A hardware redundancy monitoring method to ensure the safety of hydropower station monitoring system

Abstract

The present application relates to the technical field of hydropower safety monitoring, and discloses a hardware redundancy monitoring method for ensuring the safety of a hydropower station monitoring system, including the following steps: S1, quantum state perception, quantum measurement of redundant equipment in the hydropower station monitoring system to obtain equipment health status information; S2, quantum game optimal switching, based on quantum game theory to calculate the optimal Nash equilibrium strategy between redundant equipment and determine the optimal redundant equipment; S3, symplectic geometry path optimization, based on Hamilton dynamics to calculate the optimal equipment switching path; S4, redundant equipment switching execution, execute equipment state migration in the topological network, and continuously monitor the health status of the new equipment. Redundant equipment switching decisions are made through quantum game theory, achieving a more intelligent and efficient redundant equipment selection effect, thereby ensuring the flexibility and adaptability of redundant equipment and avoiding resource waste or unnecessary switching delays in traditional methods.

Description

Technical Field

[0001] The invention relates to the technical field of hydropower safety monitoring, and in particular to a hardware redundancy monitoring method for ensuring the safety of a monitoring system of a hydropower station. Background Art

[0002] With the continuous improvement of the scale and automation level of hydropower stations, the monitoring system of hydropower stations plays a vital role in ensuring the safe and stable operation of hydropower generation. In order to improve the reliability and fault tolerance of the monitoring system of hydropower stations, the management of redundant equipment has become a key technology. Existing redundant equipment management methods are usually based on traditional backup switching mechanisms. These methods replace the faulty equipment with redundant equipment when the equipment fails or the performance degrades, thereby ensuring the continuity of the system. Common redundancy strategies include hot backup, cold backup, dual-machine hot backup, etc. These methods perform redundant switching based on pre-set rules or thresholds, and usually rely on simple load balancing, system diagnosis or physical redundancy to achieve equipment switching.

[0003] These traditional technologies can effectively deal with short-term equipment failures in many cases and ensure that the system is not interrupted. However, with the increasing complexity of monitoring systems and the uncertainty of the operating environment, the existing redundant switching methods have also exposed some shortcomings in practical applications. First, the traditional redundant switching mechanism is usually based on fixed thresholds or rules for judgment, lacks flexibility, and cannot be adjusted intelligently according to the real-time status of the equipment, resulting in the switching of redundant equipment being too dependent on preset conditions, which may lead to problems such as resource waste or switching delays. Secondly, the selection and switching decisions of redundant equipment usually rely on manually set diagnostic rules or sensor feedback, which may have the risk of misjudgment, especially in complex environments, where errors in sensor data will affect the judgment of equipment status. Furthermore, the redundant switching process often consumes a lot of time and energy, especially when the system needs to switch frequently between multiple redundant devices, which may lead to a decrease in system efficiency. Summary of the invention

[0004] In view of the deficiencies in the prior art, the present invention provides a hardware redundancy monitoring method for ensuring the safety of a hydropower station monitoring system, which solves the problems of inflexible switching of redundant equipment, inaccurate decision-making, high switching process delay and high energy consumption in the existing hydropower station monitoring system.

[0005] To achieve the above objectives, the present invention is implemented through the following technical solutions: A hardware redundancy monitoring method for ensuring the safety of a hydropower station monitoring system comprises the following steps:

[0006] S1, quantum state perception, quantum measurement of redundant equipment in the hydropower station monitoring system to obtain equipment health status information;

[0007] S2, quantum game optimal switching, based on quantum game theory, calculate the optimal Nash equilibrium strategy between redundant devices and determine the optimal redundant device;

[0008] S3, symplectic geometry path optimization, calculates the optimal device switching path based on Hamiltonian dynamics;

[0009] S4. Redundant device switching is executed, device status migration is performed in the topology network, and the health status of the new device is continuously monitored.

[0010] Preferably, in step S1, quantum state sensing includes:

[0011] (1) Establish the quantum state density matrix of redundant devices and use quantum measurement methods to obtain device health status information;

[0012] (2) Measure the device state through the quantum measurement operator and calculate the probability distribution of the device state after quantum measurement;

[0013] (3) Calculate the quantum coherence of the device's health status and determine whether to perform device redundancy switching based on the coherence. If the coherence exceeds the set threshold, enter the redundant switching decision stage.

[0014] Preferably, the quantum measurement of the health status of the device adopts a density matrix measurement method, the measurement operator satisfies the completeness condition, and the change of the device state after measurement complies with the state collapse principle.

[0015] Preferably, in step S2, the optimal switching of quantum game includes:

[0016] (1) Establish a quantum game model and set the strategy space of multiple redundant devices in the hydropower station monitoring system;

[0017] (2) Calculate the Nash equilibrium point and obtain the optimal redundant switching strategy to maximize the stability of the equipment state;

[0018] (3) Select the optimal redundant device as the current primary device and enter the switching phase.

[0019] Preferably, the Nash equilibrium point is calculated based on quantum strategy optimization, and its optimal strategy is iteratively updated through a quantum evolution operator, and the iterative update method adopts a quantum strategy evolution algorithm.

[0020] Preferably, in step S3, the symplectic geometric path optimization includes:

[0021] (1) Construct the Hamiltonian dynamic equation of the device state and set the redundant device state variables and conjugate momentum;

[0022] (2) Solve the optimal switching path of device states based on the Hamilton-Jacobi equation;

[0023] (3) Under the premise of optimal Hamiltonian action, the shortest switching trajectory between redundant devices is calculated and the device state migration process is optimized. The boundary conditions of the switching path include the maximum switching time and the maximum energy consumption.

[0024] Preferably, in the Hamiltonian kinetic equation, the device state variables and the conjugate momentum constitute a symplectic geometric space, whose Hamiltonian quantities include the potential energy function of the device health state and satisfy the energy conservation condition.

[0025] Preferably, the Hamilton-Jacobi equation is solved by a variational method to obtain an optimal switching path and ensure minimum energy consumption during device state transition.

[0026] Preferably, in step S4, the redundant device switching execution includes:

[0027] (1) Perform device state migration in the topology network and adjust the data flow path between devices;

[0028] (2) After the switch is completed, the quantum coherence of the new device is calculated and its health status is continuously monitored;

[0029] (3) If the health status of the new device is abnormal, the quantum game optimal switching and symplectic geometry path optimization are re-executed to ensure the stable operation of the redundant devices. The switching process includes real-time optimization to minimize the switching delay based on the device health prediction.

[0030] Preferably, the method forms a closed-loop optimization system, which can dynamically adjust the quantum measurement threshold, game optimization strategy and Hamilton action optimization condition during system operation to adapt to redundant monitoring requirements in different environments.

[0031] The present invention provides a hardware redundancy monitoring method for ensuring the safety of a hydropower station monitoring system. It has the following beneficial effects:

[0032] 1. The present invention uses quantum game theory to make redundant device switching decisions, achieving a more intelligent and efficient redundant device selection effect. Compared with the redundant switching method based on fixed thresholds or preset rules in the prior art, the selection of redundant devices is optimized by quantum game, and the switching strategy can be adjusted in real time. By calculating the Nash equilibrium, the flexibility and adaptability of redundant devices can be ensured, avoiding resource waste or unnecessary switching delays in traditional methods.

[0033] 2. The present invention realizes the calculation of the shortest switching path through symplectic geometry path optimization, reduces the time delay and energy consumption in redundant switching. Different from the prior art which only relies on the shortest path algorithm for redundant switching, the present invention uses symplectic geometry to optimize the calculation of the switching path and combines multiple factors such as the health status and energy consumption of the equipment to find the optimal switching path. This not only reduces the switching delay, but also ensures the minimization of energy loss, thereby improving the overall operation efficiency of the system.

[0034] 3. The present invention effectively improves the accuracy and timeliness of redundant switching through quantum state perception and real-time health status monitoring. Compared with the traditional scheme, the monitoring of equipment health status is often affected by sensor errors or data delays, resulting in the problem of inaccurate redundant switching decisions. Through quantum measurement and quantum coherence calculation, the health status of the equipment can be grasped in real time, thereby ensuring the timeliness and accuracy of redundant switching, thereby improving the reliability of the hydropower station monitoring system.

[0035] 4. The present invention forms a closed-loop optimization system that can dynamically adjust the quantum measurement threshold and redundant switching strategy, thereby achieving a high degree of adaptability to device management in complex environments. Different from the fixed redundant strategy and hard threshold approach in the prior art, the redundant switching strategy is automatically adjusted according to changes in the device operating environment, and a closed-loop optimization mechanism is introduced, so that the system can maintain efficient operation under various working conditions, thereby maximizing the stability and flexibility of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 The figure is a flow chart of the method steps of the present invention. DETAILED DESCRIPTION

[0037] The following will be combined with the drawings in the specification of the present invention to clearly and completely describe the technical solutions 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 of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0038] Please refer to the attached Figure 1 The embodiment of the present invention provides a hardware redundancy monitoring method for ensuring the safety of a hydropower station monitoring system, comprising the following steps:

[0039] S1, quantum state perception, quantum measurement of redundant equipment in the hydropower station monitoring system to obtain equipment health status information;

[0040] S2, quantum game optimal switching, based on quantum game theory, calculate the optimal Nash equilibrium strategy between redundant devices and determine the optimal redundant device;

[0041] S3, symplectic geometry path optimization, calculates the optimal device switching path based on Hamiltonian dynamics;

[0042] S4, redundant device switching execution, device state migration in the topology network, and continuous monitoring of the health status of the new device;

[0043] The method forms a closed-loop optimization system, which can dynamically adjust the quantum measurement threshold, game optimization strategy and Hamilton action optimization condition during system operation to meet the redundant monitoring needs in different environments.

[0044] Specifically, in this embodiment, by introducing advanced technologies such as quantum games, symplectic geometry optimization and quantum state perception, the management and switching process of redundant devices is comprehensively optimized. In this method, the redundant devices in the system are first monitored in real time through the quantum state perception module, and quantum measurement technology is used to obtain the health status information of the device, and through the calculation of quantum density matrix and coherence, it is accurately evaluated whether the device is in normal working condition. If the coherence of the device exceeds the set threshold, indicating that the health of the device is poor or there is a risk of failure, the system will enter the decision-making stage of redundant device switching. This process provides accurate data support for subsequent device switching, avoiding the misjudgment problem of relying on simple rules or sensors in traditional methods.

[0045] Next, during the redundant device selection process, the system uses quantum game theory for optimization. By establishing a quantum game model and taking into account the mutual influence between redundant devices, the system calculates the optimal switching strategy for redundant devices. This process optimizes device selection by solving the Nash equilibrium point to ensure that the redundant switching decision meets the current health status of the device and system requirements, thereby maximizing system stability. The intelligent features of this module make the switching of redundant devices more flexible and can be dynamically adjusted according to real-time changes, thereby improving the system's fault tolerance.

[0046] Once the redundant devices are determined, the system enters the symplectic geometry path optimization stage. At this point, based on the device status and the health status of the selected redundant devices, the system optimizes the device status conversion through the Hamilton dynamic equation. In this process, the change of device status follows the principle of energy conservation, and the optimal switching path is calculated by solving the Hamilton-Jacobi equation. The optimization of the path not only takes into account the minimization of device switching time, but also comprehensively considers energy consumption to ensure smooth switching of devices with minimal energy loss. The introduction of the optimized path avoids the waste of resources and switching delay problems in traditional methods, and provides an efficient solution for the migration of devices from the current state to the redundant device state.

[0047] Finally, after the calculation of the switching path is completed, the switching execution phase of the redundant device begins. At this point, the system will connect the redundant device to the workflow of the main device through topological network adjustment, and monitor the health status of the new device in real time. By continuously calculating the quantum coherence, the system can determine whether the new device can stably take over the task. If the health status is abnormal, the system will automatically re-execute quantum game optimization and path optimization to ensure the stable operation of the equipment. This closed-loop monitoring and optimization process greatly improves the intelligence and reliability of redundant device switching, ensuring that the hydropower station monitoring system can quickly and accurately complete redundant switching when equipment fails.

[0048] In step S1, quantum state perception includes:

[0049] (1) Establish the quantum state density matrix of redundant devices and use quantum measurement methods to obtain device health status information;

[0050] (2) Measure the device state through the quantum measurement operator and calculate the probability distribution of the device state after quantum measurement;

[0051] (3) Calculate the quantum coherence of the health status of the device and determine whether to perform device redundancy switching based on the coherence. If the coherence exceeds the set threshold, enter the redundant switching decision stage;

[0052] The quantum measurement of the health status of the device adopts the density matrix measurement method. The measurement operator satisfies the completeness condition, and the change of the device state after measurement conforms to the state collapse principle.

[0053] Specifically, the S1 step is not only used to accurately evaluate the status of the device, but also provides the necessary basis for the subsequent redundant device switching. Specifically, quantum state perception can reveal the health status of the device from different levels through quantum measurement and quantum coherence calculation, thereby determining whether it is necessary to enter the redundant switching decision stage. Since the health status of the device is crucial to the stability of the system, accurate and timely acquisition of this information is the key to achieving efficient redundant switching.

[0054] In this embodiment, quantum state perception evaluates the status of redundant equipment in the hydropower station monitoring system through quantum measurement. First, the health status of redundant equipment is represented by a quantum state density matrix. Specifically, the status of the equipment can be represented by a quantum state density matrix. The health state of each device can be described by a set of quantum states. and the corresponding probability Quantify. The formula is:

[0055]

[0056] in:

[0057] Indicates the device The quantum state density matrix,

[0058] For equipment No. possible health states,

[0059] The device is in state

[0060] The probability of satisfying .

[0061] Through the quantum density matrix , which can accurately describe the health status of the device at a certain moment.

[0062] As an alternative, the quantum measurement operator It is used to measure the health status of the device and update the quantum state of the device based on the measurement results. According to the basic principles of quantum mechanics, the quantum state will collapse after measurement. The measurement operator Satisfy the completeness condition:

[0063]

[0064] At this time, the device health status will become , calculated by the following formula:

[0065]

[0066] in, is the probability of measurement.

[0067] In this process, quantum coherence is an important indicator for evaluating the stability of the device state. It can be calculated by the following formula:

[0068]

[0069] In general, the higher the quantum coherence, the more uncertain the health status of the device, which indicates that the device may be in an unstable state and requires redundant switching. The calculation of quantum coherence can provide a basis for redundant switching decisions in subsequent steps.

[0070] Specifically, when the calculated quantum coherence When the preset threshold is exceeded, it means that the uncertainty of the device status increases, and the system needs to enter the redundant switching decision stage. The set threshold can be dynamically adjusted according to the working environment and historical data of the device to ensure that the best redundant switching decision can be obtained in different environments.

[0071] In one possible implementation, the threshold of quantum coherence can be adaptively adjusted through a machine learning algorithm to predict the health status of the device based on historical health data, further improving the accuracy of status perception.

[0072] Through the above-mentioned quantum state perception technical solution, the present invention can obtain the health status of redundant equipment in real time and accurately, and calculate its coherence, providing reliable data support for subsequent redundant switching decisions. This solution effectively avoids the misjudgment problem caused by simple threshold judgment in traditional methods, ensures the accuracy and timeliness of redundant switching, and provides important technical support for the safety of hydropower station monitoring systems.

[0073] In step S2, the optimal switching of quantum games includes:

[0074] (1) Establish a quantum game model and set the strategy space of multiple redundant devices in the hydropower station monitoring system;

[0075] (2) Calculate the Nash equilibrium point and obtain the optimal redundant switching strategy to maximize the stability of the equipment state;

[0076] (3) Select the optimal redundant device as the current main device and enter the switching phase; the Nash equilibrium point is calculated based on quantum strategy optimization, and its optimal strategy is iteratively updated through the quantum evolution operator. The iterative update method adopts the quantum strategy evolution algorithm.

[0077] Specifically, in this embodiment, the quantum game optimal switching method uses quantum game theory to model the interaction between redundant devices. First, the system needs to set a strategy space for multiple redundant devices in the hydropower station monitoring system. Each redundant device There is a quantum strategy , whose quantum state can be expressed by the quantum evolution operator Update. The formula is:

[0078]

[0079] in, Indicates the device The quantum state of is the initial quantum state of the device. The strategy of each device is a quantum operator that controls the state of the device.

[0080] In general, redundant devices select the optimal strategy through game interaction with other devices. Specifically, the system calculates the optimal redundant device switching strategy through the Nash equilibrium principle of quantum game. Nash equilibrium is a state in which each player in the game chooses the optimal strategy. Therefore, the system finds the best redundant device switching solution by solving the Nash equilibrium point. The formula is:

[0081]

[0082] in, Indicates the device The optimal strategy, Yes Equipment The quantum state density matrix, is the quantum evolution operator, is its conjugate transpose.

[0083] In some embodiments, the calculation of the Nash equilibrium point can be achieved by the quantum strategy evolution algorithm, which uses the advantages of quantum computing to accelerate the game optimization process and make the selection of redundant devices more efficient. The core idea of ​​the algorithm is to quickly converge to the optimal strategy through the superposition and interference of quantum states. Specifically, the quantum strategy evolution algorithm iteratively updates the strategy , until the strategies of all devices reach Nash equilibrium, and finally determine the optimal switching solution for redundant devices.

[0084] In one possible implementation, quantum game optimization not only considers the health status of the current device, but also dynamically adjusts the redundancy strategy based on the historical data of the device and the game relationship between them. In this way, redundant switching can make adaptive decisions based on the real-time status and historical behavior of the device, thus avoiding the static switching method that simply relies on preset rules and improving the flexibility and fault tolerance of the system.

[0085] In addition, quantum game optimal switching is not only applicable to single device switching, it can also be extended to the collaborative work of multiple devices in the entire system. By modeling and solving the game relationship between multiple redundant devices, the system can ensure the selection of the optimal redundant device among multiple alternative devices, thereby maximizing the reliability and efficiency of the monitoring system.

[0086] In one implementation, the strategy for optimal switching of quantum games may also be combined with device performance prediction, such as predicting future health changes of the device, to further improve the foresight and accuracy of the switching.

[0087] Through the optimal switching steps of quantum games, the present invention can effectively perform dynamic selection and optimized switching of redundant devices, avoiding the problems caused by redundant device selection based on static rules in traditional methods, such as waste of device resources and response delays.

[0088] In step S3, the symplectic geometry path optimization includes:

[0089] (1) Construct the Hamiltonian dynamic equation of the device state and set the redundant device state variables and conjugate momentum;

[0090] (2) Solve the optimal switching path of device states based on the Hamilton-Jacobi equation;

[0091] (3) Under the premise of optimal Hamiltonian action, the shortest switching trajectory between redundant devices is calculated and the device state migration process is optimized, where the boundary conditions of the switching path include the maximum switching time and the maximum energy consumption;

[0092] In the Hamiltonian dynamics equation, the device state variables and conjugate momentum form a symplectic geometric space, whose Hamiltonian quantity includes the potential energy function of the device health state and satisfies the energy conservation condition;

[0093] The Hamilton-Jacobi equation is solved by the variational method to obtain the optimal switching path and ensure the minimum energy consumption during the device state transition.

[0094] Specifically, in this embodiment, in step S3, the symplectic geometric path optimization first describes the change of the redundant device state by constructing the Hamiltonian dynamic equation of the device state. The state of the device is defined as the state variable , which indicates the health status of the device; the rate of change, energy, etc. of the device can be expressed by conjugate momentum Device Status and conjugate momentum Together they form a symplectic geometric phase space that describes the entire process of a device migrating from one state to another.

[0095] In classical mechanics, Hamilton's dynamic equations are used to describe the motion of particles. In the present invention, a similar model is used to describe the health status and energy consumption of redundant devices. The state change of the device is described by the following equation:

[0096]

[0097] in:

[0098] For equipment The health status variable indicates the health status of the device at a certain moment.

[0099] For equipment The conjugate momentum of a device is usually related to the rate of change or energy consumption of the device.

[0100] It is a Hamiltonian quantity that describes the total energy of the device state.

[0101] In the present invention, the Hamiltonian of a device consists of two parts: kinetic energy and potential energy. The kinetic energy of a device is usually related to its rate of change or energy consumption, while the potential energy function The energy required to describe the change in the health state of the device is . For example, the change in the health state of the device may be affected by external factors or internal faults, so the potential energy function It can be adjusted according to actual conditions. The Hamilton quantity can be expressed as:

[0102]

[0103] in:

[0104] is the kinetic energy term, which represents the energy of the device state change;

[0105] is the potential energy term, describing the change in the health status of the equipment;

[0106] It is a quality parameter of the equipment, usually related to characteristics such as the equipment's responsiveness and fault tolerance.

[0107] In order to ensure that the change of device state is smooth and controllable, the energy conservation condition is introduced in the present invention. During the device switching process, the total energy of the system should remain constant. This energy conservation condition ensures that the device will not exceed the predetermined energy consumption range when switching. The expression of energy conservation is:

[0108]

[0109] This means that during the device state migration process, the Hamiltonian There will be no changes, thus ensuring the stability of the equipment and smooth switching.

[0110] Once the state and momentum of the device are described in symplectic geometry, the Hamilton-Jacobi equation is used to calculate the optimal device switching path. The Hamilton-Jacobi equation is used to solve the optimal path within a given time. Specifically, this equation describes the action quantity during the device state change process The equation is as follows:

[0111]

[0112] in:

[0113] is the symplectic geometric action, which represents the optimal switching path of the device state;

[0114] It is a Hamiltonian quantity that describes the total energy of the device state.

[0115] In order to obtain the optimal path, it is necessary to solve the action The optimal path from the current state of the device to the target state can be calculated through variational methods or numerical optimization methods.

[0116] In some embodiments, the Hamilton-Jacobi equation is solved using the calculus of variations to optimize the device switching path. The purpose of the calculus of variations is to minimize the energy or time of the device from the initial state to the target state. Specifically, the solution to the optimal path can be expressed by the following calculus of variations expression:

[0117]

[0118] in:

[0119] is the Lagrangian, which represents the energy form of the device state change;

[0120] and The start and end time of the device status change.

[0121] Through this process, the system can calculate the minimum energy consumption and shortest time path to ensure that the device does not cause excessive energy waste or time delay during redundant switching.

[0122] In order to further improve the stability and efficiency of the switching process, the present invention also introduces boundary conditions, which provide constraints when optimizing the path. For example, the device switching process may be limited by the maximum switching time and the maximum energy consumption. Specifically, the boundary conditions of the switching path may include:

[0123] Maximum switching time: To avoid delays during the switching process, the system will set a maximum switching time to ensure that the device can complete the switching within the specified time.

[0124] Maximum energy consumption: The system will set an upper limit on energy consumption based on the device's operating conditions and historical data to prevent excessive energy consumption during device switching.

[0125] The introduction of these boundary conditions makes the optimization path not only focus on the state changes of the equipment, but also needs to be selected under the constraints of time and energy.

[0126] In step S4, the redundant device switching execution includes:

[0127] (1) Perform device state migration in the topology network and adjust the data flow path between devices;

[0128] (2) After the switch is completed, the quantum coherence of the new device is calculated and its health status is continuously monitored;

[0129] (3) If the health status of the new device is abnormal, the quantum game optimal switching and symplectic geometry path optimization are re-executed to ensure the stable operation of the redundant devices. The switching process includes real-time optimization to minimize the switching delay based on the device health prediction.

[0130] Specifically, in this embodiment, step S4 is mainly completed by adjusting the topology network. After completing the redundant device selection, the system will migrate the load and data flow of the current device to the new redundant device. In order to ensure the continuity and reliability of data transmission during the migration process, the system needs to reconfigure the communication link between the devices so that the redundant device can seamlessly take over the task. Specifically, during the switching process, the device state will be based on the Hamiltonian dynamic equation and the optimal switching path calculated previously.

[0131] Perform the migration to ensure that data flow along the path is not affected.

[0132] In one possible implementation, the execution of device switching depends not only on the physical state of the redundant device, but also on the intelligent scheduling of the device workload. By predicting the device load in real time, the system can more accurately determine when to switch to the redundant device and how to allocate computing and communication resources during the switching process, thereby reducing delays and load fluctuations during the switching process.

[0133] For example, suppose the device is the current master device, and the device is selected as the new redundant device. In this case, the system first redirects the data flow from the device Migrate to , and reconfigures the network topology based on the optimized path. The data migration process is dynamically adjusted to ensure that network bandwidth and transmission capacity are fully utilized without causing data loss.

[0134] In general, the success of the switching process depends on good coordination between devices, especially in complex hydropower station monitoring systems. In order to ensure that the equipment after redundant switching can work stably in the shortest time, the system will perform real-time calculations of quantum coherence. The calculation of quantum coherence can help determine whether the new equipment has stably taken over the task and provide a basis for further operations. At this time, real-time monitoring of the health status of the equipment is also very necessary. Specifically, the quantum coherence of the equipment New equipment will be continuously evaluated The system will monitor the working status of the coherence value and compare it with the set threshold. If the coherence value exceeds the normal range, the system will promptly issue an alarm and initiate backup measures.

[0135] In some embodiments, the system will also combine prediction algorithms to evaluate the future health status of the device, thereby achieving forward-looking switching decisions. Through machine learning or deep learning algorithms, the system can predict the failure mode and performance changes of the device and further optimize the timing of switching execution. This method can reduce the delays or misjudgments that may occur in traditional methods and improve the intelligence level of redundant switching.

[0136] In addition, if the device If an abnormal health status is shown after switching, the system will start the self-recovery mechanism and re-enter the process of quantum game optimal switching and symplectic geometry path optimization. Specifically, the system will re-evaluate whether the current redundant equipment is suitable and perform alternative switching when necessary. This mechanism ensures that the redundant equipment can operate stably under any circumstances, avoiding the common equipment failure or data loss problems in traditional redundant switching methods.

[0137] By optimizing the execution of redundant device switching, the present invention can ensure that the stability of the system is not affected during the device switching process, data transmission remains smooth, and the new device can take over the task in a timely and stable manner. In addition, the system can also adjust the resource allocation during the redundant switching process in real time, thereby achieving efficient and stable redundant device switching and ensuring the safety and reliability of the hydropower station monitoring system.

[0138] By combining quantum state perception, quantum game optimal switching, symplectic geometry path optimization and redundant device switching execution, the present invention provides a comprehensive redundancy management solution, ensuring that the device can quickly and intelligently complete redundant switching in the event of a failure, minimizing system downtime and energy consumption.

[0139] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A hardware redundancy monitoring method for ensuring the safety of a hydropower station monitoring system, characterized in that: The following steps are involved: S1, quantum state perception, quantum measurement of redundant equipment in the hydropower station monitoring system to obtain equipment health status information; S2, quantum game optimal switching, based on quantum game theory, calculates the optimal Nash equilibrium strategy between redundant devices and determines the optimal redundant device; S3, symplectic geometry path optimization, calculates the optimal device switching path based on Hamiltonian dynamics; S4, redundant device switching execution, device state migration in the topology network, and continuous monitoring of the health status of the new device; Among them, symplectic geometry path optimization includes: (1) Construct Hamiltonian dynamic equations of device state and set redundant device state variables and conjugate momentum; (2) Solve the optimal switching path of device status based on the Hamilton-Jacobi equation; (3) Under the premise of optimal Hamiltonian action, the shortest switching trajectory between redundant devices is calculated and the device state migration process is optimized. The boundary conditions of the switching path include the maximum switching time and the maximum energy consumption.

2. A hardware redundancy monitoring system for ensuring the safety of a hydropower station monitoring system according to claim 1 The method is characterized in that In the step S1, quantum state sensing includes: (1) Establish the quantum state density matrix of redundant devices and use quantum measurement methods to obtain device health status information; (2) Measure the device state through the quantum measurement operator and calculate the probability distribution of the device state after quantum measurement; (3) Calculate the quantum coherence of the device's health status and determine whether to perform device redundancy switching based on the coherence. If the coherence exceeds the set threshold, enter the redundant switching decision stage.

3. A hardware redundancy monitoring method for ensuring the safety of a hydropower station monitoring system according to claim 2, characterized in that: The quantum measurement of the health status of the device adopts a density matrix measurement method, the measurement operator satisfies the completeness condition, and the change of the device state after measurement conforms to the state collapse principle.

4. According to claim 1, a hardware redundancy monitoring method for ensuring the safety of a hydropower station monitoring system is characterized in that: In the step S2, the optimal switching of quantum game includes: (1) Establish a quantum game model and set the strategy space of multiple redundant devices in the hydropower station monitoring system; (2) Calculate the Nash equilibrium point and obtain the optimal redundant switching strategy to maximize the stability of the equipment state; (3) Select the optimal redundant device as the current primary device and enter the switching phase.

5. A hardware redundancy monitoring method for ensuring the safety of a hydropower station monitoring system according to claim 4, characterized in that: The Nash equilibrium point is calculated based on quantum strategy optimization, and its optimal strategy is iteratively updated through quantum evolution algorithm. The iterative update method adopts quantum strategy evolution algorithm.

6. A hardware redundancy monitoring method for ensuring the safety of a hydropower station monitoring system according to claim 1, characterized in that: In the Hamiltonian dynamic equation, the device state variables and the conjugate momentum form a symplectic geometric space, the Hamiltonian quantity of which includes the potential energy function of the device health state and satisfies the energy conservation condition.

7. A method for hardware redundancy monitoring to ensure the safety of a hydropower station monitoring system according to claim 1, characterized in that: The Hamilton-Jacobi dynamic equation is solved by the variational method to obtain the optimal switching path and ensure the minimum energy consumption during the device state transition.

8. A hardware redundancy monitoring method for ensuring the safety of a hydropower station monitoring system according to claim 1, characterized in that: In the step S4, the redundant device switching execution includes: (1) Perform device state migration in the topology network and adjust the data flow path between devices; (2) After the switch is completed, the quantum coherence of the new device is calculated and its health status is continuously monitored; (3) If the health status of the new device is abnormal, the quantum game optimal switching and symplectic geometry path optimization are re-executed to ensure the stable operation of the redundant devices. The switching process includes real-time optimization to minimize the switching delay based on the device health prediction.

9. A method for hardware redundancy monitoring for ensuring the safety of a hydropower station monitoring system according to claim 1, characterized in that: The method forms a closed-loop optimization system, which can dynamically adjust the quantum measurement threshold, game optimization strategy and Hamilton action optimization condition during system operation to meet the redundant monitoring needs in different environments.

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