Calculation power migration method and device based on quantum measurement technology

Abstract

The invention discloses a computing power migration method and device based on a quantum measurement technology. The method comprises the following steps: acquiring a real-time quantum signal of a power system; determining a plurality of target feature data based on the real-time quantum signal; the multiple pieces of target feature data are input into a preset migration decision model, a Pareto optimal solution set used for cross-system computing power migration is obtained, the migration decision model is constructed based on a quantum tunneling improved non-dominated sorting genetic algorithm, and the Pareto optimal solution set comprises multiple alternative migration schemes; determining a target migration scheme in the alternative migration schemes; and migrating the computing power resources of the first region to the second region based on the target migration scheme. The technical problems that in the prior art, cross-regional computing power migration cannot accurately match new energy output in real time, so that the new energy consumption rate is not increased enough, and computing power scheduling is mismatched with the new energy output are solved.

Description

Technical Field

[0001] This invention relates to the field of quantum information technology, and more specifically, to a method and apparatus for transferring computing power based on quantum measurement technology. Background Technology

[0002] Against the backdrop of rapidly developing electronic information technology, the rise of large-scale wind and photovoltaic new energy bases in western China, and the surge in computing power demand in eastern computing centers, have presented a technological challenge: cross-regional computing power migration to promote the integration of new energy sources. Current computing power migration solutions primarily rely on traditional new energy power prediction methods and classic computing power scheduling algorithms. These methods attempt to collect new energy power data through SCADA (Supervisory Control and Data Acquisition) systems and utilize algorithms such as genetic algorithms and particle swarm optimization to allocate the scale of computing power migration, thereby matching computing power demand with new energy output. However, this traditional approach reveals significant flaws and shortcomings when faced with the complexities of practical applications.

[0003] These technical limitations manifest in three main aspects: First, the lag in new energy power sensing. SCADA systems typically sample once per minute, making it difficult to capture rapid changes in instantaneous wind or solar power, such as wind power fluctuations of ±20% within 10 seconds due to gusts. This directly leads to a deviation of over 15% between computing power migration commands and actual new energy output, failing to meet real-time consumption requirements. Second, the security of cross-regional data transmission. Traditional encryption technologies are vulnerable to quantum computing, easily cracked. Furthermore, cross-regional fiber optic transmission is affected by electromagnetic interference, resulting in packet loss rates of 0.5% to 1%. This not only threatens data integrity but may also mislead computing power migration decisions, exacerbating wind and solar curtailment. Third, the low precision of consumption-migration coordination. Classical scheduling algorithms struggle to quantify the coupling relationship between "new energy consumption - computing power migration - transmission energy consumption," limiting the effectiveness of computing power migration schemes in improving new energy consumption, typically with a consumption improvement rate below 8%.

[0004] There is currently no effective solution to the above problems. Summary of the Invention

[0005] This invention provides a computing power migration method and apparatus based on quantum measurement technology, which at least solves the technical problems in the current technology that cross-regional computing power migration cannot match the output of new energy sources in real time and accurately, resulting in insufficient improvement of the new energy consumption rate and mismatch between computing power scheduling and new energy output.

[0006] According to one aspect of the present invention, a computing power migration method based on quantum measurement technology is provided, comprising: acquiring real-time quantum signals of a power system, wherein the real-time quantum signals include real-time power quantum signals of an energy system, real-time load quantum signals of a computing power system, and real-time energy consumption quantum signals of a transmission channel, wherein the energy system is located in a first region, the computing power system is located in a second region, the first region and the second region are two different regions in the power system, and the transmission channel is a channel for data transmission in the power system; determining multiple target feature data based on the real-time quantum signals, wherein the multiple target feature data includes multiple real-time feature data and energy power prediction data; inputting the multiple target feature data into a preset migration decision model to obtain a Pareto optimal solution set for cross-system computing power migration, wherein the migration decision model is constructed based on a quantum tunneling-modified non-dominated sorting genetic algorithm, and the Pareto optimal solution set includes multiple alternative migration schemes; determining a target migration scheme from the multiple alternative migration schemes; and migrating computing power resources from the first region to the second region based on the target migration scheme.

[0007] Optionally, acquiring real-time quantum signals from a power system includes: acquiring multiple real-time power acquisitions from the energy system; converting these multiple real-time power acquisitions into multiple quantum spin frequencies based on the principle of atomic spin precession; constructing real-time power quantum signals based on the multiple quantum spin frequencies; acquiring multiple real-time processor utilization rates from a computing power system; converting these multiple real-time processor utilization rates into multiple quantum luminescence intensities based on the principle of quantum dot photoluminescence; constructing real-time load quantum signals based on the multiple quantum luminescence intensities; acquiring multiple real-time energy consumptions from a transmission channel; converting these multiple real-time energy consumptions into multiple quantum phases based on the principle of quantum interferometers; and constructing real-time energy consumption quantum signals based on the multiple quantum phases.

[0008] Optionally, based on the real-time quantum signal, multiple target feature data are determined, including: demodulating the real-time quantum signal into multiple real-time feature data; and based on the real-time power quantum signal, predicting the power prediction value of the energy system within a preset future period, as energy power prediction data.

[0009] Optionally, multiple real-time characteristic data include the real-time power of the energy system, the real-time processor utilization of the computing system, the real-time transmission latency of the first transmission channel, the real-time transmission latency of the second transmission channel, the real-time bandwidth utilization of the first transmission channel, the real-time bandwidth utilization of the second transmission channel, the real-time computing task priority of the computing system, and the quantum frequency of energy intensity in the energy system, wherein the transmission channel includes the first transmission channel and the second transmission channel.

[0010] Optionally, the target migration scheme is determined from multiple alternative migration schemes, including: calculating the energy consumption corresponding to each of the multiple alternative migration schemes to obtain multiple energy consumption amounts; comparing the size of the multiple energy consumption amounts to obtain the largest energy consumption amount among the multiple energy consumption amounts as the target energy consumption amount; and taking the alternative migration scheme corresponding to the target energy consumption amount as the target migration scheme.

[0011] According to another aspect of the present invention, a computing power migration system based on quantum measurement technology is also provided, comprising: a quantum sensing layer for acquiring real-time quantum signals of a power system, wherein the real-time quantum signals include real-time power quantum signals of an energy system, real-time load quantum signals of a computing power system, and real-time energy consumption quantum signals of a transmission channel, wherein the energy system is located in a first region, the computing power system is located in a second region, the first region and the second region are two different regions in the power system, and the transmission channel is a channel for data transmission in the power system; a quantum decision layer for determining multiple target feature data based on the real-time quantum signals, wherein the multiple target feature data includes multiple real-time feature data and energy power prediction data; inputting the multiple target feature data into a preset migration decision model to obtain a Pareto optimal solution set for cross-system computing power migration, wherein the migration decision model is constructed based on a quantum tunneling-modified non-dominated sorting genetic algorithm, and the Pareto optimal solution set includes multiple alternative migration schemes; determining a target migration scheme from the multiple alternative migration schemes; and a computing power execution layer for migrating computing resources from the first region to the second region based on the target migration scheme.

[0012] Optionally, the system further includes a quantum transmission layer for quantum encrypted transmission between the quantum sensing layer, the quantum decision-making layer, and the computing power execution layer.

[0013] According to another aspect of the present invention, a non-volatile storage medium is also provided, the non-volatile storage medium including a stored program, wherein, when the program is running, the device where the non-volatile storage medium is located is controlled to execute any of the above-described computing power transfer methods based on quantum measurement technology.

[0014] According to another aspect of the present invention, a computer device is also provided, the computer device including a processor for running a program, wherein the program executes any of the above-described computing power transfer methods based on quantum measurement technology.

[0015] According to another aspect of the present invention, a computer program product is also provided, including a computer program that, when executed by a processor, implements any of the above-described computing power transfer methods based on quantum measurement technology.

[0016] In this embodiment of the invention, a computing power transfer method based on quantum measurement technology is employed. This method acquires real-time quantum signals from the power system, including real-time power quantum signals from the energy system, real-time load quantum signals from the computing system, and real-time energy consumption quantum signals from the transmission channel. The energy system is located in a first region, and the computing system is located in a second region. The first and second regions are two different regions within the power system, and the transmission channel is a channel for data transmission within the power system. Based on the real-time quantum signals, multiple target feature data are determined, including multiple real-time feature data and energy power prediction data. These multiple target feature data are then input into a preset transfer... In the migration decision model, a Pareto optimal solution set for cross-system computing power migration is obtained. The migration decision model is constructed based on a quantum tunneling-modified non-dominated sorting genetic algorithm, and the Pareto optimal solution set includes multiple alternative migration schemes. The target migration scheme is determined from the multiple alternative migration schemes. Based on the target migration scheme, the computing power resources of the first region are migrated to the second region, which significantly improves the amount of new energy consumption. This achieves the technical effect of improving the dynamic matching degree between computing power migration and new energy output, and solves the technical problem that cross-regional computing power migration cannot match new energy output in real time and accurately, resulting in insufficient improvement of new energy consumption rate and mismatch between computing power scheduling and new energy output. Attached Figure Description

[0017] The accompanying drawings, which are included to provide a further understanding of the invention and form part of this application, illustrate exemplary embodiments of the invention and, together with their description, serve to explain the invention and do not constitute an undue limitation thereof. In the drawings:

[0018] Figure 1 A hardware structure block diagram of a computer terminal for implementing a computing power transfer method based on quantum measurement technology is shown.

[0019] Figure 2 This is a flowchart illustrating a computing power transfer method based on quantum measurement technology according to an embodiment of the present invention.

[0020] Figure 3 This is a schematic diagram of a computing power migration process based on maximizing the consumption of new energy sources, provided by an optional embodiment of the present invention. Detailed Implementation

[0021] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0022] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0023] According to an embodiment of the present invention, a computing power transfer method based on quantum measurement technology is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0024] The method embodiment provided in Embodiment 1 of this application can be executed on a mobile terminal, computer terminal, or similar computing device. Figure 1 A hardware block diagram of a computer terminal for implementing a computing power transfer method based on quantum measurement technology is shown. Figure 1 As shown, the computer terminal 10 may include one or more processors (shown as 102a, 102b, ..., 102n in the figure) (the processor may include, but is not limited to, a microprocessor MCU or a programmable logic device FPGA, etc.) and a memory 104 for storing data. In addition, it may also include: a display, an input / output interface (I / O interface), a universal serial bus (USB) port (which may be included as one of the ports of a BUS bus), a network interface, a power supply, and / or a camera. Those skilled in the art will understand that... Figure 1 The structure shown is for illustrative purposes only and does not limit the structure of the aforementioned electronic device. For example, computer terminal 10 may also include... Figure 1 The more or fewer components shown, or having the same Figure 1 The different configurations shown.

[0025] It should be noted that the aforementioned one or more processors and / or other data processing circuits are generally referred to herein as "data processing circuits". These data processing circuits may be embodied, in whole or in part, in software, hardware, firmware, or any other combination thereof. Furthermore, the data processing circuits may be a single, independent processing module, or may be integrated, in whole or in part, into any other element within the computer terminal 10. As involved in the embodiments of this application, the data processing circuits serve as a processor control mechanism (e.g., selection of a variable resistor termination path connected to an interface).

[0026] The memory 104 can be used to store software programs and modules of application software, such as the program instructions / data storage device corresponding to the computing power migration method based on quantum measurement technology in this embodiment of the invention. The processor executes various functional applications and data processing by running the software programs and modules stored in the memory 104, thereby realizing the computing power migration method based on quantum measurement technology for the aforementioned application. The memory 104 may include high-speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory 104 may further include memory remotely located relative to the processor, and these remote memories can be connected to the computer terminal 10 via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0027] The display can be, for example, a touchscreen liquid crystal display (LCD) that allows the user to interact with the user interface of the computer terminal 10.

[0028] Figure 2 This is a flowchart illustrating a computing power transfer method based on quantum measurement technology according to an embodiment of the present invention, as shown below. Figure 2 As shown, the method includes the following steps:

[0029] Step S201: Obtain the real-time quantum signal of the power system. The real-time quantum signal includes the real-time power quantum signal of the energy system, the real-time load quantum signal of the computing system, and the real-time energy consumption quantum signal of the transmission channel. The energy system is located in the first region, the computing system is located in the second region, the first region and the second region are two different regions in the power system, and the transmission channel is the channel for data transmission in the power system.

[0030] In this step, acquiring real-time quantum signals from the power system is the core component. The aim is to capture the dynamically changing power supply and demand status and transmission channel performance between the first region (Western New Energy Base) and the second region (Eastern Computing Center) with micro-nano precision, ensuring accurate resource matching and scheduling optimization. The real-time quantum signals cover three key dimensions: real-time power quantum signals from the energy system, reflecting the instantaneous output of wind and solar power in the west, converted into quantum frequency information through the principle of atomic spin precession; real-time load quantum signals from the computing system, where CPU utilization and memory usage in the eastern data center are converted into quantum luminescence intensity data through quantum dot photoluminescence; and real-time energy consumption quantum signals from the transmission channels, quantifying the energy consumption of cross-regional fiber optic links and converted into quantum phase signals using the principle of quantum interferometers. These three signals together constitute a multi-dimensional quantum sensing network, monitored in real-time with high-frequency sampling (up to 50Hz) and transmitted to the central collaborative platform. Supporting secure transmission based on quantum encryption ensures data integrity and provides precise input for building a multi-objective scheduling model aimed at maximizing renewable energy absorption, thus achieving synergistic optimization of renewable energy absorption and computing power migration.

[0031] Step S202: Based on the real-time quantum signal, determine multiple target feature data, including multiple real-time feature data and energy power prediction data.

[0032] In this step, multiple target feature data are determined based on real-time quantum signals, and a decision model is constructed using these as the core. The real-time feature data encompasses the instantaneous power output of the western new energy base, the CPU utilization and memory usage of the eastern computing center, as well as the energy consumption and latency of cross-regional transmission channels. These are converted into quantum frequency, quantum luminescence intensity, and quantum phase through the micro-nano level response of quantum technology, forming a high-precision quantum signal set. Energy power prediction data utilizes a quantum-enhanced prediction model to predict the western new energy output trend for the next hour based on real-time quantum signals, identifying peak wind power generation periods and photovoltaic peaks. This data is integrated into a multi-dimensional feature vector containing latency, energy consumption, cost, and new energy absorption capacity. This vector serves as input to the quantum-enhanced multi-objective decision model, achieving synergistic optimization of "rapid matching of computing power demand, maximization of new energy absorption, and minimization of transmission costs." This ensures the accurate issuance of computing power migration instructions, effectively improves the efficiency of new energy absorption, and guarantees the quality of computing power services.

[0033] Step S203: Input multiple target feature data into a preset migration decision model to obtain a Pareto optimal solution set for cross-system computing power migration. The migration decision model is constructed based on a quantum tunneling-improved non-dominated sorting genetic algorithm, and the Pareto optimal solution set includes multiple alternative migration schemes.

[0034] In this step, multiple target feature data, including real-time quantum signals and energy power predictions, are input into a pre-defined migration decision model. This model employs a non-dominated sorting genetic algorithm improved with quantum tunneling technology, aiming to solve multi-objective optimization problems in cross-system computing power migration. Through the model's computation, a series of Pareto optimal solutions are generated, each representing an alternative migration scheme. These schemes achieve an optimal balance between maximizing renewable energy consumption and the latency, energy consumption, and cost of computing power migration. The quantum tunneling improvement enhances the algorithm's global search capability, ensuring that while considering consumption weights, computing power demand priorities, and channel transmission costs, it can escape local optima and find the optimal set of strategies that truly meet the dynamic matching of cross-regional renewable energy consumption and computing power demand.

[0035] Specifically, the migration decision model M_sched adds the objective dimension of "maximizing the absorption of new energy sources" to the original objectives of "latency-energy consumption-cost", constructing a four-objective optimization architecture. The objective function is as follows:

[0036]

[0037] in, The amount of wind / solar curtailment in the western new energy base (unit: MWh) is represented by η, which is the weighting coefficient for new energy consumption (value ranges from 0.4 to 0.6, with the upper limit taken during peak wind / solar power generation periods). The computing power transmission delay (ms) is the time required to transmit computing power. Transmission energy consumption (kW) h), Transmission cost (yuan). The quantum feature mapping layer adds a "new energy power output quantum signal" dimension: This dimension maps the quantum states (such as the atomic spin frequency corresponding to wind speed) corresponding to the real-time power output of new energy sources in the west (wind power speed, photovoltaic irradiance). The original 8-dimensional feature vector is incorporated and expanded into a 10-dimensional vector:

[0038]

[0039] in, For the real-time power of the energy system, For the real-time processor utilization of the computing system, The real-time transmission delay of the first transmission channel. For the real-time transmission delay of the second transmission channel, This represents the real-time bandwidth utilization rate of the first transmission channel. This represents the real-time bandwidth utilization rate of the second transmission channel. Prioritize the real-time computing tasks of the computing power system. The quantum frequency of energy intensity in an energy system. The atomic spin frequency corresponding to wind speed. This represents the atomic spin frequency corresponding to photovoltaic irradiance. The model training dataset is supplemented with scenarios involving renewable energy consumption (such as wind power output > 80% of rated power in the west and fluctuating computing power demand in the east). Training is performed using the NSGA-Ⅲ algorithm improved with quantum tunneling to ensure that when renewable energy consumption increases by ≥15%, other objectives (latency and energy consumption) still meet grid requirements (latency ≤ 200ms, energy consumption ≤ 30kW). h / PFlops).

[0040] Step S204: Determine the target migration plan from multiple alternative migration plans.

[0041] In this step, determining the target migration scheme from the Pareto optimal solution set is a crucial step in the decision-making process for cross-regional computing power migration based on quantum measurement technology. By analyzing the performance of each alternative scheme across four dimensions—"renewable energy consumption," "computing power migration latency," "transmission energy consumption," and "cost"—and considering the current priority of computing power demand and the renewable energy consumption target, a comprehensive evaluation is conducted. During periods of peak renewable energy generation in western China, the scheme that "maximizes renewable energy consumption" is prioritized, even if it slightly increases the latency or cost of computing power migration, to ensure that the wind and solar curtailment rate is controlled below 5%, significantly improving consumption efficiency. Conversely, for non-real-time or low-priority computing power tasks, migration schemes with lower energy consumption and cost are preferred, even if the increase in consumption is smaller. This process ensures that the computing power migration strategy effectively promotes renewable energy consumption while also considering service quality and economic benefits, achieving the goal of optimal allocation of cross-regional resources.

[0042] Step S205: Based on the target migration plan, migrate the computing resources of the first region to the second region.

[0043] In this step, based on the determined target migration plan, the computing power resources powered by new energy sources, such as wind power direct-supply server clusters, are prioritized in the computing power center of the first region (Western New Energy Base). Taking 1.1 PFlops of computing power output as an example, this is transmitted to the second region (Eastern Computing Power Demand Center) via quantum encryption. Simultaneously, the central coordination platform issues receiving instructions to the second region to ensure smooth access and use of the computing power, marking it as "Western Wind Power Consumption Computing Power" to reflect its green source. In the cross-regional transmission channel, the channel capacity is dynamically adjusted according to the bandwidth configuration instructions of the plan, such as adjusting the channel bandwidth to 10Gbps and 15Gbps respectively, ensuring the real-time performance and efficiency of the computing power migration, and controlling the transmission latency to within 100ms. This process achieves precise matching between redundant computing power in the west and the demand in the east, promoting the consumption of new energy sources while ensuring the smooth execution of computing power tasks in the east, demonstrating the unique advantages of quantum technology in cross-regional computing power migration.

[0044] In addition, after receiving the dispatch instructions, the Western Computing Power Center prioritizes the use of "computing power nodes powered by new energy sources" (such as server clusters directly supplied by wind power) and records the corresponding new energy consumption of this computing power (e.g., 0.8 PFlops of computing power consumes 25 MWh of wind power). Quantum sensing nodes collect power data from western new energy grid-connected points in real time (using quantum power sensors with an accuracy of ±0.01 MW) and calculate the actual wind / solar curtailment after the computing power migration. If the wind curtailment rate is still >5%, it immediately reports back to the central coordination platform, triggering secondary dispatch (e.g., migrating an additional 0.2 PFlops of computing power). After receiving the computing power, the Eastern Computing Power Center marks the energy source of this computing power (e.g., "western wind power consumption computing power") with a "new energy consumption tag" and includes it in the grid's new energy consumption assessment statistics to ensure that the actual contribution of computing power migration to new energy consumption can be quantified.

[0045] Through the above steps, the goal of significantly increasing the amount of renewable energy absorbed has been achieved, thereby realizing the technical effect of improving the dynamic matching degree between computing power migration and renewable energy output. This solves the technical problem that current technologies cannot match renewable energy output in real time and accurately during cross-regional computing power migration, resulting in insufficient improvement in renewable energy absorption rate and mismatch between computing power scheduling and renewable energy output.

[0046] As an optional embodiment, acquiring real-time quantum signals from a power system includes: acquiring multiple real-time power acquisitions from the energy system; converting these multiple real-time power acquisitions into multiple quantum spin frequencies based on the principle of atomic spin precession; constructing real-time power quantum signals based on the multiple quantum spin frequencies; acquiring multiple real-time acquisition processor utilization rates from a computing power system; converting these multiple real-time acquisition processor utilization rates into multiple quantum luminescence intensities based on the principle of quantum dot photoluminescence; constructing real-time load quantum signals based on the multiple quantum luminescence intensities; acquiring multiple real-time energy consumptions from a transmission channel; converting these multiple real-time energy consumptions into multiple quantum phases based on the principle of quantum interferometers; and constructing real-time energy consumption quantum signals based on the multiple quantum phases.

[0047] Optionally, a quantum sensor network can be used to acquire core signals of the power system in real time. First, a quantum renewable energy power sensor collects multiple instantaneous power data points at the western base and converts them into corresponding quantum spin frequencies based on the principle of atomic spin precession, thereby constructing a power quantum signal reflecting the real-time output state of renewable energy. Subsequently, a quantum computing power load sensor monitors CPU utilization and memory usage at the eastern data center, using the quantum dot photoluminescence mechanism to convert these computing power indicators into quantum luminescence intensity, forming a real-time load quantum signal revealing computing power demand. Furthermore, in the cross-regional transmission channel, a quantum channel energy consumption sensor, based on a quantum interferometer, accurately measures real-time energy consumption and maps it to a quantum phase, thus constructing a real-time energy consumption quantum signal. In summary, by converting physical quantities into quantum states, not only is ultra-sensitive capture of the power system's operating state achieved, but also absolute secure data transmission is ensured, providing high-precision real-time quantum signal input for subsequent computing power migration decisions, thereby effectively optimizing the dynamic matching of renewable energy consumption and computing power services.

[0048] For example, one quantum new energy power sensor is deployed for every 50MW of installed capacity in the western new energy base to collect instantaneous power data for wind / photovoltaic power; one quantum computing load sensor is deployed for every 100 servers in the eastern computing center to collect computing power demand. One quantum channel energy consumption sensor is deployed for every 100km of cross-regional transmission channels (such as the "West-East Computing-East Data" fiber optic link) to collect transmission energy consumption data. A standard power signal (e.g., 1000kW) is sent to the quantum new energy power sensor, and the deviation Δ is recorded. Compensation is performed using the formula Δ_cal=Δ×k1 (k1=0.995~1.005) to ensure a power measurement accuracy of ±0.1kW; a standard CPU utilization signal (50%) is sent to the quantum computing load sensor, and compensation is performed using Δ_cal=Δ×k2 (k2=0.99~1.01) to ensure a utilization accuracy of ±0.5%; a standard energy consumption signal (10kW) is sent to the quantum channel energy consumption sensor. h), through compensation of Δ_cal=Δ×k3 (k3=0.98~1.02), ensure energy consumption accuracy of ±0.1kW. h. Time synchronization of all sensing nodes can be achieved through a quantum synchronization clock (accuracy <1ns), avoiding data timing deviations.

[0049] The quantum new energy power sensor, based on the principle of atomic spin precession, converts power P into quantum spin frequency f_P (unit: kHz), with a sampling frequency of 100Hz (capturing power fluctuations of ±20% within 10 seconds), generating a power quantum subset S_P={f_P1,f_P2,...,f_Pn}; the power-frequency mapping relationship is: P=a×f_P (a=10kW / kHz), such as f_P=50kHz corresponding to P=500kW. The quantum computing power load sensor converts CPU utilization U into quantum luminescence intensity I_U (unit: μW / cm²), with a sampling frequency of 100Hz, generating a load quantum subset S_U={I_U1,I_U2,...,I_Um}; the utilization-luminescence intensity mapping is: U=c×I_U+d (c=0.1% / (μW / cm²), d=0%), such as I_U=600μW / cm² corresponding to U=60%. Quantum channel energy sensors convert transmission energy E into quantum phase. _E (unit: rad), sampling frequency 50Hz, generating energy-consuming quantum subset S_E={ _E1, _E2,..., _Ep}; Energy consumption and phase mapping: E=e× _E+f(e=1kW) h / rad, f=0kW h), such as _E=20rad corresponds to E=20kW h. Align S_P, S_U, and S_E according to timestamps to generate the original quantum dataset S_total=[S_P,S_U,S_E]^T (3×max(n,m,p)). Quantum sensing nodes generate "one-time pad" keys using QKD to encrypt sensitive data such as new energy power and computing load in S_total; quantum teleportation technology is used to transmit the encrypted quantum state to the central collaborative platform, avoiding packet loss in traditional optical fibers, with a transmission latency of ≤10ms.

[0050] As an optional embodiment, multiple target feature data are determined based on real-time quantum signals, including: demodulating the real-time quantum signals into multiple real-time feature data; and predicting the power prediction value of the energy system within a preset future period based on real-time power quantum signals, as energy power prediction data.

[0051] Optionally, based on real-time quantum signals, the instantaneous power of the western new energy base, the processor utilization rate of the eastern computing center, and the real-time energy consumption of the cross-regional transmission channel are first demodulated and converted into accurate real-time feature data. Then, using quantum-enhanced prediction algorithms, such as the LSTM-quantum hybrid model, the real-time power quantum signal from the western new energy base is used as input to predict the power trend over the next hour, obtaining energy power prediction data. This process relies on a quantum entanglement filtering algorithm to reduce signal noise, ensuring the accuracy of the prediction. Through demodulation and prediction, multiple target feature data are determined, including the power output of the western new energy base, the computing power demand in the east, the energy consumption and latency of cross-regional transmission, and the future prediction of new energy power. This provides comprehensive and accurate data support for subsequent computing power migration decisions, ensuring the coordinated optimization of new energy consumption and computing power migration.

[0052] As an optional embodiment, multiple real-time characteristic data include the real-time power of the energy system, the real-time processor utilization of the computing system, the real-time transmission latency of the first transmission channel, the real-time transmission latency of the second transmission channel, the real-time bandwidth utilization of the first transmission channel, the real-time bandwidth utilization of the second transmission channel, the real-time computing task priority of the computing system, and the quantum frequency of energy intensity in the energy system, wherein the transmission channel includes the first transmission channel and the second transmission channel.

[0053] Optionally, multiple real-time data points cover the instantaneous power of the western energy system, converted into quantum frequency by a quantum new energy power sensor based on the principle of atomic spin precession; the real-time processor utilization rate of the eastern computing center, converted into quantum luminescence intensity by a quantum computing load sensor based on the principle of quantum dot photoluminescence; and the real-time transmission latency and bandwidth occupancy of the first and second transmission channels, quantized into quantum phase by a quantum channel energy consumption sensor using quantum interferometer technology. Simultaneously, the quantum frequency of real-time task priorities and energy intensity of the computing system, such as wind speed, is identified as one of the features. These data collectively constitute an important basis for computing power migration decisions. Through a quantum-enhanced multi-objective optimization model, efficient coordination between energy consumption and computing power services in cross-regional transmission is ensured, achieving precise allocation of resources for the first and second transmission channels.

[0054] As an optional embodiment, determining the target migration scheme from multiple alternative migration schemes includes: calculating the energy consumption corresponding to each of the multiple alternative migration schemes to obtain multiple energy consumption amounts; comparing the size of the multiple energy consumption amounts to obtain the largest energy consumption amount among the multiple energy consumption amounts as the target energy consumption amount; and taking the alternative migration scheme corresponding to the target energy consumption amount as the target migration scheme.

[0055] Optionally, a target migration scheme is determined from multiple alternatives. The core of this process is to quantify and compare the renewable energy absorption capacity of each scheme. Based on multidimensional data constructed from real-time quantum signals, a quantum-enhanced decision model is applied to calculate the changes in wind and solar curtailment in western renewable energy bases under each scheme, obtaining the corresponding absorption capacity dataset. By comparison, the scheme that maximizes renewable energy absorption capacity is identified, i.e., the one with the largest target energy absorption capacity. This scheme not only prioritizes renewable energy absorption but also takes into account the latency, energy consumption, and cost of computing power migration, ensuring that while meeting the computing power needs of the east, the curtailment rate in the west is effectively reduced. Ultimately, this scheme is selected as the target migration scheme to guide subsequent cross-regional allocation of computing power resources, thereby achieving efficient renewable energy absorption and optimized computing power allocation.

[0056] Specifically, when the output of new energy in the western region exceeds the threshold (e.g., wind power output > 90% of rated power, wind curtailment risk ≥ 20%), the model automatically adjusts η to 0.6, prioritizing the selection of computing power migration schemes based on "maximizing new energy consumption"—for example, if the western region has 0.8 PFlops of dispatchable computing power, corresponding to the consumption of 25 MWh of wind power, the eastern region's demand scheme, which can fully absorb this computing power, is prioritized. Furthermore, if there is multi-regional computing power demand (e.g., both the eastern and central regions require computing power), it is allocated through a "new energy consumption-computing power demand matching matrix": 0.5 PFlops are migrated from the west to the east (consuming 15 MWh of wind power), and 0.3 PFlops are migrated to the central region (consuming 10 MWh of wind power), ensuring that the amount of new energy curtailment is reduced to below 5%. The quantum sensing node can also predict the output trend of western new energy sources in the next hour (such as a 30% decrease in photovoltaic output), and adjust the computing power migration sequence in advance. During the peak period of new energy output (10:00-14:00), 0.2 PFlops of computing power will be migrated, and the amount of migration will be reduced during the off-peak period (20:00-6:00 the next day) to avoid mismatch between computing power migration and new energy output.

[0057] As an optional embodiment, Figure 3 This is a schematic diagram of a computing power migration process based on maximizing the consumption of new energy sources, provided by an optional embodiment of the present invention. Figure 3 As shown, the above process includes the following steps:

[0058] Step 1: Deployment of Quantum Sensing Nodes in the Western New Energy Base. Following a density of one quantum new energy power sensor per 50MW of installed capacity, sensors will be deployed at the bottom of wind turbine towers and next to photovoltaic array combiner boxes. Simultaneously, one quantum power curtailment monitoring sensor will be deployed at the new energy grid connection point (110kV / 220kV substation). The sensors will have waterproof and rust-proof housings and will be fixed 2-3 meters above the ground to avoid obstruction. This will ensure coverage of the core monitoring points for new energy output, providing fundamental data for subsequent consumption calculations, with an accuracy of ±0.1kW.

[0059] Step 2: Deployment of Quantum Sensing Nodes in the Eastern Computing Power Center. Sensors will be deployed on the top of server racks at a density of one quantum computing load sensor for every 100 servers. Racks near areas with concentrated computing power demand (such as load forecasting server clusters) will be prioritized. The sensors will connect to the rack monitoring unit via USB interfaces to collect CPU utilization and memory usage data. This will accurately capture the computing power demand in the eastern region, providing demand-side data for matching computing power with new energy sources. The utilization measurement accuracy must reach ±0.5%.

[0060] Step 3: Deployment of quantum sensing nodes for cross-regional transmission channels. Deploy these sensors within the channel relay stations at a density of one energy consumption sensor per 100km. The sensors are connected to the channel's power system to synchronously collect transmission delay, bandwidth utilization, and energy consumption data. The spacing error between adjacent sensors should be ≤5km to ensure full channel coverage. Monitor channel transmission costs to provide a basis for selecting "low-energy-consumption, high-absorption" migration paths. Energy consumption measurement accuracy must reach ±0.1kW. h.

[0061] Step 4: Quantum New Energy Power Sensor Calibration. Send standard power signals (e.g., 1000kW, 500kW, and 100kW standard signals) to the quantum new energy power sensors already deployed in the western new energy base; record the deviation Δ between the sensor output signal and the standard signal, and compensate using the formula Δ_cal=Δ×k1 (k1 is the calibration coefficient, ranging from 0.995 to 1.005, calibrated by the laboratory using 100 sets of standard data); repeat the calibration 3 times to ensure that the power measurement error after calibration is ≤±0.1kW, and the stability fluctuation is ≤±0.05kW for one hour. This ensures the accuracy of the new energy output data and avoids errors in the calculation of the consumption capacity due to data deviation.

[0062] Step 5: Quantum Computing Load Sensor Calibration. Send standard CPU utilization signals (30%, 50%, and 80% standard signals) to the quantum computing load sensors already deployed in the Eastern Computing Center; record the deviation value Δ, and compensate using the formula Δ_cal=Δ×k2 (k2=0.99-1.01); verify the calibration effect: simulate the dynamic process of CPU utilization increasing from 20% to 90%, with sensor tracking error ≤±0.5%, meeting the accuracy required for capturing computing power. Ensure that the computing power load data accurately reflects the demand in the east, avoiding "excessive / insufficient computing power migration" affecting the consumption of new energy.

[0063] Step 6: Quantum Channel Power Consumption Sensor Calibration. Send a standard power consumption signal (10kW) to the quantum channel power consumption sensors already deployed across the region. h, 20kW h, 30kW h is a level 3 standard signal); record the deviation value Δ, and compensate using the formula Δ_cal=Δ×k3 (k3=0.98-1.02); test channel delay: send standard delay signals (50ms, 100ms, 200ms), sensor measurement error ≤±1ms, energy consumption error ≤±0.1kW. h. Accurately quantify channel transmission costs to provide reliable data for selecting "low energy consumption - high absorption" solutions.

[0064] Step 7: Time Synchronization of the Quantum Sensing Network. Deploy a quantum synchronization clock server (accuracy <1ns) and connect it to each node via optical fiber; send synchronization signals to all nodes, calibrating the timestamp every 10ms; verify the synchronization effect: collect western wind power, eastern computing load, and channel energy consumption data at the same time, with a time deviation ≤5ns, ensuring the consistency of the dataset's timing. Avoid time misalignment leading to distortion in the "new energy output - computing power demand" match, affecting consumption decisions.

[0065] Step 8: Western New Energy Power Quantum Signal Acquisition. Based on the principle of atomic spin precession, the sensor converts wind / photovoltaic power P into a quantum spin frequency f_P (unit: kHz). The sampling frequency is set to 100Hz (capturing power fluctuations of ±20% within 10 seconds). Power values ​​are recorded according to the formula P=10×f_P+0 (a=10kW / kHz, laboratory calibration), e.g., f_P=50kHz corresponds to P=500kW. A new energy power quantum subset S_P={f_P1,f_P2,...,f_Pn} is generated, storing data once per second and marking it with a timestamp (accurate to ns). Real-time capture of new energy output fluctuations provides high-frequency data for determining the timing of grid integration.

[0066] Step 9: Acquisition of Quantum Signals from Eastern Computing Load. The sensor, based on the quantum dot photoluminescence principle, converts the CPU utilization U into quantum luminescence intensity I_U (unit: μW / cm²), with a sampling frequency of 100Hz. The utilization rate is calculated using the formula U = 0.1 × I_U + 0 (c = 0.1% / (μW / cm²)), e.g., I_U = 600 μW / cm² corresponds to U = 60%. A quantum subset S_U = {I_U1, I_U2, ..., I_Um} is generated to handle the computing load, simultaneously recording the computing task type (real-time / non-real-time), such as "load prediction task (real-time)". This prioritizes computing demand, providing a basis for "ensuring latency for real-time tasks and ensuring load capacity for non-real-time tasks".

[0067] Step 10: Cross-regional channel energy consumption quantum signal acquisition. The sensor, based on the principle of quantum interferometer, converts the transmission energy consumption E into quantum phase. _E (unit: rad), sampling frequency 50Hz; according to the formula E=1× _E+0 (e=1kW) Energy consumption is calculated using h / rad, such as _E=20rad corresponds to E=20kW h; Synchronously acquire channel delay t_chan and bandwidth band_chan, and generate energy consumption quantum subset S_E={ _E1, _E2,..., _Ep}. Quantify the transmission costs of different channels to provide data support for multi-channel computing power allocation.

[0068] Step 11: Integration of the Original Quantum Dataset. Based on the timestamps of the quantum synchronization clock, align the three subsets of data to ensure a one-to-one correspondence between P, U, and E at the same time (e.g., 16:00:00.000000000); generate the original quantum dataset S_total=[S_P,S_U,S_E]^T, with dimensions 3×max(n,m,p), where each data entry contains a timestamp, quantum power signal, quantum load signal, and quantum energy consumption signal; add "new energy output tags" (e.g., "wind power output" and "photovoltaic power output") and "computing power task tags" (e.g., "real-time" and "non-real-time") to S_total. This forms a complete multi-dimensional dataset, providing a unified input for subsequent encrypted transmission and decision-making.

[0069] Step 12: Quantum Key Distribution (QKD) Generation. Quantum sensing nodes in the western, eastern, and central areas establish QKD links with the central collaborative platform, generating "one-time pad" quantum keys through entangled photon pairs. The key is updated every minute, with a key length of 256 bits, ensuring the encryption and security of sensitive data (such as renewable energy curtailment and computing power requirements). Key security is verified through quantum key consistency checks, ensuring no key leakage during transmission, with a pass rate ≥99.99%. This ensures the secure transmission of renewable energy and computing power data, preventing data tampering that could lead to errors in consumption decisions.

[0070] Step 13: Quantum Teleportation Dataset Transmission. Quantum teleportation technology is used to directly transmit the quantum states of S_total to the central collaborative platform, avoiding packet loss issues associated with traditional fiber optic transmission. During transmission, data integrity is monitored in real-time using "quantum entanglement verification": if quantum state distortion is detected, a retransmission mechanism is immediately triggered, with a retransmission latency ≤5ms. Transmission performance is recorded: transmission latency ≤10ms, packet loss rate ≤0.01%, ensuring data real-time performance meets the requirements for consumption decisions. This prevents data loss from causing "signals for new energy generation not to be transmitted in time," thus missing the computing power migration window.

[0071] Step 14: Quantum signal denoising and demodulation. A quantum entanglement filtering algorithm is employed, using the formula ρ_clean = ρ_noisy × |ψ ψ|(ρ_noisy is the noisy quantum density matrix, |ψ Electromagnetic interference is filtered out for Bell entangled states, improving the signal-to-noise ratio to 60dB; power demodulation: P = 10 × f_P (kW); load demodulation: U = 0.1 × I_U (%); energy consumption demodulation: E = 1 × _E (kW) h); Generate a standardized feature vector V_total=[P_west,U_east,t_chan1,t_chan2,band_chan1,band_chan2,P_task,f_wind]^T (P_west is the power of new energy in western China, P_task is the priority of computing tasks, and f_wind is the quantum frequency corresponding to wind speed). Convert the quantum signal into practical parameters that can be directly used for decision-making, ensuring that the data accuracy meets the requirements for absorption calculation.

[0072] Step 15: Predicting the Output Trend of New Energy. Input the quantum signals corresponding to f_wind (wind speed) and photovoltaic irradiance in V_total, and predict the output trend of new energy in western China for the next hour using an LSTM-quantum hybrid model; output the prediction results, such as "wind power output will decrease from 800MW to 500MW and photovoltaic output will decrease from 300MW to 100MW in the next hour"; mark the peak output periods (e.g., 10:00-14:00, output ≥900MW) and off-peak periods (20:00-6:00 the next day, output ≤400MW). Plan the computing power migration sequence in advance to avoid "mismatch between computing power migration and new energy output" and maximize the absorption during peak periods.

[0073] Step 16: Initialization and Training of the Quantum-Enhanced Multi-Objective Decision Model. Set a four-objective optimization function: MaxF=[0.6×C_new,-t_trans,-E_trans,-Cost_trans] (C_new is the amount of renewable energy curtailed, t_trans is the latency, E_trans is the energy consumption, Cost_trans is the cost, and 0.6 is the absorption weight coefficient); expand the feature vector to 10 dimensions (adding the predicted renewable energy output value); train the model using 1000 sets of scenario data (including wind power fluctuations and computing power spikes); optimize using the quantum tunneling-improved NSGA-Ⅲ algorithm to ensure that the absorption improvement rate is ≥15% after model convergence; load the trained model parameters and set constraints: western curtailment rate ≤5%, eastern CPU load ≤90%, transmission energy consumption ≤30kW. h / PFlops. Construct a decision-making model centered on absorption to balance multiple objective requirements.

[0074] Step 17: Optimal Computing Power Migration Scheme Selection. Input real-time V_total (e.g., P_west=850MW, U_east=70%, t_chan1=80ms), the model outputs the Pareto optimal solution set; prioritize the "maximizing renewable energy consumption" scheme: such as "migrate 0.8PFlops from west to east via Channel 1 (consuming 25MWh of wind power), migrate 0.3PFlops via Channel 2 (consuming 10MWh of wind power), total consumption 35MWh, curtailment rate reduced to 7%"; if there is demand in multiple regions (e.g., east + central), allocate according to the "consumption-demand matching matrix": east 0.5PFlops (consuming 15MWh), central 0.3PFlops (consuming 10MWh), ensuring curtailment rate ≤5%. Directly maximize renewable energy consumption and avoid waste of computing power resources.

[0075] Step 18: Issuance and Execution of Computing Power Migration Instructions. The central platform issues a "Computing Power Output Instruction" to the Western Computing Power Center: "Prioritize the use of wind power direct supply nodes, outputting 1.1 PFlops of computing power (Channel 1: 0.8 PFlops, Channel 2: 0.3 PFlops)"; it issues a "Receiving Preparation Instruction" to the Eastern Computing Power Center: "Reserve a 1.1 PFlops interface, marked as 'Western Wind Power Consumption Computing Power'"; and it issues a "Bandwidth Configuration Instruction" to the channels: "Adjust the bandwidth of Channel 1 to 10Gbps, adjust the bandwidth of Channel 2 to 5Gbps, and control the latency to ≤100ms". The Western Computing Power Center initiates the migration: wake up the wind power direct supply nodes within 30 seconds and achieve computing power output within 1 minute. Prioritize the use of computing power resources supplied by new energy sources to ensure the actual contribution of computing power migration to consumption.

[0076] Step 19: Real-time monitoring and secondary scheduling of renewable energy consumption. Quantum curtailment sensors collect real-time power data from western grid-connected points and calculate the curtailment rate after relocation. If the curtailment rate is still >5% (e.g., an actual curtailment rate of 6.5%), it immediately reports back to the central platform. The central platform triggers secondary scheduling: "Add 0.2 PFlops of computing power to the east (channel 2) to consume the remaining 5 MWh of wind power." The computing power load in the east is monitored: if the CPU load in the east exceeds 90%, the additional computing power is transferred to the central computing center (load 60%) to ensure no impact on real-time tasks in the east. This forms a closed-loop consumption mechanism, avoiding renewable energy waste caused by insufficient relocation.

[0077] Step 20: Evaluation of Energy Consumption Effect and Data Archiving. Calculate the renewable energy consumption improvement rate: Improvement rate = (Consumption after relocation - Consumption before relocation) / Curtailment before relocation × 100%. For example, if the curtailment rate decreases from 20% to 7%, the improvement rate is 65%. Statistical analysis of economic and ecological benefits: Annual increase in wind power consumption of 100 GWh, reduction of standard coal consumption by 32,000 tons, C 80,000 tons of emissions will be emitted; archived data will be stored in the historical database, including the original quantum data, solutions, and effects of this migration, for model iteration and optimization (model parameters will be updated monthly). Quantitative assessment of the contribution to energy consumption will provide data support for subsequent optimization, ensuring that the technical solution continues to adapt to the needs of new energy consumption.

[0078] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that the present invention is not limited to the described order of actions, because according to the present invention, some steps can be performed in other orders or simultaneously. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions and modules involved are not necessarily essential to the present invention.

[0079] Through the above description of the embodiments, those skilled in the art can clearly understand that the computing power migration method based on quantum measurement technology according to the above embodiments can be implemented by means of software plus necessary general-purpose hardware platform. Of course, it can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, or network device, etc.) to execute the methods described in the various embodiments of the present invention.

[0080] According to an embodiment of the present invention, a computing power migration system based on quantum measurement technology is also provided, comprising: a quantum sensing layer for acquiring real-time quantum signals of a power system, wherein the real-time quantum signals include real-time power quantum signals of an energy system, real-time load quantum signals of a computing power system, and real-time energy consumption quantum signals of a transmission channel, wherein the energy system is located in a first region, the computing power system is located in a second region, the first region and the second region are two different regions in the power system, and the transmission channel is a channel for data transmission in the power system; a quantum decision layer for determining multiple target feature data based on the real-time quantum signals, wherein the multiple target feature data includes multiple real-time feature data and energy power prediction data; inputting the multiple target feature data into a preset migration decision model to obtain a Pareto optimal solution set for cross-system computing power migration, wherein the migration decision model is constructed based on a quantum tunneling-modified non-dominated sorting genetic algorithm, and the Pareto optimal solution set includes multiple alternative migration schemes; determining a target migration scheme from the multiple alternative migration schemes; and a computing power execution layer for migrating computing resources from the first region to the second region based on the target migration scheme.

[0081] Optionally, the system leverages quantum measurement technology to construct a three-layer architecture of quantum sensing, decision-making, and execution, enabling dynamic cross-regional computing power migration. The quantum sensing layer deploys sensors in the energy and computing power systems, converting real-time power, processor utilization, and energy consumption into quantum signals to ensure high data accuracy and security. The quantum decision-making layer, based on collected real-time quantum signals, predicts future power trends in the energy system using an improved quantum tunneling algorithm and determines the optimal computing power migration strategy, balancing energy consumption and computing power service demands. The selection of the target migration scheme prioritizes maximizing consumption while considering latency, energy consumption, and cost during the migration. The computing power execution layer, based on the decisions, executes the migration of computing resources from the energy-rich first region to the second region with concentrated computing power demand, ensuring efficient allocation and use of computing power while promoting the efficient consumption of new energy sources.

[0082] For example, the quantum sensing layer can be deployed in western new energy bases, eastern computing centers, and cross-regional transmission channels, including three types of quantum sensors: quantum new energy power sensors (based on the principle of atomic magnetometers, monitoring instantaneous power of wind / photovoltaics with an accuracy of ±0.1kW); quantum computing load sensors (based on the principle of quantum dot photoluminescence, monitoring CPU utilization / memory occupancy with an accuracy of ±0.5%); and quantum channel energy consumption sensors (based on the principle of quantum interferometers, monitoring transmission channel energy consumption / latency with an energy consumption accuracy of ±0.1kW). h). The quantum decision-making layer is deployed on the central collaborative platform, using a quantum enhancement absorption-transfer model (QNN+multi-objective optimization) to output computing power migration schemes. The computing power execution layer connects the western computing power center (redundant computing power output end) and the eastern computing power center (computing power demand end), executes computing power migration instructions, and supports real-time migration of 1PFlops-level computing power.

[0083] As an optional embodiment, the system further includes a quantum transmission layer for quantum encrypted transmission between the quantum sensing layer, the quantum decision layer, and the computing power execution layer.

[0084] Optionally, the quantum transport layer plays a crucial communication bridge role in the system. Utilizing quantum key distribution (QKD) and quantum teleportation technologies, it ensures the secure and lossless transmission of real-time quantum signals acquired by the quantum sensing layer to the quantum decision-making layer for processing, and then efficiently transmits decision instructions to the computing power execution layer. This layer encrypts transmitted data by generating "one-time pad" quantum keys, ensuring data security even in the face of quantum computing attacks. Simultaneously, it employs quantum teleportation to directly transmit quantum states, avoiding packet loss and tampering issues inherent in traditional optical fibers, ensuring data integrity and real-time performance. It supports transmission latency as low as 10ms and a packet loss rate of less than 0.01%, providing stable and reliable communication guarantees for precise decision-making and execution in cross-regional computing power migration. For example, the quantum transport layer can integrate quantum key distribution (QKD) units and quantum teleportation modules to construct secure cross-regional transmission links with transmission latency ≤10ms and a packet loss rate ≤0.01%.

[0085] Embodiments of the present invention may provide a computer device. Optionally, in this embodiment, the computer device may be located in at least one of a plurality of network devices in a computer network. The computer device includes a memory and a processor.

[0086] The memory can be used to store software programs and modules, such as the program instructions / modules corresponding to the quantum measurement technology-based computing power migration method and apparatus in this embodiment of the invention. The processor executes various functional applications and data processing by running the software programs and modules stored in the memory, thereby realizing the aforementioned quantum measurement technology-based computing power migration method. The memory may include high-speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory may further include memory remotely located relative to the processor, and these remote memories can be connected to a computer terminal via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0087] The processor can access information and applications stored in memory via a transmission device to perform the following steps: acquiring real-time quantum signals of the power system, wherein the real-time quantum signals include real-time power quantum signals of the energy system, real-time load quantum signals of the computing system, and real-time energy consumption quantum signals of the transmission channel. The energy system is located in a first region, and the computing system is located in a second region. The first and second regions are two different regions within the power system, and the transmission channel is a channel for data transmission within the power system; determining multiple target feature data based on the real-time quantum signals, wherein the multiple target feature data includes multiple real-time feature data and energy power prediction data; inputting the multiple target feature data into a preset migration decision model to obtain a Pareto optimal solution set for cross-system computing power migration, wherein the migration decision model is constructed based on a quantum tunneling-modified non-dominated sorting genetic algorithm, and the Pareto optimal solution set includes multiple alternative migration schemes; determining the target migration scheme from the multiple alternative migration schemes; and migrating the computing resources of the first region to the second region based on the target migration scheme.

[0088] Optionally, the processor may also execute program code for the following steps: acquiring real-time quantum signals from the power system, including: acquiring multiple real-time acquisition powers from the energy system; converting the multiple real-time acquisition powers into multiple quantum spin frequencies based on the principle of atomic spin precession; constructing real-time power quantum signals based on the multiple quantum spin frequencies; acquiring the utilization rates of multiple real-time acquisition processors from the computing power system; converting the utilization rates of the multiple real-time acquisition processors into multiple quantum luminescence intensities based on the principle of quantum dot photoluminescence; constructing real-time load quantum signals based on the multiple quantum luminescence intensities; acquiring multiple real-time acquisition energy consumptions from the transmission channel; converting the multiple real-time acquisition energy consumptions into multiple quantum phases based on the principle of quantum interferometers; and constructing real-time energy consumption quantum signals based on the multiple quantum phases.

[0089] Optionally, the processor may also execute program code for the following steps: determining multiple target feature data based on real-time quantum signals, including: demodulating the real-time quantum signals into multiple real-time feature data; and predicting the power prediction value of the energy system within a preset future period based on real-time power quantum signals, as energy power prediction data.

[0090] Optionally, the processor may also execute program code for the following steps: multiple real-time feature data including the real-time power of the energy system, the real-time processor utilization of the computing system, the real-time transmission delay of the first transmission channel, the real-time transmission delay of the second transmission channel, the real-time bandwidth utilization of the first transmission channel, the real-time bandwidth utilization of the second transmission channel, the real-time computing task priority of the computing system, and the quantum frequency of energy intensity in the energy system, wherein the transmission channel includes the first transmission channel and the second transmission channel.

[0091] Optionally, the processor may also execute program code that performs the following steps: determining a target migration scheme from multiple alternative migration schemes, including: calculating the energy consumption corresponding to each of the multiple alternative migration schemes to obtain multiple energy consumption; comparing the size of the multiple energy consumption to obtain the largest energy consumption among the multiple energy consumption, which is taken as the target energy consumption; and taking the alternative migration scheme corresponding to the target energy consumption as the target migration scheme.

[0092] This invention provides a computing power migration method based on quantum measurement technology. The method involves acquiring real-time quantum signals from a power system, including real-time power quantum signals from the energy system, real-time load quantum signals from the computing system, and real-time energy consumption quantum signals from the transmission channel. The energy system is located in a first region, and the computing system is located in a second region. The first and second regions are two different regions within the power system, and the transmission channel is a channel for data transmission within the power system. Based on the real-time quantum signals, multiple target feature data are determined, including multiple real-time feature data and energy power prediction data. These multiple target feature data are then input into a preset migration decision model to obtain a method for cross-system migration. The Pareto optimal solution set for computing power migration is proposed. The migration decision model is constructed based on a quantum tunneling-modified non-dominated sorting genetic algorithm. The Pareto optimal solution set includes multiple alternative migration schemes. The target migration scheme is determined from the multiple alternative migration schemes. Based on the target migration scheme, the computing power resources of the first region are migrated to the second region, which significantly improves the amount of renewable energy absorbed. This achieves the technical effect of improving the dynamic matching degree between computing power migration and renewable energy output, and solves the technical problems in the current technology where cross-regional computing power migration cannot match renewable energy output in real time and accurately, resulting in insufficient improvement of renewable energy absorption rate and mismatch between computing power scheduling and renewable energy output.

[0093] Those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be implemented by a program instructing the hardware related to the terminal device. The program can be stored in a non-volatile storage medium, which may include: flash drive, read-only memory (ROM), random access memory (RAM), disk or optical disk, etc.

[0094] Embodiments of the present invention also provide a non-volatile storage medium. Optionally, in this embodiment, the aforementioned non-volatile storage medium can be used to store the program code executed by the computing power migration method based on quantum measurement technology provided in the above embodiments.

[0095] Optionally, in this embodiment, the non-volatile storage medium may be located in any computer terminal in a group of computer terminals in a computer network, or in any mobile terminal in a group of mobile terminals.

[0096] Optionally, in this embodiment, the non-volatile storage medium is configured to store program code for performing the following steps: acquiring real-time quantum signals of the power system, wherein the real-time quantum signals include real-time power quantum signals of the energy system, real-time load quantum signals of the computing system, and real-time energy consumption quantum signals of the transmission channel, the energy system is located in a first region, the computing system is located in a second region, the first region and the second region are two different regions in the power system, and the transmission channel is a channel for data transmission in the power system; determining multiple target feature data based on the real-time quantum signals, wherein the multiple target feature data includes multiple real-time feature data and energy power prediction data; inputting the multiple target feature data into a preset migration decision model to obtain a Pareto optimal solution set for cross-system computing power migration, wherein the migration decision model is constructed based on a quantum tunneling-modified non-dominated sorting genetic algorithm, and the Pareto optimal solution set includes multiple alternative migration schemes; determining a target migration scheme from the multiple alternative migration schemes; and migrating computing resources from the first region to the second region based on the target migration scheme.

[0097] Optionally, in this embodiment, the non-volatile storage medium is configured to store program code for performing the following steps: acquiring real-time quantum signals of a power system, including: acquiring multiple real-time acquisition powers of the energy system; converting the multiple real-time acquisition powers into multiple quantum spin frequencies based on the principle of atomic spin precession; constructing real-time power quantum signals based on the multiple quantum spin frequencies; acquiring multiple real-time acquisition processor utilization rates of a computing power system; converting the multiple real-time acquisition processor utilization rates into multiple quantum luminescence intensities based on the principle of quantum dot photoluminescence; constructing real-time load quantum signals based on the multiple quantum luminescence intensities; acquiring multiple real-time acquisition energy consumptions of a transmission channel; converting the multiple real-time acquisition energy consumptions into multiple quantum phases based on the principle of quantum interferometers; and constructing real-time energy consumption quantum signals based on the multiple quantum phases.

[0098] Optionally, in this embodiment, the non-volatile storage medium is configured to store program code for performing the following steps: determining multiple target feature data based on real-time quantum signals, including: demodulating the real-time quantum signals into multiple real-time feature data; and predicting the power prediction value of the energy system within a preset future period based on real-time power quantum signals, as energy power prediction data.

[0099] Optionally, in this embodiment, the non-volatile storage medium is configured to store program code for performing the following steps: multiple real-time characteristic data include the real-time power of the energy system, the real-time processor utilization of the computing system, the real-time transmission delay of the first transmission channel, the real-time transmission delay of the second transmission channel, the real-time bandwidth utilization of the first transmission channel, the real-time bandwidth utilization of the second transmission channel, the real-time computing task priority of the computing system, and the quantum frequency of the energy intensity in the energy system, wherein the transmission channel includes the first transmission channel and the second transmission channel.

[0100] Optionally, in this embodiment, the non-volatile storage medium is configured to store program code for performing the following steps: determining a target migration scheme from multiple alternative migration schemes, including: calculating the energy consumption corresponding to each of the multiple alternative migration schemes to obtain multiple energy consumption; comparing the size of the multiple energy consumption to obtain the largest energy consumption among the multiple energy consumption, which is taken as the target energy consumption; and taking the alternative migration scheme corresponding to the target energy consumption as the target migration scheme.

[0101] Embodiments of the present invention also provide a computer program product, including a computer program. Optionally, in this embodiment, when the computer program is executed by a processor, it can: acquire real-time quantum signals of a power system, wherein the real-time quantum signals include real-time power quantum signals of an energy system, real-time load quantum signals of a computing system, and real-time energy consumption quantum signals of a transmission channel. The energy system is located in a first region, and the computing system is located in a second region. The first region and the second region are two different regions in the power system, and the transmission channel is a channel for data transmission in the power system; based on the real-time quantum signals, determine multiple target feature data, wherein the multiple target feature data includes multiple real-time feature data and energy power prediction data; input the multiple target feature data into a preset migration decision model to obtain a Pareto optimal solution set for cross-system computing power migration, wherein the migration decision model is constructed based on a quantum tunneling-modified non-dominated sorting genetic algorithm, and the Pareto optimal solution set includes multiple alternative migration schemes; determine a target migration scheme from the multiple alternative migration schemes; and based on the target migration scheme, migrate computing resources from the first region to the second region.

[0102] The sequence numbers of the above embodiments of the present invention are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0103] In the above embodiments of the present invention, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0104] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of units can be a logical functional division, and in actual implementation, there may be other division methods. For instance, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual coupling, direct coupling, or communication connection may be through some interfaces; the indirect coupling or communication connection between units or modules may be electrical or other forms.

[0105] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0106] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0107] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a non-volatile storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0108] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for transferring computing power based on quantum measurement technology, characterized in that, include: The real-time quantum signal of the power system is acquired, wherein the real-time quantum signal includes the real-time power quantum signal of the energy system, the real-time load quantum signal of the computing system, and the real-time energy consumption quantum signal of the transmission channel. The energy system is located in a first region, the computing system is located in a second region, the first region and the second region are two different regions of the power system, and the transmission channel is a channel for data transmission in the power system. Based on the real-time quantum signal, multiple target feature data are determined, wherein the multiple target feature data includes multiple real-time feature data and energy power prediction data; The multiple target feature data are input into a preset migration decision model to obtain a Pareto optimal solution set for cross-system computing power migration. The migration decision model is constructed based on a quantum tunneling-improved non-dominated sorting genetic algorithm, and the Pareto optimal solution set includes multiple alternative migration schemes. Determine the target migration plan from the plurality of alternative migration plans; Based on the target migration scheme, the computing resources of the first region are migrated to the second region.

2. The method according to claim 1, characterized in that, The acquisition of real-time quantum signals from the power system includes: Obtain multiple real-time power samples from the energy system; Based on the principle of atomic spin precession, the multiple real-time acquired powers are converted into multiple quantum spin frequencies; The real-time power quantum signal is constructed based on the multiple quantum spin frequencies; Obtain the utilization rate of multiple real-time acquisition processors in the computing power system; Based on the quantum dot photoluminescence principle, the utilization rate of the multiple real-time acquisition processors is converted into multiple quantum luminescence intensities; The real-time load quantum signal is constructed based on the multiple quantum luminescence intensities. Obtain multiple real-time energy consumption data for the transmission channel; Based on the principle of quantum interference devices, the multiple real-time energy consumptions are converted into multiple quantum phases; The real-time energy-consuming quantum signal is constructed based on the multiple quantum phases.

3. The method according to claim 1, characterized in that, The determination of multiple target feature data based on the real-time quantum signal includes: The real-time quantum signal is demodulated into the plurality of real-time feature data; Based on the real-time power quantum signal, the power prediction value of the energy system in a preset future period is predicted and used as the energy power prediction data.

4. The method according to claim 1, characterized in that, The multiple real-time feature data include the real-time power of the energy system, the real-time processor utilization of the computing power system, the real-time transmission latency of the first transmission channel, the real-time transmission latency of the second transmission channel, the real-time bandwidth utilization of the first transmission channel, the real-time bandwidth utilization of the second transmission channel, the real-time computing power task priority of the computing power system, and the quantum frequency of energy intensity in the energy system, wherein the transmission channel includes the first transmission channel and the second transmission channel.

5. The method according to claim 1, characterized in that, The step of determining the target migration plan from the plurality of alternative migration plans includes: Calculate the energy consumption corresponding to each of the multiple alternative migration schemes to obtain multiple energy consumption amounts; By comparing the magnitudes of the multiple energy consumption amounts, the largest energy consumption amount among the multiple energy consumption amounts is obtained as the target energy consumption amount; The alternative migration schemes corresponding to the target energy consumption are taken as the target migration scheme.

6. A computing power transfer system based on quantum measurement technology, characterized in that, include: A quantum sensing layer is used to acquire real-time quantum signals of a power system. The real-time quantum signals include real-time power quantum signals of the energy system, real-time load quantum signals of the computing system, and real-time energy consumption quantum signals of the transmission channel. The energy system is located in a first region, the computing system is located in a second region, the first region and the second region are two different regions of the power system, and the transmission channel is a channel for data transmission in the power system. A quantum decision layer is used to determine multiple target feature data based on the real-time quantum signal, wherein the multiple target feature data includes multiple real-time feature data and energy power prediction data; the multiple target feature data are input into a preset migration decision model to obtain a Pareto optimal solution set for cross-system computing power migration, wherein the migration decision model is constructed based on a quantum tunneling-modified non-dominated sorting genetic algorithm, and the Pareto optimal solution set includes multiple candidate migration schemes; a target migration scheme is determined from the multiple candidate migration schemes; The computing power execution layer is used to migrate computing resources from the first region to the second region based on the target migration scheme.

7. The system according to claim 6, characterized in that, Also includes: A quantum transport layer is used for quantum-encrypted transmission between the quantum sensing layer, the quantum decision layer, and the computing power execution layer.

8. A non-volatile storage medium, characterized in that, The non-volatile storage medium includes a stored program, wherein, when the program is executed, it controls the device containing the non-volatile storage medium to perform the computing power migration method based on quantum measurement technology as described in any one of claims 1 to 5.

9. A computer device, characterized in that, include: Memory and processor The memory stores computer programs; The processor is configured to execute a computer program stored in the memory, wherein when the computer program is executed, the processor performs the computing power transfer method based on quantum measurement technology as described in any one of claims 1 to 5.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the computing power transfer method based on quantum measurement technology as described in any one of claims 1 to 5.

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