System management method and device, storage medium and electronic equipment

Abstract

The invention discloses a system management method and device, a storage medium and electronic equipment, and relates to the field of financial science and technology. The method comprises the steps that change information of a first subsystem in a target system is acquired, and the first subsystem is any subsystem in the target system; determining a first target combination and a second target combination according to the target algorithm and the change information of the first subsystem; a screening result of the target personnel on the first target combination and the second target combination is received, the screening result at least comprises a target combination, and the target combination is a combination with an influence value on target system performance smaller than a first preset threshold value in the first target combination and the second target combination; and adjusting state information of other subsystems except the first subsystem in the target system according to the target combination. The technical problems of high labor cost and low decision-making efficiency due to the fact that bank system architecture management only depends on expert review in the prior art are solved.

Description

Technical Field

[0001] The present application relates to the field of financial technology, and specifically, to a system management method, device, storage medium and electronic device. Background Art

[0002] In today's era of rapid development of information technology in the banking industry, bank system architecture management faces unprecedented challenges. With the diversification and complexity of business, the number of bank back-end systems and projects has increased dramatically, and the dependencies between systems are complex. Traditional management models that rely on expert experience and manual review are no longer able to meet the needs of high-efficiency and low-cost management.

[0003] While expert review can provide accurate decision-making evidence to a certain extent, it is often time-consuming and labor-intensive. The limitations of expert review become increasingly apparent, especially when dealing with large-scale system state combination and optimization problems. On the one hand, expert review relies on personal experience and expertise, and different experts may provide different review results, leading to subjectivity and uncertainty in decision-making. On the other hand, faced with massive amounts of system state data, manual review cannot fully consider all possible optimization solutions, and the optimal solution is easily missed. Furthermore, communication and coordination during the review process consumes a significant amount of time and resources.

[0004] To address the above-mentioned problems, no effective solutions have been proposed so far. Summary of the Invention

[0005] The present application provides a system management method, device, storage medium and electronic device to at least solve the technical problem in the prior art that the management of bank system architecture relies solely on expert review, resulting in high labor costs and low decision-making efficiency.

[0006] According to one aspect of the present application, a system management method is provided, comprising: obtaining change information of a first subsystem in a target system, wherein the first subsystem is any subsystem in the target system; determining a first target combination and a second target combination based on a target algorithm and the change information of the first subsystem, wherein the target algorithm is used to search for a state combination that meets a preset condition in a state combination space, and the first target combination and the second target combination are combinations including state information of each subsystem in the target subsystem set, determined by the target algorithm after traversing and searching for state information of a target subsystem set in the target system based on the change information of the first subsystem, wherein the preset condition is used to constrain the scope of influence of the first target combination and the second target combination on the performance of the target system, and the target subsystem set is used to represent a set of all subsystems in the target system that are associated with the first subsystem; receiving screening results of the first target combination and the second target combination by a target person, wherein the screening results at least include a target combination, wherein the target combination is a combination of the first target combination and the second target combination whose impact value on the performance of the target system is less than a first preset threshold; and adjusting the state information of other subsystems in the target system except the first subsystem according to the target combination.

[0007] Optionally, determining the first target combination and the second target combination based on the target algorithm and the change information of the first subsystem includes: setting N state combinations, where N is an integer greater than 1, each state combination in the N state combinations includes M subsystems, and the state information of each subsystem in the M subsystems will change with the change of the state information of other subsystems except the subsystem, where M is an integer greater than or equal to 1; based on the change information of the first subsystem, initializing the state information of the M subsystems in each state combination in the N state combinations to obtain N first state combinations; obtaining the target function, where the target function is used to quantify the impact of each first state combination on the performance of the target system; determining the first target combination and the second target combination based on the target algorithm, the N first state combinations and the target function.

[0008] Optionally, based on the change information of the first subsystem, the state information of the M subsystems in each state combination in the N state combinations is initialized to obtain N first state combinations, including: based on the change information of the first subsystem, the state information of the M subsystems in each state combination in the N state combinations is initialized and assigned values ​​to obtain N initial state combinations; according to the target algorithm, the state information of each initial state combination in the N initial state combinations is encoded into a quantum bit probability amplitude to obtain N first state combinations, wherein the quantum bit probability amplitude is used to characterize the encoding of the state information of the subsystem in the target system as a complex value of the quantum state.

[0009] Optionally, the first target combination and the second target combination are determined according to the target algorithm, N first state combinations and the target function, including: calculating the target value corresponding to each first state combination in the N first state combinations according to the target function; based on the target value corresponding to each first state combination, using the target algorithm to perform multiple target operations on the N first state combinations until the number of target operations is greater than or equal to a preset number of iterations, to obtain a target state combination, wherein each target operation is used to update the quantum bit probability amplitude of each first state combination according to the target value of each first state combination; and determining the first target combination and the second target combination according to the target state combination.

[0010] Optionally, the target operation includes the following steps: based on the target algorithm, according to the target value of each first state combination, using a quantum rotation gate to update the quantum bit probability amplitude of each first state combination in N first state combinations to obtain N second state combinations; calculating the target value corresponding to each second state combination in the N second state combinations according to the target function; according to the target value corresponding to each second state combination, determining the target state combination from the N second state combinations, wherein the target state combination is a combination of the N second state combinations whose corresponding target value is less than the corresponding second preset threshold.

[0011] Optionally, according to the target value of each first-state combination, a quantum rotation gate is used to update the qubit probability amplitude of each first-state combination in the N first-state combinations, including: in the case of the first target operation, according to the target value of each first-state combination, the quantum rotation gate is used to update the qubit probability amplitude of each first-state combination in the N first-state combinations; in the case of the i-th target operation, if the target value of the j-th first-state combination is less than the target value of the j-th first-state combination under the i-1 target operation, the quantum rotation gate is used to update the qubit probability amplitude of the j-th first-state combination based on each first-state combination corresponding to the i-th target operation, wherein i is an integer greater than 1 and j is a positive integer less than or equal to N; in the case of the i-th target operation, if the target value of the j-th first-state combination is greater than or equal to the target value of the j-th first-state combination under the i-1 target operation, the quantum rotation gate is used to update the qubit probability amplitude of the j-th first-state combination based on each first-state combination corresponding to the i-1 target operation.

[0012] Optionally, determining the first target combination and the second target combination according to the target state combination includes: decoding the quantum bit probability amplitude in the target state combination into actual state information to obtain the first target combination and the second target combination.

[0013] According to another aspect of the present application, a system management device is provided, comprising: an acquisition unit, configured to acquire change information of a first subsystem in a target system, wherein the first subsystem is any subsystem in the target system; a determination unit, configured to determine a first target combination and a second target combination based on a target algorithm and the change information of the first subsystem, wherein the target algorithm is configured to search for a state combination that meets a preset condition in a state combination space, and the first target combination and the second target combination are combinations including state information of each subsystem in the target subsystem set, determined by the target algorithm after traversing and searching for state information of a target subsystem set in the target system based on the change information of the first subsystem, wherein the preset condition is configured to constrain the scope of influence of the first target combination and the second target combination on the performance of the target system, and the target subsystem set is configured to represent a set of all subsystems in the target system that are associated with the first subsystem; a receiving unit, configured to receive a screening result of the first target combination and the second target combination by a target person, wherein the screening result at least includes a target combination, wherein the target combination is a combination of the first target combination and the second target combination whose influence value on the performance of the target system is less than a first preset threshold; and an adjustment unit, configured to adjust the state information of other subsystems in the target system except the first subsystem according to the target combination.

[0014] According to another aspect of an embodiment of the present application, a computer-readable storage medium is further provided, which includes a stored executable program, wherein when the executable program runs, the device where the computer-readable storage medium is located is controlled to execute the above-mentioned system management method.

[0015] According to another aspect of an embodiment of the present application, an electronic device is also provided, comprising one or more processors and a memory, wherein the memory is used to store one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors execute the above-mentioned system management method.

[0016] In the present application, change information of a first subsystem in a target system is first obtained, wherein the first subsystem is any subsystem in the target system. Then, a first target combination and a second target combination are determined based on a target algorithm and the change information of the first subsystem. The target algorithm is used to search for a state combination that meets a preset condition in a state combination space. The first target combination and the second target combination are determined by the target algorithm after traversing and searching the state information of a target subsystem set in the target system based on the change information of the first subsystem, and include combinations of state information of each subsystem in the target subsystem set. The preset condition is used to constrain the scope of influence of the first target combination and the second target combination on the performance of the target system. The target subsystem set is used to represent the set of all subsystems in the target system that are associated with the first subsystem. Then, a screening result of the first target combination and the second target combination by a target person is received, wherein the screening result at least includes a target combination, wherein the target combination is a combination of the first target combination and the second target combination whose impact value on the performance of the target system is less than a first preset threshold. Finally, the state information of other subsystems in the target system except the first subsystem is adjusted based on the target combination. That is, by combining the preliminary screening of the intelligent optimization algorithm with the final decision of the target personnel, the purpose of quickly responding to subsystem changes and automatically adjusting the status of related subsystems is achieved, thereby realizing the technical effect of efficient and intelligent management of the bank system architecture, and further solving the technical problem in the existing technology that the bank system architecture management relies solely on expert review, resulting in high labor costs and low decision-making efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0018] Figure 1 is a flowchart of an optional system management method according to an embodiment of the present application;

[0019] Figure 2 This is a schematic diagram of an optional system management device according to an embodiment of the present application. DETAILED DESCRIPTION

[0020] In order to enable those skilled in the art to better understand the present invention, the following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts should fall within the scope of protection of this application.

[0021] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequential order. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in a sequence other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0022] It should be noted that the collected information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data for display, data for analysis, etc.) involved in this application are information and data authorized by the user or fully authorized by all parties, and the collection, storage, use, processing, transmission, provision, disclosure and application of the relevant data comply with relevant laws, regulations and standards, take necessary confidentiality measures, do not violate public order and good morals, and provide corresponding operation portals for users to choose to authorize or refuse. If an interface is set up between this system and relevant users or institutions, a corresponding operation portal will be provided for users to choose to agree or refuse the automated decision-making results; if the user chooses to refuse, the expert decision-making process will be entered.

[0023] It should be noted that an intelligent management system can be used as the execution subject of the system management method of the embodiment of the present application. It is understandable that the system management method provided in the embodiment of the present application can also be executed by other systems or devices, and the embodiment of the present application does not specifically limit this.

[0024] According to an embodiment of the present application, a method embodiment of a system management 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 a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0025] Figure 1 is a flow chart of an optional system management method according to an embodiment of the present application, such as Figure 1 As shown, the method includes the following steps:

[0026] Step S101: Acquire change information of a first subsystem in a target system.

[0027] In step S101 , the first subsystem is any subsystem in the target system.

[0028] Alternatively, the target system can be the bank's global system architecture, which consists of multiple interrelated subsystems, such as a big data platform, a transaction system, etc.

[0029] Optionally, the first subsystem is any subsystem in the target system, and its state change will trigger the combinatorial optimization process of the entire system, for example, a table in the big data platform is changed.

[0030] Optionally, the change information of the first subsystem state refers to the specific circumstances of the change of the first subsystem state, including the change time, the change type, etc.

[0031] Alternatively, the bank's global system architecture is very complex, and even the slightest change in any subsystem can affect other related subsystems. Therefore, the first step is crucial. It ensures that the system can capture any subsystem status changes in a timely manner, providing basic data for subsequent portfolio optimization.

[0032] Step S102: determining a first target combination and a second target combination according to the target algorithm and the change information of the first subsystem.

[0033] In step S102, the target algorithm is used to search for a state combination that meets preset conditions in the state combination space. The first target combination and the second target combination are determined by the target algorithm after traversing and searching the state information of the target subsystem set in the target system based on the change information of the first subsystem, and include combinations of state information of each subsystem in the target subsystem set.

[0034] In step S102, the preset conditions are used to constrain the influence range of the first target combination and the second target combination on the performance of the target system, and the target subsystem set is used to represent the set of all subsystems in the target system that are associated with the first subsystem.

[0035] Optionally, in this embodiment, the target algorithm refers to a quantum swarm intelligence algorithm, which is used to search for an optimal state combination in a state combination space.

[0036] It should be noted that the quantum swarm intelligence algorithm is an innovative algorithm that combines quantum computing principles with swarm intelligence optimization strategies. Its core is to leverage concepts from quantum mechanics to enhance the optimization capabilities and computational efficiency of traditional swarm intelligence algorithms (such as particle swarm optimization and genetic algorithms). Traditional swarm intelligence algorithms use classical bits for encoding, while quantum swarm intelligence algorithms use qubits. Qubits can exist in a superposition state between 0 and 1, meaning that a single qubit can simultaneously represent multiple states, significantly enhancing the algorithm's parallel processing capabilities. In quantum swarm intelligence algorithms, the state of each qubit is described by its probability amplitude, which can be complex. Quantum swarm intelligence algorithms update the probability amplitude of qubits using quantum rotation gates. This operation is similar to mutation and crossover operations in traditional algorithms, but it enables more efficient global search through the superposition and interference of quantum states. Quantum swarm intelligence algorithms can simultaneously represent and search for multiple possible solutions in multidimensional space. This is due to the superposition property of qubits. In each dimension of the traversed space, the qubit is in the probability amplitude range [-1, 1]. This allows the algorithm to explore a wider set of states and search for more optimal solutions. The most significant advantage of quantum swarm intelligence algorithms is their ability to leverage quantum parallelism to simultaneously process a large number of possible solutions in a single computation. This property enables the algorithm to exponentially expand its search space for complex optimization problems, significantly increasing the speed of optimization and the likelihood of finding the global optimal solution. In banking system architecture management, quantum swarm intelligence algorithms can efficiently handle complex inter-system connections and quickly find the optimal configuration within the combined space of system states, reducing reliance on expert review, thereby lowering labor costs and improving decision-making efficiency.

[0037] Alternatively, in quantum space, since the position and velocity of a particle cannot be determined simultaneously, the state of the particle must be described by a wave function ψ(X,t), where X is the particle's position vector and t is the dynamic characteristic that reflects the change of the quantum state over time. The physical meaning of the wave function is that the square of its modulus represents the probability density of the particle appearing at position X in space, as shown in formula (1):

[0038] |ψ| 2 dxdydz=Qdxdydz (1)

[0039] Where Q is the probability density function, which also satisfies formula (2):

[0040]

[0041] Alternatively, in the quantum particle swarm algorithm, the movement of the particle position is realized by a quantum rotating gate. Therefore, the update of the particle position is converted into the update of the probability amplitude of the qubit on the particle. i(corresponding to an individual in the particle swarm optimization algorithm, it is a point in the solution space of the optimization problem. The optimal position currently searched is the cosine position, as shown in formula (3):

[0042] P i1 =(cos(θ il1 ),cos(θ il2 ),...,cos(θ iln )) (3)

[0043] Among them, cos(θ iln ) corresponds to particle P i The real part of the probability amplitude of the qubit in the nth dimension (each dimension represents a subsystem), where θ iln is an angle value, which can be regarded as the rotation angle of the particle quantum bit in this dimension.

[0044] The optimal position P currently searched by the entire population g The expression of is shown in formula (4):

[0045] P g =(cos(θ g1 ),cos(θ g2 ),...,cos(θ gn )) (4)

[0046] The particle position is obtained by random simulation using Monte Carlo method (a statistical simulation technique), and its update equations are shown in formulas (5)-(9):

[0047] P(t)=θ·P b (t)+(1-θ)P g (t) (5)

[0048]

[0049] L(t+1)=2α·|m(t)-X(t)| (7)

[0050]

[0051] Among them, in formulas (5)-(9), P b (t) and P g(t) represents the individual optimal position of the particle and the global optimal position of the population respectively; θ is a random variable that obeys uniform distribution; P(t) is the local attraction domain of the particle at the tth iteration, indicating that the position of each particle is a random position between the individual optimal position and the global optimal position; m(t) is the average value of the individual optimal positions of all particles in the population; N represents the size of the population, that is, the number of particles; L represents the weighted distance between the particle and the average optimal position of the population; μ is a random variable that obeys uniform distribution on [0,1]; α is called the contraction-expansion coefficient, which is used to control the convergence speed of the particle. The larger α is, the weaker the convergence and the stronger the global search ability; conversely, the stronger the convergence, the stronger the local search ability. As the iteration proceeds, α changes linearly from a to b, usually setting a=1 and b=0.5; G max Indicates the maximum number of iterations.

[0052] Combining the above five equations (Formulas (5)-(9)), we can obtain Formula (10):

[0053]

[0054] Where X(t+1) represents the position of the particle at iteration (t+1). (P(t)) represents the individual optimal position of the particle at iteration (t). m(t) represents the average of the individual optimal positions of the entire particle swarm at iteration (t), representing the current optimal search direction of the particle swarm. α is the contraction-expansion coefficient, which controls the particle's convergence speed and search range. A larger α value results in a more global search; a smaller α value results in a more local search. μ is a random variable uniformly distributed in the interval ([0,1]) and is used to introduce randomness to prevent particles from prematurely falling into local optima.

[0055] Optionally, the process of the target algorithm is as follows:

[0056] (1) Initialization parameters: Set the particle swarm size N, particle dimension D, particle initial position, and individual optimal position.

[0057] (2) By calculating the fitness function value of the particle, the initial individual optimal position and the global optimal position are obtained.

[0058] (3) Iteratively update the position of each particle according to the set of equations.

[0059] (4) After iteration, the fitness function value of the particle is calculated again.

[0060] (5) Assuming that the goal is to find the minimum value of the fitness function, the individual optimal position P of the particle is bi The update method of is shown in formula (11).

[0061]

[0062] Among them, formula (11) means that for each particle (i) in the particle swarm, after each iteration (t+1), the current particle position (X i (t+1)) and the individual optimal position of the particle in history (P bi (t)) fitness function value. If the fitness value of the current position is better than the fitness value of the individual's optimal position (i.e. f(X i (t+1) <f(P bi (t)))), then the individual optimal position of the particle will be updated to the current position (X i (t+1)); If the fitness value of the current position is not as good as the individual's optimal position (fX i (t+1)≥f(P bi (t)) / ), then the individual optimal position of the particle remains unchanged, which is still (P bi (t)).

[0063] (6) Calculate the global optimal position P g As shown in formula (12):

[0064] P g (t+1)=arg min 1≤i≤N {f[P bi (t+1)]} (12)

[0065] Among them, after each iteration (t+1), the global optimal position (P g (t+1)) is updated to the individual optimal position (P bi (t+1)), the fitness function value (f[P bi (t+1)]) is the smallest position. In simple terms, (P g (t+1)) is the best solution among the best solutions found by all particles in the entire particle swarm in the current iteration.

[0066] (7) Determine whether the termination condition is met. If not, go to step (3); otherwise, terminate.

[0067] Optionally, the first target combination and the second target combination are the two most likely optimal solutions found by the quantum swarm intelligence algorithm in the state combination space. Based on the change information of the first subsystem, the algorithm determines the two possible optimal combinations by traversing the state information of the set of target subsystems.

[0068] Alternatively, the intelligent management system leverages the parallel search capabilities of quantum swarm intelligence algorithms to quickly find two combinations in the state combination space that satisfy pre-defined conditions. These pre-defined conditions typically involve the impact on system performance, such as how to minimize system disruption while ensuring efficient and stable operation of all subsystems.

[0069] Step S103: receiving the screening results of the target person on the first target combination and the second target combination.

[0070] In step S103, the screening result includes at least the target combination.

[0071] In step S103, the target combination is a combination of the first target combination and the second target combination whose impact value on the target system performance is less than a first preset threshold.

[0072] Alternatively, the target personnel refer to experts from the bank's system architecture management team who are responsible for the final screening and decision-making.

[0073] Optionally, from the two target combinations found by the quantum swarm intelligence algorithm, the target personnel will further screen the combination with the least impact on system performance based on their professional knowledge and experience. This step is to ensure that the algorithm's output is feasible and safe in practical applications, avoiding blindly pursuing theoretical optimality while ignoring the limitations and risks of actual operation.

[0074] Step S104: adjusting the status information of the subsystems other than the first subsystem in the target system according to the target combination.

[0075] Optionally, after determining the target combination, the intelligent management system automatically or manually adjusts the states of other subsystems to ensure the optimal operating state of the entire target system. This adjustment is based on the global optimal solution found by the quantum swarm intelligence algorithm, avoiding the local optimality trap that may be encountered in traditional methods, and improving the accuracy and efficiency of system architecture management.

[0076] As can be seen from the contents of steps S101 to S104, in the present application, first, change information of the first subsystem in the target system is obtained, wherein the first subsystem is any subsystem in the target system. Then, a first target combination and a second target combination are determined based on the target algorithm and the change information of the first subsystem. The target algorithm is used to search for a state combination that meets a preset condition in the state combination space. The first target combination and the second target combination are determined by the target algorithm after traversing and searching the state information of the target subsystem set in the target system based on the change information of the first subsystem, and include combinations of state information of each subsystem in the target subsystem set. The preset condition is used to constrain the scope of influence of the first target combination and the second target combination on the performance of the target system. The target subsystem set is used to represent the set of all subsystems in the target system that are associated with the first subsystem. Then, the target personnel receives the screening results of the first target combination and the second target combination, wherein the screening results at least include the target combination, wherein the target combination is a combination of the first target combination and the second target combination whose impact value on the performance of the target system is less than a first preset threshold. Finally, the state information of other subsystems in the target system except the first subsystem is adjusted according to the target combination. That is, by combining the preliminary screening of the intelligent optimization algorithm with the final decision of the target personnel, the purpose of quickly responding to subsystem changes and automatically adjusting the status of related subsystems is achieved, thereby realizing the technical effect of efficient and intelligent management of the bank system architecture, and further solving the technical problem in the existing technology that the bank system architecture management relies solely on expert review, resulting in high labor costs and low decision-making efficiency.

[0077] In an optional embodiment, the intelligent management system first sets N state combinations, where N is an integer greater than 1, each of the N state combinations includes M subsystems, and the state information of each subsystem in the M subsystems will change with the change of the state information of other subsystems except the subsystem, where M is an integer greater than or equal to 1, and then based on the change information of the first subsystem, the state information of the M subsystems in each state combination of the N state combinations is initialized to obtain N first state combinations, and then the objective function is obtained, where the objective function is used to quantify the impact of each first state combination on the performance of the target system, and finally the first target combination and the second target combination are determined according to the target algorithm, the N first state combinations and the objective function.

[0078] Optionally, in the intelligent management system of this embodiment, after a subsystem change occurs, in the process of determining the optimal combination, N state combinations are first set, where N is an integer greater than 1. These N state combinations represent different configurations or state sets that may be encountered in system architecture management. Each state combination contains M subsystems, where M represents any integer greater than or equal to 1, representing the number of subsystems that may be involved in the bank's back-end information system. These subsystems are closely interdependent, that is, the state information of any subsystem will change with changes in the state information of other subsystems except for that subsystem. This dependency reflects the complexity and dynamic nature of bank system architecture management.

[0079] Optionally, based on the change information for the first subsystem, the intelligent management system initializes the M subsystems within each of the N state combinations, resulting in N first state combinations. This step involves presetting or calculating the likely performance of the subsystems under different states by collecting and analyzing impact data before and after the first subsystem changes. For example, if the first subsystem undergoes an online change, the associated subsystems may need to adjust their operating parameters or reconfigure their workflows to adapt to the new state of the first subsystem. This initialization process ensures the rationality and predictability of each state combination, laying the foundation for subsequent optimization calculations. The intelligent management system then obtains an objective function, a mathematical model that quantifies the impact of each first state combination on the performance of the target system. Target system performance can include system stability, efficiency, response time, or resource utilization. The objective function requires comprehensive consideration of the banking system's business needs, technical indicators, and risk assessments to ensure that the optimization results meet the actual needs of the banking business while minimizing potential risks and costs. In this system, the objective function treats each state combination as a decision point and assesses its impact on the performance of the entire banking system. Finally, the intelligent management system determines the first and second target combinations based on the target algorithm, combining the N first state combinations and the objective function. This process involves iteratively optimizing the N state combinations, leveraging the parallel processing capabilities and global search advantages of the quantum swarm intelligence algorithm to find the optimal state combination that can minimize or optimize the objective function. The target algorithm gradually approaches the global optimal solution by updating the state information of the subsystems in each state combination until the pre-set convergence conditions are met or the maximum number of iterations is reached. The final first and second target combinations are the two state combinations that can provide the best system performance among the N state combinations. These two combinations will be manually reviewed to confirm their practical feasibility and optimality, and the most reasonable one will be selected as the final management decision.

[0080] As can be seen from the above, intelligent management systems can effectively reduce the labor costs and computing time associated with system architecture management. The application of quantum swarm intelligence algorithms in data processing and decision-making significantly enhances the ability to optimize bank system state combinations, enabling rapid and accurate identification of state combinations with the least or most impactful impact on target system performance. This approach not only increases the level of automation in bank system architecture management but also improves overall system stability and efficiency by reducing human intervention and errors.

[0081] In an optional embodiment, the intelligent management system initializes and assigns values ​​to the state information of the M subsystems in each of the N state combinations based on the change information of the first subsystem, to obtain N initial state combinations, and then encodes the state information of each of the N initial state combinations into a quantum bit probability amplitude according to the target algorithm, to obtain N first state combinations, wherein the quantum bit probability amplitude is used to characterize the encoding of the state information of the subsystems in the target system into a complex value of the quantum state.

[0082] Optionally, the intelligent management system first receives change information of the first subsystem, which can be an update of the system state, the execution of an operation command, or the occurrence of a fault. Based on this information, the system initializes and assigns values ​​to the M subsystems in each possible state combination (i.e., each of the N state combinations). Next, the intelligent management system encodes the subsystem state information in each initialized state combination into a probability amplitude of a quantum bit. As a basic unit of quantum information, the state of a quantum bit is described by two complex probability amplitudes, which correspond to the probabilities of the quantum state being 0 and 1, respectively. During encoding, the state information of each subsystem is converted into a probability amplitude of a quantum bit through a specific mapping rule.

[0083] Optionally, through the above process, each subsystem state information is encoded into a qubit probability amplitude, which reflects the complex-valued representation of the subsystem state in quantum space. For example, if a subsystem state is assigned a value of +0.25 (normal operation), it may correspond to a qubit probability amplitude of [cosα, sinα], where α is an angle calculated based on the system state and quantum encoding rules. This encoding method fully utilizes the superposition principle and parallel processing capabilities of quantum mechanics, allowing the system to simultaneously explore and evaluate multiple state combinations in quantum space.

[0084] Alternatively, for each of the N state combinations, the intelligent management system combines the qubit probability amplitudes encoded with the state information of all subsystems in each combination to form a quantum state. This quantum state is a vector containing the quantum representation of the state of each subsystem in the N state combinations. In this way, the system can process and optimize the state combination of the entire target system in the form of quantum states.

[0085] Optionally, each state combination is treated as an n-dimensional quantum vector in the target algorithm, where n represents the number of variables (subsystems) involved in the optimization problem. In quantum computing, a qubit can be in a superposition of both 0 and 1, and this superposition is described by its sine and cosine components. Specifically, in the quantum swarm intelligence algorithm, each state combination is encoded as a quantum vector, and each dimension of this quantum vector (i.e., each state information) is represented by the probability amplitude of a qubit. This probability amplitude consists of two key components: a sine component (sinα) and a cosine component (cosα), where α is the rotation angle parameter in the quantum rotary gate operation (the quantum rotary gate adjusts this rotation angle to update the probability amplitude of the qubit for each state combination). Therefore, when the intelligent management system encodes the state information of the bank system architecture management onto qubits, each state information is actually represented by the sine and cosine components of a qubit. This encoding method allows the state information to occupy two positions simultaneously in the quantum computation: the sine position and the cosine position, providing the algorithm with the opportunity to explore two potential optimal solutions.

[0086] Alternatively, the solution of the optimization problem (seeking the optimal combination) is regarded as a point or vector in n-dimensional space, and the continuous optimization problem can be expressed as min f(x1,x2,...,x n ), where a i ≤X i ≤b i ,i=1,2,...,n;n is the number of optimization variables; [a i ,b i ] is the variable X i The domain of definition; f is the objective function (fitness function), whose value can be used as the fitness of the particle. Then the probability amplitude of the quantum bit is used as the encoding of the current position of the particle, as shown in formula (13):

[0087]

[0088] Where θ ij = 2π × rnd; rnd is a random number in the interval (0, 1); i = 1, 2, ..., m; j = 1, 2, ..., n; m is the number of particles in the population; n is the spatial dimension. Each particle in the population occupies the following two positions in the ergodic space, which correspond to the probability amplitudes of the quantum states |0> and |1>, respectively, as shown in Equations (14) and (15):

[0089] P ic =(cos(θ i1 ),cos(θ i2 ),...,cos(θ in )) (14)

[0090] P is =(sin(θ i1 ),sin(θ i2 ),...,sin(θ in )) (15)

[0091] Among them, P ic is the cosine position, P is is the sinusoidal position.

[0092] From the above, it can be seen that by adopting the above-mentioned specific implementation methods, the intelligent management system can effectively process and optimize the combination of multiple subsystem states in the target system. Especially in the scenario of bank system architecture management, it can significantly reduce labor costs and shorten calculation time. The encoding method of quantum bit probability amplitude enables the system to process multiple state combinations simultaneously in quantum space, thereby improving the efficiency of searching for the global optimal solution. In addition, by encoding the subsystem state information as a complex value of the quantum state, it can not only accurately represent the state of the subsystem, but also utilize the characteristics of quantum computing to achieve rapid evaluation and optimization of state combinations. This method can help banks solve combinatorial optimization problems that are difficult to handle with traditional computing methods, especially when there are many systems and complex relationships, and can provide more accurate and faster solutions.

[0093] In an optional embodiment, the intelligent management system first calculates the target value corresponding to each first state combination in the N first state combinations according to the objective function, and then uses the target algorithm to perform multiple target operations on the N first state combinations based on the target value corresponding to each first state combination, until the number of target operations is greater than or equal to the preset number of iterations, thereby obtaining the target state combination, wherein each target operation is used to update the quantum bit probability amplitude of each first state combination according to the target value of each first state combination, and finally the first target combination and the second target combination are determined according to the target state combination.

[0094] Optionally, the intelligent management system first calculates a quantitative target value for each of the N first-state combinations based on a preset objective function. The objective function is designed to quantitatively assess the impact of each state combination on the bank's system performance. It converts the state combination into a numerical value that serves as a measure of its performance. For example, the objective function can be set to optimize metrics such as system runtime, resource consumption, and data processing efficiency. The smaller the calculated value, the more effectively that state combination optimizes bank system performance. Next, based on the target value calculated for each state combination, the intelligent management system uses a quantum swarm intelligence algorithm to perform multiple target operations on the N first-state combinations. These target operations, namely, updates to the qubit probability amplitudes, aim to iteratively optimize each state combination and find the optimal state combination that minimizes the objective function. During each operation, the algorithm uses the target value as feedback to adjust the qubit probability amplitude corresponding to the subsystem state information in each state combination, exploring the state combination space closer to the optimal solution. This process is repeated until the number of target operations is greater than or equal to the preset number of iterations, ensuring that the algorithm fully explores the entire state space and increasing the likelihood of finding the global optimal solution.

[0095] Optionally, after a preset number of iterations of the target operation, the intelligent management system will obtain a series of updated state combinations. From these state combinations, the system will determine two optimal state combinations based on the optimization results of the objective function: a first target combination and a second target combination. These two combinations represent two possible system state configurations that can achieve optimal banking system performance given the current first subsystem change information. In actual applications, these two target combinations will undergo further manual review and analysis to ultimately select the most reasonable and applicable one as the basis for decision-making in system architecture management.

[0096] As can be seen from the above, the intelligent management system, using the aforementioned implementation, can significantly improve the efficiency and accuracy of bank system architecture management. The introduction of quantum swarm intelligence algorithms not only accelerates the search for the global optimal solution but also overcomes the limitations of traditional optimization methods, which are prone to falling into local optimal solutions. By updating the quantum bit probability amplitude multiple times, the system can more comprehensively explore the state combination space, thereby finding a state combination that is closer to the global optimal solution within a preset number of iterations. This approach can effectively reduce the labor cost of system architecture management, shorten decision-making time, and ensure the accuracy and reliability of decision results, ultimately achieving efficient and stable operation of the banking system.

[0097] In an optional embodiment, the target operation includes the following steps: based on the target algorithm, according to the target value of each first state combination, using a quantum rotation gate to update the quantum bit probability amplitude of each first state combination in N first state combinations to obtain N second state combinations; calculating the target value corresponding to each second state combination in the N second state combinations according to the target function; according to the target value corresponding to each second state combination, determining the target state combination from the N second state combinations, wherein the target state combination is a combination of the N second state combinations whose corresponding target value is less than the corresponding second preset threshold.

[0098] Optionally, the target operation includes the following key steps, which aim to optimize the banking system architecture through quantum swarm intelligence algorithms to ensure that the global system performance after subsystem state adjustments meets the preset standards. The following is a detailed explanation:

[0099] First, based on a target algorithm, the intelligent management system uses a quantum rotation gate to update the qubit probability amplitude for each of the N first-state combinations, according to the target value of each first-state combination (usually quantified by an objective function, reflecting the impact of that state combination on the target system performance). A quantum rotation gate is an operation used in quantum computing to change the state of a qubit. By adjusting the angular parameters of the qubit, the qubit probability amplitude corresponding to the state information of each subsystem is updated. This process leverages the parallelism of quantum computing to rapidly explore and optimize the system state combinations. The updated state combinations are referred to as the N second-state combinations.

[0100] Next, the intelligent management system calculates the target value for each of the N second-state combinations based on an objective function. The objective function is a mathematical model that maps each state combination to a real number and is used to assess its impact on the target system's performance, such as stability and resource consumption. By comparing the updated target value with the pre-updated value, we can evaluate the effectiveness of the quantum rotating gate operation and identify which state combinations are closest to the global optimal solution.

[0101] Finally, the intelligent management system determines a target state combination from the N second-state combinations based on the target value of each second-state combination. This target state combination is the combination among all updated state combinations whose target value is less than the corresponding second preset threshold. The second preset threshold is set based on the performance requirements and risk tolerance of the bank's system architecture and provides a quantitative standard for finding the optimal state combination. Through screening, the intelligent management system can eliminate state combinations with high target values ​​(i.e., those with a significant impact on system performance) and retain those state combinations that meet performance requirements and reduce risk as the target state combination.

[0102] Optionally, the N second state combinations obtained in each target operation are the updated N first state combinations, and the updated N first state combinations (N second state combinations) are used as the first state combinations for the next target operation.

[0103] As can be seen from the above, the intelligent management system's targeted operations achieve efficient optimization of the bank's system architecture through the aforementioned steps. Leveraging quantum swarm intelligence algorithms, the system can rapidly process a large number of possible state combinations and dynamically adjust subsystem states through the operation of quantum revolving gates, thus avoiding the local optimum trap inherent in traditional optimization methods. Furthermore, by setting a second preset threshold, the intelligent management system ensures that the final target state combination not only improves system performance but also meets the bank's specific business needs and risk control standards. This approach reduces labor costs, shortens computation time, and improves the accuracy and efficiency of bank system architecture management, demonstrating significant advantages when dealing with high-dimensional and complex system state combinations.

[0104] In an optional embodiment, in the case of the first target operation, the intelligent management system uses a quantum rotating gate to update the quantum bit probability amplitude of each first state combination in the N first state combinations according to the target value of each first state combination; in the case of the i-th target operation, if the target value of the j-th first state combination is less than the target value of the j-th first state combination under the i-1-th target operation, the intelligent management system uses a quantum rotating gate to update the quantum bit probability amplitude of the j-th first state combination based on each first state combination corresponding to the i-th target operation, where i is an integer greater than 1 and j is a positive integer less than or equal to N; in the case of the i-th target operation, if the target value of the j-th first state combination is greater than or equal to the target value of the j-th first state combination under the i-1-th target operation, the intelligent management system uses a quantum rotating gate to update the quantum bit probability amplitude of the j-th first state combination based on each first state combination corresponding to the i-1-th target operation.

[0105] Optionally, when the intelligent management system starts the quantum swarm intelligence algorithm, that is, in the case of the first target operation (i=1), the system will update the quantum bit probability amplitude based on the target values ​​of the N first state combinations. First, the target value of each first state combination is calculated, which is usually achieved by evaluating the degree of influence of the state combination on the performance of the target system. Subsequently, a quantum rotary gate operation is used to update the quantum bit probability amplitude in each first state combination in order to search for a better state combination in the state combination space. The quantum rotary gate is a quantum operation that can change the probability amplitude of the quantum bit, simulating the movement of particles in the quantum space. In this way, the system attempts to explore new areas in the state combination space and find possible better solutions.

[0106] Optionally, in the case of the i-th target operation (i>1), the intelligent management system will compare the target value of the current state combination with the target value of the previous target operation (i-1) to determine the next quantum bit probability amplitude update strategy. Specifically, for the j-th first-state combination (j is a positive integer less than or equal to N), if its target value under the i-th target operation is less than the target value under the i-1 target operation, it indicates that the optimization effect of the state combination has improved. The system will use a quantum rotating gate to update the quantum bit probability amplitude of the j-th first-state combination based on each first-state combination corresponding to the i-th target operation. This means that the system will continue to explore and improve the state combination based on the most recent optimization results, in order to further improve its target value.

[0107] Alternatively, if the target value of the jth first-state combination in the i-th target operation is greater than or equal to the target value in the i-1-th target operation, that is, the optimization effect has not improved or has decreased, the system will use a quantum rotating gate to update the qubit probability amplitude of the jth first-state combination based on each first-state combination corresponding to the i-1-th target operation. This means that the system will go back and update the qubit probability amplitude of the jth first-state combination based on the global optimal combination determined in the previous operation (the combination with the smallest corresponding target value among all first-state combinations in the previous operation) and the j-th first-state combination, avoiding further deviation from the optimal path when the optimization effect is poor.

[0108] From the above content, it can be seen that by adopting the strategy of dynamically updating the quantum bit probability amplitude through the quantum revolving gate, the intelligent management system can significantly improve the efficiency and accuracy of the intelligent management system when searching for state combination optimization solutions. By comparing the changes in the target value, the intelligent management system can intelligently determine the update direction of the quantum bit probability amplitude, which avoids blind search and reduces the waste of computing resources. In the specific application scenario of bank system architecture management, this method can quickly respond to changes in the status of subsystems in the bank system and continuously adjust the optimization strategy to achieve the optimal operating state of the global system. Compared with traditional methods, this embodiment can find a better system architecture management solution at a lower cost and a shorter time period, effectively improving the stability and operating efficiency of the bank system, while also reducing the frequency and cost of manual review, and realizing intelligent and automated system management.

[0109] In an optional embodiment, the intelligent management system decodes the quantum bit probability amplitude in the target state combination into actual state information to obtain the first target combination and the second target combination.

[0110] Optionally, the intelligent management system needs to decode the qubits in the target state combination. In quantum swarm intelligence algorithms, the probability amplitude of each qubit consists of sine and cosine components, which represent the state information in the quantum computing domain. The decoding process involves converting these probability amplitudes into specific state information, such as system online, offline, normal operation, or fault status. This conversion is based on the mapping relationship between the qubit probability amplitude and the actual state information, ensuring that the quantum computing results can be understood and applied to practical banking system architecture management scenarios.

[0111] Optionally, the target state combination includes optimized qubit probability amplitude information. After decoding, the intelligent management system will obtain the corresponding state information combination. Because the probability amplitude of each qubit represents two possible state information (sine position and cosine position), the decoding process will generate two different state information combinations: the first target combination and the second target combination. These two combinations represent the two possible optimal state configurations of the quantum swarm intelligence algorithm under the current optimization conditions. They are selected from the quantum computing solution space through the algorithm's iterative optimization process, and each combination contains the optimized state information of all subsystems.

[0112] Alternatively, since each dimension of the traversal space of each particle is [-1, 1], each particle occupies two positions (sine position and cosine position), and each probability amplitude corresponds to an optimization variable in the solution space of the combinatorial optimization problem. j The i-th qubit is Then the corresponding solution space variables are shown in formulas (16)-(17):

[0113]

[0114] in, is the cosine position solution space variable of particle j in the i-th dimension, which is obtained by dividing the qubit probability amplitude by (real part) is mapped to the domain of the variable for calculation. The value is b i ;when The value is a i .therefore, The value of A specific location in the domain of definition, thereby converting the probability amplitude of the qubit into a specific value in the solution space. is the spatial variable of the sinusoidal position solution of particle j in the i-th dimension, similar to It is achieved through the quantum bit probability amplitude (imaginary part). Similarly, The value of At a specific location in the domain of definition, the mapping from quantum bit probability amplitude to solution space variables is realized.

[0115] Alternatively, through equations (16)-(17), the target algorithm can convert the probability amplitude information of the qubit into specific optimization variable values ​​in the solution space, thereby completing the conversion from quantum coding to the solution space of the traditional optimization problem and solving the combinatorial optimization problem. This conversion mechanism utilizes the flexibility of qubit encoding in probability amplitude and the boundary information of the optimization variable definition domain, effectively applying the advantages of quantum computing to practical optimization problems.

[0116] Optionally, each particle position corresponds to two combined optimization solutions. After completing the convergence iteration, the most reasonable optimization solution is selected through manual investigation. That is, the input is the particle swarm size N, particle dimension D, and maximum number of iterations G. max , the initial position of the particle and the individual optimal position, and the global optimal position P g , and then converted into two combined optimization solutions through coding, and manually screened to obtain the final combined optimization solution. The parameter input data type is all integer, the initial position is in vector form, and the output is also in vector form of the same dimension.

[0117] From the above content, it can be seen that through the above implementation method, the intelligent management system effectively converts the calculation results of the quantum swarm intelligence algorithm into optimization decisions in actual operations, solving the problem of over-reliance on expert review in traditional banking system architecture management.

[0118] In an optional embodiment, in actual bank operations, various systems are closely interrelated, and the status of a single system directly affects the status of other systems. For example, when a system undergoes an online change, another associated system will be affected by the change and may not operate normally. Changes to tables on a data platform will cause table changes and scheduling on multiple platforms, thus causing other systems to not operate normally. To ensure optimal control of the global system, the following equation can be established by referring to engineering control methods:

[0119] Assume that the global system of a certain business line consists of two subsystems. Since the impact function of the upgrading and transformation of the banking system is a nonlinear second-order function, it can be defined as shown in formula (18)-formula (19):

[0120]

[0121] Among them, formula (18)-formula (19) describes the change of the system state over time. That is, the global system has four states y i , two control quantities μ1 and μ2.

[0122] The performance index of the global system is shown in formula (20):

[0123]

[0124] Wherein, formula (20) defines the performance index J of the global system, which is calculated by integrating the function F over the time interval [0, T]. Function F depends on the state variables y1, y2, y3, y4 of the system and the control variables μ1, μ2

[0125] The minimum value of J is the optimal indicator for system management (which can also be used as the fitness function in this embodiment). When the optimal control argmin J(v1,μ2) (which represents the parameters μ1 and μ2 that minimize the objective function J) exists, the combined optimization equation should meet the following constraints, as shown in formulas (21) and (22):

[0126]

[0127] Among them, Equations (21) and (22) are necessary conditions for determining the optimal control strategy. They are based on the variational principle and are used to describe the dynamic behavior of the system under optimal control. In control theory, these conditions are often referred to as the Euler-Lagrange equations, which are the key to finding control strategies μ1 and μ2 that minimize the performance indicator J. In optimization problems, finding the control strategies μ1 and μ2 that minimize the performance indicator J is equivalent to finding a set of control variables such that the above Euler-Lagrange equations hold at all time points.

[0128] Similarly, when applied to a global system of a business line with N subsystems, the optimal control equation can be expressed as shown in formula (23):

[0129]

[0130] Because solving this combinatorial optimization equation using traditional methods is computationally complex, the intelligent management system encodes the equation into quantum bit probability amplitudes. Ultimately, the optimal solution is obtained, which is the execution command for each subsystem to change operations when a business system is stable.

[0131] For example, if we define the subsystem control states as being on-line (value +0.25), off-line (+0.25), normal operation (+0.25), and system failure (-0.75), the input particle positions, i.e., the optimal states of the four systems, are assumed to be (+0.25, +0.25, +0.25, -0.75). After solving the equations and converting the code, we obtain two sets of vectors. We select the set with the appropriate data range as the global optimal position and set the corresponding value for the optimal state. Whenever the subsystem state is updated, we query the corresponding value range based on the output result to obtain the optimal control for the global system.

[0132] The embodiment of the present application further provides a system management device. It should be noted that the system management device of the embodiment of the present application can be used to execute the system management method provided in the embodiment of the present application. The system management device provided in the embodiment of the present application is introduced below.

[0133] According to an embodiment of the present application, a device for implementing the above system management method is also provided. Figure 2 is a schematic diagram of an optional system management device according to an embodiment of the present application, such as Figure 2 As shown, the device includes: an acquiring unit 201 , a determining unit 202 , a receiving unit 203 and an adjusting unit 204 .

[0134] Optionally, an acquisition unit 201 is configured to acquire change information of a first subsystem in a target system, wherein the first subsystem is any subsystem in the target system; a determination unit 202 is configured to determine a first target combination and a second target combination based on a target algorithm and the change information of the first subsystem, wherein the target algorithm is configured to search for a state combination that meets a preset condition in a state combination space, and the first target combination and the second target combination are combinations including state information of each subsystem in the target subsystem set, determined by the target algorithm after traversing and searching the state information of a target subsystem set in the target system based on the change information of the first subsystem, wherein the preset condition is configured to constrain the scope of influence of the first target combination and the second target combination on the performance of the target system, and the target subsystem set is configured to represent a set of all subsystems in the target system that are associated with the first subsystem; a receiving unit 203 is configured to receive a screening result of the first target combination and the second target combination by a target person, wherein the screening result at least includes a target combination, wherein the target combination is a combination of the first target combination and the second target combination whose impact value on the performance of the target system is less than a first preset threshold; and an adjustment unit 204 is configured to adjust the state information of other subsystems in the target system except the first subsystem according to the target combination.

[0135] Optionally, the determination unit 202 includes: a first setting subunit, a first processing subunit, a first acquisition subunit, and a first determination subunit. The first setting subunit is used to set N state combinations, where N is an integer greater than 1, each of the N state combinations includes M subsystems, and the state information of each subsystem in the M subsystems changes with the change of the state information of other subsystems except the subsystem, where M is an integer greater than or equal to 1; the first processing subunit is used to initialize the state information of the M subsystems in each state combination of the N state combinations based on the change information of the first subsystem, to obtain N first state combinations; the first acquisition subunit is used to obtain an objective function, where the objective function is used to quantify the impact of each first state combination on the performance of the target system; and the first determination subunit is used to determine the first target combination and the second target combination based on the target algorithm, the N first state combinations, and the objective function.

[0136] Optionally, the first processing subunit includes: a first processing module and a first encoding module. The first processing module is configured to initialize and assign values ​​to the state information of M subsystems in each of N state combinations based on the change information of the first subsystem, thereby obtaining N initial state combinations; and the first encoding module is configured to encode the state information of each of the N initial state combinations into a qubit probability amplitude according to a target algorithm, thereby obtaining N first state combinations, wherein the qubit probability amplitude is used to represent the complex value of encoding the state information of the subsystems in the target system into a quantum state.

[0137] Optionally, the first determination subunit includes: a first calculation module, a second processing module, and a first determination module. The first calculation module is used to calculate the target value corresponding to each first state combination in the N first state combinations according to the target function; the second processing module is used to use the target algorithm to perform multiple target operations on the N first state combinations based on the target value corresponding to each first state combination, until the number of target operations is greater than or equal to a preset number of iterations, thereby obtaining a target state combination, wherein each target operation is used to update the quantum bit probability amplitude of each first state combination according to the target value of each first state combination; and the first determination module is used to determine the first target combination and the second target combination based on the target state combination.

[0138] Optionally, the second processing module includes: a first updating submodule, a first calculating submodule, and a first determining submodule. The first updating submodule is configured to update the quantum bit probability amplitude of each first state combination in the N first state combinations using a quantum rotation gate based on a target algorithm and according to a target value of each first state combination, thereby obtaining N second state combinations; the first calculating submodule is configured to calculate a target value corresponding to each second state combination in the N second state combinations according to a target function; and the first determining submodule is configured to determine a target state combination from the N second state combinations based on the target value corresponding to each second state combination, wherein the target state combination is a combination of the N second state combinations whose corresponding target value is less than a corresponding second preset threshold.

[0139] Optionally, the first update submodule includes: a first update component, a second update component, and a third update component. The first update component, in the case of the first target operation, uses a quantum rotation gate to update the qubit probability amplitude of each first-state combination in N first-state combinations based on the target value of each first-state combination; the second update component, in the case of the i-th target operation, if the target value of the j-th first-state combination is less than the target value of the j-th first-state combination under the i-1 target operation, uses a quantum rotation gate to update the qubit probability amplitude of the j-th first-state combination based on each first-state combination corresponding to the i-th target operation, where i is an integer greater than 1 and j is a positive integer less than or equal to N; and the third update component, in the case of the i-th target operation, if the target value of the j-th first-state combination is greater than or equal to the target value of the j-th first-state combination under the i-1 target operation, uses a quantum rotation gate to update the qubit probability amplitude of the j-th first-state combination based on each first-state combination corresponding to the i-1 target operation.

[0140] Optionally, the first determination module includes: a first decoding submodule, configured to decode the quantum bit probability amplitude in the target state combination into actual state information to obtain the first target combination and the second target combination.

[0141] According to another aspect of the present application, a computer-readable storage medium is provided, which includes a stored executable program, wherein when the executable program runs, the device where the computer-readable storage medium is located is controlled to execute the above-mentioned system management method.

[0142] According to another aspect of an embodiment of the present application, an electronic device is also provided, comprising one or more processors and a memory, wherein the memory is used to store one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors execute the above-mentioned system management method.

[0143] The serial numbers of the above embodiments of the present application are for description only and do not represent the advantages or disadvantages of the embodiments.

[0144] In the above embodiments of the present application, the description of each embodiment has its own focus. For parts that are not described in detail in a certain embodiment, please refer to the relevant description of other embodiments.

[0145] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are only exemplary. For example, the division of the units can be a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of units or modules, which can be electrical or other forms.

[0146] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple units. Some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0147] In addition, the functional units in the various embodiments of the present application may be integrated into a single processing unit, or each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0148] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for enabling a computer device (which can be a personal computer, a server or a network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a read-only memory (ROM), a random access memory (RAM), a mobile hard disk, a magnetic disk or an optical disk.

[0149] The above is only a preferred embodiment of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present application. These improvements and modifications should also be regarded as the scope of protection of the present application.

Claims

1. A system management method, characterized in that: include: Acquire change information of a first subsystem in a target system, wherein the first subsystem is any subsystem in the target system; Determining a first target combination and a second target combination based on a target algorithm and change information of the first subsystem, wherein the target algorithm is used to search for a state combination that meets a preset condition in a state combination space, and the first target combination and the second target combination are combinations including state information of each subsystem in the target subsystem set, determined by the target algorithm after performing a traversal search for state information of a target subsystem set in the target system based on the change information of the first subsystem, wherein the preset condition is used to constrain the scope of influence of the first target combination and the second target combination on the performance of the target system, and the target subsystem set is used to represent a set of all subsystems in the target system that are associated with the first subsystem; receiving a screening result of the first target combination and the second target combination by a target person, wherein the screening result includes at least a target combination, wherein the target combination is a combination of the first target combination and the second target combination whose impact value on the performance of the target system is less than a first preset threshold; The status information of other subsystems in the target system except the first subsystem is adjusted according to the target combination.

2. The system management method according to claim 1, characterized in that: Determining a first target combination and a second target combination according to a target algorithm and change information of the first subsystem includes: Setting N state combinations, where N is an integer greater than 1, each of the N state combinations includes M subsystems, and the state information of each subsystem in the M subsystems changes with the change of the state information of other subsystems except the subsystem, where M is an integer greater than or equal to 1; Initializing the state information of the M subsystems in each of the N state combinations based on the change information of the first subsystem to obtain N first state combinations; Obtaining an objective function, wherein the objective function is used to quantify the impact of each first state combination on the performance of the target system; The first target combination and the second target combination are determined according to the target algorithm, N first state combinations and the target function.

3. The system management method according to claim 2, characterized in that: Initializing the state information of the M subsystems in each of the N state combinations based on the change information of the first subsystem to obtain N first state combinations, including: Initializing and assigning values ​​to the state information of the M subsystems in each of the N state combinations based on the change information of the first subsystem to obtain N initial state combinations; According to the target algorithm, the state information of each of the N initial state combinations is encoded into a quantum bit probability amplitude to obtain N first state combinations, wherein the quantum bit probability amplitude is used to represent the complex value of encoding the state information of the subsystem in the target system into a quantum state.

4. The system management method according to claim 3, characterized in that: Determining the first target combination and the second target combination according to the target algorithm, the N first state combinations, and the target function includes: Calculating a target value corresponding to each of the N first state combinations according to the target function; Based on the target value corresponding to each first-state combination, perform a plurality of target operations on the N first-state combinations using the target algorithm until the number of target operations is greater than or equal to a preset number of iterations, to obtain a target state combination, wherein each target operation is used to update the qubit probability amplitude of each first-state combination according to the target value of each first-state combination; The first target combination and the second target combination are determined according to the target state combination.

5. The system management method according to claim 4, characterized in that: The target operation includes the following steps: Based on the target algorithm, according to the target value of each first-state combination, using a quantum rotating gate to update the qubit probability amplitude of each first-state combination in the N first-state combinations to obtain N second-state combinations; Calculate the target value corresponding to each of the N second state combinations according to the target function; According to the target value corresponding to each second state combination, the target state combination is determined from N second state combinations, wherein the target state combination is a combination of the N second state combinations whose corresponding target values ​​are less than the corresponding second preset threshold.

6. The system management method according to claim 5, characterized in that: Updating the qubit probability amplitude of each of the N first-state combinations using a quantum rotating gate according to a target value of each of the first-state combinations includes: In the case of the first target operation, according to the target value of each first state combination, a quantum rotation gate is used to update the qubit probability amplitude of each first state combination in the N first state combinations; In the case of the i-th target operation, if the target value of the j-th first-state combination is less than the target value of the j-th first-state combination in the i-1-th target operation, using the quantum rotating gate to update the qubit probability amplitude of the j-th first-state combination based on each first-state combination corresponding to the i-th target operation, where i is an integer greater than 1 and j is a positive integer less than or equal to N; In the case of the i-th target operation, if the target value of the j-th first-state combination is greater than or equal to the target value of the j-th first-state combination under the i-1-th target operation, the quantum rotating gate is used to update the quantum bit probability amplitude of the j-th first-state combination based on each first-state combination corresponding to the i-1-th target operation.

7. The system management method according to claim 4, characterized in that: Determining the first target combination and the second target combination according to the target state combination includes: The qubit probability amplitude in the target state combination is decoded into actual state information to obtain the first target combination and the second target combination.

8. A system management device, characterized in that: include: an acquiring unit, configured to acquire change information of a first subsystem in a target system, wherein the first subsystem is any subsystem in the target system; a determination unit, configured to determine a first target combination and a second target combination based on a target algorithm and change information of the first subsystem, wherein the target algorithm is configured to search for a state combination that meets a preset condition in a state combination space, and the first target combination and the second target combination are combinations of state information of each subsystem in the target subsystem set, determined by the target algorithm after performing a traversal search for state information of a target subsystem set in the target system based on the change information of the first subsystem, wherein the preset condition is configured to constrain a range of influence of the first target combination and the second target combination on the performance of the target system, and the target subsystem set is configured to represent a set of all subsystems in the target system that are associated with the first subsystem; a receiving unit, configured to receive a screening result of the first target combination and the second target combination by a target person, wherein the screening result includes at least a target combination, wherein the target combination is a combination of the first target combination and the second target combination whose impact value on the performance of the target system is less than a first preset threshold; An adjusting unit is configured to adjust status information of other subsystems in the target system except the first subsystem according to the target combination.

9. A computer-readable storage medium, characterized in that The computer-readable storage medium includes a stored executable program, wherein when the executable program is run, the device where the computer-readable storage medium is located is controlled to execute the system management method according to any one of claims 1 to 7.

10. An electronic device, characterized in that: The system comprises one or more processors and a memory, wherein the memory is used to store one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors execute the system management method according to any one of claims 1 to 7.

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