A method for analyzing local spectral properties of Hamiltonian operators

Abstract

The present invention relates to the field of data processing technology, and in particular to a method for analyzing the local spectral properties of a Hamiltonian operator. The method comprises: obtaining the real-time temperature, power consumption, and coolant flow of a grid; determining a high temperature difference grid; determining a temporary grid; determining an analysis grid; inputting data into a Hamiltonian operator model; adjusting a temperature gradient threshold, and generating an abnormal property analysis report. The present invention collects temperature, power consumption, and coolant flow data from a quantum computer cooling unit in real time, and combines this with local spectral energy eigenvalue analysis to accurately identify and adjust temperature control anomalies. This not only improves the stability and efficiency of the cooling unit, but also dynamically optimizes the temperature gradient threshold. Through the generated abnormal property analysis report, potential problems can be discovered and maintained in a timely manner, effectively extending the life of the equipment and optimizing the utilization of cooling resources. This effectively solves the problem of low output accuracy due to over-reliance on historical data.

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 computation times and low accuracy when dealing with large-scale, complex data. To meet the demand for real-time data analysis and accurate prediction, new analysis methods have been proposed. In particular, algorithms that can efficiently process high-dimensional data and achieve high precision have become a hot research topic. Fields such as quantum computing and cooling systems have particularly stringent requirements for analysis speed and accuracy. Therefore, advanced mathematical models, optimization algorithms, and efficient computing frameworks are needed to achieve accurate and rapid real-time analysis and early warning.

[0003] Patent document with publication number CN107895206A discloses a multi-step wind energy prediction method based on singular spectrum analysis and local sensitive hashing, which includes: 1) obtaining historical wind energy data of the 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 fluctuation component obtained in step 2) into an average trend segment and a fluctuation component segment in phase space; 4) using local sensitive hashing to select a similar average trend segment of the average trend segment to be predicted; 5) using the combination of the obtained similar average trend segment and the corresponding fluctuation component segment as the training input of the support vector regression model, and the forecast 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 local sensitive hashing has the following problems: this method relies 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 process; local sensitive hashing cannot fully capture complex nonlinear relationships when selecting similar segments, resulting in the accuracy of the prediction results being limited; the model training and prediction process is more dependent on the effect of the support vector regression model. If the training data is insufficient or the features are improperly selected, the generalization ability of the model is weak, affecting the accuracy of the final prediction results. Summary of the Invention

[0005] To this end, the present invention provides a Hamiltonian operator local spectral property analysis method for overcoming the problem of low output accuracy in the prior art due to over-reliance 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, comprising:

[0007] Obtaining the real-time temperature, real-time power consumption, and real-time coolant flow of each to-be-processed grid in a monitoring area divided into grids in a quantum computer cooling unit;

[0008] 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;

[0009] 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;

[0010] 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;

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

[0012] Adjusting the preset temperature gradient threshold according to the local spectral energy eigenvalues ​​of any two analysis grids to form an adjusted gradient threshold;

[0013] An abnormal property analysis report is generated according to the local spectrum energy eigenvalue formed by adjusting the gradient threshold.

[0014] Furthermore, 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:

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

[0016] Calculating the ratio of the difference between the two corresponding 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, it is determined that the corresponding two to-be-processed grids are both the high temperature difference grids, forming a plurality of high temperature difference grids.

[0018] Furthermore, 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 includes:

[0019] Calculating a standard deviation of the real-time power consumption within a predetermined first time period to form a consumption fluctuation value;

[0020] Calculating a standard deviation of the real-time coolant flow rate within the preset first determined time period to form a first flow rate fluctuation value;

[0021] A number of temporary grids are determined according to the consumption fluctuation value and the first flow fluctuation value.

[0022] Furthermore, determining a number of temporary grids according to the consumption fluctuation value and the flow fluctuation value includes:

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

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

[0025] Calculating 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, the high temperature difference grid is determined to be a temporary grid, and a plurality of temporary grids are formed.

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

[0028] Calculating a 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;

[0029] Calculating a 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;

[0030] determining a degree of synchronization according to the temperature fluctuation value and the second flow rate fluctuation value;

[0031] A plurality of analysis grids are determined according to the synchronization degree of any two adjacent temporary grids.

[0032] Furthermore, determining the degree of synchronization according to the consumption fluctuation value and the second flow fluctuation value includes:

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

[0034] Drawing a variation curve of the second flow fluctuation value to form a second flow curve;

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

[0036] Furthermore, determining a number of analysis grids according to the synchronization degree of any two adjacent temporary grids includes:

[0037] When both synchronization degrees are greater than a preset synchronization degree threshold, a relative deviation between the two synchronization degrees is calculated to form a synchronization deviation value;

[0038] 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, forming a plurality of analysis grids.

[0039] Furthermore, adjusting the preset temperature gradient threshold according to the local spectral energy eigenvalues ​​of any two analysis grids to form an adjusted gradient threshold includes:

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

[0041] 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 eigengradient value;

[0042] 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 a preset adjustment coefficient to form an adjusted gradient threshold.

[0043] Furthermore, generating an abnormal property analysis report according to the local spectrum energy eigenvalue formed by adjusting the gradient threshold includes:

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

[0045] 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.

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

[0047] Calculating the standard deviation of the eigenvalue distribution within a preset analysis time 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 abnormality property analysis report is generated.

[0049] Compared with the existing technology, the beneficial effect of the present invention is that by real-time collection of temperature, power consumption and coolant flow data in the quantum computer cooling unit, combined with local spectral energy eigenvalue analysis, it can accurately identify and adjust temperature control anomalies, which not only improves the stability and efficiency of the cooling system, but also dynamically optimizes the temperature gradient threshold, reduces overheating or inefficient cooling problems, and through the generated abnormal property analysis report, potential problems are discovered and maintained in time, effectively extending equipment life, improving system performance, reducing the risk of failure, optimizing the utilization of cooling resources, and effectively solving the problem of low output accuracy due to over-reliance on historical data.

[0050] Furthermore, by identifying high-temperature gradient grids, areas of significant temperature fluctuation can be precisely located, preventing local overheating from impacting the stability and efficiency of the quantum computer. This helps promptly identify potential cooling deficiencies or overheating issues, allowing for optimized cooling system adjustments and ensuring long-term, efficient operation of the equipment. By presetting temperature gradient thresholds, human error can be effectively avoided, improving system automation and precision, reducing maintenance costs, and enhancing overall reliability and sustainability.

[0051] Furthermore, by calculating fluctuations in power consumption and coolant flow, grid regions with abnormal fluctuations can be identified, allowing targeted optimization and adjustment. This helps improve the cooling system's responsiveness, allowing for timely detection and repair of potential anomalies. This prevents localized fluctuations in energy consumption and flow from impacting the normal operation of the quantum computer, thereby ensuring overall system stability and efficiency.

[0052] Furthermore, by comparing the change curves of consumption and flow fluctuation values, grids where power and flow changes are not synchronized can be effectively identified. This helps to find areas where problems or anomalies may exist and adjust the cooling strategy in a timely manner.

[0053] Furthermore, by examining the consistency of temperature and coolant flow fluctuations in adjacent grids, we ensure that only grids with similar change patterns are selected for analysis, effectively reducing interference caused by system anomalies or errors. By calculating synchronization, we can more accurately identify true system behavior, avoid unnecessary misjudgments, and improve the accuracy and reliability of the overall analysis.

[0054] Furthermore, by calculating the degree of synchronization between temperature and coolant flow, the stability and normal operating status of the system can be effectively determined. 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 degree of synchronization is low, possible fault points can be accurately identified and eliminated.

[0055] Furthermore, by calculating 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 that meet specific standards 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 the analysis grids, it is possible to ensure that the system's sensitivity to temperature differences is adaptively adjusted according to actual energy changes, thereby improving the rationality of the temperature gradient threshold.

[0057] Furthermore, by analyzing the distribution of eigenvalues ​​to determine whether there is a risk of potential failure or reduced efficiency, and setting a reasonable distribution threshold, it can ensure that abnormal reports are triggered only when the system abnormal fluctuations are large, avoiding frequent false alarms.

[0058] Furthermore, by analyzing the fluctuation of the distribution, it is possible to promptly identify any anomalies in the temperature control system. If the distribution fluctuation is small, it indicates that the system temperature control is relatively stable. Conversely, if the fluctuation exceeds the threshold, it indicates a possible temperature control problem. Generating an anomaly analysis report can help to take timely corrective measures, thereby ensuring normal system operation and avoiding potential failures or performance degradation. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] Figure 1 Flowchart of the local spectral property analysis method of the Hamiltonian operator in this embodiment;

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

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

[0062] Figure 4 A decision logic diagram for determining the analysis grid for this embodiment. DETAILED DESCRIPTION

[0063] In order to make the objects and advantages of the present invention more clearly understood, the present invention is further described below in conjunction with embodiments; it should be understood that the specific embodiments described herein are merely used to explain the present invention and are not intended to limit the present invention.

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

[0065] See also Figure 1 As shown, it is a flow chart of the local spectral property analysis method of the Hamiltonian operator in this embodiment;

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

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

[0068] 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;

[0069] 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;

[0070] 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;

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

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

[0073] An abnormal property analysis report is generated according to the local spectrum energy eigenvalue formed by adjusting the gradient threshold.

[0074] Obtaining the real-time temperature, power consumption, and coolant flow rate of each grid to be processed within the grid-divided monitoring area of ​​the quantum computer cooling unit is typically achieved through a series of sensors and monitoring equipment. Each grid area is equipped with a temperature sensor (such as a thermocouple or RTD sensor) to monitor the temperature in real time. Power consumption is obtained using a power meter, sensor, or current / voltage measurement device to calculate the energy consumption of each grid. Coolant flow rate is measured in real time using a flow meter (such as a turbine flow meter or 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. It depends on the design requirements of the cooling unit, the operating environment, and the thermal 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. It can ensure a rapid response to high-temperature difference grids during the cooling process, while avoiding frequent adjustments or false alarms due to too low a threshold, thereby 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, used to describe the energy state of a physical system. It includes not only the system's kinetic energy term but also its potential energy term, reflecting the system's energy distribution under different states. In many applications, the Hamiltonian operator is used to determine the system's eigenvalues ​​and eigenstates. These eigenvalues ​​can be used to understand and analyze the system's dynamic behavior, stability, and energy distribution. In particular, 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 a quantum computer cooling unit. Specifically, data such as the real-time temperature, power consumption, and coolant flow rate of each grid in the system are input into the Hamiltonian operator model. Using this model, the system calculates the local spectral energy eigenvalues ​​for each grid. These eigenvalues ​​reflect the energy state of each grid during the cooling process. By adjusting these eigenvalues, the system's temperature gradient threshold can be precisely adjusted, thereby optimizing temperature control and maintaining efficient and stable operation of the cooling unit. The specific configuration of the Hamiltonian operator model depends on the operating characteristics of the cooling unit and the system design. Typically, model parameters (such as power consumption and temperature gradients) are adjusted based on actual applications. The preset parameters in this embodiment enable the model to accurately reflect the energy distribution of the temperature control system under different workloads, thereby efficiently predicting and adjusting cooling strategies to avoid overheating or undercooling, ensuring the stability and performance optimization of the quantum computer cooling unit.

[0077] By acquiring real-time temperature, power consumption, and coolant flow data for each grid in the quantum computer's cooling unit, the system first identifies high-temperature-difference grids based on the real-time temperature and a preset temperature gradient threshold. Temporary grids are then selected by analyzing the power consumption and coolant flow of these grids. Analysis grids are then determined based on the temperature and coolant flow of these temporary grids. The relevant data for the analysis grids is then fed into a Hamiltonian operator model to calculate the local spectral energy eigenvalues. The temperature gradient threshold is then adjusted based on these eigenvalues, ultimately generating an anomaly analysis report to evaluate the system's temperature control and cooling effectiveness.

[0078] By collecting temperature, power consumption and coolant flow data in the quantum computer cooling unit in real time and combining it with local spectral energy eigenvalue analysis, temperature control anomalies can be accurately identified and adjusted. This not only improves the stability and efficiency of the cooling unit, but also dynamically optimizes the temperature gradient threshold to reduce overheating or inefficient cooling problems. Through the generated abnormal property analysis report, potential problems can be discovered and maintained in a timely manner, effectively extending equipment life, improving system performance, reducing the risk of failure, optimizing the utilization of cooling resources, and effectively solving the problem of low output accuracy due to over-reliance on historical data.

[0079] Please continue reading Figure 2 As shown, it is a decision logic diagram for determining high temperature difference grids in this embodiment. Determining a number of high temperature difference grids according to the real-time temperature and the preset temperature gradient threshold of any two grids to be processed includes:

[0080] Obtaining the center distance between any two grids to be processed to form a processing grid distance;

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

[0082] 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, forming a plurality of high temperature difference grids.

[0083] The processing grid distance is formed by obtaining the center distance between any two grids to be processed. The temperature gradient is calculated by calculating the ratio of the real-time temperature difference between the two grids to the processing grid distance. When the temperature gradient exceeds the preset temperature gradient threshold, the two grids are determined to have a high temperature difference and are listed as high temperature difference grids requiring further analysis.

[0084] By identifying high-temperature gradient grids, we can precisely locate areas of significant temperature fluctuations, preventing local overheating from impacting the stability and efficiency of quantum computers. This helps promptly identify potential cooling deficiencies or overheating issues, allowing for optimized cooling unit adjustments and ensuring long-term, efficient operation. Preset temperature gradient thresholds effectively prevent human error, improve system automation and precision, reduce maintenance costs, and enhance overall reliability and sustainability.

[0085] Specifically, 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 includes:

[0086] Calculating a standard deviation of the real-time power consumption within a predetermined first time period to form a consumption fluctuation value;

[0087] Calculating a standard deviation of the real-time coolant flow rate within the preset first determined time period to form a first flow rate fluctuation value;

[0088] A number of temporary grids are determined according to the consumption fluctuation value and the first flow fluctuation value.

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

[0090] First, the standard deviation of the real-time power consumption and coolant flow rate for each high-temperature difference grid is calculated over a predetermined first period of time, yielding consumption fluctuation values ​​and flow rate fluctuation values, respectively. By analyzing these fluctuation values, several temporary grids are identified, indicating areas with potentially significant fluctuations and requiring further attention.

[0091] By calculating fluctuations in power consumption and coolant flow, grid regions with abnormal fluctuations can be identified, allowing targeted optimization and adjustment. This helps improve the cooling unit's responsiveness, allowing for timely detection and repair of potential anomalies. This prevents localized unstable energy consumption and flow variations from impacting the normal operation of the quantum computer, thereby ensuring overall system stability and efficiency.

[0092] Please continue reading Figure 3 As shown, it is a decision logic diagram for determining a temporary grid in this embodiment. Determining a number of temporary grids according to the consumption fluctuation value and the flow fluctuation value includes:

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

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

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

[0096] 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.

[0097] Based on the consumption and flow fluctuation values, the consumption and flow curves are first drawn. Then, the cosine similarity between the two is calculated to form the change consistency. If the change consistency is less than the preset consistency threshold, the corresponding high temperature difference grid is determined to be a temporary grid.

[0098] The preset consistency threshold is a standard value used to judge the consistency of changes in the consumption fluctuation curve and the flow fluctuation curve. It depends on the performance requirements, monitoring accuracy and tolerance to fluctuation changes of the cooling unit. It is usually set between 0.8 and 0.95. In this embodiment, it is set to 0.85. While ensuring accuracy, it can avoid being overly sensitive to small fluctuations, thereby avoiding over-frequency misjudgment and maintaining stable system operation.

[0099] By comparing the changing curves of consumption and flow fluctuation values, grids where power and flow changes are not synchronized can be effectively identified. This helps to find areas where problems or anomalies may exist and adjust the cooling strategy in a timely manner.

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

[0101] Calculating a 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;

[0102] Calculating a 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;

[0103] determining a degree of synchronization according to the temperature fluctuation value and the second flow rate fluctuation value;

[0104] A plurality of analysis grids are determined according to the synchronization degree of any two adjacent temporary grids.

[0105] The preset second determination time duration is a time window for calculating temporary grid temperature and coolant flow fluctuations, 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 fluctuation changes. It is usually set between several minutes and tens of minutes. In this embodiment, it is set to 10 minutes, which can balance the response speed of the system and the stability of the data, so that the short-term fluctuations of the system can be effectively captured, while avoiding misjudgment or data noise caused by too short a time duration.

[0106] First, the standard deviation of the real-time temperature and coolant flow rate for each temporary grid is calculated over a predetermined second time period to obtain the temperature fluctuation value and the second flow rate fluctuation value. Then, based on these two fluctuation values, the degree of synchronization is calculated to assess the consistency of temperature and flow rate changes. Finally, by comparing the synchronization degrees of any adjacent temporary grids, the grids are determined to be suitable for further analysis.

[0107] By examining the consistency of temperature and coolant flow fluctuations in adjacent grids, we ensure that only grids with similar change patterns are selected for analysis, effectively reducing interference caused by system anomalies or errors. By calculating synchronization, we can more accurately identify true system behavior, avoid unnecessary misjudgments, and improve the accuracy and reliability of the overall analysis.

[0108] Specifically, determining the degree of synchronization according to the consumption fluctuation value and the second flow fluctuation value includes:

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

[0110] Drawing a variation curve of the second flow fluctuation value to form a second flow curve;

[0111] The cosine similarity between the temperature curve and the second flow curve is calculated to form a synchronization degree.

[0112] First, we plotted the temperature fluctuations and coolant flow rate fluctuations over time, forming a temperature curve and a secondary flow rate curve, respectively. We then calculated the cosine similarity between the temperature curve and the secondary flow rate curve to determine their synchronization. This synchronization reflects the coordination of temperature and coolant flow rate fluctuations, specifically whether they follow similar patterns.

[0113] By calculating the degree of synchronization between temperature and coolant flow, we can effectively determine system stability and normal operating conditions. If the fluctuation patterns of the two are highly consistent, the system is operating in the expected stable state. Conversely, if the degree of synchronization is low, possible fault points can be accurately identified and eliminated.

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

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

[0116] 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, forming a plurality of analysis grids.

[0117] The preset synchronization threshold is a standard value used to determine whether adjacent grids are sufficiently synchronized. It depends on the stability requirements of the cooling unit, the range of temperature and coolant flow, 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 due to too high a threshold, and avoid excessive screening due to too low a threshold, thereby 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 of two grids is within the tolerance range. It depends on the system's requirements for synchronization accuracy and the normal range of possible temperature and flow fluctuations. It is usually set between 0.01 and 0.1. In this embodiment, it is set to 0.05. While ensuring the consistency of synchronization, it can avoid too small a threshold causing too many unnecessary grids to be selected as analysis objects, thereby effectively improving analysis efficiency and reducing the misjudgment rate.

[0119] First, the synchronization degree of any two adjacent temporary grids is calculated and determined to be greater than a preset synchronization threshold. If both synchronization degrees meet the requirements, the relative deviation between the two synchronization degrees is further calculated to obtain a synchronization deviation value. Finally, if the synchronization deviation value is less than the preset synchronization deviation threshold, the two temporary grids are determined to be analysis grids, thus forming a number of analysis grids.

[0120] By calculating the synchronization degree and synchronization deviation values, we can further screen out grids with similar characteristics and strong consistency, and determine these grids as analysis grids for in-depth analysis. This can ensure that only grids that meet specific standards 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 eigenvalues ​​of any two analysis grids to form an adjusted gradient threshold includes:

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

[0123] 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 eigengradient value;

[0124] 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, where T'=T×[1-k×(Q-Q0) / Q0], T' is the adjusted gradient threshold, T is the preset temperature gradient threshold, Q is the intrinsic gradient value, Q0 is the preset intrinsic gradient threshold, and k is the preset adjustment coefficient.

[0125] The preset intrinsic gradient threshold is a standard value used to determine whether the difference in the local spectral energy eigenvalues ​​of two analysis grids is significant. It depends on the accuracy requirements of the system, the distance between the 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 too frequent adjustments due to too small thresholds, and maintain the stability and responsiveness of the system.

[0126] The preset adjustment coefficient is used to dynamically adjust the proportional factor of the temperature gradient threshold based on the relative deviation between the intrinsic gradient value and the preset intrinsic gradient threshold. It depends on the sensitivity requirements and adjustment accuracy of the system, and needs to balance the adjustment sensitivity and system stability. 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 and avoid over-adjustment. At the same time, it ensures sufficient response to areas with large temperature changes, so that the cooling unit can adaptively maintain efficient operation.

[0127] First, the center distance between any two analysis grids is obtained to form the analysis grid distance. Next, the ratio of the difference in the local spectral energy eigenvalues ​​of the two analysis grids to the analysis grid distance is calculated to form the intrinsic gradient value. If the intrinsic gradient value is greater than a preset intrinsic gradient threshold, the preset temperature gradient threshold is reduced based on the relative deviation between the intrinsic gradient value and the preset intrinsic gradient threshold, combined with a preset adjustment coefficient, to obtain the adjusted gradient threshold.

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

[0129] Specifically, generating an abnormal property analysis report according to the local spectrum energy eigenvalue formed by adjusting the gradient threshold includes:

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

[0131] 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.

[0132] The preset distribution threshold refers to a numerical standard used to determine whether there is significant fluctuation in the distribution of local spectral energy eigenvalues ​​when generating an abnormal property analysis report. It depends on the system's fault tolerance, operational stability, and sensitivity to abnormal fluctuations. It is usually set between 0.1 and 1.0. In this embodiment, it is set to 0.3. While ensuring that larger abnormal fluctuations can be discovered in a timely manner, it avoids overreaction to small, normal fluctuations, thereby maintaining the stability and efficient operation of the system.

[0133] Based on the adjusted gradient threshold, the standard deviation of all local spectral energy eigenvalues ​​is calculated to determine the distribution of the eigenvalues. When the eigenvalue distribution exceeds the preset distribution threshold, an abnormal property analysis report is generated. Specifically, the standard deviation reflects the amplitude of the eigenvalue fluctuations across all grids. If the distribution exceeds the preset threshold, it indicates significant abnormal fluctuations in the system, requiring detailed analysis and adjustment.

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

[0135] Specifically, generating an abnormal property analysis report based on the eigenvalue distribution includes:

[0136] Calculating the standard deviation of the eigenvalue distribution within a preset analysis time to form a distribution fluctuation value;

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

[0138] The preset distribution fluctuation threshold is a set value used to determine whether the fluctuation of the eigenvalue distribution exceeds the normal range. It depends on the system's temperature control accuracy requirements, the stability of the operating environment, and the sensitivity requirements for abnormality 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 normal operating fluctuations, ensuring that in actual operation, abnormalities can be identified in a timely manner without misjudging normal fluctuations.

[0139] First, the standard deviation of the distribution of all local spectral energy eigenvalues ​​within the preset analysis time is calculated to obtain the distribution fluctuation value. Then, if the distribution fluctuation value is less than the preset distribution fluctuation threshold, the temperature control is judged to be abnormal and an abnormality analysis report is generated. The abnormality analysis report details the abnormal grid, abnormality type, abnormality cause, and adjustment suggestions.

[0140] By analyzing the fluctuation of the distribution, we can promptly identify any anomalies in the temperature control system. If the distribution fluctuation is small, it indicates that the system temperature control is relatively stable. Conversely, if the fluctuation exceeds the threshold, it indicates a possible temperature control problem. Generating an anomaly analysis report can help you take timely corrective measures to ensure the normal operation of the system and avoid potential failures or performance degradation.

[0141] Thus far, the technical solutions of the present invention have been described in conjunction with the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art may make equivalent changes or substitutions to the relevant technical features, and the technical solutions after such changes or substitutions will fall within the scope of protection of the present invention.

Claims

1. A method for analyzing the 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 a monitoring area divided into grids in a quantum computer cooling unit; 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 of each analysis grid into a preset Hamiltonian operator model to obtain a plurality 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 grids to be processed 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, forming a plurality of high temperature difference grids.

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 predetermined first time period to form a consumption fluctuation value; Calculating a 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 number 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 change 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 between 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 includes: Calculating a 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 a 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 rate fluctuation value; A plurality 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 includes: Drawing a change curve of the temperature fluctuation value to form a temperature curve; Drawing a variation curve of the second flow fluctuation value to form a second flow curve; The cosine similarity between 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 includes: When both synchronization degrees are greater than a preset synchronization degree threshold, a relative deviation between 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, forming 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 includes: 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 eigengradient 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 a 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 based on the eigenvalue distribution includes: Calculating 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 abnormality property analysis report is generated.

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