.

[0075] Optionally, the financial risk data processing system defines the individual best position of each particle and the group best position, which represent the optimal objective function values reached by the individual and the group in each iteration, respectively, and play a decisive role in guiding the search direction and optimization target of the particle swarm.

[0076] Optionally, there is an introduction example for the particle swarm algorithm: assuming that the particle swarm contains M particles, the current position of particle i is x i , the optimal position of particle i is Pbest i , the optimal position of the particle swarm is Gbest, and the flight speed of particle i in the search space is v i To prevent the particle speed from being too large to exceed the set search range, the particle speed can be set to satisfy -v max <=v<=v max If it exceeds the set range, take the boundary value, and the iteration number is n.

[0077] The current optimal position of particle i is shown in formula (3):

[0078]

[0079] The current optimal position of the entire particle swarm is shown in formula (4):

[0080] Gbest(n) = min{Pbest1(n), Pbest2(m),..., Pbest M (n)} (4)

[0081] The velocity and position update formula of the particle i in the nth generation is shown in formula (5):

[0082] v i (n) = ωv i (n-1) + c1r1(Pbest i -x i (n-1)) + c2r2(Gbest-x i (n-1))

[0083] x i (n) = x i (n-1) + v i (n) (5)

[0084] Where ω is an inertia factor, c1 and c2 are learning factors, and r1 and r2 are random numbers between 0 and 1.

[0085] From the above, the financial risk data processing system deeply analyzes the initialization parameter setting logic of the particle swarm algorithm and the role of each particle in dynamically solving the minimum value of the objective function, can intelligently and efficiently explore the financial risk regulation parameter space, find the optimal solution that makes the objective function reach the minimum value, and thus provide accurate risk management strategies for financial institutions.

[0086] In an optional embodiment, the financial risk data processing system first acquires the maximum number of iterations, the maximum inertia factor, the minimum inertia factor and the random velocity parameter set for the particle swarm, and then determines the inertia factor corresponding to each particle in the particle swarm at the current number of iterations according to the current number of iterations, the maximum number of iterations, the maximum inertia factor, the minimum inertia factor and the random velocity parameter of the particle swarm.

[0087] Optionally, the maximum number of iterations is based on the evaluation of the problem complexity and the consideration of the computing resources, aiming to balance the convergence speed and solution accuracy of the algorithm.

[0088] Optionally, the maximum inertia factor and the minimum inertia factor together constitute the adjustment range of the inertia factor, reflecting the change trend of the dependence on historical information and the exploration intensity of the current direction of the particle in the search process.

[0089] Optionally, there is a method of dynamically adjusting the inertia factor and the learning factor, so that the algorithm can find better points in the early iterations, and quickly converge, and in the later iterations, can maintain the diversity of the population, and expand the possibility of searching for other optimal solutions as much as possible. The value of the inertia factor ω is improved to be nonlinear, which ensures that the algorithm can more effectively seek the optimal solution, as shown in formula (6):

[0090]

[0091] where t represents the number of current iterations, T represents the maximum number of iterations, ω max represents the set maximum inertia factor, ω min represents the set minimum inertia factor, and r is a random speed parameter, which is generally set to 2. It can be found that the inertia factor changes nonlinearly, and the speed slows down significantly, which improves the search ability of the algorithm in the whole cycle.

[0092] From the above, it can be seen that the financial risk data processing system determines the inertia factor corresponding to each particle in the particle swarm at the current iteration number according to the current iteration number of the particle swarm, the maximum iteration number, the maximum inertia factor, the minimum inertia factor and the random speed parameter, so that the particle swarm algorithm can dynamically adjust the search strategy and continuously optimize the solution process of the target function.

[0093] In an optional embodiment, the financial risk data processing system first determines the aggregation parameter and the speed parameter corresponding to the particle swarm in the nth iteration when the particle swarm performs the nth iteration, where n is an integer greater than 1, the aggregation parameter is used to quantify the aggregation degree of the particle swarm, and the speed parameter is used to quantify the iteration speed of the particle swarm, and then determines the learning factor corresponding to the particle swarm in the nth iteration according to the initialized learning factor, the aggregation parameter and the speed parameter corresponding to the particle swarm in the nth iteration, the weight value corresponding to the aggregation parameter, and the weight value corresponding to the speed parameter.

[0094] Optionally, the financial risk data processing system calculates and determines two key parameters reflecting the dynamic characteristics of the particle swarm: the aggregation parameter and the speed parameter.

[0095] Optionally, the aggregation parameter (denoted as k a ) quantifies the concentration of the particle swarm in the search space, reveals the exploration and convergence state of the group in the iteration process, and helps to accurately judge the diversity and information exchange efficiency of the particle swarm.

[0096] Optionally, the speed parameter (denoted as k v ) quantifies the iteration speed of the particle swarm, i.e., the speed of the whole particle swarm moving in the search space, and provides real-time insight into the convergence dynamics and search efficiency of the algorithm.

[0097] Optionally, the learning factor can be adjusted in time according to the particle evolution speed and the aggregation degree, so that the algorithm can find the optimal solution more efficiently.

[0098] Optionally, the optimization formula of the learning factor is shown in formula (7):

[0099] c1(n)=c2(n)=c start -k a c a +k v c v (7)

[0100] where c start is the initialized learning factor, c a , c v are constant parameters for adjusting the aggregation parameter k a and the aggregation parameter k a , respectively, and here can be set to 2.

[0101] As can be seen from the above, the financial risk data processing system determines the learning factor corresponding to the particle swarm in the nth iteration according to the initialized learning factor, the aggregation parameter and the speed parameter corresponding to the particle swarm in the nth iteration, the weight value corresponding to the aggregation parameter, and the weight value corresponding to the speed parameter, which promotes the global search ability of the particle swarm algorithm to play in the early iteration, also ensures that the algorithm maintains sufficient population diversity and convergence stability in the later iteration, and avoids the common trap of falling into local optimum.

[0102] In an optional embodiment, the financial risk data processing system first takes the solution value of the xth particle in the particle swarm to the target function as a first value when the particle swarm performs the nth iteration, where the xth particle is any particle in the particle swarm, then takes the solution value of the xth particle in the particle swarm to the target function as a second value when the particle swarm performs the (n-1)th iteration, then determines the minimum value and the maximum value of the first value and the second value, and finally calculates the ratio of the minimum value and the maximum value of the first value and the second value to obtain the speed parameter corresponding to the particle swarm in the nth iteration.

[0103] Optionally, when the particle swarm enters the nth iteration period, the financial risk data processing system monitors and analyzes the solution state of the individual particle in real time. Any particle in the particle swarm, i.e. the xth particle, is selected, and the current solution value of the particle to the target function is recorded and evaluated, and this value is defined as a first value.

[0104] Optionally, in the (n-1)th iteration, the solution value of the xth particle to the target function is recorded and defined as a second value.

[0105] Optionally, for the speed parameter k v, defined as formula (8) :

[0106]

[0107] Wherein, L[x(n)] represents the solution value of the xth particle in the particle swarm to the objective function (i.e. the first value) when the particle swarm performs the nth iteration; L[x(n-1)] represents the solution value of the xth particle in the particle swarm to the objective function (i.e. the second value) when the particle swarm performs the (n-1)th iteration.

[0108] As can be seen from the formula, when the nth generation and the (n-1)th generation particles are far apart, k v The smaller the value is, the faster the particle evolution speed is, and the value of the learning factor should be reduced to keep the algorithm in a large search range. If the particle evolution speed is slow, the value of the learning factor should be expanded to reduce the search range and make the algorithm converge.

[0109] From the above, it can be seen that the financial risk data processing system provides direct guidance for dynamic adjustment of the algorithm in the later stage by calculating the speed parameter corresponding to the nth iteration, ensuring that the particle swarm algorithm can quickly converge to the potential optimal solution in the iteration process, and avoiding falling into local optimum too early, maintaining the diversity and dynamics of the population.

[0110] In an alternative embodiment, the financial risk data processing system first obtains the solution value of each particle in the particle swarm to the objective function in the iteration process after the particle swarm performs the nth iteration, and calculates the average of all solution values corresponding to all particles. Then, the solution value of the xth particle in the particle swarm to the objective function is taken as the first value when the particle swarm performs the nth iteration, wherein the xth particle is any particle in the particle swarm. Then, the maximum and minimum values of the average and the first value are determined. Finally, the ratio of the minimum and maximum values of the average, the first value is calculated to obtain the aggregation parameter corresponding to the nth iteration of the particle swarm.

[0111] Optionally, the financial risk data processing system obtains the latest solution value of each particle in the particle swarm to the objective function, and then calculates the average of the solution values corresponding to all particles, which provides a key quantitative index for evaluating the overall solution efficiency and convergence state of the particle swarm.

[0112] Optionally, for the aggregation parameter k a , the formula is shown in formula (9) :

[0113]

[0114] Wherein, L avg(n) is the average value of the solution of the objective function of all particles in the nth iteration, which represents the solution value (i.e., the first numerical value) of the xth particle in the particle swarm to the objective function when the particle swarm performs the nth iteration.

[0115] As can be seen from the formula, if the particles are more concentrated, k a The greater the value is, in order to improve the population diversity, the value of the learning factor should be reduced, and the search ability of the algorithm in the later stage should be expanded, otherwise, the value of the learning factor should be increased, and the convergence speed of the algorithm should be accelerated.

[0116] From the above, it can be seen that the financial risk data processing system provides an important basis for dynamic adjustment of the algorithm in the later stage by calculating the corresponding aggregation parameters of the particle swarm in the nth iteration, and ensures that the particle swarm algorithm can maintain population diversity and accelerate convergence to the optimal solution when solving complex financial risk problems.

[0117] From the above, it can be seen that the technical solution according to the present application can at least achieve the following technical effects:

[0118] 1. The nonlinear financial model fully considers the complex internal and external risks, as well as the mutual influence of internal and external risks, and can better predict the risk of the financial market, playing an important role in avoiding and controlling risks.

[0119] 2. The optimized particle swarm algorithm can quickly find the optimal value in the early stage of the algorithm, promoting the convergence speed of the algorithm, and still maintaining excellent search ability in the later stage of the algorithm, preventing the loss of population diversity.

[0120] 3. Combined with the optimized particle swarm algorithm, the appropriate control parameter c value can be accurately and quickly found for the nonlinear financial risk model, so that the financial risk system enters a stable motion.

[0121] In an alternative embodiment, Figure 3 An alternative improved particle swarm algorithm according to an embodiment of the present application is shown in a flowchart of application in a financial risk model, which improves the values of the inertia factor and the learning factor of the particle swarm algorithm, strengthens the search ability of the algorithm in the early stage, improves the convergence ability, guarantees the population diversity in the later stage of the algorithm, and prevents the algorithm from falling into local optimum. The improved particle swarm algorithm is applied in the financial risk model to solve the appropriate control parameter c, so that the total risk value is minimized. The main process is as follows:

[0122] Step one, first initialize the population, wherein each c value represents a particle, and initialize the population size, speed, position, and Pbest and Gbest.

[0123] Step two, calculate the value of each particle in the objective function L, and update the historical optimal position of each particle, update the historical optimal position of the whole population.

[0124] Step three, update the speed and position of each particle.

[0125] Step four, according to whether the particle swarm reaches the maximum iteration number, if not, return to step two, continue to calculate the value of each particle in the objective function L, and update the historical optimal position of each particle, update the historical optimal position of the whole population.

[0126] Step five, if the maximum iteration number is reached, end the algorithm and get the optimal c.

[0127] According to another aspect of the embodiment of the application, a financial risk data processing device is also provided, wherein, Figure 4 is a schematic diagram of an optional financial risk data processing device according to an embodiment of the application, as Figure 4 shown, the financial risk data processing device comprises a first determination unit 401, a second determination unit 402, and a third determination unit 403.

[0128] The first determination unit 401 determines a system risk value and a regulation parameter from the financial risk data of the financial transaction system, wherein the system risk value represents the risk value caused by data interference of the financial transaction system; the regulation parameter is used to adjust the risk value; the second determination unit 402 determines an inertia factor and a learning factor which change with the continuous iteration of the particle swarm in the iteration process of the particle swarm, wherein the particle swarm is used to determine the minimum value of the objective function according to the inertia factor and the learning factor, the inertia factor is a nonlinearly changing value, the learning factor is a value which changes according to the evolution speed and the aggregation degree of the particle swarm, and the objective function is used to determine the volatility of the system risk value; the third determination unit 403 determines a target control coefficient for the financial transaction system according to the minimum value of the objective function, wherein the target control coefficient is used as a reference threshold when the financial institution regulates the financial transaction system.

[0129] Optionally, the first determination unit 401 comprises a first determination subunit and a first processing subunit. The first determination subunit is used to determine a first type of risk value and a second type of risk value from the financial risk data, wherein the first type of risk value represents the transaction risk value caused by the internal data interference of the financial transaction system and / or the transaction risk value caused by the external data interference of the financial transaction system; the second type of risk value represents the transaction risk value derived from the first type of risk value; and the first processing subunit is used to take the first type of risk value and the second type of risk value as the system risk value.

[0130] Optionally, the financial risk data processing apparatus further comprises a processing unit configured to set a number of particles in the particle swarm, a current position of each particle, a target position of each particle, a target position of the particle swarm, a flight speed of each particle in the search space, and an upper limit value and a lower limit value of the flight speed, wherein each particle represents a process for solving the target function, the current position of each particle represents a progress of the particle in solving the target function, the target position of each particle represents a minimum value of the particle in solving the target function, the target position of the particle swarm represents minimum values of all particles in the particle swarm in solving the target function, and the flight speed of each particle represents a speed of the particle in solving the target function.

[0131] Optionally, the second determining unit 402 comprises a first obtaining subunit and a second determining subunit. The first obtaining subunit is configured to obtain the maximum iteration number, the maximum inertia factor, the minimum inertia factor, and the random speed parameter set for the particle swarm. The second determining subunit is configured to determine, according to the current iteration number, the maximum iteration number, the maximum inertia factor, the minimum inertia factor, and the random speed parameter of the particle swarm, the inertia factor corresponding to each particle in the particle swarm at the current iteration number.

[0132] Optionally, the second determining unit 402 comprises a third determining subunit and a fourth determining subunit. The third determining subunit is configured to determine, when the particle swarm performs the nth iteration, the aggregation parameter and the speed parameter corresponding to the particle swarm at the nth iteration, wherein n is an integer greater than 1, the aggregation parameter is used to quantitatively represent an aggregation degree of the particle swarm, and the speed parameter is used to quantitatively represent an iteration speed of the particle swarm. The fourth determining subunit is configured to determine, according to the initialized learning factor, the aggregation parameter and the speed parameter corresponding to the particle swarm at the nth iteration, a weight value corresponding to the aggregation parameter, and a weight value corresponding to the speed parameter, the learning factor corresponding to the particle swarm at the nth iteration.

[0133] Optionally, the third determining subunit comprises a first processing module, a second processing module, a first determining module, and a first calculating module. The first processing module is configured to take, when the particle swarm performs the nth iteration, a solution value of the xth particle in the particle swarm to the target function as a first value, wherein the xth particle is any particle in the particle swarm. The second processing module is configured to take, when the particle swarm performs the (n-1)th iteration, a solution value of the xth particle in the particle swarm to the target function as a second value. The first determining module is configured to determine minimum and maximum values in the first value and the second value. The first calculating module is configured to calculate a ratio of the minimum and maximum values in the first value and the second value to obtain the speed parameter corresponding to the particle swarm at the nth iteration.

[0134] Optionally, the third determining subunit includes: a first acquisition module, a third processing module, a second determining module, and a second calculation module. The first acquisition module is used to acquire the solution value of each particle in the particle swarm for the objective function during the current iteration after the nth iteration, and to calculate the average of all solution values ​​for all particles. The third processing module is used to take the solution value of the x-th particle in the particle swarm for the objective function as the first value during the nth iteration, where the x-th particle is any particle in the particle swarm. The second determining module is used to determine the maximum and minimum values ​​among the average value and the first value. The second calculation module is used to calculate the ratio of the average value, the minimum value, and the maximum value among the first values ​​to obtain the aggregation parameter of the particle swarm during the nth iteration.

[0135] Embodiments of this application may provide an electronic device. Figure 5 This is a structural block diagram of an electronic device according to an embodiment of this application. Figure 5 As shown, the electronic device may include: one or more ( Figure 5 Only one is shown in the diagram. The processor, memory, memory controller, and peripheral interface are connected to the radio frequency module, audio module, and display.

[0136] The memory can be used to store software programs and modules, such as the program instructions / modules corresponding to the methods and apparatus in the embodiments of this application. The processor executes various functional applications and data processing by running the software programs and modules stored in the memory, thereby implementing the above-described methods. The memory may include high-speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory may further include memory remotely located relative to the processor, and these remote memories can be connected to the terminal via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0137] Those skilled in the art will understand that Figure 5 The structure shown is for illustrative purposes only. Electronic devices can also be smartphones (such as Android phones, iOS phones, etc.), tablets, PDAs, mobile internet devices (MIDs), PADs, and other terminal devices. Figure 5 This does not limit the structure of the aforementioned electronic device. For example, electronic devices may also include components that are more... Figure 5 The more or fewer components shown (such as network interfaces, display devices, etc.), or having the same Figure 5 The different configurations shown.

[0138] Those skilled in the art can understand that all or part of the steps of the various methods in the above embodiments can be completed by instructing the terminal device related hardware through a program, and the program can be stored in a computer readable storage medium, and the storage medium can include a flash disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, etc.

[0139] According to another aspect of the present application, a computer readable storage medium is also provided, wherein the computer readable storage medium stores a computer program, and when the computer program runs, the computer readable storage medium makes the device where the computer readable storage medium is located execute the above-mentioned processing method of financial risk data.

[0140] According to another aspect of the present application, a computer program product is also provided, wherein the computer program product includes computer instructions, and when the computer instructions execute, the computer program product makes the device where the computer program product is located execute the above-mentioned processing method of financial risk data.

[0141] The above-mentioned embodiments or examples disclosed in the present application are not exhaustive, and only illustrate some embodiments or examples, and are not specific limitations on the protection scope of the present application. In the case of no contradiction, each step in a certain embodiment or example in the present application can be implemented as an independent example, and the steps can be combined arbitrarily, for example, the scheme after removing some steps in a certain embodiment or example can be implemented as an independent example, and the order of the steps in a certain embodiment or example can be exchanged arbitrarily, in addition, the optional mode or optional example in a certain embodiment or example can be combined arbitrarily; in addition, the embodiments or examples can be combined arbitrarily, for example, the steps of different embodiments or examples can be combined arbitrarily, a certain embodiment or example can be combined with the optional mode or optional example of other embodiments or examples.

[0142] The above-mentioned embodiment serial numbers of the present application are only for description, and do not represent the advantages or disadvantages of the embodiments.

[0143] In the above-mentioned embodiments of the present application, the description of each embodiment has its own emphasis, and the parts not described in detail in a certain embodiment can be referred to the related description of other embodiments.

[0144] In several embodiments provided in the present application, it should be understood that the disclosed technology can be implemented by other ways. Among them, the above-described device embodiments are only schematic, for example, the division of the units can be a logical function division, and actual implementation can have another division manner, for example, a plurality of units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between the displayed or discussed units can be indirect coupling or communication connection through some interfaces, units or modules, and can be electrical or other forms.

[0145] The units described as separate components can or can not be physically separate, and the components shown as units can or can not be physical units, that is, they can be located in one place or distributed to multiple units. Part or all of the units can be selected according to actual needs to achieve the purpose of the embodiment.

[0146] In addition, the functional units in each embodiment of the present application can be integrated in one processing unit, or each unit can exist physically, or two or more units can be integrated in one unit. The integrated unit can be realized in the form of hardware or in the form of a software functional unit.

[0147] The integrated unit, if realized in the form of a software functional unit and sold or used as an independent product, can be stored in a computer readable storage medium. Based on this understanding, the technical solutions of the present application essentially or the part that contributes to the prior art or the whole or part of the technical solutions can be embodied in the form of a software product. The computer software product is stored in a storage medium, including a plurality of instructions to make a computer device (which can be a personal computer, a server or a network device, etc.) execute all or part of the steps of the method described in each embodiment of the present application. The foregoing storage medium includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), mobile hard disk, magnetic disk or optical disk and various program codes that can be stored in the medium.

[0148] The above is only the preferred embodiment of the present application, and it should be pointed out that for ordinary skilled in the art, without departing from the principles of the present application, a number of improvements and refinements can be made, and these improvements and refinements should be considered as the protection scope of the present application.

Claims

1. A method of processing financial risk data, characterized by, The method comprises: determining a system risk value and a regulation parameter from financial risk data of a financial transaction system, wherein the system risk value represents a risk value of the financial transaction system caused by data interference, and the regulation parameter is used to adjust the risk value; in the iteration process of the particle swarm, determining an inertia factor and a learning factor that change with the continuous iteration of the particle swarm, wherein the particle swarm is used to determine the minimum value of the objective function according to the inertia factor and the learning factor, the inertia factor belongs to a nonlinearly changing value, the learning factor belongs to a value that changes according to the evolution speed and the aggregation degree of the particle swarm, and the objective function is used to determine the volatility of the system risk value; determining a target control coefficient for the financial transaction system according to the minimum value of the objective function, wherein the target control coefficient is used as a reference threshold for the financial institution when regulating the financial transaction system; wherein, in the iteration process of the particle swarm, determining the inertia factor that changes with the continuous iteration of the particle swarm comprises: obtaining the maximum iteration number, the maximum inertia factor, the minimum inertia factor and the random speed parameter set for the particle swarm; determining the inertia factor corresponding to each particle in the particle swarm at the current iteration number according to the current iteration number, the maximum iteration number, the maximum inertia factor, the minimum inertia factor and the random speed parameter; wherein, in the iteration process of the particle swarm, determining the learning factor that changes with the continuous iteration of the particle swarm comprises: determining the aggregation parameter and the speed parameter corresponding to the particle swarm at the nth iteration when the particle swarm performs the nth iteration, wherein n is an integer greater than 1, the aggregation parameter is used to quantify the aggregation degree of the particle swarm, and the speed parameter is used to quantify the iteration speed of the particle swarm; determining the learning factor corresponding to the particle swarm at the nth iteration according to the initialized learning factor, the aggregation parameter and the speed parameter corresponding to the particle swarm at the nth iteration, the weight value corresponding to the aggregation parameter, and the weight value corresponding to the speed parameter.

2. The method of processing financial risk data according to claim 1, wherein, Determining a system risk value from financial risk data of a financial transaction system comprises: determining a first risk value and a second risk value from the financial risk data, wherein the first risk value represents a transaction risk value caused by internal data interference of the financial transaction system and / or a transaction risk value caused by external data interference of the financial transaction system, and the second risk value represents a transaction risk value derived from the first risk value; taking the first risk value and the second risk value as the system risk value.

3. The method of processing financial risk data according to claim 2, wherein, After determining the system risk value and the regulation parameter from the financial risk data of the financial transaction system, the method further comprises: Set the number of particles in the particle swarm, the current position of each particle, the target position of each particle, the target position of the particle swarm, the flight speed of each particle in the search space and the upper and lower limits of the flight speed, wherein each particle represents a process for solving the objective function, the current position of each particle represents the progress of the particle for solving the objective function, the target position of each particle represents the minimum value of the particle for solving the objective function, and the target position of the particle swarm represents the minimum value of all particles in the particle swarm for solving the objective function.

4. The method of processing financial risk data according to claim 1, wherein, When the particle swarm is performing the nth iteration, the speed parameter corresponding to the nth iteration of the particle swarm is determined, including: When the particle swarm is performing the nth iteration, the solution value of the xth particle in the particle swarm to the objective function is taken as a first value, wherein the xth particle is any particle in the particle swarm; When the particle swarm is performing the nth iteration, the solution value of the xth particle in the particle swarm to the objective function is taken as a first value, wherein the xth particle is any particle in the particle swarm; Determine the minimum and maximum values of the first value and the second value; Calculate the ratio of the minimum and maximum values of the first value, the second value, the minimum value and the maximum value to obtain the speed parameter corresponding to the nth iteration of the particle swarm.

5. The method of processing financial risk data according to claim 1, wherein, When the particle swarm is performing the nth iteration, the aggregation parameter corresponding to the nth iteration of the particle swarm is determined, including: After the particle swarm performs the nth iteration, the solution value of each particle of the particle swarm to the objective function during the iteration process is obtained, and the average value of all solution values corresponding to all particles is calculated; When the particle swarm is performing the nth iteration, the solution value of the xth particle in the particle swarm to the objective function is taken as a first value, wherein the xth particle is any particle in the particle swarm; Determine the maximum and minimum values of the average value and the first value; Calculate the ratio of the minimum and maximum values of the average value, the minimum value and the maximum value to obtain the aggregation parameter corresponding to the nth iteration of the particle swarm.

6. An apparatus for processing financial risk data for executing the method for processing financial risk data according to any one of claims 1 to 5, characterized by Including: The first determination unit determines the system risk value and the control parameter from the financial risk data of the financial transaction system, wherein the system risk value represents the risk value caused by data interference of the financial transaction system; and the control parameter is used to adjust the risk value; The second determination unit determines the inertia factor and the learning factor which change with the continuous iteration of the particle swarm during the iteration process of the particle swarm, wherein the particle swarm is used to determine the minimum value of the objective function according to the inertia factor and the learning factor, the inertia factor is a nonlinear changing value, the learning factor is a value which changes according to the evolution speed and aggregation degree of the particle swarm, and the objective function is used to determine the volatility of the system risk value; A third determining unit is configured to determine a target control coefficient for the financial transaction system according to the minimum value of the target function, wherein the target control coefficient is used as a reference threshold for the financial institution when regulating the financial transaction system.

7. A computer-readable storage medium, characterized in that, The computer readable storage medium comprises a stored executable program, wherein the executable program, when executed, controls a device in which the computer readable storage medium is located to perform the financial risk data processing method of any one of claims 1 to 5.

8. An electronic device, comprising: The computer readable storage medium comprises a stored executable program, wherein the executable program, when executed, controls a device in which the computer readable storage medium is located to perform the financial risk data processing method of any one of claims 1 to 5.

9. A computer program product comprising computer instructions, characterized in that, The computer readable storage medium comprises a stored executable program, wherein the executable program, when executed, controls a device in which the computer readable storage medium is located to perform the financial risk data processing method of any one of claims 1 to 5. The computer readable storage medium comprises a stored executable program, wherein the executable program, when executed, controls a device in which the computer readable storage medium is located to perform the financial risk data processing method of any one of claims 1 to 5.

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