Hamiltonian operator local spectrum property analysis method

Abstract

The invention relates to the technical field of data processing, in particular to a Hamiltonian operator local spectrum property analysis method, which comprises the following steps of: acquiring real-time temperature, power consumption and cooling liquid flow of a grid; determining a high temperature difference grid; determining a temporary grid; determining an analysis grid; inputting data to the Hamiltonian operator model; and adjusting the temperature gradient threshold and generating an abnormal property analysis report. Temperature, power consumption and cooling liquid flow data in the quantum computer cooling unit are collected in real time, local spectrum energy eigenvalue analysis is combined, temperature control abnormity can be accurately recognized and adjusted, the stability and efficiency of the cooling unit are improved, the temperature gradient threshold value can be dynamically optimized, and the accuracy of temperature control is improved. Through the generated abnormal property analysis report, potential problems are timely found and maintained, the service life of equipment is effectively prolonged, the utilization of cooling resources is optimized, and the problem of low accuracy of an output result caused by excessive dependence on historical data is effectively solved.

Description

Technical Field

[0001] The present invention relates to the technical field of quantum computing data processing, and in particular to a method for analyzing the local spectral properties of a Hamiltonian operator. Background Art

[0002] With the rapid development of computer technology, especially in the fields of quantum computing and high-performance computing, the demand for data analysis is increasing. Traditional data analysis methods often face challenges such as long calculation time and low accuracy when dealing with large-scale and complex data. To meet the needs of real-time data analysis and accurate prediction, new analysis methods have been proposed. In particular, algorithms that can efficiently process high-dimensional data and have high accuracy have become a research hotspot. The requirements for analysis speed and accuracy in fields such as quantum computing and cooling systems are particularly stringent. Therefore, advanced mathematical models, optimization algorithms, and efficient computing frameworks are needed to achieve accurate and rapid real-time analysis and warning.

[0003] The patent document with the publication number CN107895206A discloses a multi-step wind energy prediction method based on singular spectrum analysis and locality-sensitive hashing. The method includes: 1) obtaining historical wind energy data of a wind farm; 2) using singular spectrum analysis to decompose the historical wind energy data of the wind farm into two components: a low-frequency average trend component and a high-frequency fluctuation component; 3) reconstructing the average trend component and the fluctuation component obtained in step 2) into an average trend segment and a fluctuation component segment in the phase space; 4) using locality-sensitive hashing to select a similar average trend segment of the average trend segment to be predicted; 5) taking the combination of the obtained similar average trend segment and the corresponding fluctuation component segment as the training input of a support vector regression model, and the prediction result is the wind power output.

[0004] It can be seen that the multi-step wind energy prediction method based on singular spectrum analysis and locality-sensitive hashing has the following problems: This method depends on the accuracy of historical data. If the historical wind energy data is inaccurate or incomplete, it will affect the reliability of the decomposition and reconstruction processes; Locality-sensitive hashing cannot fully capture complex non-linear relationships when selecting similar segments, resulting in limited accuracy of the prediction results; The training and prediction processes of the model are relatively dependent on the effect of the support vector regression model. If the training data is insufficient or the feature selection is inappropriate, the generalization ability of the model is weak, affecting the accuracy of the final prediction result. Summary of the Invention

[0005] For this reason, the present invention provides a method for analyzing the local spectral properties of a Hamiltonian operator, which is used to overcome the problem of low accuracy of the output result in the prior art due to excessive dependence on historical data by optimizing temperature gradient adjustment and local spectral energy analysis.

[0006] To achieve the above object, the present invention provides a method for analyzing the local spectral properties of a Hamiltonian operator, including:

[0007] Obtain the real-time temperature, real-time power consumption, and real-time coolant flow rate of each grid to be processed in the monitored area divided by a grid in the quantum computer cooling unit;

[0008] Determine a number of high-temperature difference grids based on the real-time temperature of any two of the grids to be processed and a preset temperature gradient threshold;

[0009] Determine a number of temporary grids based on the real-time power consumption and the real-time coolant flow rate of each of the high-temperature difference grids;

[0010] Determine a number of analysis grids based on the real-time temperature and the real-time coolant flow rate of any two adjacent temporary grids;

[0011] Input the real-time temperature, the real-time power consumption, and the real-time coolant flow rate of each of the analysis grids into a preset Hamiltonian operator model to obtain a number of local spectral energy eigenvalues;

[0012] Adjust the preset temperature gradient threshold based on the local spectral energy eigenvalues of any two of the analysis grids to form an adjusted gradient threshold;

[0013] Generate an abnormal property analysis report based on the local spectral energy eigenvalues formed by the adjusted gradient threshold.

[0014] Further, determining a number of high-temperature difference grids based on the real-time temperature of any two of the grids to be processed and a preset temperature gradient threshold includes:

[0015] Obtain the center distance between any two of the grids to be processed to form a processing grid distance;

[0016] Calculate the ratio of the difference between the corresponding two real-time temperatures and the processing grid distance to form a temperature gradient;

[0017] When the temperature gradient is greater than the preset temperature gradient threshold, determine that the corresponding two grids to be processed are both the high-temperature difference grids to form a number of high-temperature difference grids.

[0018] Further, determining a number of temporary grids based on the real-time power consumption and the real-time coolant flow rate of each of the high-temperature difference grids includes:

[0019] Calculate the standard deviation of the real-time power consumption within a preset first determination duration to form a consumption fluctuation value;

[0020] Calculate the standard deviation of the real-time coolant flow rate within the preset first determination duration to form a first flow fluctuation value;

[0021] Determine a number of temporary grids based on the consumption fluctuation value and the first flow fluctuation value.

[0022] Further, determining a number of temporary grids based on the consumption fluctuation value and the flow fluctuation value includes:

[0023] Draw a change curve of the consumption fluctuation value to form a consumption curve;

[0024] Draw a change curve of the flow fluctuation value to form a first flow curve;

[0025] Calculate the cosine similarity between the consumption curve and the first flow curve to form a change consistency;

[0026] When the change consistency is less than a preset consistency threshold, determine that the high temperature difference grid is a temporary grid to form a number of temporary grids.

[0027] Further, determining a number of analysis grids based on the real-time temperature and the real-time coolant flow of any two adjacent temporary grids includes:

[0028] Calculate the standard deviation of the real-time temperature of a single temporary grid within a preset second determination duration to form a temperature fluctuation value;

[0029] Calculate the standard deviation of the real-time coolant flow of a single temporary grid within the preset second determination duration to form a second flow fluctuation value;

[0030] Determine a synchronization degree based on the temperature fluctuation value and the second flow fluctuation value;

[0031] Determine a number of analysis grids based on the synchronization degrees of any two adjacent temporary grids.

[0032] Further, determining a synchronization degree based on the consumption fluctuation value and the second flow fluctuation value includes:

[0033] Draw a change curve of the temperature fluctuation value to form a temperature curve;

[0034] Draw a change curve of the second flow fluctuation value to form a second flow curve;

[0035] Calculate the cosine similarity between the temperature curve and the second flow curve to form a synchronization degree.

[0036] Further, determining a number of analysis grids based on the synchronization degrees of any two adjacent temporary grids includes:

[0037] When both of the two synchronization degrees are greater than a preset synchronization threshold, calculate the relative deviation of the two synchronization degrees to form a synchronization deviation value;

[0038] When the synchronization deviation value is less than a preset synchronization deviation threshold, it is determined that the corresponding two temporary grids are the analysis grids, and a number of analysis grids are formed.

[0039] Further, adjusting the preset temperature gradient threshold according to the local spectral energy eigenvalue of any two of the analysis grids, the formation of the adjusted gradient threshold includes:

[0040] Obtain the center distance between any two of the analysis grids to form an analysis grid distance;

[0041] Calculate the ratio of the difference between the local spectral energy eigenvalues of the corresponding two analysis grids to the analysis grid distance to form an eigenvalue gradient value;

[0042] When the eigenvalue gradient value is greater than a preset eigenvalue gradient threshold, reduce the preset temperature gradient threshold according to the relative deviation between the eigenvalue gradient value and the preset eigenvalue gradient threshold and a preset adjustment coefficient to form an adjusted gradient threshold.

[0043] Further, generating an abnormal property analysis report according to the local spectral energy eigenvalue formed by the adjusted gradient threshold includes:

[0044] Calculate the standard deviation of all the local spectral energy eigenvalues to form an eigenvalue distribution degree;

[0045] When the eigenvalue distribution degree is greater than a preset distribution degree threshold, generate an abnormal property analysis report according to the eigenvalue distribution degree.

[0046] Further, generating an abnormal property analysis report according to the eigenvalue distribution degree includes:

[0047] Calculate the standard deviation of the eigenvalue distribution degree within a preset analysis duration to form a distribution fluctuation value;

[0048] When the distribution fluctuation value is less than a preset distribution fluctuation threshold, it is determined that a temperature control abnormality occurs, and an abnormal property analysis report is generated.

[0049] Compared with the prior art, the beneficial effects of the present invention are that by collecting the temperature, power consumption, and coolant flow data in the cooling unit of the quantum computer in real time and combining with the local spectral energy eigenvalue analysis, it can accurately identify and adjust the temperature control abnormality, not only improving the stability and efficiency of the cooling system, but also dynamically optimizing the temperature gradient threshold, reducing overheating or inefficient cooling problems, and through the generated abnormal property analysis report, timely discovering potential problems and performing maintenance, effectively extending the equipment life, improving the system performance, reducing the risk of failure, optimizing the utilization of cooling resources, and effectively solving the problem of low output result accuracy due to over-reliance on historical data.

[0050] Furthermore, by identifying high-temperature-difference grids, areas with significant temperature fluctuations can be accurately located, avoiding the impact of local overheating on the stability and efficiency of the quantum computer. This helps to promptly detect potential cooling deficiencies or overheating problems, and then optimize the adjustment of the cooling system to ensure the long-term efficient operation of the device. By judging through a preset temperature gradient threshold, human errors can be effectively avoided, the automation and precision of the system can be improved, maintenance costs can be reduced, and the overall reliability and sustainability can be enhanced.

[0051] Furthermore, by calculating the fluctuation values of power consumption and coolant flow rate, grid areas with abnormal fluctuations can be identified, enabling targeted optimization and adjustment. This helps to improve the response sensitivity of the cooling system, promptly detect potential abnormalities and repair them, and avoid the normal operation of the quantum computer being affected by local unstable energy consumption and flow rate changes, ensuring the overall stability and efficiency of the system.

[0052] Furthermore, by comparing the change curves of consumption and flow rate fluctuation values, grids with asynchronous power and flow rate changes can be effectively identified, which helps to discover areas that may have problems or abnormalities and promptly adjust the cooling strategy.

[0053] Furthermore, by examining the consistency of temperature and coolant flow rate fluctuations in adjacent grids, only grids with similar change patterns are selected for analysis, effectively reducing interference caused by system abnormalities or errors. Through the calculation of the synchronization degree, the true system behavior can be more accurately identified, unnecessary misjudgments are avoided, and the accuracy and reliability of the overall analysis are improved.

[0054] Furthermore, by calculating the synchronization degree of temperature and coolant flow rate, the stability and normal working state of the system can be effectively judged. If the fluctuation patterns of the two are highly consistent, it indicates that the system is operating in the expected stable state; conversely, if the synchronization degree is low, possible fault points can be accurately identified and excluded.

[0055] Furthermore, through the calculation of the synchronization degree and synchronization deviation value, grids with similar characteristics and strong consistency are further screened out, and these grids are determined as analysis grids for in-depth analysis, which can ensure that only grids meeting specific criteria are monitored in detail, thereby improving the accuracy of system detection.

[0056] Furthermore, by dynamically adjusting the temperature gradient threshold according to the energy difference between analysis grids, the sensitivity of the system to temperature differences can be ensured to be adaptively adjusted according to actual energy changes, improving the rationality of the temperature gradient threshold.

[0057] Further, by analyzing the distribution degree of the eigenvalues to determine whether there is a risk of potential failure or efficiency reduction, and setting a reasonable distribution degree threshold, it can ensure that the abnormal report is triggered only when the system fluctuates greatly abnormally, avoiding frequent false alarms.

[0058] Further, by analyzing the volatility of the distribution degree, it is possible to timely identify whether there is an abnormality in the temperature control system. If the distribution fluctuation is small, it indicates that the system temperature control is relatively stable; on the contrary, if the fluctuation value exceeds the threshold, it indicates that there may be a temperature control problem. Generating an abnormal nature analysis report can help take corrective measures in a timely manner, thus ensuring the normal operation of the system and avoiding potential failures or performance degradation. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] Figure 1 is a flowchart of the method for analyzing the local spectral properties of the Hamiltonian operator in this embodiment;

[0060] Figure 2 is a decision logic diagram for determining the high temperature difference grid in this embodiment;

[0061] Figure 3 is a decision logic diagram for determining the temporary grid in this embodiment;

[0062] Figure 4 is a decision logic diagram for determining the analysis grid in this embodiment. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0063] In order to make the objectives and advantages of the present invention clearer and more understandable, the present invention will be further described below in conjunction with embodiments; it should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0064] The preferred embodiments of the present invention will be described below with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are only used to explain the technical principles of the present invention and do not limit the protection scope of the present invention.

[0065] Please refer to Figure 1 shown, which is a flowchart of the method for analyzing the local spectral properties of the Hamiltonian operator in this embodiment;

[0066] This embodiment provides a method for analyzing the local spectral properties of a Hamiltonian operator, including:

[0067] Obtaining the real-time temperature, real-time power consumption, and real-time coolant flow rate of each grid to be processed in the monitoring area divided by grids in the cooling unit of the quantum computer;

[0068] Determining a plurality of high temperature difference grids according to the real-time temperature of any two of the grids to be processed and a preset temperature gradient threshold;

[0069] Determine a number of temporary grids based on the real-time power consumption and the real-time coolant flow rate of each of the high temperature difference grids;

[0070] Determine a number of analysis grids based on the real-time temperature and the real-time coolant flow rate of any two adjacent temporary grids;

[0071] Input the real-time temperature, the real-time power consumption, and the real-time coolant flow rate of each of the analysis grids into a preset Hamiltonian operator model to obtain a number of local spectral energy eigenvalues;

[0072] Adjust the preset temperature gradient threshold according to the local spectral energy eigenvalues of any two of the analysis grids to form an adjusted gradient threshold;

[0073] Generate an abnormal property analysis report based on the local spectral energy eigenvalues formed by the adjusted gradient threshold.

[0074] Obtain the real-time temperature, the real-time power consumption, and the real-time coolant flow rate of each grid to be processed in the monitoring area divided by grids in the quantum computer cooling unit, which is usually achieved through a series of sensors and monitoring devices. A temperature sensor (such as a thermocouple or an RTD sensor) is installed in each grid area to monitor the temperature of the area in real time; the power consumption is obtained through a power meter, a sensor, or a current / voltage measuring device, and then the energy consumption of each grid is calculated; the coolant flow rate is measured in real time through a flow meter (such as a turbine flow meter or an electromagnetic flow meter).

[0075] The preset temperature gradient threshold is a standard value used to determine whether the temperature difference between grids exceeds the normal range, which depends on the design requirements of the cooling unit, the operating environment, and the heat capacity characteristics of the equipment. It is usually set between 3°C / m and 10°C / m. In this embodiment, it is set to 5°C / m, which can ensure a quick response to high temperature difference grids during the cooling process, and at the same time avoid frequent adjustments or false alarms caused by too low a threshold, improving the stability and energy efficiency of the system.

[0076] The preset Hamiltonian operator model is a mathematical tool derived from the Hamiltonian operator in quantum mechanics and is used to describe the energy state of a physical system. It includes not only the kinetic energy term of the system but also the potential energy term, reflecting the energy distribution of the system in different states. In many applications, the Hamiltonian operator is used to solve the eigenvalues and eigenstates of the system, and these eigenvalues can be used to understand and analyze the dynamic behavior, stability, and energy distribution of the system. Especially in the fields of thermodynamics and quantum computing, the Hamiltonian operator model is used to analyze the performance of physical systems under different operating conditions. In this embodiment, the preset Hamiltonian operator model is applied to the temperature control system of the quantum computer cooling unit. Specifically, data such as the real-time temperature, power consumption, and coolant flow rate of each grid of the system are input into the Hamiltonian operator model. Through this model, the system calculates the local spectral energy eigenvalues of each grid, which reflect the energy states of different grids during the cooling process. By adjusting these eigenvalues, the temperature gradient threshold of the system can be precisely adjusted, thereby achieving the optimization of temperature control and maintaining the efficient and stable operation of the cooling unit. The specific setting of the Hamiltonian operator model depends on the working characteristics of the cooling unit and the system design. Usually, the model parameters (such as power consumption, temperature gradient, etc.) are adjusted according to the actual application. The preset parameters in this embodiment enable the model to accurately reflect the energy distribution of the temperature control system under different working loads, so as to efficiently predict and adjust the cooling strategy, avoid overheating or overcooling, and ensure the stability and performance optimization of the quantum computer cooling unit.

[0077] By obtaining the temperature, power consumption, and coolant flow rate data of each grid in the quantum computer cooling unit in real time, the high-temperature difference grids are first determined based on the real-time temperature and the preset temperature gradient threshold. Subsequently, by analyzing the power consumption and coolant flow rate of these grids, the temporary grids are further screened out, and the analysis grids are determined according to the temperature and coolant flow rate of the temporary grids. The relevant data of the analysis grids are input into the Hamiltonian operator model, the local spectral energy eigenvalues are calculated, and the temperature gradient threshold is adjusted based on these eigenvalues. Finally, an abnormal property analysis report is generated to evaluate the temperature control and cooling effects of the system.

[0078] By collecting the temperature, power consumption, and coolant flow rate data in the quantum computer cooling unit in real time and combining with the analysis of the local spectral energy eigenvalues, it is possible to accurately identify and adjust the temperature control anomalies, which not only improves the stability and efficiency of the cooling unit but also dynamically optimizes the temperature gradient threshold, reduces overheating or inefficient cooling problems, discovers potential problems in time through the generated abnormal property analysis report and conducts maintenance, effectively extends the equipment life, improves the system performance, reduces the risk of failure, optimizes the utilization of cooling resources, and effectively solves the problem of low accuracy of output results due to excessive dependence on historical data.

[0079] Please continue to refer toFigure 2 As shown, it is a determination logic diagram for high temperature difference grids in this embodiment. Determining several high temperature difference grids based on the real-time temperatures and the preset temperature gradient threshold of any two of the to-be-processed grids includes:

[0080] Obtain the central distance between any two of the to-be-processed grids to form a processed grid distance;

[0081] Calculate the ratio of the difference between the corresponding two real-time temperatures and the processed grid distance to form a temperature gradient;

[0082] When the temperature gradient is greater than the preset temperature gradient threshold, determine that the corresponding two to-be-processed grids are both the high temperature difference grids to form several high temperature difference grids.

[0083] By obtaining the central distance between any two to-be-processed grids to form a processed grid distance, and calculating the ratio of the difference between the real-time temperatures of the two grids and the processed grid distance, the temperature gradient is obtained. When the temperature gradient is greater than the preset temperature gradient threshold, determine that these two grids are high temperature difference grids and list them as high temperature difference grids that need further analysis.

[0084] By identifying high temperature difference grids, areas with large temperature fluctuations can be accurately located, avoiding affecting the stability and efficiency of the quantum computer due to local overheating. This helps to timely detect potential problems of insufficient cooling or overheating, and then optimize the adjustment of the cooling unit to ensure the long-term efficient operation of the device. By judging through the preset temperature gradient threshold, human errors can be effectively avoided, and the automation and accuracy of the system can be improved, reducing maintenance costs and enhancing the overall reliability and sustainability.

[0085] Specifically, determining several temporary grids based on the real-time power consumption and the real-time coolant flow rate of each of the high temperature difference grids includes:

[0086] Calculate the standard deviation of the real-time power consumption within a preset first determination duration to form a consumption fluctuation value;

[0087] Calculate the standard deviation of the real-time coolant flow rate within the preset first determination duration to form a first flow fluctuation value;

[0088] Determine several temporary grids based on the consumption fluctuation value and the first flow fluctuation value.

[0089] The preset first determination duration is the time period used to calculate the fluctuation values of the power consumption and the coolant flow rate, which depends on the response time of the cooling unit and the frequency of temperature change, and is usually set between several minutes and several hours. In this embodiment, it is set to 10 minutes, which can ensure capturing sufficient data fluctuations, while avoiding misjudgment caused by too short a time, and effectively balancing the response speed and data accuracy.

[0090] First, calculate the standard deviations of the real-time power consumption and real-time coolant flow rate for each high-temperature difference grid within a preset first determination duration, respectively obtaining a consumption fluctuation value and a flow rate fluctuation value. Then, by analyzing these two fluctuation values, several temporary grids are determined, and these grids may have regions with relatively large fluctuations that require further attention.

[0091] By calculating the fluctuation values of the power consumption and coolant flow rate, grid regions with abnormal fluctuations can be identified, thereby enabling targeted optimization and adjustment. This helps improve the response sensitivity of the cooling unit, promptly detect potential abnormalities and repair them, and avoid affecting the normal operation of the quantum computer due to locally unstable energy consumption and flow rate changes, ensuring the overall stability and efficiency of the system.

[0092] Please continue to refer to Figure 3 As shown, it is the determination logic diagram for determining temporary grids in this embodiment. Determining several temporary grids based on the consumption fluctuation value and the flow rate fluctuation value includes:

[0093] Plot the change curve of the consumption fluctuation value to form a consumption curve;

[0094] Plot the change curve of the flow rate fluctuation value to form a first flow rate curve;

[0095] Calculate the cosine similarity between the consumption curve and the first flow rate curve to form a change consistency;

[0096] When the change consistency is less than a preset consistency threshold, determine that the high-temperature difference grid is a temporary grid to form several temporary grids.

[0097] Based on the consumption fluctuation value and the flow rate fluctuation value, first plot the consumption curve and the flow rate curve, and then calculate the cosine similarity between the two to form a change consistency. If the change consistency is less than the preset consistency threshold, determine that the corresponding high-temperature difference grid is a temporary grid.

[0098] The preset consistency threshold is a standard value used to judge the change consistency between the consumption fluctuation curve and the flow rate fluctuation curve, depending on the performance requirements of the cooling unit, the monitoring accuracy, and the tolerance for fluctuation changes. It is usually set between 0.8 and 0.95. In this embodiment, it is set to 0.85, which can avoid being overly sensitive to minor fluctuations while ensuring accuracy, thereby avoiding frequent misjudgments and maintaining the stable operation of the system.

[0099] By comparing the change curves of the consumption and flow rate fluctuation values, grid regions where the power and flow rate changes are out of sync can be effectively identified, which helps discover regions that may have problems or abnormalities and promptly adjust the cooling strategy.

[0100] Specifically, determining a number of analysis grids based on the real-time temperature and the real-time coolant flow rate of any two adjacent ones of the temporary grids includes:

[0101] Calculating the standard deviation of the real-time temperature of a single temporary grid within a preset second determination duration to form a temperature fluctuation value;

[0102] Calculating the standard deviation of the real-time coolant flow rate of a single temporary grid within the preset second determination duration to form a second flow rate fluctuation value;

[0103] Determining a synchronization degree based on the temperature fluctuation value and the second flow rate fluctuation value;

[0104] Determining a number of analysis grids based on the synchronization degrees of any two adjacent ones of the temporary grids.

[0105] The preset second determination duration is a time window for calculating the temperature and coolant flow rate fluctuations of the temporary grids, which depends on the response time of the cooling unit and the data acquisition frequency, as well as the system stability and the time scale of the fluctuation change. It is usually set between several minutes and dozens of minutes. In this embodiment, it is set to 10 minutes, which can balance the response speed of the system and the data stability, enabling effective capture of the short-term fluctuations of the system while avoiding misjudgment or data noise caused by too short a duration.

[0106] First, by calculating the standard deviations of the real-time temperature and the coolant flow rate of each temporary grid within the preset second determination duration, a temperature fluctuation value and a second flow rate fluctuation value are obtained. Then, based on these two fluctuation values, the synchronization degree is calculated to evaluate the consistency of the temperature and flow rate changes. Finally, by comparing the synchronization degrees of any two adjacent temporary grids, it is determined which grids can be used as analysis grids for further processing.

[0107] By examining the consistency of the temperature and coolant flow rate fluctuations of adjacent grids, it is ensured that only those grids with similar change patterns are selected for analysis, thereby effectively reducing the interference caused by system anomalies or errors. Through the calculation of the synchronization degree, the real system behavior can be more accurately identified, avoiding unnecessary misjudgment and improving the accuracy and reliability of the overall analysis.

[0108] Specifically, determining the synchronization degree based on the consumption fluctuation value and the second flow rate fluctuation value includes:

[0109] Plotting a change curve of the temperature fluctuation value to form a temperature curve;

[0110] Plotting a change curve of the second flow rate fluctuation value to form a second flow rate curve;

[0111] Calculating the cosine similarity of the temperature curve and the second flow rate curve to form the synchronization degree.

[0112] First, draw the curves of the temperature fluctuation value and the coolant flow rate fluctuation value changing with time, respectively forming a temperature curve and a second flow rate curve. Then, by calculating the cosine similarity of the temperature curve and the second flow rate curve, the synchronization degree between them is obtained. The synchronization degree reflects the coordination of the temperature and the coolant flow rate fluctuations, that is, whether they fluctuate in a similar pattern.

[0113] By calculating the synchronization degree of the temperature and the coolant flow rate, the stability and normal working state of the system can be effectively judged. If the fluctuation patterns of the two are highly consistent, it indicates that the system is operating in the expected stable state; on the contrary, if the synchronization degree is low, possible fault points can be accurately identified and excluded.

[0114] Please continue to refer to Figure 4 shown, which is the decision logic diagram for determining the analysis grid in this embodiment. Determining several analysis grids according to the synchronization degree of any two adjacent temporary grids includes:

[0115] When both of the two synchronization degrees are greater than the preset synchronization degree threshold, calculate the relative deviation of the two synchronization degrees to form a synchronization deviation value;

[0116] When the synchronization deviation value is less than the preset synchronization deviation threshold, determine the corresponding two temporary grids as the analysis grids to form several analysis grids.

[0117] The preset synchronization degree threshold is a standard value used to determine whether adjacent grids have sufficient synchronization, depending on the stability requirements of the cooling unit, the change ranges of the temperature and the coolant flow rate, and the system's tolerance for errors. It is usually set between 0.8 and 1.0. In this embodiment, it is set to 0.9, which can effectively distinguish grids with strong synchronization, avoid ignoring real abnormal grids caused by too high a threshold, and also avoid excessive screening caused by too low a threshold, improving the accuracy and reliability of the analysis.

[0118] The preset synchronization deviation threshold is a standard value used to determine whether the synchronization deviation between two grids is within the tolerable range, depending on the system's requirements for synchronization accuracy and the normal ranges of possible temperature and flow rate fluctuations. It is usually set between 0.01 and 0.1. In this embodiment, it is set to 0.05, which can ensure the consistency of synchronization while avoiding too many unnecessary grids being selected as analysis objects due to too small a threshold, thereby effectively improving the analysis efficiency and reducing the misjudgment rate.

[0119] First, calculate the synchronization degree of any two adjacent temporary grids, and determine whether their synchronization degrees are greater than a preset synchronization degree threshold. If both synchronization degrees meet the conditions, further calculate the relative deviation of these two synchronization degrees to obtain a synchronization deviation value. Finally, if the synchronization deviation value is less than a preset synchronization deviation threshold, determine these two temporary grids as analysis grids, thereby forming several analysis grids.

[0120] By calculating the synchronization degree and the synchronization deviation value, further screen out grids with similar characteristics and strong consistency, and determine these grids as analysis grids for in-depth analysis, which can ensure that only grids meeting specific criteria are monitored in detail, thereby improving the accuracy of system detection.

[0121] Specifically, adjusting the preset temperature gradient threshold according to the local spectral energy eigenvalue of any two of the analysis grids to form an adjusted gradient threshold includes:

[0122] Obtain the center distance between any two of the analysis grids to form an analysis grid distance;

[0123] Calculate the ratio of the difference between the local spectral energy eigenvalues of the corresponding two analysis grids to the analysis grid distance to form an eigenvalue gradient value;

[0124] When the eigenvalue gradient value is greater than a preset eigenvalue gradient threshold, reduce the preset temperature gradient threshold according to the relative deviation between the eigenvalue gradient value and the preset eigenvalue gradient threshold and a preset adjustment coefficient to form an adjusted gradient threshold, where T' = T × [1 - k × (Q - Q0) / Q0], T' is the adjusted gradient threshold, T is the preset temperature gradient threshold, Q is the eigenvalue gradient value, Q0 is the preset eigenvalue gradient threshold, and k is the preset adjustment coefficient.

[0125] The preset eigenvalue gradient threshold is a standard value used to determine whether the difference in local spectral energy eigenvalues between two analysis grids is significant, depending on the accuracy requirements of the system, the distance between grids, and the typical range of energy changes. It is usually set between 0.1 and 1.0. In this embodiment, it is set to 0.5, which can ensure effective adjustment of the gradient in areas with large energy changes, avoid overly frequent adjustments caused by too small a threshold, and maintain the stability and response ability of the system.

[0126] The preset adjustment coefficient is a proportional factor used to dynamically adjust the temperature gradient threshold according to the relative deviation between the eigenvalue gradient value and the preset eigenvalue gradient threshold. It depends on the sensitivity requirements and adjustment accuracy of the system, and needs to balance the sensitivity of adjustment and the stability of the system. It is usually set between 0.1 and 0.5. In this embodiment, it is set to 0.3, which can effectively balance the adjustment range of the temperature gradient threshold, avoid over-adjustment, and at the same time ensure sufficient response to areas with large temperature changes, enabling the cooling unit to adaptively maintain efficient operation.

[0127] First, obtain the central distance between any two analysis grids to form an analysis grid distance. Then, calculate the ratio of the difference between the local spectral energy eigenvalues of these two analysis grids to the analysis grid distance to form an eigen-gradient value. If the eigen-gradient value is greater than a preset eigen-gradient threshold, then according to the relative deviation between the eigen-gradient value and the preset eigen-gradient threshold, combined with a preset adjustment coefficient, reduce the preset temperature gradient threshold to obtain an adjusted gradient threshold.

[0128] By dynamically adjusting the temperature gradient threshold according to the energy difference between analysis grids, it can ensure that the sensitivity of the system to temperature differences is adaptively adjusted according to the actual energy changes, improving the rationality of the temperature gradient threshold.

[0129] Specifically, generating an abnormal property analysis report based on the local spectral energy eigenvalues formed by the adjusted gradient threshold includes:

[0130] Calculate the standard deviation of all the local spectral energy eigenvalues to form an eigenvalue distribution degree;

[0131] When the eigenvalue distribution degree is greater than a preset distribution degree threshold, generate an abnormal property analysis report according to the eigenvalue distribution degree.

[0132] The preset distribution degree threshold refers to a numerical standard used to determine whether there is a significant fluctuation in the distribution of local spectral energy eigenvalues when generating an abnormal property analysis report. It depends on the fault tolerance of the system, operating stability, and sensitivity to abnormal fluctuations. Usually, it is set between 0.1 and 1.0. In this embodiment, it is set to 0.3, which can ensure that large-amplitude abnormal fluctuations can be detected in a timely manner while avoiding overreaction to small and normal fluctuations, maintaining the stability and efficient operation of the system.

[0133] According to the adjusted gradient threshold, calculate the distribution degree of eigenvalues by calculating the standard deviation of all local spectral energy eigenvalues. When the eigenvalue distribution degree is greater than the preset distribution degree threshold, generate an abnormal property analysis report. Specifically, the standard deviation reflects the amplitude of the fluctuation of eigenvalues in all grids. If the distribution degree exceeds the preset threshold, it indicates that there are obvious abnormal fluctuations in the system, and detailed analysis and adjustment are required.

[0134] By analyzing the distribution degree of eigenvalues to determine whether there is a risk of potential failure or efficiency reduction, setting a reasonable distribution degree threshold can ensure that an abnormal report is triggered only when there are large abnormal fluctuations in the system, avoiding frequent false alarms.

[0135] Specifically, generating an abnormal property analysis report according to the eigenvalue distribution degree includes:

[0136] Calculate the standard deviation of the eigenvalue distribution degree within the preset analysis duration to form a distribution fluctuation value;

[0137] When the distribution fluctuation value is less than the preset distribution fluctuation threshold, it is determined that a temperature control anomaly occurs, and an abnormal property analysis report is generated.

[0138] The preset distribution fluctuation threshold is a set value used to judge whether the fluctuation of the eigenvalue distribution degree exceeds the normal range, depending on the temperature control precision requirements of the system, the stability of the operating environment, and the sensitivity requirements for anomaly detection. It is usually set between 0.01 and 0.1. In this embodiment, it is set to 0.05, which can balance the response sensitivity of the temperature control system and the normal operation fluctuation, ensuring that anomalies can be identified in time during actual operation without misjudging normal fluctuations.

[0139] First, calculate the standard deviation of the distribution degree of all local spectral energy eigenvalues within the preset analysis duration to obtain the distribution fluctuation value. Then, if the distribution fluctuation value is less than the preset distribution fluctuation threshold, it is judged that the temperature control is abnormal, and an abnormal property analysis report is generated. The abnormal property analysis report will detail the abnormal grid, abnormal type, abnormal reason, and adjustment suggestions.

[0140] By analyzing the volatility of the distribution degree, it is possible to identify in time whether there is an anomaly in the temperature control system. If the distribution fluctuation is small, it indicates that the system temperature control is relatively stable; on the contrary, if the fluctuation value exceeds the threshold, it indicates that there may be a temperature control problem. Generating an abnormal property analysis report can help take corrective measures in time, thus ensuring the normal operation of the system and avoiding potential failures or performance degradation.

[0141] So far, the technical solution of the present invention has been described in combination with the preferred embodiments shown in the accompanying drawings. However, it is easy for those skilled in the art to understand that the protection scope of the present invention is obviously not limited to these specific embodiments. Without departing from the principle of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will all fall within the protection scope of the present invention.

Claims

1. A method for analyzing local spectral properties of a Hamiltonian operator, characterized in that: include: Obtaining the real-time temperature, real-time power consumption and real-time coolant flow of each to-be-processed grid in the monitoring area divided into grids in the cooling unit of the quantum computer; Determine a plurality of high temperature difference grids according to the real-time temperatures of any two grids to be processed and a preset temperature gradient threshold; Determine a number of temporary grids according to the real-time power consumption and the real-time coolant flow of each high temperature difference grid; Determine a number of analysis grids according to the real-time temperature and the real-time coolant flow rate of any two adjacent temporary grids; Inputting the real-time temperature, the real-time power consumption and the real-time coolant flow rate of each analysis grid into a preset Hamiltonian operator model to obtain a number of local spectral energy eigenvalues; Adjusting the preset temperature gradient threshold according to the local spectral energy eigenvalues ​​of any two analysis grids to form an adjusted gradient threshold; An abnormal property analysis report is generated according to the local spectrum energy eigenvalue formed by adjusting the gradient threshold.

2. The method for analyzing local spectral properties of Hamiltonian operators according to claim 1, characterized in that: Determining a number of high temperature difference grids according to the real-time temperatures of any two grids to be processed and a preset temperature gradient threshold comprises: Obtaining the center distance between any two of the to-be-processed grids to form a processing grid distance; Calculating the ratio of the difference between the two corresponding real-time temperatures and the processing grid distance to form a temperature gradient; When the temperature gradient is greater than the preset temperature gradient threshold, it is determined that the corresponding two to-be-processed grids are both the high temperature difference grids, and a plurality of high temperature difference grids are formed.

3. The method for analyzing local spectral properties of Hamiltonian operators according to claim 2, characterized in that: Determining a number of temporary grids according to the real-time power consumption and the real-time coolant flow of each high temperature difference grid comprises: Calculating a standard deviation of the real-time power consumption within a preset first determined time period to form a consumption fluctuation value; Calculating the standard deviation of the real-time coolant flow rate within the preset first determined time period to form a first flow rate fluctuation value; A plurality of temporary grids are determined according to the consumption fluctuation value and the first flow fluctuation value.

4. The method for analyzing local spectral properties of Hamiltonian operators according to claim 3, characterized in that: Determining a number of temporary grids according to the consumption fluctuation value and the flow fluctuation value includes: Drawing a variation curve of the consumption fluctuation value to form a consumption curve; Drawing a change curve of the flow fluctuation value to form a first flow curve; Calculating the cosine similarity of the consumption curve and the first flow curve to form a change consistency; When the change consistency is less than a preset consistency threshold, the high temperature difference grid is determined to be a temporary grid, and a plurality of temporary grids are formed.

5. The method for analyzing local spectral properties of Hamiltonian operators according to claim 4, characterized in that: Determining a number of analysis grids according to the real-time temperature and the real-time coolant flow of any two adjacent temporary grids comprises: Calculate the standard deviation of the real-time temperature of a single temporary grid within a preset second determined time period to form a temperature fluctuation value; Calculating the standard deviation of the real-time coolant flow rate of a single temporary grid within the preset second determined time period to form a second flow fluctuation value; determining a degree of synchronization according to the temperature fluctuation value and the second flow fluctuation value; A number of analysis grids are determined according to the synchronization degree of any two adjacent temporary grids.

6. The method for analyzing local spectral properties of Hamiltonian operators according to claim 5, characterized in that: Determining the degree of synchronization according to the consumption fluctuation value and the second flow fluctuation value comprises: Draw a change curve of the temperature fluctuation value to form a temperature curve; Draw a variation curve of the second flow fluctuation value to form a second flow curve; The cosine similarity of the temperature curve and the second flow curve is calculated to form a synchronization degree.

7. The method for analyzing local spectral properties of Hamiltonian operators according to claim 5, characterized in that: Determining a number of analysis grids according to the synchronization degree of any two adjacent temporary grids comprises: When both of the synchronization degrees are greater than a preset synchronization degree threshold, a relative deviation of the two synchronization degrees is calculated to form a synchronization deviation value; When the synchronization deviation value is less than a preset synchronization deviation threshold, the corresponding two temporary grids are determined to be the analysis grids to form a plurality of analysis grids.

8. The method for analyzing local spectral properties of Hamiltonian operators according to claim 7, characterized in that: Adjusting the preset temperature gradient threshold according to the local spectral energy eigenvalues ​​of any two analysis grids to form an adjusted gradient threshold comprises: Obtaining the center distance between any two analysis grids to form an analysis grid distance; Calculating the ratio of the difference between the local spectral energy eigenvalues ​​of the two corresponding analysis grids and the analysis grid distance to form an eigenvalue gradient value; When the intrinsic gradient value is greater than the preset intrinsic gradient threshold, the preset temperature gradient threshold is reduced according to the relative deviation between the intrinsic gradient value and the preset intrinsic gradient threshold and the preset adjustment coefficient to form an adjusted gradient threshold.

9. The method for analyzing local spectral properties of Hamiltonian operators according to claim 8, characterized in that: Generating an abnormal property analysis report according to the local spectrum energy eigenvalue formed by adjusting the gradient threshold comprises: Calculating the standard deviation of all the local spectral energy eigenvalues ​​to form an eigenvalue distribution; When the eigenvalue distribution degree is greater than a preset distribution degree threshold, an abnormal property analysis report is generated according to the eigenvalue distribution degree.

10. The method for analyzing local spectral properties of Hamiltonian operators according to claim 9, characterized in that: Generating an abnormal property analysis report according to the eigenvalue distribution degree includes: Calculate the standard deviation of the eigenvalue distribution within a preset analysis time to form a distribution fluctuation value; When the distribution fluctuation value is less than a preset distribution fluctuation threshold, it is determined that a temperature control abnormality occurs, and an abnormal property analysis report is generated.

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