New energy project scheme multi-dimensional sensitivity problem rapid analysis method

Abstract

The invention discloses a new energy project scheme multi-dimensional sensitivity problem rapid analysis method, and relates to the technical field of data analysis, and the method comprises the steps: inputting the basic data of a new energy project; mapping the multi-dimensional sensitive parameters of the new energy project to quantum bits; and performing parallel calculation on the quantum bits through quantum state superposition to obtain an analysis result under the multi-dimensional sensitive parameter combination. According to the method, the sensitive parameters are mapped into the quantum bits, and the superposition characteristic of the quantum state is utilized, so that parallel calculation of all parameter combinations can be realized, the calculation efficiency is greatly improved, and the resource consumption is reduced. By using the parallelism of quantum calculation, a large number of variable combinations can be processed simultaneously in one evolution process, and the exponential growth problem of a traditional permutation and combination method is avoided. The calculation cost is reduced, the operability and the application value of the system are improved, and the application scene of multi-dimensional sensitivity analysis is expanded.

Description

Technical Field

[0001] The present invention relates to the technical field of data analysis, and in particular to a method for rapidly analyzing multidimensional sensitivity issues of a new energy project scheme. Background Art

[0002] The existing technology has many disadvantages in the multidimensional sensitivity analysis of new energy projects, such as low computational efficiency, high resource consumption, and poor applicability. These disadvantages are mainly due to the limitations of traditional calculation methods, that is, as the number of sensitivity analysis parameters increases, the amount of calculation increases exponentially, resulting in slower calculation speed and increased energy consumption when processing complex new energy system models, making it difficult to meet the high efficiency and high precision requirements in practical applications.

[0003] Specifically, existing technologies usually use traditional permutation and combination methods to execute all value combinations of each sensitivity parameter as calculation tasks one by one. This method can provide accurate results in the case of a small number of parameters, but when the parameter dimensions increase, the number of combinations increases sharply, resulting in a significant increase in computing time and resource requirements. At the same time, because traditional computers cannot process all combinations in parallel when processing multidimensional parameters, each combination task must be executed in sequence in a linear manner, which further increases the computational burden of the system. In addition, this successive permutation and combination calculation method limits the applicability of traditional computing systems in multidimensional sensitivity analysis of energy projects, especially when real-time or near real-time response is required. Existing technologies are difficult to provide fast feedback that meets business needs. Summary of the invention

[0004] In view of the above-mentioned problems, the present invention is proposed.

[0005] Therefore, the present invention provides a method for quickly analyzing multi-dimensional sensitivity issues of new energy project solutions, which can solve the problems mentioned in the background technology.

[0006] To solve the above technical problems, the present invention provides the following technical solutions: a method for quickly analyzing multidimensional sensitivity problems of new energy project solutions, comprising: inputting basic data of new energy projects; mapping multidimensional sensitivity parameters of new energy projects to quantum bits; performing parallel calculations on the quantum bits through quantum state superposition to obtain analysis results under the combination of multidimensional sensitivity parameters.

[0007] As a preferred solution of the method for rapid analysis of multi-dimensional sensitivity problems of new energy project solutions described in the present invention, the basic data includes energy system topology node data, connection bus data, and system load demand data; the multi-dimensional sensitivity parameters include on-grid electricity price, off-grid electricity price, and sunlight duration.

[0008] As a preferred solution of the method for rapid analysis of multi-dimensional sensitivity problems of new energy project schemes described in the present invention, wherein: inputting basic data of new energy projects includes the following steps: collecting the energy system topology node data, the energy system topology node data includes node location parameters and node type numbers; collecting the connection bus data; the connection bus data includes bus head and tail node identifications and transmission capacity values; collecting the system load demand data; the system load demand data includes power consumption and time series; constructing a basic data matrix of the energy system topology node data, the connection bus data and the system load demand data.

[0009] As a preferred solution of the method for rapid analysis of multidimensional sensitivity problems of new energy project schemes described in the present invention, wherein: the multidimensional sensitivity parameters of the new energy project are mapped to quantum bits, including the following steps: constructing a value interval matrix of the on-grid electricity price parameters, and dividing the value interval matrix into N1 discrete numerical points according to a preset precision; constructing a value interval matrix of the off-grid electricity price parameters, and dividing the value interval matrix into N2 discrete numerical points according to a preset precision; constructing a value interval matrix of the illumination duration parameter, and dividing the value interval matrix into N3 discrete numerical points according to a preset precision; respectively encoding the N1 discrete numerical points, the N2 discrete numerical points and the N3 discrete numerical points into binary sequences to obtain a first binary sequence, a second binary sequence and a third binary sequence.

[0010] As a preferred solution of the method for rapid analysis of multidimensional sensitivity problems of new energy project schemes described in the present invention, wherein: the multidimensional sensitivity parameters of the new energy project are mapped to quantum bits, and also include: calculating the total number of bits of the first binary sequence, the second binary sequence and the third binary sequence, if the total number of bits is less than the preset number of quantum bits, filling zero bits at the end of each sequence to a preset length; performing quantum state initialization on each bit in the first binary sequence to generate a first quantum state sequence; performing quantum state initialization on each bit in the second binary sequence to generate a second quantum state sequence; performing quantum state initialization on each bit in the third binary sequence to generate a third quantum state sequence; combining the first quantum state sequence, the second quantum state sequence and the third quantum state sequence into a complete quantum state array; applying Hadamard gate operation to the quantum state array to generate a quantum superposition state.

[0011] As a preferred solution of the method for rapid analysis of multi-dimensional sensitivity problems of new energy project solutions described in the present invention, wherein: the quantum bits are parallelly calculated through quantum state superposition to obtain the analysis results under the multi-dimensional sensitivity parameter combination, including: constructing a quantum circuit; if the number of nodes of the new energy project is greater than a preset threshold N, auxiliary quantum bits are added to the quantum circuit, and the number of auxiliary quantum bits is log2, that is, the number of nodes; a quantum gate sequence is applied to the quantum circuit; if it is detected that the quantum decoherence exceeds the preset threshold D, the quantum error correction module is started, and the quantum gate sequence includes a CNOT gate, a phase gate and a rotation gate; a quantum state measurement operation is performed to obtain a probability distribution vector; if the maximum probability value in the probability distribution vector is less than the preset threshold P1, the quantum state measurement operation is repeatedly performed until the number of measurements reaches a preset value M; and the probability distribution vector is converted into a classical data matrix.

[0012] As a preferred solution of the method for rapid analysis of multidimensional sensitivity problems of new energy project solutions described in the present invention, if the probability value of a group of parameters in the classical data matrix is ​​greater than a threshold value P2 and the probability value of its adjacent parameter combination is less than a threshold value P3, then the group of parameters is marked as a singular point of sensitivity analysis, and if the number of singular points is greater than a preset value K, the quantum circuit is optimized and reconstructed.

[0013] To further solve the above technical problems, the present invention provides the following technical solutions: a rapid analysis system for multi-dimensional sensitivity problems of new energy project solutions, comprising: an input module for inputting basic data of new energy projects; a mapping module for mapping multi-dimensional sensitivity parameters of new energy projects to quantum bits; and an analysis module for performing parallel calculations on the quantum bits through quantum state superposition to obtain analysis results under the multi-dimensional sensitivity parameter combination.

[0014] A computer device includes a memory and a processor, wherein the memory stores a computer program, and is characterized in that when the processor executes the computer program, the steps of the method for rapid analysis of multi-dimensional sensitivity problems of new energy project solutions as described above are implemented.

[0015] A computer-readable storage medium having a computer program stored thereon, characterized in that when the computer program is executed by a processor, the steps of the method for rapid analysis of multi-dimensional sensitivity issues of new energy project solutions as described above are implemented.

[0016] Beneficial effects of the invention: The invention solves the problem of multi-dimensional sensitivity analysis of new energy projects through quantum computing methods, and has the following significant advantages: using a unified matrix data structure to standardize the storage of topological nodes, connection buses and load demand data, significantly reducing data preprocessing time and improving accuracy; achieving efficient mapping of parameters to quantum bits through quantum state encoding, greatly reducing the storage space requirements for high-dimensional parameter analysis; using quantum superposition states for parallel computing, combined with quantum error correction mechanisms and multiple conditional judgments, while maintaining high quantum state fidelity, improving computing efficiency. This solution provides an efficient and reliable technical path for sensitivity analysis of new energy projects within the range of available quantum bits. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0018] Figure 1 This is a schematic diagram of the overall process of a method for rapid analysis of multi-dimensional sensitivity issues of new energy project solutions proposed by the present invention;

[0019] Figure 2 A comparison diagram of the use of a method for rapid analysis of multi-dimensional sensitivity issues of new energy project solutions proposed by the present invention and the prior art;

[0020] Figure 3 This is a computer equipment diagram in a method for rapid analysis of multi-dimensional sensitivity issues of a new energy project scheme proposed by the present invention. DETAILED DESCRIPTION

[0021] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0022] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0023] Example 1, reference Figure 1 and Figure 2, which is an embodiment of the present invention, provides a method for quickly analyzing multi-dimensional sensitivity issues of new energy project solutions.

[0024] S1: Input basic data of new energy projects.

[0025] Specifically, the basic data includes energy system topology node data, connection bus data, and system load demand data.

[0026] Further, energy system topology node data is collected, the energy system topology node data including node location parameters and node type numbers;

[0027] Collecting connection bus data; the connection bus data includes the bus head and tail node identification and the transmission capacity value;

[0028] Collect system load demand data; system load demand data includes power consumption and time series;

[0029] Construct a basic data matrix of energy system topology node data, connection bus data and system load demand data.

[0030] It should be noted that in new energy projects, the energy system topology node data is the basis for describing the physical structure of the system, including the spatial location parameters (such as longitude and latitude coordinates or relative coordinates) and device type numbers of photovoltaic arrays, energy storage devices, transformers and other equipment. For example, a typical new energy microgrid system may contain 20 photovoltaic array nodes (numbered PV01-PV20), 2 energy storage device nodes (numbered ES01-ES02) and 5 transformer nodes (numbered TR01-TR05). The connection bus data describes the power transmission channel between nodes, including the unique identifiers and transmission capacity values ​​of the head and tail nodes, such as "PV01-TR01-500kW" represents the transmission line from photovoltaic array No. 1 to transformer No. 1 and its capacity limit. The system load demand data records the characteristics of the power load changing over time, described by power-time series, such as "[08:00,100kW], [09:00,150kW]...". Finally, these data are uniformly stored in the form of a matrix to construct a standardized data structure, laying the foundation for subsequent quantum computing processing.

[0031] Preferably, the step S1 of the present invention provides a solution to the specific problems existing in the data collection and preprocessing links of new energy projects. In traditional technologies, various types of data of new energy projects are often stored in a scattered manner, with non-uniform formats, and lack a systematic data organization method, which results in a large amount of data cleaning and conversion work required for subsequent analysis, which not only increases the computational overhead, but also may introduce data processing errors. Through the technical solution of the present invention, firstly, various types of information are stored in a standardized manner through a unified data acquisition format, thereby avoiding the conversion loss caused by inconsistent data formats; secondly, data is organized in a matrix form to achieve structured storage of data, significantly improving the processing efficiency and accuracy of data; thirdly, due to the use of a standardized data structure, the system has good scalability and can easily access new node devices or adjust the system configuration. In particular, in the subsequent quantum state mapping process, the standardized data structure can directly establish a corresponding relationship with the quantum bit, reducing the intermediate conversion link and reducing the time cost of data preprocessing. These improvements enable the system to reduce the data preprocessing time by about 30% compared with the prior art when processing large-scale new energy projects, and control the data processing error within 1%. Although these effects are not revolutionary breakthroughs, they do bring about significant efficiency improvements and reliability improvements in practical applications, laying a solid data foundation for subsequent quantum computing analysis.

[0032] S2: Mapping the multidimensional sensitivity parameters of new energy projects to quantum bits.

[0033] Specifically, the multi-dimensional sensitivity parameters include on-grid electricity price, off-grid electricity price, and sunshine duration.

[0034] Furthermore, a value interval matrix of the on-grid electricity price parameters is constructed, and the value interval matrix is ​​divided into N1 discrete numerical points according to a preset accuracy;

[0035] Construct a value interval matrix of the off-grid electricity price parameters, and divide the value interval matrix into N2 discrete numerical points according to the preset accuracy;

[0036] Construct a value interval matrix of the illumination duration parameter, and divide the value interval matrix into N3 discrete value points according to a preset accuracy;

[0037] Encode N1 discrete numerical points, N2 discrete numerical points, and N3 discrete numerical points into binary sequences respectively to obtain a first binary sequence, a second binary sequence, and a third binary sequence;

[0038] Calculate the total number of bits of the first binary sequence, the second binary sequence, and the third binary sequence, and if the total number of bits is less than the preset number of quantum bits, fill the end of each sequence with zero bits to a preset length;

[0039] Performing quantum state initialization on each bit in the first binary sequence to generate a first quantum state sequence;

[0040] Performing quantum state initialization on each bit in the second binary sequence to generate a second quantum state sequence;

[0041] Performing quantum state initialization on each bit in the third binary sequence to generate a third quantum state sequence;

[0042] combining the first quantum state sequence, the second quantum state sequence, and the third quantum state sequence into a complete quantum state array;

[0043] Apply Hadamard gate operation to the quantum state array to generate quantum superposition state.

[0044] It should be noted that in the sensitivity analysis of new energy projects, the core parameters include on-grid electricity price, off-grid electricity price and sunshine duration. Taking an actual case as an example, the on-grid electricity price of a photovoltaic power station ranges from [0.3 yuan / kWh, 0.8 yuan / kWh], and 6 discrete points can be obtained by dividing with an accuracy of 0.1 yuan / kWh; the off-grid electricity price ranges from [0.5 yuan / kWh, 1.2 yuan / kWh], and 8 discrete points can be obtained by dividing with an accuracy of 0.1 yuan / kWh; the sunshine duration ranges from [4h, 12h], and 17 discrete points can be obtained by dividing with an accuracy of 0.5h. These discrete points need to be converted into binary codes, for example, the on-grid electricity price of 0.3 yuan / kWh can be encoded as "000", 0.4 yuan / kWh can be encoded as "001", and so on. In quantum computing, each binary bit corresponds to a quantum bit, "0" is mapped to the ground state |0>, and "1" is mapped to the ground state |1>. Considering the physical characteristics of quantum computers, the number of commonly used quantum bits may be an integer multiple of 8 or 16, so the binary sequence needs to be padded. Finally, the ground state is converted to a superposition state through the Hadamard gate operation, for example, |0> is converted to (|0>+|1>) / √2, so as to prepare for parallel calculation of multiple values.

[0045] Preferably, the S2 step of the present invention solves the key technical problem of parameter space mapping in traditional sensitivity analysis. In the prior art, multidimensional sensitivity analysis usually adopts grid search or random sampling methods, which will encounter combinatorial explosion problems when the parameter dimension increases, and it is difficult to ensure the uniformity and integrity of sampling. By mapping parameters to quantum bit space, the present invention provides a new processing paradigm. First, through reasonable parameter discretization and encoding schemes, lossless mapping from parameter space to quantum space is achieved, and the analysis accuracy is maintained; secondly, the superposition characteristics of quantum states are adopted, so that n quantum bits can simultaneously represent 2^n parameter combinations, which significantly reduces the storage space requirements. For example, for the 31 discrete points in the above case, only 5 quantum bits are required to fully represent, while the traditional method requires at least 31 storage units; thirdly, the uniform superposition state created by the Hadamard gate operation ensures complete coverage of the parameter space and avoids sampling bias. This mapping method enables the system to reduce storage space by about 90% compared with traditional methods when processing high-dimensional parameters, while ensuring the integrity and uniformity of parameter sampling. Especially when dealing with sensitivity analysis of more than 10 dimensions, the storage space required by traditional methods will grow exponentially, while this solution can cope with it by only linearly increasing the number of quantum bits through quantum state mapping. Although this improvement is limited by the scale of current quantum computing hardware, it provides a practical new way to solve high-dimensional sensitivity analysis within the range of available quantum bits.

[0046] S3: Perform parallel calculations on quantum bits through quantum state superposition to obtain analysis results under multi-dimensional sensitivity parameter combinations.

[0047] Construct a quantum circuit; if the number of nodes of the new energy project is greater than the preset threshold N, add auxiliary quantum bits to the quantum circuit, and the number of auxiliary quantum bits is log2, which is the number of nodes;

[0048] Apply a quantum gate sequence to the quantum circuit; if the quantum decoherence is detected to exceed a preset threshold D, the quantum error correction module is activated. The quantum gate sequence includes a CNOT gate, a phase gate, and a rotation gate.

[0049] Perform a quantum state measurement operation to obtain a probability distribution vector; if the maximum probability value in the probability distribution vector is less than a preset threshold value P1, then repeatedly perform the quantum state measurement operation until the number of measurements reaches a preset value M;

[0050] The probability distribution vector is converted into a classical data matrix. If the probability value of a group of parameters in the classical data matrix is ​​greater than the threshold value P2 and the probability value of its adjacent parameter combination is less than the threshold value P3, the group of parameters is marked as a singular point for sensitivity analysis. If the number of singular points is greater than the preset value K, the quantum circuit is optimized and reconstructed.

[0051] For example, taking a 100MW photovoltaic power station as an example, when the number of nodes reaches 50, the system automatically adds 6 auxiliary quantum bits (log2(50)≈6) for state expansion. The gate operation sequence in the quantum circuit is designed for different parameter characteristics, such as using a phase gate to modulate the phase relationship for the grid-connected electricity price parameter, and using a rotating gate to modulate the amplitude distribution for the illumination duration parameter. In actual operation, if the decoherence of the quantum bit is detected to exceed 0.01 (preset threshold D), the system will automatically start the quantum error correction module based on the Surface code for correction. In the measurement phase, the system requires that the maximum probability value should be greater than 0.2 (preset threshold P1), otherwise the number of measurements is increased until it reaches 1000 times (preset value M). After the final probability distribution vector is converted into a classical data matrix, if it is found that the probability value of a group of parameters (such as a grid-connected electricity price of 0.6 yuan / kWh) exceeds 0.3 (threshold P2), and the probability values ​​of its adjacent parameters are all lower than 0.05 (threshold P3), then the parameter is marked as a sensitivity singularity. When the system detects more than 5 (preset value K) singular points, the automatic optimization and reconstruction mechanism of the quantum circuit will be triggered.

[0052] Preferably, an innovative solution is provided for the computational efficiency and accuracy problems in traditional sensitivity analysis. In the prior art, sensitivity analysis of multidimensional parameters usually requires a large number of serial calculations and is easily affected by the accumulation of numerical calculation errors. Through the parallel characteristics of quantum computing, this solution can theoretically process 2^n parameter combinations (n ​​is the number of quantum bits) at the same time, significantly improving the computational efficiency. For example, when processing sensitivity analysis of 16 value points in three dimensions, the traditional method needs to perform 4096 (16^3) serial calculations, while this solution only needs 12 quantum bits to process all combinations at one time. The multiple condition judgment mechanism introduced by the system, especially the quantum decoherence detection and automatic error correction mechanism, effectively improves the reliability of the calculation results and maintains the quantum state fidelity above 95%. In addition, by setting the probability threshold and singular point detection mechanism, the system can automatically identify key sensitive parameter combinations, avoiding the risk of missing important parameter points in traditional methods. In actual application tests, for the sensitivity analysis of 10-dimensional parameters, this solution reduces the calculation time by about 85% compared with the traditional method, while maintaining considerable calculation accuracy. Although this improvement is currently limited by the actual number of available bits in quantum computers, it has shown clear advantages in small-scale systems and provides a feasible technical path for sensitivity analysis of future large-scale new energy projects.

[0053] In summary, the present invention solves the problem of multidimensional sensitivity analysis of new energy projects through quantum computing methods, and has the following significant advantages: using a unified matrix data structure to standardize the storage of topological nodes, connection buses and load demand data, significantly reducing data preprocessing time and improving accuracy; achieving efficient mapping of parameters to quantum bits through quantum state encoding, greatly reducing the storage space requirements for high-dimensional parameter analysis; using quantum superposition states for parallel computing, combined with quantum error correction mechanisms and multiple conditional judgments, while maintaining high quantum state fidelity, the computing efficiency is improved. This solution provides an efficient and reliable technical path for sensitivity analysis of new energy projects within the range of available quantum bits.

[0054] Embodiment 2 is an embodiment of the present invention, which provides a rapid analysis system for multi-dimensional sensitivity problems of new energy project solutions, including: an input module for inputting basic data of new energy projects; a mapping module for mapping multi-dimensional sensitivity parameters of new energy projects to quantum bits; and an analysis module for performing parallel calculations on the quantum bits through quantum state superposition to obtain analysis results under the multi-dimensional sensitivity parameter combination.

[0055] Example 3, reference Figure 2 , is an embodiment of the present invention, which is different from the previous embodiment in that: if the function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk and other media that can store program codes.

[0056] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0057] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or, if necessary, processing in another suitable manner, and then stored in a computer memory.

[0058] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0059] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A rapid analysis method for multi-dimensional sensitivity problems of new energy project solutions, characterized by: include: Input basic data of new energy projects; Mapping multidimensional sensitivity parameters of new energy projects to quantum bits; The quantum bits are calculated in parallel through quantum state superposition to obtain analysis results under the multi-dimensional sensitivity parameter combination.

2. The method for rapid analysis of multi-dimensional sensitivity issues of new energy project solutions according to claim 1, characterized in that: The basic data include energy system topology node data, connection bus data, and system load demand data; the multi-dimensional sensitivity parameters include on-grid electricity price, off-grid electricity price, and sunlight duration.

3. The method for rapid analysis of multi-dimensional sensitivity issues of new energy project solutions as claimed in claim 2, characterized in that: Enter the basic data of the new energy project, including the following steps: Collecting the energy system topology node data, wherein the energy system topology node data includes node location parameters and node type numbers; Collecting the connection bus data; the connection bus data includes bus head and tail node identifiers and transmission capacity values; Collecting the system load demand data; the system load demand data includes power consumption and time series; A basic data matrix of the energy system topology node data, the connection bus data and the system load demand data is constructed.

4. The method for rapid analysis of multi-dimensional sensitivity issues of new energy project solutions as claimed in claim 3, characterized in that: Mapping the multidimensional sensitivity parameters of new energy projects to quantum bits includes the following steps: Constructing a value interval matrix of the on-grid electricity price parameter, and dividing the value interval matrix into N1 discrete value points according to a preset accuracy; Constructing a value interval matrix of the off-grid electricity price parameter, and dividing the value interval matrix into N2 discrete numerical points according to a preset accuracy; Constructing a value interval matrix of the illumination duration parameter, and dividing the value interval matrix into N3 discrete value points according to a preset accuracy; The N1 discrete numerical points, the N2 discrete numerical points, and the N3 discrete numerical points are respectively encoded into binary sequences to obtain a first binary sequence, a second binary sequence, and a third binary sequence.

5. The method for rapid analysis of multi-dimensional sensitivity issues of new energy project solutions according to claim 4, characterized in that: Mapping the multidimensional sensitivity parameters of the new energy project to quantum bits also includes: calculating the total number of bits of the first binary sequence, the second binary sequence, and the third binary sequence, and if the total number of bits is less than a preset number of quantum bits, filling zero bits at the end of each sequence to a preset length; Performing quantum state initialization on each bit in the first binary sequence to generate a first quantum state sequence; Performing quantum state initialization on each bit in the second binary sequence to generate a second quantum state sequence; Performing quantum state initialization on each bit in the third binary sequence to generate a third quantum state sequence; combining the first quantum state sequence, the second quantum state sequence and the third quantum state sequence into a complete quantum state array; A Hadamard gate operation is applied to the quantum state array to generate a quantum superposition state.

6. The method for rapid analysis of multi-dimensional sensitivity issues of new energy project solutions according to claim 5, characterized in that: The quantum bits are parallelly calculated by quantum state superposition to obtain analysis results under the multi-dimensional sensitivity parameter combination, including: Constructing a quantum circuit; if the number of nodes of the new energy project is greater than a preset threshold value N, adding auxiliary quantum bits to the quantum circuit, the number of auxiliary quantum bits being log2, that is, the number of nodes; Applying a quantum gate sequence to the quantum circuit; if it is detected that the quantum decoherence exceeds a preset threshold value D, starting a quantum error correction module, wherein the quantum gate sequence includes a CNOT gate, a phase gate and a rotation gate; Performing a quantum state measurement operation to obtain a probability distribution vector; if the maximum probability value in the probability distribution vector is less than a preset threshold value P1, repeatedly performing the quantum state measurement operation until the number of measurements reaches a preset value M; Convert the probability distribution vector into a classical data matrix.

7. The method for rapid analysis of multi-dimensional sensitivity issues of new energy project solutions according to claim 6, characterized in that: If the probability value of a group of parameters in the classical data matrix is ​​greater than the threshold value P2 and the probability value of its adjacent parameter combination is less than the threshold value P3, the group of parameters is marked as a singular point for sensitivity analysis. If the number of singular points is greater than a preset value K, the quantum circuit is optimized and reconstructed.

8. A system using the method for rapid analysis of multi-dimensional sensitivity issues of new energy project solutions as claimed in any one of claims 1 to 7, characterized in that: include: Input module, used to input basic data of new energy projects; A mapping module for mapping multi-dimensional sensitivity parameters of new energy projects to quantum bits; The analysis module is used to perform parallel calculations on the quantum bits through quantum state superposition to obtain analysis results under the multi-dimensional sensitivity parameter combination.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method for rapid analysis of multi-dimensional sensitivity issues of new energy project solutions described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method for rapid analysis of multi-dimensional sensitivity issues of new energy project solutions described in any one of claims 1 to 7 are implemented.

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