High-efficiency holographic data storage system

Abstract

The invention discloses a high-efficiency holographic data storage system which comprises a data input module, a spatial light modulation module, a holographic optical disc module, a recording medium module, an intelligent algorithm application module and a hardware architecture module. Wherein the spatial light modulator modulates to-be-stored data to signal light to form a two-dimensional information page, the two-dimensional information page interferes with reference light in a recording medium to complete information recording, the intelligent algorithm application module optimizes a modulation amplitude process by using a deep learning model and a sparse coding technology, and the deep learning model learns characteristics of a large amount of data, so that the information recording efficiency is improved. According to the method, an accurate modulation prediction model is constructed, modulation parameters are intelligently adjusted according to different data types and storage requirements, and redundant information is removed by searching for sparse representation of data through a sparse coding technology. Through cooperation of all the parts, high-density storage and rapid data access are achieved, and the data storage efficiency and quality are improved.

Description

Technical Field

[0001] The present invention relates to a high-efficiency holographic data storage system, in particular to a holographic data storage system based on a spider wasp optimization algorithm. Background Art

[0002] With the rapid development of information technology, data is growing explosively, and demands for data storage capacity and speed are increasing. Traditional data storage technologies, such as hard drives and optical disks, are gradually facing bottlenecks in storage density and data transfer rates. Hard disk storage is limited by the physical properties of its mechanical components, with read and write speeds capped and storage density difficult to significantly increase. While optical disk storage technology provides a relatively stable storage method, its storage capacity is struggling to meet demand as data volumes continue to rise.

[0003] Holographic data storage technology has emerged as an emerging storage method. It utilizes the principle of light interference to record data in the form of holograms on a storage medium. This technology offers potential advantages such as large storage capacity, high data redundancy, and excellent security. However, existing holographic data storage technology still faces numerous challenges. During the data modulation process, traditional methods struggle to efficiently and accurately convert large amounts of data into a format suitable for holographic storage, limiting storage efficiency. Furthermore, the hologram recording and readout processes are susceptible to noise interference, optical aberrations, and other factors, making it difficult to ensure data storage quality.

[0004] Another challenge is the lack of effective intelligent algorithms to optimize the storage process. Traditional storage technologies often rely on fixed encoding and modulation schemes, unable to dynamically adjust to data characteristics and storage environments, making it difficult to fully realize the potential of holographic data storage. Therefore, a new holographic data storage technology is urgently needed to overcome these issues and meet the growing demand for data storage. Summary of the Invention

[0005] Purpose of the invention: The purpose of the present invention is to provide a high-efficiency holographic data storage system to achieve high-density storage and fast data access, thereby improving data storage efficiency and quality.

[0006] Technical solution: The high-efficiency holographic data storage system of the present invention comprises:

[0007] Data input module: used to receive computer stored data or real-time data streams, check data integrity and accuracy, and prompt and repair abnormal data;

[0008] Spatial light modulation module: used to receive the data to be stored, control the various units of the spatial light modulator through optical or electrical signals to change the optical properties, and modulate the data onto the signal light to form a two-dimensional information page;

[0009] Holographic disc module: used to integrate data positioning and indexing systems, quickly locate holographic data, and access data;

[0010] Intelligent algorithm application module: Utilizes deep learning models and sparse coding technology to optimize the modulation amplitude process. The deep learning model builds a modulation prediction model by learning data features and intelligently adjusts modulation parameters based on data type and storage requirements. Sparse coding technology removes redundant information by finding a sparse representation of the data.

[0011] Hardware architecture module: includes a readout unit, an optical unit, a detector unit and a data encoding unit, wherein the readout unit is used to generate the same reference light as that used during recording, address the storage medium, and reproduce the signal light in the direction of the original signal light using the diffraction effect of the volume holographic grating; the optical unit includes lenses and optical elements, which are used to control the focusing and guidance of the light beam and to achieve interference between the signal light and the reference light; the detector unit is used to detect the signal light reproduced during readout and convert the optical signal into an electrical signal; the data encoding unit is responsible for encoding the electrical signal data into an optical signal and decoding the readout optical signal back into an electrical signal.

[0012] Furthermore, the system also includes a recording medium module for receiving the signal light and the reference light modulated by the spatial light modulator, and using the photorefractive effect to record the volume hologram formed by the interference of the signal light and the reference light.

[0013] Furthermore, the spatial light modulator is composed of multiple independent units and is distributed in a one-dimensional or two-dimensional array in space; the holographic optical disc is a multi-layer structure with an anti-reflection coating on the surface.

[0014] Furthermore, the spider wasp optimization algorithm in the intelligent algorithm application module uses a deep learning model and sparse coding technology to optimize the modulation amplitude, including: initializing parameters, randomly generating an initialized population; setting an objective function, initializing the population, and evaluating the fitness function of each object; introducing an adaptive step size operator and a dynamic trade-off rate strategy into the spider wasp optimization algorithm, and introducing Gaussian mutation into the adaptive step size operation operator for optimization.

[0015] Furthermore, the initialization parameters are randomly generated to initialize the population, and the process is as follows:

[0016] Randomly generate a number of population individuals, which are randomly placed in the space as candidate solutions represented by d-dimensional vectors x = (x1, x2, ..., x d ), each variable value (x1,x2,...,x d ) are all floating point types, setting the maximum number of iterations of the algorithm and setting the search space of the algorithm according to the number of iterations.

[0017] Furthermore, the objective function is:

[0018]

[0019] E(x)=P e (SNR(x6))

[0020]

[0021] Where C(x) represents the storage capacity, η SLM (x1) is the modulation efficiency of the spatial light modulator, N(x2) represents the number of layers of the holographic disc, Δn(x3) 2 represents the refractive index modulation amplitude of the recording medium, λ is the wavelength of light, V(x4) is the effective volume factor related to the optical system, γ(x5) is the data encoding efficiency, A(x4) is the effective cross-sectional area of ​​the light beam in the recording medium, d(x4) is the effective depth of action, E(x) represents the data reading error rate, P e is the data decoding error probability model, SNR(x6) is the power ratio of signal light to noise light, C max is the theoretical maximum storage capacity, E max is the maximum acceptable data reading error rate, ω1 and ω2 are weight coefficients used to balance the importance of storage capacity and data reading accuracy in the optimization objective, satisfying ω1+ω2=1, and f(x) is the holographic data storage efficiency.

[0022] Furthermore, the adaptive step-size operator and dynamic trade-off strategy are introduced as follows: in the hunting behavior of the spider-wasp optimization algorithm, a large step-size search is used to maintain the global search capability of the population, while a small step-size search focuses on exploring the neighborhood of known solutions. Female wasps search for spiders suitable for offspring through continuous large-step search.

[0023] The adaptive step size operator and dynamic trade-off rate strategy formulas are as follows:

[0024]

[0025] Among them, μ1 controls the movement direction of the spider wasp:

[0026] μ1=|rn|*r1

[0027] Explore the area around the discarded spider using small steps:

[0028]

[0029] The adaptive step size operator h is introduced, and the formula is as follows:

[0030]

[0031] in, is the optimal holographic data storage efficiency, t is the number of iterations, is the current population exploration state, that is, the current holographic data storage efficiency, is the current exploration status of the spider, is the wasp's current exploration state, Δn(x3) 2 That is, the spider's movement amplitude, μ1 controls the movement direction of the spider wasp, μ2 represents the degree of change in the refractive index of the recording medium after being irradiated with light, and rn represents the relative angle between the spider and the wasp. represents the abandoned spider exploration state, r1 and r2 represent the influence range regional factors, L represents the distance between the spider and the exploration target, H represents the distance between the wasp and the exploration target, h is the adaptive step size operator, t max is the maximum number of iterations.

[0032] Furthermore, the spider-wasp optimization algorithm also introduces a Gaussian mutation strategy, and the formula is as follows:

[0033]

[0034] By simulating the lens imaging effect, the search range is dynamically adjusted:

[0035]

[0036] Among them, the parameter k is expressed as:

[0037]

[0038] Where TR n represents the trade-off rate, x * To dynamically adjust the search range, k is an iteration parameter, and a+b represents the distance between the spider and the wasp.

[0039] Furthermore, the spider wasp optimization algorithm applies regulation modulation efficiency, optimizes storage efficiency, brings the initial population into the objective function, searches for individuals with the best population fitness, calculates the fitness value f(x), and then iterates to output the optimal solution.

[0040] Furthermore, the optimization process also includes taking storage capacity and data reading error rate as input and holographic data storage efficiency as output, iterating the optimization of the target environment, and finally outputting the optimal solution f(x) to optimize the storage efficiency; the stopping condition is that the same output value f(x) is obtained twice in a row.

[0041] Beneficial effects: Compared with the existing technology, the present invention has the following significant advantages: (1) It performs real-time regulation according to changes in storage capacity, data reading error rate, etc., and the various parts work together to achieve high-density storage and fast data access, thereby improving data storage efficiency and quality; (2) It adopts deep learning optimization and sparse representation, and the deep learning model can optimize the modulation function. The sparse coding technology can reduce the required amount of modulation information, which is efficient and stable, and the data interaction with the spatial light modulator (SLM) is smooth without introducing additional noise or data loss; (3) It adopts the spider wasp optimization algorithm, which has high adaptability to conditional control. In addition, this algorithm is not only simple in structure, but also improves the exploration ability of the algorithm by introducing an adaptive step size operator and a dynamic trade-off rate for optimization, and is superior to other metaheuristic algorithms in terms of convergence to the global optimal solution. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 Schematic diagram of the system framework of the present invention.

[0043] Figure 2 This is a flow chart of the spider wasp optimization algorithm described in the present invention. DETAILED DESCRIPTION

[0044] The technical solution of the present invention will be further described below with reference to the accompanying drawings.

[0045] This embodiment provides a high-efficiency holographic data storage system, such as Figure 1 As shown, including:

[0046] Data input module: used to receive computer stored data or real-time data streams, check data integrity and accuracy, and prompt and repair abnormal data;

[0047] Spatial light modulation module: used to receive the data to be stored, control the various units of the spatial light modulator through optical or electrical signals to change the optical properties, and modulate the data onto the signal light to form a two-dimensional information page;

[0048] Holographic disc module: used to integrate data positioning and indexing systems, quickly locate holographic data, and access data;

[0049] Intelligent algorithm application module: Utilizes deep learning models and sparse coding technology to optimize the modulation amplitude process. The deep learning model builds a modulation prediction model by learning data features and intelligently adjusts modulation parameters based on data type and storage requirements. Sparse coding technology removes redundant information by finding a sparse representation of the data.

[0050] Hardware architecture module: includes a readout unit, an optical unit, a detector unit and a data encoding unit, wherein the readout unit is used to generate the same reference light as that used during recording, address the storage medium, and reproduce the signal light in the direction of the original signal light using the diffraction effect of the volume holographic grating; the optical unit includes lenses and optical elements, which are used to control the focusing and guidance of the light beam and to achieve interference between the signal light and the reference light; the detector unit is used to detect the signal light reproduced during readout and convert the optical signal into an electrical signal; the data encoding unit is responsible for encoding the electrical signal data into an optical signal and decoding the readout optical signal back into an electrical signal.

[0051] Among them, the data input module is compatible with various data formats and sources, and is equipped with functions such as data verification and a cache unit for temporary data storage. It can receive computer-stored data or real-time data streams, check data integrity and accuracy, and can prompt or automatically repair errors to ensure the smooth progress of the holographic storage process.

[0052] A spatial light modulator (SLM) consists of multiple independent units distributed in a one-dimensional or two-dimensional array in space. It can receive digital or analog data to be stored, and control each unit to change its own optical properties through optical or electrical signals, thereby modulating the data onto the signal light to form a two-dimensional information page.

[0053] The holographic disc has a multi-layer structure with an anti-reflective coating on the surface. It has an integrated data positioning and indexing system, which can quickly locate holographic data and provide efficient access. It has self-repair and redundant backup functions to ensure that the data is complete and recoverable.

[0054] The system also includes a recording medium module, which is a material such as photorefractive crystal or photosensitive polymer. It is set in a stable environment and can receive signal light and reference light modulated by SLM, and use the photorefractive effect to record the volume hologram formed by the interference of the two.

[0055] The application of intelligent algorithms includes deep learning optimization and sparse representation. The deep learning model can optimize the modulation function M(x,y), and sparse coding technology can reduce the amount of modulation information required.

[0056] In the sparse representation, the algorithm of the sparse coding technology is efficient and stable, and the data interaction with the spatial light modulator (SLM) is smooth without introducing additional noise or data loss.

[0057] Furthermore, the intelligent algorithm application module obtains information through spatial light modulation (SLM), holographic optical disc, and recording medium. The spider wasp optimization algorithm uses deep learning models and sparse coding technology to optimize the modulation amplitude process. The deep learning model builds an accurate modulation prediction model by learning a large amount of data features, and intelligently adjusts the modulation parameters according to different data types and storage requirements. The sparse coding technology removes redundant information by finding a sparse representation of the data, and then transmits it to the hardware architecture for implementation and regulation.

[0058] In a holographic data storage system, the optimization of the modulation function M(x,y) can be achieved by the following steps:

[0059] Data input: The system receives data to be stored, which can be images, videos or other forms of digital information.

[0060] Data preprocessing: Preprocess the input data, including format conversion, noise removal, etc., to adapt to the requirements of the modulation function.

[0061] Modulation function optimization: Use a deep learning model to train the modulation function M(x,y) so that it can automatically adjust its parameters based on the characteristics of the input data.

[0062] Apply sparse coding technology to reduce the amount of modulation information and improve data compression efficiency.

[0063] Data modulation: The preprocessed data is mapped to the SLM through the optimized modulation function M(x,y) to form a two-dimensional information page.

[0064] Hologram recording: The light field distribution on the SLM is used to record a hologram in the recording medium through interference with the reference light.

[0065] Data reading and decoding: During the reading process, the reproduced signal light is detected by the detector unit, and the data encoding unit decodes the optical signal back into an electrical signal to complete the data reading.

[0066] In this way, the optimization of the modulation function M(x,y) not only improves the efficiency and quality of holographic data storage, but also ensures smooth data interaction with the SLM without introducing additional noise or data loss.

[0067] Among them, the optimization process of the spider wasp optimization algorithm is as follows:

[0068] (1) Initialize parameters and randomly generate the initial population;

[0069] (2) Setting the objective function, initializing the population, and evaluating the fitness function of each object;

[0070] (3) An adaptive step-size operator and a dynamic trade-off rate strategy are introduced into the spider-wasp optimization algorithm, and Gaussian mutation is introduced into the adaptive step-size operation operator for optimization.

[0071] In step (1), the initialization population is specifically:

[0072] Randomly generate several population individuals and place them randomly in space as candidate solutions x = (x1, x2, ..., xd) represented by a d-dimensional vector. Each variable value (x1, x2, ..., xd) is a floating-point type. Set the maximum number of iterations of the algorithm and set the search space of the algorithm according to the number of iterations.

[0073] In step (2), the objective function of the setting is as follows:

[0074]

[0075] E(x)=P e (SNR(x6))

[0076]

[0077] Where C(x) represents the storage capacity, η SLM (x1) is the modulation efficiency of the spatial light modulator (SLM), N(x2) represents the number of layers of the holographic disc, Δn(x3) 2 represents the refractive index modulation amplitude of the recording medium, λ is the wavelength of light, V(x4) is the effective volume factor related to the optical system, which depends on the focusing characteristics of the optical system, beam size and other parameters, γ(x5) is the data encoding efficiency, A(x4) is the effective cross-sectional area of ​​the light beam in the recording medium, d(x4) is the effective depth of action, E(x) represents the data reading error rate, P e is the data decoding error probability model, SNR(x6) is the power ratio of signal light to noise light, C max is the theoretical maximum storage capacity, E max is the maximum acceptable data reading error rate, ω1 and ω2 are weight coefficients used to balance the importance of storage capacity and data reading accuracy in the optimization objective. They can be adjusted according to actual needs and satisfy ω1+ω2=1. f(x) is the holographic data storage efficiency.

[0078] In step (3), an adaptive step-size operator and a dynamic trade-off strategy are introduced. Specifically, in the hunting behavior of the Spider Wasp Optimizer (SWO) algorithm, two different step-sizes are used to explore the solution space: a large step-size search is used to maintain the global search capability of the population, while a small step-size search focuses on exploring the neighborhood of known solutions. Female wasps use continuous large-step search to find spiders suitable for offspring. An adaptive step-size operator h is introduced, which ensures that the global search capability is maintained in the early stages and accelerates the convergence speed in the later stages.

[0079] The adaptive step size operator and dynamic trade-off rate strategy formulas are as follows:

[0080]

[0081] Among them, μ1 controls the movement direction of the spider wasp:

[0082] μ1=|rn|*r1

[0083] Explore the area around the discarded spider using small steps:

[0084]

[0085] An adaptive step size operator h is introduced, which ensures that the global search capability is maintained in the early stages and accelerates the convergence speed in the later stages:

[0086]

[0087] in, is the optimal exploration state, that is, the optimal holographic data storage efficiency, t is the number of iterations, is the current population exploration state, that is, the current holographic data storage efficiency, is the current exploration status of the spider, is the wasp's current exploration state, Δn(x3) 2 That is, the spider moves in a certain range, μ1 controls the direction of movement of the spider wasp, and rn represents the relative angle between the spider and the wasp. represents the abandoned spider exploration state, r1 and r2 represent the influence range regional factors, L represents the distance between the spider and the exploration target, H represents the distance between the wasp and the exploration target, h is the adaptive step size operator, t max is the maximum number of iterations.

[0088] To prevent the Spider Wasp Optimizer (SWO) algorithm from falling into a local optimal solution during the search process, a Gaussian mutation strategy is introduced. Gaussian mutation uses a Gaussian distribution to generate mutation vectors, which can generate data points with high probability near the mean, thereby performing a fine search near the current optimal solution and improving local search capabilities. Gaussian mutation is introduced into the adaptive step size operator and search phase update:

[0089]

[0090] This strategy dynamically adjusts the search range by simulating the lens imaging effect:

[0091]

[0092] Among them, the parameter k is expressed as:

[0093]

[0094] Where TR n represents the trade-off rate, x * To dynamically adjust the search range, k is an iteration parameter, and a+b represents the distance between the spider and the wasp.

[0095] The improved spider wasp optimization algorithm applies regulation modulation efficiency and optimizes storage efficiency. After bringing the initial population into the objective function, it searches for the individual with the best fitness in the population, calculates the fitness value f(x), and then iterates to output the optimal solution.

[0096] In addition, with storage capacity, data reading error rate, etc. as input and holographic data storage efficiency as output, the target environment is continuously optimized and iterated, and finally the optimal solution f(x) is output, thereby optimizing the storage efficiency; the stopping condition refers to obtaining the same output value f(x) twice in a row.

[0097] In the hardware architecture module, the recording process is to receive the data to be stored through a spatial light modulator (SLM) and modulate the data onto the signal light to form a two-dimensional information page. Subsequently, the lenses and optical elements in the optical unit control the focus and guidance of the light beam to achieve interference between the signal light and the reference light in the recording medium. The recording medium (such as a photorefractive crystal or photosensitive polymer) uses the photorefractive effect to record the volume hologram formed by the interference of the signal light and the reference light, thereby completing the data storage.

[0098] This invention proposes a holographic data storage technology based on a spider-wasp optimization algorithm. Through its unique system architecture and intelligent algorithm application, it effectively solves many problems existing in existing storage technologies, achieving significant breakthroughs in data storage capacity and access speed. In practical applications, it can be widely applied to various scenarios with high data storage requirements, such as big data centers and scientific research data repositories. In the future, as the technology continues to develop and improve, its application scope is expected to be further expanded, and it is expected to integrate and innovate with more related technologies, providing strong support for the overall advancement of intelligent control and data storage technology, and promoting the data management level of related industries to a new level.

Claims

1. A high-efficiency holographic data storage system, characterized in that: include: Data input module: used to receive computer stored data or real-time data streams, check data integrity and accuracy, and prompt and repair abnormal data; Spatial light modulation module: used to receive the data to be stored, control the various units of the spatial light modulator through optical or electrical signals to change the optical properties, and modulate the data onto the signal light to form a two-dimensional information page; Holographic disc module: used to integrate data positioning and indexing systems, quickly locate holographic data, and access data; Intelligent algorithm application module: Utilizes deep learning models and sparse coding technology to optimize the modulation amplitude process. The deep learning model builds a modulation prediction model by learning data features and intelligently adjusts modulation parameters based on data type and storage requirements. Sparse coding technology removes redundant information by finding a sparse representation of the data. Hardware architecture module: includes a readout unit, an optical unit, a detector unit and a data encoding unit, wherein the readout unit is used to generate the same reference light as that used during recording, address the storage medium, and reproduce the signal light in the direction of the original signal light using the diffraction effect of the volume holographic grating; the optical unit includes lenses and optical elements, which are used to control the focusing and guidance of the light beam and to achieve interference between the signal light and the reference light; the detector unit is used to detect the signal light reproduced during readout and convert the optical signal into an electrical signal; the data encoding unit is responsible for encoding the electrical signal data into an optical signal and decoding the readout optical signal back into an electrical signal.

2. The high-efficiency holographic data storage system according to claim 1, characterized in that: The system also includes a recording medium module for receiving the signal light and the reference light modulated by the spatial light modulator, and using the photorefractive effect to record the volume hologram formed by the interference of the signal light and the reference light.

3. The high-efficiency holographic data storage system according to claim 1, characterized in that: The spatial light modulator is composed of multiple independent units and is distributed in a one-dimensional or two-dimensional array in space; the holographic optical disc is a multi-layer structure with an anti-reflection coating on the surface.

4. The high-efficiency holographic data storage system according to claim 1, characterized in that: The spider wasp optimization algorithm in the intelligent algorithm application module utilizes a deep learning model and sparse coding technology to optimize the modulation amplitude, including: initializing parameters and randomly generating an initialized population; setting an objective function, initializing the population, and evaluating the fitness function of each object; introducing an adaptive step-size operator and a dynamic trade-off rate strategy into the spider wasp optimization algorithm, and introducing Gaussian mutation into the adaptive step-size operation operator for optimization.

5. The high-efficiency holographic data storage system according to claim 4, characterized in that: The initialization parameters are randomly generated to initialize the population. The process is as follows: Randomly generate a number of population individuals, which are randomly placed in the space as candidate solutions represented by d-dimensional vectors x = (x1, x2, ..., x d ), each variable value (x1,x2,...,x d ) are all floating point types, setting the maximum number of iterations of the algorithm and setting the search space of the algorithm according to the number of iterations.

6. The high-efficiency holographic data storage system according to claim 4, characterized in that: The objective function is: E(x)=P e (SNR(x6)) Where C(x) represents the storage capacity, η SLM (x1) is the modulation efficiency of the spatial light modulator, N(x2) represents the number of layers of the holographic disc, Δn(x3) 2 represents the refractive index modulation amplitude of the recording medium, λ is the wavelength of light, V(x4) is the effective volume factor related to the optical system, γ(x5) is the data encoding efficiency, A(x4) is the effective cross-sectional area of ​​the light beam in the recording medium, d(x4) is the effective depth of action, E(x) represents the data reading error rate, P e is the data decoding error probability model, SNR(x6) is the power ratio of signal light to noise light, C max is the theoretical maximum storage capacity, E max is the maximum acceptable data reading error rate, ω1 and ω2 are weight coefficients used to balance the importance of storage capacity and data reading accuracy in the optimization objective, satisfying ω1+ω2=1, and f(x) is the holographic data storage efficiency.

7. The high-efficiency holographic data storage system according to claim 4, characterized in that: The adaptive step-size operator and dynamic trade-off strategy are introduced as follows: in the hunting behavior of the spider-wasp optimization algorithm, a large step-size search is used to maintain the global search capability of the population, while a small step-size search focuses on exploring the neighborhood of known solutions. Female wasps use continuous large-step search to find spiders suitable for offspring. The adaptive step size operator and dynamic trade-off rate strategy formulas are as follows: Among them, μ1 controls the movement direction of the spider wasp: μ1=|rn|*r1 Explore the area around the discarded spider using small steps: The adaptive step size operator h is introduced, and the formula is as follows: in, is the optimal holographic data storage efficiency, t is the number of iterations, is the current population exploration state, that is, the current holographic data storage efficiency, is the current exploration status of the spider, is the wasp's current exploration state, Δn(x3) 2 That is, the spider's movement amplitude, μ1 controls the movement direction of the spider wasp, μ2 represents the degree of change in the refractive index of the recording medium after being irradiated with light, and rn represents the relative angle between the spider and the wasp. represents the abandoned spider exploration state, r1 and r2 represent the influence range regional factors, L represents the distance between the spider and the exploration target, H represents the distance between the wasp and the exploration target, h is the adaptive step size operator, t max is the maximum number of iterations.

8. The high-efficiency holographic data storage system according to claim 7, characterized in that: The spider-wasp optimization algorithm also introduces a Gaussian mutation strategy, and the formula is as follows: By simulating the lens imaging effect, the search range is dynamically adjusted: Among them, the parameter k is expressed as: Where TR n represents the trade-off rate, x * To dynamically adjust the search range, k is an iteration parameter, and a+b represents the distance between the spider and the wasp.

9. The high-efficiency holographic data storage system according to claim 4, characterized in that: The spider wasp optimization algorithm applies regulation modulation efficiency, optimizes storage efficiency, brings the initial population into the objective function, searches for the individual with the best population fitness, calculates the fitness value f(x), and then iterates to output the optimal solution.

10. The high-efficiency holographic data storage system according to claim 4, characterized in that: The optimization process further includes taking storage capacity and data reading error rate as input and holographic data storage efficiency as output, iterating the optimization of the target environment, and finally outputting the optimal solution f(x) to optimize the storage efficiency; The stopping condition is to obtain the same output value f(x) twice in a row.

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