Quantum circuit parallel compiling acceleration system based on multi-objective optimization

Abstract

The invention discloses a quantum circuit parallel compiling acceleration system based on multi-objective optimization, and relates to the technical field of quantum computing, and the system comprises an environment and equipment parameter collection module which is responsible for collecting transmission data in real time; the parameter processing module quantifies the environmental influence; the quantum entanglement module constructs an equipment association quantum model; the quantum circuit parallel compiling module accelerates to generate an operation sequence; the multi-target collaborative optimization module outputs an adaptive control strategy, and the efficiency and reliability of the power system are comprehensively improved; according to the method, efficient solution of multi-objective optimization of the power system is realized through a quantum circuit parallel compiling acceleration technology, a traditional method needs to sequentially process operation efficiency, equipment life and environment adaptation sub-objectives, calculation is time-consuming and is easy to fall into local optimum, and the system splits multiple objectives into sub-tasks, so that the efficiency of the system is greatly improved. And quantum resources are dynamically allocated by using a quantum acceleration operator algorithm, sub-quantum circuits are compiled in parallel, and the optimization period is shortened.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of quantum computing, in particular to a quantum circuit parallel compilation acceleration system based on multi-objective optimization. BACKGROUND

[0002] With the continuous expansion of the scale and the increase of the complexity of the power system, the management and optimization methods of traditional power systems are facing unprecedented challenges, especially in the context of large-scale access of new energy, such as the widespread application of photovoltaic power generation, the dynamic balance and stable operation of the power system become more complex, the collaborative work between photovoltaic inverters, energy storage converters, transformer devices, and the performance optimization of these devices under different environmental conditions become the key to improve the overall efficiency and reliability of the power system, at the same time, environmental factors such as temperature and humidity also cannot be ignored, therefore, it is particularly important to develop a power system management and optimization technology that can comprehensively consider multi-objective optimization, under this background, quantum computing technology gradually becomes a new way to solve the optimization problem of power system with its powerful parallel computing ability and the advantage of simulating complex systems.

[0003] Traditional power system optimization methods mainly rely on mathematical modeling and classical computing technology, these methods often face the problems of low computational efficiency and local optimal optimization results when dealing with large-scale and high-complexity power systems, specifically, when dealing with multi-objective optimization problems, traditional methods usually need to convert multiple objectives into a single objective or process them through weighted sum, which leads to the difficulty of reflecting the real needs of each target in the optimization results, in addition, traditional methods often lack effective modeling and computing means when dealing with complex coupling relationship between devices and the influence of environmental factors on device performance, leading to poor effect of optimization strategy in practical application, especially in the context of new energy access, traditional methods are difficult to adapt to the rapid changes and uncertainties of the power system, and cannot meet the needs of modern power system for efficient, reliable and flexible operation.

[0004] Therefore, the development of a quantum circuit parallel compilation acceleration system based on multi-objective optimization improves the optimization efficiency and decision-making accuracy of the power system, enhances the adaptability and robustness of multi-device collaborative control, and provides a new solution for new energy power stations, microgrids and industrial power distribution systems. SUMMARY

[0005] The purpose of the present application is to make up for the shortcomings of the prior art, provide a quantum circuit parallel compilation acceleration system based on multi-objective optimization, accurately quantify the influence of the environment on the equipment through an environment-equipment quantum parameter dynamic evolution model, and construct a multi-equipment collaborative quantum model using quantum entanglement gates to realize the quantum expression of reactive power compensation and power flow matching, the system uses multi-line parallel compilation and quantum acceleration operators, dynamically allocates resources and compresses the compilation depth, combines quantum multi-objective constraint algorithms and iterative optimization mechanisms, and efficiently generates adaptive control strategies.

[0006] To solve the above technical problems, the present application provides the following technical solutions: a quantum circuit parallel compilation acceleration system based on multi-objective optimization, the system comprises:

[0007] An environment and equipment parameter acquisition module: temperature, humidity, and power, voltage, current, and self-state parameters of a photovoltaic inverter, energy storage converter, and transformer of a power system environment are acquired using a sensing device, and are transmitted after being packaged and encrypted through an MQTT protocol;

[0008] An environment and equipment parameter processing module: receives raw data, performs wavelet denoising and normalization processing, and converts environment parameters into dynamic evolution of equipment quantum parameters through an environment-equipment quantum parameter dynamic evolution model to quantify the influence of the environment on the electrical characteristics of the equipment;

[0009] A quantum entanglement-multi-device correlation construction module: analyzes the results of the environment-equipment quantum parameter dynamic evolution model, extracts the coupling constraint conditions between photovoltaic inverters, energy storage converters, and transformer devices, quantizes the control variables of each device, designs corresponding multi-body entanglement gates, and embeds the constraint conditions therein to construct a power system quantum model;

[0010] A quantum circuit parallel compilation acceleration module: divides the multi-objective optimization requirements of the power system into operation efficiency, equipment life, and environment adaptation sub-tasks, constructs sub-quantum circuits for each sub-task, uses a multi-line parallel compilation quantum acceleration operator algorithm to perform quantum resource dynamic allocation and compilation depth compression optimization, and generates an executable quantum operation sequence;

[0011] A multi-objective collaborative optimization decision module: uses quantum gate operations to simulate selection, crossover, and mutation in the quantum state space based on the quantum operation sequence, pre-synchronizes device control instructions through entanglement gates based on the quantum operation sequence, fuses multi-objective weights by means of a quantum multi-objective constraint algorithm, and decodes and outputs an adaptive control strategy after iterative optimization.

[0012] Further, in the environment and equipment parameter acquisition module, the sensor device used is:

[0013] An environment parameter acquisition sensor: a digital temperature and humidity sensor and a temperature and humidity transmitter;

[0014] Sensors for acquiring equipment operating parameters: multi-functional digital smart meters, electronic current / voltage transformers, fiber Bragg grating sensors, vibration sensors, and infrared thermal imagers.

[0015] Furthermore, in the environment and equipment parameter processing module, the calculation formula for constructing the dynamic evolution model of environment-equipment quantum parameters is as follows: in It is the quantized resistance operator of the device at time t. The initial quantized resistance operator of the device is obtained by quantizing the device's factory parameters, and γ is the environment-device quantum coupling coefficient, calibrated through quantum state tomography experiments. The quantum perturbation operator for the superposition state of the environment is first encoded as a quantum superposition state, where T is the temperature and H is the humidity, and t is the time, which is synchronized with the sampling period of the acquisition module.

[0016] Furthermore, in the quantum entanglement-multi-device association construction module, the specific steps for extracting constraint conditions are as follows:

[0017] (1) Deconstruct the dynamic evolution model of environmental-equipment quantum parameters output by the environmental and equipment parameter processing module, and sort out the dynamic correlation between environmental parameters and equipment quantum parameters presented by the model;

[0018] (2) Extract key quantum parameters from the model that affect the operating status of the equipment and the collaboration of multiple equipment;

[0019] (3) Analyze the evolution trend of key quantum parameters under different environmental conditions, and the potential correlation between parameters;

[0020] (4) Combining power system operation theory and historical operation data, for photovoltaic inverters and energy storage converters, analyze the reactive power output coordination rules under different grid loads and photovoltaic power output scenarios, and extract reactive power compensation coordination constraints.

[0021] (5) Considering the power transmission capacity limitation of the transformer, and combining the power output characteristics of the photovoltaic inverter and the energy storage converter, analyze the power matching relationship among the three and extract the power flow matching constraints.

[0022] (6) Extract the equipment operating status constraints based on the equipment's own safety operation standards and performance parameters.

[0023] Furthermore, in the quantum entanglement-multi-device association construction module, the specific steps for constructing the quantum model of the power system are as follows:

[0024] (1) Identify the core control variables of photovoltaic inverters, energy storage converters and transformers, and map them to corresponding qubits and quantum states;

[0025] (2) For different coupling constraints, dedicated multi-body entanglement gates are designed for reactive power compensation coordination and power flow matching respectively;

[0026] (3) Transform the mathematical relationships of the constraints into the quantum entanglement gate operation logic;

[0027] (4) Combine the quantized control variables with the corresponding entangled gates according to the device association to form a preliminary quantum logic chain;

[0028] (5) Verify the integrity of the quantum logic chain, eliminate conflicts in entanglement gate operations, and form a complete model framework;

[0029] (6) Introduce the operating state constraints of the quantum measurement operator conversion device, adjust the parameters through feedback, and construct the final quantum model of the power system.

[0030] Furthermore, in the quantum entanglement-multi-device association construction module, the calculation formula for the power system quantum model is as follows: And satisfy: ∑ l,j∈{0,1} |lj| 2 ·| <lj|Ψ PV-ESS >| 2 ≤∈, where |Ψ PV-ESS > is the system quantum state composed of a photovoltaic inverter and an energy storage converter, |ψ PV The reactive power control qubit of a photovoltaic inverter represents the reactive power control state of the photovoltaic inverter, |ψ ESS The reactive power control qubits of the energy storage converter represent the reactive power control state of the energy storage converter. It is a controllable quantum entanglement gate, where e, f, g, h are the probability amplitudes of the entangled states, and |e| 2 +|f| 2 +|g| 2 +|h| 2 =1, |lj| 2 This is the penalty term for the state difference of the control variable, where l,j is the measurement result of the qubit, reflecting the coordination deviation between PV and ESS reactive power control. <lj|Ψ PV-ESS > represents the projection of the entangled state onto the basis |ij>, i.e., the probability amplitudes e, f, g, h, where ∈ is the quantum cooperative error threshold, set by the safety margin of reactive power cooperation in the power system. is the tensor product symbol, and |00>, |01>, |10>, and |11> are the joint states of qubits.

[0031] Furthermore, in the quantum circuit parallel compilation acceleration module, the calculation formula for the quantum accelerated operator algorithm of multi-circuit parallel compilation is as follows: in, Quantum-accelerated operators compiled in parallel across multiple lines It is the quantum circuit for the k-th multi-objective sub-task. It is a quantum resource allocation operator that dynamically allocates qubits and entanglement gate resources, θ k is the compilation acceleration phase factor, optimized by the quantum variational compilation algorithm; k is the summation / product index used to traverse multi-objective subtasks; i is the imaginary unit; and N is the number of multi-objective subtasks.

[0032] Furthermore, in the multi-objective collaborative optimization decision-making module, the weights of multiple objectives are fused using a quantum multi-objective constraint algorithm, and the calculation formula is as follows: in λ is the quantum observation operator for the m-th target. m The target weights are dynamically adjusted based on environmental and system requirements, and are achieved through the superposition of quantum states. It is the unit operator, and M is the number of multi-targets. It is a quantum multi-objective optimization operator, and m is the summation index.

[0033] Furthermore, in the multi-objective collaborative optimization decision module, a multi-objective optimization convergence determination algorithm is used to determine whether the iterative optimization process has converged. The calculation formula is as follows: Where Υ is the convergence criterion. Let be the observation values ​​of the m-th target at times t and t-1, respectively, where M is the number of targets. The convergence threshold is set according to system requirements. Convergence is determined at that time.

[0034] Compared with existing technologies, this quantum circuit parallel compilation acceleration system based on multi-objective optimization has the following advantages:

[0035] I. This invention achieves efficient solutions for multi-objective optimization of power systems through quantum circuit parallel compilation acceleration technology. Traditional methods require sequential processing of sub-objectives such as operating efficiency, equipment lifespan, and environmental adaptability, which is computationally time-consuming and prone to getting trapped in local optima. In contrast, this system decomposes multiple objectives into sub-tasks, uses quantum acceleration operator algorithms to dynamically allocate quantum resources, and compiles sub-quantum circuits in parallel, shortening the optimization cycle. At the same time, the dynamic evolution model of environment-equipment quantum parameters accurately quantifies the impact of environmental factors such as temperature and humidity on the electrical characteristics of equipment through wavelet denoising and normalization, reducing parameter bias in traditional models. Combined with quantum entanglement gate pre-synchronization of equipment instructions, the system can quickly converge to the global optimum during iterative optimization, improving the adaptability of control strategies and decision accuracy.

[0036] Second, this invention solves the problem of coordinated control among photovoltaic inverters, energy storage converters and transformers by applying quantum entanglement theory to multi-device correlation modeling. By extracting reactive power compensation coordination and power flow matching constraints, a dedicated multi-body entanglement gate is designed to transform device control variables into quantum state operations, achieving seamless connection of cross-device quantum logic chains. Compared with traditional rule bases, this model can dynamically respond to changes in grid load and photovoltaic output and automatically adjust the operating status of devices. In addition, the quantum multi-objective constraint algorithm integrates multi-objective requirements through dynamic weight fusion and combines convergence determination formula to ensure the stability and controllability of the optimization process, improving the adaptability of the power system in complex environments.

[0037] Other advantages, objectives and features of the invention will be set forth in part in the description which follows, and in part will be apparent to those skilled in the art from the following examination or study, or may be learned from the practice of the invention. Attached Figure Description

[0038] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are merely some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without any creative effort.

[0039] Figure 1 A schematic diagram of the workflow of a quantum circuit parallel compilation acceleration system based on multi-objective optimization;

[0040] Figure 2 A module connection and data flow diagram for a quantum circuit parallel compilation acceleration system based on multi-objective optimization. Detailed Implementation

[0041] To further illustrate the technical means and effects of the present invention in achieving its intended purpose, the following detailed description of the specific implementation methods, structures, features, and effects of the present invention, in conjunction with the accompanying drawings and preferred embodiments, is provided below.

[0042] Example 1:

[0043] Optimization scenario of photovoltaic power generation and energy storage synergy in industrial parks.

[0044] Environmental and Equipment Parameter Acquisition Module: Digital temperature and humidity sensors and transmitters are deployed around the photovoltaic arrays, energy storage battery packs, and transformers in the industrial park to capture real-time changes in ambient temperature and humidity. This data provides a foundation for subsequent analysis of the environment's impact on the equipment. Simultaneously, multi-functional digital smart meters and electronic current / voltage transformers are installed to collect real-time output and voltage stability data from the photovoltaic inverters, charging and discharging efficiency and current fluctuations from the energy storage converters, and power losses and vibration frequencies from the transformers. This provides a comprehensive understanding of the equipment's operating status. All data is encapsulated and encrypted using the MQTT protocol before transmission, ensuring data security and integrity during transmission and preventing information leakage or tampering. Figure 1 As shown.

[0045] Environmental and Equipment Parameter Processing Module: After receiving the raw data, the system first filters out noise from external electromagnetic interference and sensor errors through wavelet denoising, making the data more accurate. Then, it performs normalization to unify the magnitude and unit of different parameters, facilitating subsequent model calculations. Subsequently, using the environment-equipment quantum parameter dynamic evolution model, the environmental temperature and humidity parameters are transformed into the dynamic evolution process of equipment quantum parameters. This quantifies the specific impact of increased temperature on equipment resistance changes and increased humidity on equipment insulation performance, thus affecting the electrical characteristics of the equipment. The calculation formula for the environment-equipment quantum parameter dynamic evolution model is as follows: in: It is the quantized resistance operator of the device at time t. The initial quantized resistance operator of the device is obtained by quantizing the device's factory parameters, and γ is the environment-device quantum coupling coefficient, calibrated through quantum state tomography experiments. As a quantum perturbation operator for the superposition state of the environment, the environmental parameters T and H are first encoded as quantum superposition states, where T is the temperature and H is the humidity, and t is the time, which is synchronized with the sampling period of the acquisition module to provide data support for subsequent optimization.

[0046] Quantum Entanglement-Multi-Device Association Construction Module: This module analyzes the results of the dynamic evolution model of environment-device quantum parameters, extracts coupling constraints between devices, and for example, considers a scenario where an industrial park experiences high daytime load and high photovoltaic output, followed by reduced load and zero photovoltaic output at night. It extracts reactive power compensation coordination constraints between photovoltaic inverters and energy storage converters to ensure grid voltage stability. Considering the maximum power capacity of transformers, it extracts power flow matching constraints among the three components to prevent equipment overload. Simultaneously, it incorporates safe operating temperature and voltage range constraints for each device. After quantizing the control variables of each device, it designs dedicated multi-body entanglement gates for different constraints, embedding these constraints into the model. The constructed power system quantum model accurately reflects the coordination relationships between devices. The calculation formula for the power system quantum model is as follows: And satisfy: ∑ l,j∈{0,1} |lj| 2 ·| <lj|Ψ PV-ESS >| 2 ≤∈, where |Ψ PV-ESS > is the system quantum state composed of a photovoltaic inverter and an energy storage converter, |ψ PV The reactive power control qubit of a photovoltaic inverter represents the reactive power control state of the photovoltaic inverter, |ψ ESS The reactive power control qubits of the energy storage converter represent the reactive power control state of the energy storage converter. It is a controllable quantum entanglement gate, where e, f, g, h are the probability amplitudes of the entangled states, and |e| 2 +|f| 2 +|g| 2 +|h| 2 =1, |lj| 2 This is the penalty term for the state difference of the control variable, where l,j is the measurement result of the qubit, reflecting the coordination deviation between PV and ESS reactive power control. <lj|Ψ PV-ESS > represents the projection of the entangled state onto the basis |ij>, i.e., the probability amplitudes e, f, g, h, where ∈ is the quantum cooperative error threshold, set by the safety margin of reactive power cooperation in the power system. The symbol is the tensor product symbol. |00>, |01>, |10>, and |11> are the joint states of the qubits, which lay the model foundation for subsequent optimization.

[0047] The quantum circuit parallel compilation acceleration module breaks down the multi-objective optimization requirements of the industrial park's power system into three sub-tasks: improving operational efficiency (e.g., reducing power loss), extending equipment lifespan (e.g., avoiding long-term full-load operation of transformers), and enhancing environmental adaptability (e.g., adjusting equipment operation strategies at high temperatures). After constructing a sub-quantum circuit for each sub-task, the module uses a multi-circuit parallel compilation quantum acceleration operator algorithm to dynamically allocate qubits and entanglement gate resources. The calculation formula for the multi-circuit parallel compilation quantum acceleration operator algorithm is as follows: in, Quantum-accelerated operators compiled in parallel across multiple lines It is the quantum circuit for the k-th multi-objective sub-task. It is a quantum resource allocation operator that dynamically allocates qubits and entanglement gate resources, θ k It is a compilation acceleration phase factor optimized by a quantum variational compilation algorithm. k is a summation / product index used to traverse multi-objective subtasks. i is the imaginary unit, and N is the number of multi-objective subtasks. This avoids resource waste, compresses compilation depth, and speeds up computation. The generated executable quantum operation sequence can be directly used for subsequent optimization decisions.

[0048] Multi-objective collaborative optimization decision-making module: Based on quantum operation sequences, it simulates selection, crossover, and mutation processes in quantum state space through quantum gate operations, expanding the optimization range and increasing the probability of finding the optimal solution. Entanglement gates are used to pre-synchronize control commands for each device, ensuring coordinated operation of the photovoltaic inverter, energy storage converter, and transformer. A quantum multi-objective constraint algorithm is used to fuse multi-objective weights; the calculation formula for the quantum multi-objective constraint algorithm is as follows: in λ is the quantum observation operator for the m-th target. m The target weights are dynamically adjusted based on environmental and system requirements, and are achieved through the superposition of quantum states. It is the unit operator, and M is the number of multi-targets. It is a quantum multi-objective optimization operator, where m is the summation index, balancing the importance of each objective. After multiple iterations of optimization, a multi-objective optimization convergence determination algorithm is used to determine whether the iterations have converged. The calculation formula for the multi-objective optimization convergence determination algorithm is as follows: Where: Υ is the convergence criterion. Let be the observation values ​​of the m-th target at times t and t-1, respectively, where M is the number of targets. The convergence threshold is set according to system requirements. When convergence is determined, the final decoded output control strategy can be adapted to the complex scenarios of industrial parks, achieving efficient synergy between photovoltaic power generation and energy storage, reducing operating costs, and improving system stability.

[0049] In summary, in the scenario of synergistic optimization of photovoltaic power generation and energy storage in industrial parks, this invention comprehensively acquires environmental and equipment data through an environmental and equipment parameter acquisition module, purifies the data through a processing module, quantifies the environmental impact using an environmental-equipment quantum parameter dynamic evolution model, establishes precise correlations between devices through a quantum entanglement-multi-device association construction module, improves computational efficiency through a quantum circuit parallel compilation acceleration module, and finally outputs an adaptation strategy through a multi-objective collaborative optimization decision module. The entire process fully leverages the role of each module, achieving efficient synergy between photovoltaic and energy storage systems in industrial parks, effectively balancing operating efficiency, equipment lifespan, and environmental adaptability, and providing a reliable solution for the optimized operation of complex power systems in industrial parks.

[0050] Example 2:

[0051] Integrated control scenario for microgrids in residential communities.

[0052] Environmental and Equipment Parameter Acquisition Module: Digital temperature and humidity sensors and transmitters are deployed near distributed photovoltaic panels, energy storage cabinets, and distribution transformers in residential communities to collect environmental temperature and humidity data in real time at different times of the day. This data is crucial for analyzing the impact of the environment on the equipment. Multifunctional digital smart meters are used to collect parameters such as the power generation of the photovoltaic inverter (e.g., maximum output at noon on sunny days, fluctuations on cloudy days), the charging and discharging speed of the energy storage converter, and the temperature rise of the transformer. This allows for real-time monitoring of equipment operation. The data is encapsulated and encrypted using the MQTT protocol, ensuring data authenticity and security and providing reliable raw data for subsequent processing.

[0053] Environmental and Equipment Parameter Processing Module: After receiving the raw data, it uses wavelet denoising to eliminate interference from current surges during peak residential electricity consumption periods, making the data more reliable; normalization processing unifies the parameter format for easier model calculation; and using the environmental-equipment quantum parameter dynamic evolution model, it transforms environmental parameters into the dynamic evolution of equipment quantum parameters, quantifying the impact of low winter temperatures on battery energy storage efficiency and high summer temperatures on transformer heat dissipation pressure. The calculation formula for the environmental-equipment quantum parameter dynamic evolution model is as follows: This provides a basis for developing targeted optimization strategies.

[0054] Quantum Entanglement-Multi-Device Association Construction Module: Analyzing model results, extracting coupling constraints between devices, and considering the peak electricity consumption periods during morning and evening commutes and the off-peak periods during the day, extracting reactive power compensation coordination constraints between photovoltaic inverters and energy storage converters to maintain stable household voltage; based on transformer capacity, extracting power flow matching constraints among the three components to prevent equipment damage due to overload; simultaneously incorporating equipment safety operation parameter constraints, quantizing control variables, designing multi-body entanglement gates and embedding constraints, the constructed microgrid quantum model accurately reflects the coordination relationships between devices, providing a precise model for optimization. The calculation formula for the power system quantum model is: And satisfy: ∑ l,j∈{0,1} |lj| 2 ·| <lj|Ψ PV-ESS >| 2 ≤∈.

[0055] The quantum circuit parallel compilation acceleration module breaks down the multi-objective optimization needs of residential community microgrids into three sub-tasks: improving operational efficiency (e.g., increasing photovoltaic power utilization), ensuring long-term stable operation of equipment (e.g., reducing the number of charge-discharge cycles of energy storage batteries), and adapting to changes in the community environment (e.g., adjusting equipment moisture protection strategies during the rainy season). After constructing sub-quantum circuits for each sub-task, the module utilizes a multi-circuit parallel compilation quantum acceleration operator algorithm to dynamically allocate quantum resources, compress compilation depth, and accelerate the computation process. The calculation formula for the multi-circuit parallel compilation quantum acceleration operator algorithm is as follows: The generated sequence of executable quantum operations provides direct instructions for optimization decisions.

[0056] Multi-objective collaborative optimization decision-making module: Based on quantum operation sequences, it simulates selection, crossover, and mutation through quantum gate operations in quantum state space to broaden the optimization path. Entanglement gates are used to pre-synchronize control commands for each device to ensure coordinated device actions. A quantum multi-objective constraint algorithm is used to fuse multi-objective weights. The calculation formula for the quantum multi-objective constraint algorithm is as follows: Balancing efficiency and equipment lifespan; after iterative optimization and convergence determination by a multi-objective optimization convergence algorithm, the calculation formula for the multi-objective optimization convergence determination algorithm is as follows: when The convergence determination and decoding output of the comprehensive control strategy can be adapted to the electricity consumption characteristics of residential communities, realize the efficient and stable operation of the microgrid, ensure the quality of electricity consumption for residents, and reduce electricity costs.

[0057] In summary, such as Figure 2 As shown, in the scenario of integrated control of microgrids in residential communities, this invention starts with the collection of environmental and equipment parameters. The data is processed by the processing module, and the impact of the environment on the equipment is clarified by the dynamic evolution model of environment-equipment quantum parameters. The equipment collaboration model is constructed through the quantum entanglement-multi-device association construction module. The computation speed is improved by the quantum circuit parallel compilation acceleration module. Finally, the multi-objective collaborative optimization decision module generates the control strategy. All modules work together to accurately adapt to the time-varying characteristics of electricity consumption in residential communities. While improving the operating efficiency of the microgrid and ensuring the stable operation of equipment, it also enhances the adaptability to the community environment, providing strong support for the safe, economical and stable operation of microgrids in residential communities.

[0058] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any simple modifications, equivalent changes and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.

Claims

1. A quantum circuit parallel compilation acceleration system based on multi-objective optimization, characterized in that, The system includes: Environmental and equipment parameter acquisition module: It uses sensors to collect temperature and humidity data of the power system environment, as well as power, voltage, current, and self-status parameters of photovoltaic inverters, energy storage converters, and transformers. The data is then encapsulated and encrypted using the MQTT protocol before being transmitted. Environment and equipment parameter processing module: Receives raw data, performs wavelet denoising and normalization processing, and transforms environmental parameters into dynamic evolution of equipment quantum parameters through an environment-equipment quantum parameter dynamic evolution model, quantifying the impact of the environment on the electrical characteristics of the equipment; Quantum Entanglement-Multi-Device Association Construction Module: Analyze the results of the dynamic evolution model of environment-device quantum parameters, extract the coupling constraints between photovoltaic inverters, energy storage converters, and transformers, quantize the control variables of each device, design corresponding multi-body entanglement gates, and embed the constraints into them to construct a quantum model of the power system; Quantum circuit parallel compilation acceleration module: It breaks down the multi-objective optimization requirements of the power system into sub-tasks of operating efficiency, equipment life and environmental adaptation, constructs sub-quantum circuits for each sub-task, and uses quantum acceleration operator algorithms for multi-circuit parallel compilation to perform dynamic allocation of quantum resources and deep compression optimization of compilation to generate executable quantum operation sequences. Multi-objective collaborative optimization decision module: Based on quantum operation sequences, in quantum state space, it uses quantum gate operations to simulate selection, crossover, and mutation. Based on quantum operation sequences, it uses entanglement gates to pre-synchronize device control commands. It uses quantum multi-objective constraint algorithms to fuse multi-objective weights, and after iterative optimization, it decodes and outputs an adapted control strategy.

2. The quantum circuit parallel compilation acceleration system based on multi-objective optimization according to claim 1, characterized in that, The sensor devices used in the environmental and equipment parameter acquisition module are: Environmental parameter acquisition sensors: digital temperature and humidity sensors and temperature and humidity transmitters; Sensors for acquiring equipment operating parameters: multi-functional digital smart meters, electronic current / voltage transformers, fiber Bragg grating sensors, vibration sensors, and infrared thermal imagers.

3. The quantum circuit parallel compilation acceleration system based on multi-objective optimization according to claim 1, characterized in that, In the environment and equipment parameter processing module, the calculation formula for constructing the dynamic evolution model of environment-equipment quantum parameters is as follows: in It is the quantized resistance operator of the device at time t. The initial quantized resistance operator of the device is obtained by quantizing the device's factory parameters, and γ is the environment-device quantum coupling coefficient, calibrated through quantum state tomography experiments. The quantum perturbation operator for the superposition state of the environment is first encoded as a quantum superposition state, where T is the temperature and H is the humidity, and t is the time, which is synchronized with the sampling period of the acquisition module.

4. The quantum circuit parallel compilation acceleration system based on multi-objective optimization according to claim 1, characterized in that, The specific steps for extracting constraint conditions in the quantum entanglement-multi-device association construction module are as follows: (1) Deconstruct the dynamic evolution model of environmental-equipment quantum parameters output by the environmental and equipment parameter processing module, and sort out the dynamic correlation between environmental parameters and equipment quantum parameters presented by the model; (2) Extract key quantum parameters from the model that affect the operating status of the equipment and the collaboration of multiple equipment; (3) Analyze the evolution trend of key quantum parameters under different environmental conditions, and the potential correlation between parameters; (4) Combining power system operation theory and historical operation data, for photovoltaic inverters and energy storage converters, analyze the reactive power output coordination rules under different grid loads and photovoltaic power output scenarios, and extract reactive power compensation coordination constraints. (5) Considering the power transmission capacity limitation of the transformer, and combining the power output characteristics of the photovoltaic inverter and the energy storage converter, analyze the power matching relationship among the three and extract the power flow matching constraints. (6) Extract the equipment operating status constraints based on the equipment's own safety operation standards and performance parameters.

5. The quantum circuit parallel compilation acceleration system based on multi-objective optimization according to claim 1, characterized in that, The specific steps for constructing the quantum model of the power system in the quantum entanglement-multi-device association construction module are as follows: (1) Identify the core control variables of photovoltaic inverters, energy storage converters and transformers, and map them to corresponding qubits and quantum states; (2) For different coupling constraints, dedicated multi-body entanglement gates are designed for reactive power compensation coordination and power flow matching respectively; (3) Transform the mathematical relationships of the constraints into the quantum entanglement gate operation logic; (4) Combine the quantized control variables with the corresponding entangled gates according to the device association to form a preliminary quantum logic chain; (5) Verify the integrity of the quantum logic chain, eliminate conflicts in entanglement gate operations, and form a complete model framework; (6) Introduce the operating state constraints of the quantum measurement operator conversion device, adjust the parameters through feedback, and construct the final quantum model of the power system.

6. The quantum circuit parallel compilation acceleration system based on multi-objective optimization according to claim 1, characterized in that, In the quantum entanglement-multi-device association construction module, the calculation formula for the power system quantum model is as follows: And satisfy: ∑ l,j∈{0,1} |lj| 2 ·| <lj|Ψ PV-ESS >| 2 ≤∈, where |Ψ PV-ESS > is the system quantum state composed of a photovoltaic inverter and an energy storage converter, |ψ PV The reactive power control qubit of a photovoltaic inverter represents the reactive power control state of the photovoltaic inverter, |ψ ESS The reactive power control qubits of the energy storage converter represent the reactive power control state of the energy storage converter. It is a controllable quantum entanglement gate, where e, f, g, h are the probability amplitudes of the entangled states, and |e| 2 +|f| 2 +|g| 2 +|h| 2 =1, |lj| 2 This is the penalty term for the state difference of the control variable, where l,j is the measurement result of the qubit, reflecting the coordination deviation between PV and ESS reactive power control. <lj|Ψ PV-ESS > represents the projection of the entangled state onto the basis |ij>, i.e., the probability amplitudes e, f, g, h, where ∈ is the quantum cooperative error threshold, set by the safety margin of reactive power cooperation in the power system. is the tensor product symbol, and |00>, |01>, |10>, and |11> are the joint states of qubits.

7. The quantum circuit parallel compilation acceleration system based on multi-objective optimization according to claim 1, characterized in that, In the quantum circuit parallel compilation acceleration module, the calculation formula for the quantum accelerated operator algorithm of multi-circuit parallel compilation is as follows: in, Quantum-accelerated operators compiled in parallel across multiple lines It is the quantum circuit for the k-th multi-objective sub-task. It is a quantum resource allocation operator that dynamically allocates qubits and entanglement gate resources, θ k is the compilation acceleration phase factor, optimized by the quantum variational compilation algorithm; k is the summation / product index used to traverse multi-objective subtasks; i is the imaginary unit; and N is the number of multi-objective subtasks.

8. The quantum circuit parallel compilation acceleration system based on multi-objective optimization according to claim 1, characterized in that, In the multi-objective collaborative optimization decision-making module, the weights of multiple objectives are fused using a quantum multi-objective constraint algorithm, and the calculation formula is as follows: in λ is the quantum observation operator for the m-th target. m The target weights are dynamically adjusted based on environmental and system requirements, and are achieved through the superposition of quantum states. It is the unit operator, and M is the number of multi-targets. It is a quantum multi-objective optimization operator, and m is the summation index.

9. The quantum circuit parallel compilation acceleration system based on multi-objective optimization according to claim 1, characterized in that, In the multi-objective collaborative optimization decision module, the convergence determination algorithm for multi-objective optimization is used to determine whether the iterative optimization process has converged. The calculation formula is: Y = Where Υ is the convergence criterion. Let be the observation values ​​of the m-th target at times t and t-1, respectively, where M is the number of targets. The convergence threshold is set according to system requirements. Convergence is determined at that time.

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