Data Processing Method, System and Storage Medium of a Storage Device

Abstract

The present invention relates to the technical field of storage data processing, and specifically to a data processing method, system and storage medium for a storage device. The method includes the following steps: receiving an input data stream, the data stream including a plurality of data units, and performing noise filtering and signal normalization through an initial preprocessing module to improve the integrity of the data stream; splitting the preprocessed data stream through a multi-mode interference divider, wherein the divider performs spatial and frequency-domain decomposition of signals according to the length and wavelength differences of the optical paths for subsequent parallel processing. The present invention combines photon computing and storage technologies, and replaces the traditional electronic computing part with an efficient photon signal processing module. Specifically, by using the photon computing module, the data processing speed is accelerated, especially when processing large-scale and high-concurrency data requests, and a performance improvement by several times can be provided.

Description

Technical Field

[0001] The present invention relates to the technical field of storage data processing, and specifically provides a data processing method, system and storage medium for a storage device. Background Art

[0002] Due to the physical limitations and architecture design of storage devices, the data processing of storage devices is gradually unable to meet the requirements of the big data era for high performance, high throughput and low latency. Most current storage devices rely on the transmission and storage of electronic signals, and their speed and expansion capabilities are restricted by semiconductor materials and microelectronic designs. In addition, with the exponential growth of information volume, storage devices gradually expose bottlenecks in data access speed, parallel processing capabilities and energy consumption. Therefore, the technical problem proposed by the present invention is: how to improve the efficiency of data processing of storage devices. Summary of the Invention

[0003] The present invention provides a data processing method, system and storage medium for a storage device to solve the technical problem of low efficiency in data processing of current storage devices.

[0004] The technical solution of the present invention to solve the above technical problems is as follows:

[0005] On the one hand, a data processing method for a storage device is provided, and the method includes the following steps:

[0006] Receive an input data stream, where the data stream includes multiple data units, and perform noise filtering and signal standardization through an initial preprocessing module to improve the integrity of the data stream;

[0007] Split the preprocessed data stream through a multi-mode interference divider, where the divider performs spatial and frequency-domain decomposition of the signal according to the length and wavelength differences of the optical path for subsequent parallel processing;

[0008] Import the split data stream into a photonic computing module, and the photonic computing module uses a photonic transistor array to perform logical operations and phase modulation calculations for synchronous processing of multiple data units to improve the computing efficiency;

[0009] Use an error correction code module to perform quality detection and error correction on the data stream output by the photonic computing module. The error correction process includes data correction based on a function with dynamically adjusted parameters to correct errors introduced by parallel processing;

[0010] Store the error-corrected data stream in the storage device, and the storage device is combined with a high-density controller to ensure the fast access characteristics of the data;

[0011] In response to a user data request, target data is retrieved from the storage device through a data extraction and decoding module, and the data extraction and decoding module uses a photon inverse transformation method to parse and output the target data, which is used to reduce latency.

[0012] Furthermore, the steps of performing noise filtering and signal normalization by the initial preprocessing module include:

[0013] Represent the data stream as a multi-dimensional matrix, where multiple dimensions represent signals in multiple different frequency bands;

[0014] Calculate the energy distribution of the signal and apply a self-adjusting filter bank , and the calculation formula is:

[0015] , where x represents the input signal, and α and β are parameters obtained through iterative optimization, which are used to minimize the influence of noise signals within the frequency band;

[0016] Perform point-by-point filtering and apply a recursive algorithm to update the filter parameters to enhance the fidelity of the target signal;

[0017] The signal obtained after being processed by the filter is amplitude-adjusted by a normalization unit to ensure that the signal amplitude is within the defined range;

[0018] Perform frequency-domain analysis on the normalized signal using the fast Fourier transform to filter out interference frequencies.

[0019] Furthermore, the splitting of the preprocessed data stream by the multi-mode interferometric splitter includes the splitter performing a beam splitting operation based on coherent optics, and the steps include:

[0020] Generate an initial light beam using a tunable laser, and the initial light beam covers all signal frequencies;

[0021] Import the initial light beam into an optical network composed of a liquid crystal tunable phase surface, and the operating mechanism expression of the initial light beam is:

[0022] , where γ n and θ n are phase offset values dynamically adjusted through a preset algorithm;

[0023] The initial light beam is controlled by the phase surface to obtain a phase-controlled light beam, and the phase-controlled light beam realizes phase alignment of the signal channels through a spatial light modulator to obtain a modulated light beam;

[0024] The modulated light beam is spectrally dispersed through a dispersion delay line to achieve spatial encoding of the signal;

[0025] Use a ratio detection device to separate the horizontally and vertically polarized state transmission signals.

[0026] Furthermore, the steps for the photon computing module to perform logic operations and phase modulation calculations using a photon transistor array include:

[0027] Perform phase error detection on the input optical signal using a phase-locked loop;

[0028] Call the phase modulation control function , where δ, ε, and λ represent parameters adjusted according to feedback, and σ represents the input phase, which is used to adjust the system response and signal gain;

[0029] Perform phase error compensation and inject the error correction signal into the modulation frequency response of the photon transistor;

[0030] After the input optical signal passes through the photon transistor array, use a target phase transformation to make the beam phase reach the system standard;

[0031] The input optical signal is normalized again in terms of the full signal amplitude after calculation, so as to stabilize the input optical signal within a limited dynamic range.

[0032] Furthermore, the phase modulation mechanism in the photon computing module includes a photon multiplication cascaded suppression oscillator network, and the network is configured through the following steps:

[0033] Determine the relationship matrix M to describe the topological structure of the transistor network;

[0034] Use a signal optimization algorithm to optimize signal routing. The expression of the signal optimization algorithm is:

[0035] , where σ i and σ j respectively represent the phase offsets at adjacent transistor nodes;

[0036] Adjust the frequency and feedback gain of the oscillator to minimize oscillation damping and maximize network stability;

[0037] Enable the multiplication signal path and select the target channel with a gain higher than the preset threshold for output signal enhancement;

[0038] Comprehensively adjust the propagation delay parameter through feedback to maintain the coherent integration of the output signal.

[0039] Furthermore, the storage device includes non-volatile storage units, and the storage units cooperate with the controller to implement the following steps:

[0040] Receive the data stream after error correction, and perform full-process bit scaling adjustment through an encoder to adapt to the storage space;

[0041] The data stream enters the storage buffer, and a priority caching mechanism is used to optimize the access speed;

[0042] Apply random access memory combined with dynamic refresh rate and power management strategies to optimize energy consumption;

[0043] When the data stream is written, monitor the writing integrity and the status of the storage unit;

[0044] During the storage process of the data stream, introduce a redundant storage strategy to ensure data disaster recovery security.

[0045] Furthermore, the storage unit is connected to the interface of the storage device through an optoelectronic hybrid integrated circuit, and the interface includes:

[0046] A high-speed data transmission channel that supports a transmission rate of up to several hundred megabits per second;

[0047] Use a programmable logic array to dynamically optimize the encoding scheme to improve data storage efficiency;

[0048] During the writing process, monitor the integrity of the photon signal in real time, and use a feedback mechanism to instantaneously adjust the storage voltage and pulse width;

[0049] Perform the mutual conversion between optical signals and electrical signals for stable and real-time data transmission;

[0050] Adopt photon caching technology for high-speed caching and latency management.

[0051] Furthermore, the data extraction and decoding module uses a photon inverse transformation method to parse and output the target data, including the following steps:

[0052] Monitor the request mode of the target data, and optimize the decoding priority using the historical request frequency;

[0053] Pre-decode the requested target data to extract the location information and structured identifier;

[0054] Adopt a prediction model to enhance the decoding accuracy, and the prediction model obtains the best decoding path through dynamic learning of historical data sets;

[0055] During the decoding process, adjust the signal gain and phase shift in real time to ensure complete and accurate decoding;

[0056] Adopt a photon inverse transformation algorithm to reconstruct the decoded optical signal into an available data stream for optimized output.

[0057] On the other hand, a data processing system for a storage device is provided, the system comprising:

[0058] An input interface module configured to receive an input data stream and perform preliminary preprocessing;

[0059] A multi-mode interference splitter for splitting the preprocessed data stream into a plurality of signal channels;

[0060] A photonic computing module including a photonic transistor array and a phase modulation sub-module, configured to perform parallel data processing;

[0061] An error correction code module coupled to the photonic computing module, using a multi-step cyclic correction mechanism for data correction;

[0062] A data storage module connected to a storage controller to implement data storage;

[0063] A data extraction and decoding module for responding to a user request and performing photonic inverse transform decoding.

[0064] In still another aspect, a computer-readable storage medium is provided, storing computer instructions, and when the instructions are executed, a processor of a computer executes the data processing method as described above.

[0065] The beneficial effects of the present invention are:

[0066] By combining photonic computing and storage technologies, the present invention replaces the traditional electronic computing part with an efficient photonic signal processing module. Specifically, by using the photonic computing module, the data processing speed is accelerated, especially when processing large-scale and high-concurrency data requests, it can provide a several-fold performance improvement. At the same time, through the data stream splitting achieved by the multi-mode interference splitter, the data space and frequency domain can be decomposed for parallel processing, breaking through the bottleneck of data interaction in complex computing processes. In addition, the error correction module uses a dynamic adjustment function for error detection and correction, improving the accuracy of data processing. By a large number of storage units being adopted, the fast access ability of data is ensured, enabling both storage and extraction operations to be performed with lower latency and higher reliability.

[0067] The above description is only an overview of the technical solution of the present invention. In order to be able to understand the technical means of the present invention more clearly and to implement it in accordance with the content of the specification, the following will be described in detail with reference to the preferred embodiments of the present invention and the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0068] Figure 1 It is a flowchart of the data processing method in an embodiment of the present invention;

[0069] Figure 2 It is a schematic diagram of noise cancellation of a self-adjusting filter bank in an embodiment of the present invention;

[0070] Figure 3 Schematic diagram of the principle of a multi-mode interference splitter in an embodiment of the present invention;

[0071] Figure 4 Optical path diagram of a multi-mode interference splitter in an embodiment of the present invention;

[0072] Figure 5 Schematic diagram of the phase modulation of a photonic transistor array in an embodiment of the present invention;

[0073] Figure 6 Schematic diagram of a multi-dimensional matrix for data stream preprocessing in an embodiment of the present invention;

[0074] Figure 7 Effect diagram of noise suppression for data stream preprocessing in an embodiment of the present invention;

[0075] Figure 8 Schematic diagram of optoelectronic hybrid integration in an embodiment of the present invention. Detailed implementation manners

[0076] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all the embodiments.

[0077] In the embodiments of the present invention, words such as "exemplary" or "for example" are used to represent examples, illustrations or explanations. Any embodiment or design solution described as "exemplary" or "for example" in the embodiments of the present invention should not be construed as being more preferred or having more advantages than other embodiments or design solutions. Rather, the use of words such as "exemplary" or "for example" is intended to present relevant concepts in a specific manner.

[0078] The present invention provides the following preferred embodiments:

[0079] Embodiment 1

[0080] To address the deficiencies of traditional storage devices in terms of data processing speed and accuracy, this embodiment proposes a data processing method for a storage device to improve processing efficiency and data integrity. As Figure 1 shown, the method includes the following steps:

[0081] S100. Receive the input data stream, which includes multiple data units, and perform noise filtering and signal normalization through the initial preprocessing module to improve the integrity of the data stream.

[0082] S200. Split the preprocessed data stream through a multi-mode interferometer. The interferometer decomposes the signal in the spatial and frequency domains according to the differences in the optical path length and wavelength for subsequent parallel processing.

[0083] S300. Import the split data stream into the photonic computing module. The photonic computing module uses a photonic transistor array to perform logic operations and phase modulation calculations for synchronous processing of multiple data units to improve the computing efficiency.

[0084] S400. Use an error correction code module to perform quality detection and error correction on the data stream output by the photonic computing module. The error correction process includes data correction based on a function with dynamically adjusted parameters to correct the errors introduced by parallel processing.

[0085] S500. Store the error-corrected data stream in a storage device. The storage device is combined with a high-density controller to ensure the fast access characteristics of the data.

[0086] S600. In response to a user data request, retrieve the target data from the storage device through a data extraction and decoding module. The data extraction and decoding module uses a photonic inverse transformation method to parse and output the target data to reduce the latency.

[0087] In this embodiment, after the input data stream enters the processing system, it first passes through an initial preprocessing module. In the preprocessing module, an adaptive filtering technique is used to remove noise. The selection of the filtering technique can be fine-tuned according to the characteristics and quality requirements of the input data. The specific mechanism mostly uses a dynamically adjustable adaptive filter bank to cope with signal interference in different frequency bands, as Figure 2 shown. After the noise removal is completed, the data stream is normalized. During this process, the adjustment of the signal amplitude ensures that the data remains within a specific range during subsequent processing for the convenience of the computing module. It should be understood that the optimization and selection of the initial preprocessing module affect the efficiency of the entire data processing chain.

[0088] Furthermore, the preprocessed data stream is split through a multi-mode interferometer, and the principle is as Figure 3 and Figure 4 shown. In addition, the basis for splitting is the difference in the optical path length and wavelength, enabling the signal to be decomposed in an optimized form in the spatial and frequency domains. It can be understood that the selection and adjustment of the optical path largely determine the efficiency of subsequent parallel processing. The multi-mode interferometer is designed considering different optical path length ratios to ensure the minimum phase error and maximum signal intensity when the signal passes through.

[0089] The following process involves the use of a photonic computing module that performs synchronous logic operations and phase modulation calculations through a photonic transistor array, as Figure 5 shown. Photonic transistors are typically selected for their stable performance and fast response to meet the complex operation requirements of parallel data units. In photonic computing, phase modulation calculation is a complex task based on detecting and adjusting the phase of the incoming signal. It should be understood that controlling signal gain and optimizing phase modulation parameters are of practical significance for achieving efficient computing. In this embodiment, the photonic transistors are finely tuned to handle various input signals while ensuring the controllability and efficiency of synchronous computing.

[0090] After the photonic computing module completes the operation, the quality detection and error correction of the data stream are performed using an error correction code module for data correction, where a function based on dynamically adjusted parameters is used for real-time correction to address data distortion and errors that may be caused by parallel processing. When the data stream passes through the error correction code module, on the one hand, it undergoes strict quality detection to ensure the reliability of the output data, and on the other hand, through continuous adjustment and optimization of the function parameters, the high precision of the data is ensured. The design of the error correction mechanism lies in its real-time nature and self-adaptability, which endows this module with a strong fault recovery ability.

[0091] Further, the data after error correction processing is stored in a storage device combined with a high-density controller to ensure the speed and efficiency of data storage and access. It should be understood that the interface technology of the storage device and the coordination working relationship of the controller affect the fast access time. The high-density controller optimizes the data write and read paths and rationally allocates storage resources, thereby shortening the data access time.

[0092] Further, in the process of responding to user data requests, the data extraction and decoding module uses the photonic inverse transformation method to parse and output the target data to reduce system latency. In this link, the application of the photonic inverse transformation algorithm improves the accuracy of decoding and the efficiency of data output. Ensure that users can obtain high-accuracy data with a low waiting time after sending requests.

[0093] Through this embodiment, the performance improvement and reliability enhancement in each stage of data processing are achieved, especially in the integrated optimization of steps such as noise filtering, signal normalization, photonic computing, and error correction. It not only improves the overall processing speed of the system but also enhances the accuracy of data processing and the effectiveness of data storage and access, providing a practical path for efficient data processing in complex computing environments.

[0094] Embodiment 2

[0095] To address the impact of noise and non-standardization in the input signal on data processing accuracy, this embodiment further refines the noise filtering and signal standardization steps in the signal preprocessing process. In practical applications, the dynamic change characteristics and spectral complexity of signals often pose challenges to stable data processing. Therefore, in this embodiment, the input data stream is represented as a multi-dimensional matrix, such as Figure 6 shown, where each dimension corresponds to signals in different frequency bands, so as to conduct a detailed analysis and processing of signal energy.

[0096] Furthermore, to improve the efficiency and accuracy of noise filtering, a self-adjusting filter bank is adopted in this embodiment. Specifically, by calculating the energy distribution of the input signal, the signal characteristics of each frequency band are identified, and thus a matching multi-element filter bank is enabled . The specific implementation of filtering is based on the following calculation formula:

[0097] (1)

[0098] It should be noted that in the calculation formula (1), the meanings of the parameters are as follows:

[0099] X represents the input signal. In practical applications, x represents the signal value to be filtered. In other words, x can represent the signal amplitude or power at a certain time point.

[0100] F i (x) is the output of a specific filter in the self-adjusting filter bank, representing the result after filtering the input signal x. i represents the index of a specific filter in the filter bank, which means that there are multiple filters F1, F2, …, F n acting on the input signal x together. Different filters focus on filtering signal noise in different frequency bands.

[0101] α is used to control the sensitivity and response curve of the filter, obtained through iterative optimization and affecting the gain change of the filter for the input signal. When the amplitude of the input signal changes, α determines the change rate of the filter output.

[0102] β is used to adjust the signal offset, thereby optimizing the performance of the filter so that the filter can more effectively focus on the target frequency region.

[0103] e represents the natural exponential constant, approximately 2.718, and is used for the calculation of the exponential function. The exponential function form helps to describe the non-linear adjustment process of the filter response to the signal intensity, especially when the input signal deviates from the set range.

[0104] The overall purpose of Equation (1) in this embodiment is to provide a non - linear filtering mechanism. By adjusting the α and β parameters, the filter can adapt to different signal conditions and minimize the influence of noise signals within a specific frequency band.

[0105] Equation (1) filters based on multiple frequency bands of the signal, such that each frequency band is processed by an adaptive filter. This kind of filter can dynamically respond to signal changes, adjust the gain and offset, to ensure that the effective components of the signal are retained during the filtering process while the noise components are minimized. By iteratively optimizing α and β, the system can achieve the best noise suppression effect under different signal conditions, as Figure 7 shown. It is applicable to complex and diverse signal processing environments where the filter is required to autonomously adapt to the continuously changing input signals.

[0106] Furthermore, during the process of performing point - by - point filtering, this embodiment introduces a recursive algorithm to update the filter parameters, enabling the filter to achieve the optimal effect in terms of enhancing the fidelity of the target signal. The recursive algorithm relies on the information of the previous input and output to adjust the filtering parameters in real - time.

[0107] The filtered signal enters the normalization unit for amplitude adjustment. It should be emphasized that normalization is not only to ensure that the signal amplitude is within the set range, but also to provide a uniformly - standard signal input for subsequent signal detection and processing. The normalization process analyzes the statistical characteristics of the signal and dynamically adjusts the gain of the amplification neural network. This method not only improves the relative sensitivity of the signal, but also reduces the distortion risk introduced by non - linear gain operations.

[0108] After signal normalization, the fast Fourier transform (FFT) is applied to perform frequency - domain analysis on the normalized signal to further filter out the interference frequencies outside the effective signal spectrum. This frequency - domain analysis relies on the FFT to quickly decompose the spectral characteristics of the signal, enabling the system to real - time identify and eliminate the noise components in the frequency domain. This processing ensures that the signal exists in the purest and most efficient state before entering the subsequent calculation module.

[0109] The benefits of this embodiment lie in the optimization of the signal pre - processing steps. By combining the representation of multi - dimensional matrices, self - adjusting filter banks, and fast - calculation frequency - domain analysis, it not only improves the integrity and accuracy of data processing, but also lays a solid foundation for subsequent data calculation and storage. This multi - level and multi - stage signal pre - processing method effectively solves the problems of data noise filtering and normalization under different background signal conditions, and can significantly improve the applicable range and stability of the system when applied in practice.

[0110] Embodiment Three

[0111] To address the issue of accurately and orderly implementing signal splitting and distribution in high-speed multi-signal frequency data stream processing, this embodiment further optimizes the design of the multi-mode interference divider and refines its application in coherent optical beam splitting operations. Specifically, this embodiment introduces a processing method that combines phase modulation and spatial light modulation to improve the accuracy and efficiency of data processing.

[0112] Specifically, a tunable laser is used to generate an initial light beam, which is set to cover all signal frequencies. This light source selection can ensure that a broadband optical carrier is provided for the entire system at the initial stage. This initial light beam is further transmitted to an optical network composed of a liquid crystal tunable phase surface to achieve specific optical regulation. It should be understood that the role of the liquid crystal tunable phase surface in this network is to finely adjust the phase of the light beam, and its process is represented by a calculation formula:

[0113] (2)

[0114] It can be understood that in the calculation formula (2), it means that by dynamically adjusting the phase offset values γ n and θ n , the system can flexibly respond to the dynamic changes of the signal flow, thereby optimizing the phase beam control effect. In the calculation formula (2), the specific meanings of the parameters are as follows:

[0115] Δφ represents the change in phase, which is used to describe the result of phase adjustment, and the result depends on the superposition of multiple harmonic signals.

[0116] N represents the maximum number of harmonics, which defines the number of harmonics used in phase modulation and is used to describe more complex waveforms. n represents the specific nth harmonic.

[0117] γ n represents the amplitude coefficient corresponding to the nth harmonic. It is an adjustable parameter that determines the contribution of each harmonic. By adjusting γ n , the influence of each harmonic on the total phase change is adjusted.

[0118] t represents the time variable, which is a parameter of signal change over time. Usually, it is a continuously changing quantity, making the phase modulated continuously over time.

[0119] Furthermore, the initial light beam is transformed into a phase-controlled light beam after phase surface control. At this stage, the spatial light modulator aligns the phases of the signal channels, laying the foundation for the generation of the modulated light beam. The selection of the spatial light modulator lies in its ability to achieve efficient switching of the light beam through spatial modulation, forming an interactive feedback with the liquid crystal phase surface to achieve the purpose of phase synchronization.

[0120] After the modulated light beam is formed, spectral dispersion is achieved via a dispersion delay line, and the spatial encoding of the signal is maintained. The application of the dispersion delay line ensures the complete spread of the signal in the spectral space, enabling each signal to be accurately identified and accessed in subsequent processing. It should be understood that this spectral dispersion is not a simple frequency division, but rather distance encoding of the signal is achieved through structured dispersion.

[0121] Furthermore, the modulated light beam obtained based on the above operations is used with a ratio detection device to complete the separation of the signals transmitted in the horizontal and vertical polarization states. The ratio detection device is configured with high-sensitivity polarization separation ability, capable of reliably separating polarization signals at the microscale. It can be understood that this polarization-based signal separation provides a basis for the independent transmission and subsequent processing of different signals in the data stream.

[0122] Through the specific processing flow of this embodiment, the challenges of phase accuracy and carrier stability in the splitting of multi-signal frequency data streams are effectively solved, demonstrating a new mode of multi-layer optimization and precise control for beam splitting. The most significant benefit is that through the close combination of phase regulation and spatial light modulation, efficient data separation and processing are achieved, enhancing the overall stability and adaptability of the system. It not only enhances the compatibility of the device with diverse input signals but also improves the signal processing efficiency in complex optical environments.

[0123] Embodiment Four

[0124] In the photonic computing module, to address the efficiency bottleneck problem of traditional electronic devices in high-speed data processing, this embodiment further refines the phase modulation and logic operation functions of the photonic transistor array to achieve faster and more accurate data processing. First, this embodiment introduces a phase-locked loop specifically for detecting the phase error of the input optical signal. The phase-locked loop not only enhances the control ability of the initial phase of the input signal but also provides a reliable error feedback source for subsequent phase modulation.

[0125] In this embodiment, the phase modulation control process uses an optimized control function, and its calculation formula is:

[0126] (3)

[0127] In calculation formula (3), δ, ε, and λ are parameters based on system feedback regulation. It should be understood that σ, as a representative of the input phase, can better respond to system requirements and optimize signal gain through the regulation of this function. The square term and sine term in the function respectively provide means for quadratic regulation and harmonic regulation, and the λ term is used to set the reference phase level to ensure stable operation.

[0128] Furthermore, the phase error compensation section accurately injects the detected error signal into the system through the modulation frequency response of the photonic transistor. This process can quickly correct the phase error by controlling the response characteristics of the modulation frequency. The configuration of the photonic transistor ensures the reliability of high-frequency modulation, and the selection of its modulation frequency response depends on specific signal frequency bandwidth and dynamic range requirements.

[0129] Furthermore, the input optical signal after passing through the photonic transistor array will perform a target phase transformation, making the phase of the signal beam reach the standard set by the system. Here, the "target phase transformation" refers to achieving signal phase normalization through a series of preset phase offsets and compensations. The photonic transistor not only serves as a simple signal channel in this process but also acts as a dynamic regulator of the signal phase, ensuring that the output signal meets the requirements of specific application scenarios.

[0130] Furthermore, for the calculated signal, this embodiment also considers the amplitude normalization process of the signal. The normalization of the final full-signal amplitude ensures the stable operation of the system within a limited dynamic range. Through this embodiment, the excessive fluctuations of the signal amplitude can be effectively suppressed, avoiding signal distortion and processing failure caused by an overly large dynamic range.

[0131] Through the specific method design of this embodiment, the photonic computing module not only achieves effective phase management of high-speed signals but also greatly improves the accuracy of signal processing through dynamic parameter adjustment and feedback optimization. This design significantly enhances the adaptability and stability of the photonic computing module in high-speed and large-data processing environments. At the same time, it provides an optical signal processing solution that can effectively replace the deficiencies of traditional electronic devices in multitasking. The implementation of this method lays a solid foundation for the development of future high-performance optical computing systems and also puts forward new ideas and directions for the further development of phase modulation technology.

[0132] Embodiment Five

[0133] To solve the problems of the accuracy and stability of phase modulation in the photonic computing module, this embodiment optimizes the configuration of the photonic multiplication cascaded suppression oscillator network. Specifically, this network improves the reliability of phase modulation and the stability of signal output by establishing an optical signal processing mechanism.

[0134] Specifically, this embodiment determines the relationship matrix M to describe the topological structure of the transistor network. The selection of the relationship matrix M affects the signal routing and the physical connection form of the oscillator network in the network design. Through the reasonable configuration of the M matrix, the connection characteristics between transistor nodes and the signal transmission path can be optimized. For different topological structures, the influence of the relationship matrix needs to be verified and fine-tuned through simulation and experiments.

[0135] Furthermore, this embodiment adopts a signal optimization algorithm for signal routing optimization. The objective calculation formula of the signal optimization algorithm is:

[0136] (4)

[0137] It should be understood that this optimization algorithm reduces energy loss and signal attenuation by minimizing the cosine distance of the phase difference between adjacent nodes. Among them, minΦ represents the optimal signal, i, j represent the nodes of adjacent transistors, and σ i and σ j are the phase offsets of adjacent transistor nodes respectively. Fine phase control and reduction of differences enable the network to achieve a high degree of phase synchronization. The algorithm also adjusts the phase of each node to minimize the overall phase offset of the network, thereby achieving efficient energy transmission. It can be understood that this algorithm aims to ensure the coherence and stability of the multi-node network and provides support for the frequency adjustment of subsequent oscillators.

[0138] In the oscillator configuration, this embodiment pays particular attention to the adjustment of frequency and feedback gain to minimize oscillation damping and maximize network stability. The adjustment of feedback gain is controlled by an algorithm, and then the appropriate operating frequency of the oscillator is determined. The optimized oscillator not only improves the signal amplification effect but also ensures that the system operates near the stable point, guaranteeing the phase modulation efficiency of the network.

[0139] Furthermore, the function of enabling the doubling signal path is to enhance the output signal by selecting channels with a gain higher than a preset threshold. The doubling signal path enhances those signal channels that perform well in terms of gain during transmission, so that the output signal can be enhanced without adding additional noise. This strategy allows the system to maintain a low noise level when processing high-gain signals, ensuring signal quality. For those paths that do not meet the gain requirements, feedback and hysteresis regulation are selectively used to reduce their impact on the overall signal processing efficiency.

[0140] Furthermore, the propagation delay parameter is comprehensively adjusted by feedback to maintain the coherent integration of the output signal. The propagation delay parameter is crucial for the synchronization of multi-node signals in the system. During the adjustment process, the actual propagation characteristics and potential delay changes of each signal path need to be considered. By precisely setting the delay parameter, the system can effectively compensate each signal path to ensure that the overall output signal is phase-consistent in both the time domain and the frequency domain.

[0141] Through the configuration optimization and phase regulation of this embodiment, the photonic computing module can not only improve its signal processing ability in complex networks but also exhibit enhanced stability in high-frequency and high-density data applications. This method combining optical network topology optimization and phase modulation lays a solid technical foundation for further improving the efficiency of photonic computing.

[0142] Example Six

[0143] To address the efficiency and reliability issues of non-volatile storage units in data processing, this embodiment further refines the data processing flow in the storage device. Specifically, by working in coordination with the controller, it ensures that data remains efficient, secure, and low-power throughout the storage process.

[0144] In this embodiment, when the error-corrected data stream enters the storage device, the encoder performs full-process bit scaling adjustment. The goal of bit scaling adjustment is to optimize the space utilization efficiency of data in the storage unit. The precision in operation directly affects the data compression effect and access speed. Therefore, the settings of the encoder parameters must be cautious and dynamically adjusted according to real-time storage requirements. This adjustment mechanism needs to be understood as in different storage scenarios, the corresponding bit scaling strategies should also change accordingly to adapt to the differentiated characteristics of the storage space.

[0145] Furthermore, after the data stream enters the storage buffer, the priority caching mechanism is used to optimize the access speed. The priority caching mechanism relies on caching algorithms, which ensure that frequently accessed data is processed quickly through judgment and scheduling, reducing access latency and improving the system response ability. It should be understood that the priority caching not only considers the data access frequency but also intelligently optimizes potential access patterns based on prediction algorithms, thereby enhancing the overall access efficiency.

[0146] In terms of energy management, this embodiment applies random access memory combined with dynamic refresh rate and power management strategies to reduce unnecessary energy consumption. The implementation of the power management strategy includes dynamically adjusting the refresh rate to reduce energy consumption according to actual usage requirements. It can be understood that this dynamic management not only extends the service life of the storage device but also provides an optimized energy efficiency performance under various operating conditions. Such a design can ensure that the device saves energy to the maximum extent without affecting performance.

[0147] Meanwhile, during the process of writing the data stream into the storage unit, the system monitors the integrity of the write and the working state of the storage unit in real time. By introducing a real-time monitoring mechanism, the system can capture any potential errors or abnormal states and quickly take appropriate corrective measures to ensure the high integrity of data writing. The implementation of storage unit state monitoring also includes dynamic detection of physical wear to help prevent potential hardware failures.

[0148] In addition, during the data storage process, a redundant storage strategy is introduced in the embodiments to ensure the security of data disaster backup. The redundancy strategy replicates critical data to distributed storage units, so that data will not be lost when a single storage unit fails. The implementation of this strategy relies on complex redundancy algorithms to ensure the reasonable allocation of data backups in space and time without increasing the storage cost.

[0149] Through this embodiment, the non-volatile storage unit not only achieves improved storage efficiency in the storage device, but also realizes higher reliability and energy efficiency. The multi-level optimization strategy in the embodiment ensures the security and integrity of the data storage process and is applicable to applications in various storage environments.

[0150] Embodiment Seven

[0151] To solve the efficiency and reliability problems of the storage device during high-speed data transmission, this embodiment optimizes the connection between the storage unit and the storage device interface through an optoelectronic hybrid integrated circuit, which not only improves the data transmission speed but also enhances the overall processing capacity of the system, as Figure 8 shown.

[0152] Specifically, in the interface design, the high-speed data transmission channel supports a transmission rate of up to hundreds of megabits per second. Considering the high requirements of current information systems for data transmission speed, through signal transmission technology and optimized line structures, a large data volume and low-latency transmission performance are achieved. It can be understood that at such a high transmission rate, it is necessary to control signal integrity and bit error rate. Therefore, anti-interference and signal strengthening measures need to be taken at the physical level.

[0153] Furthermore, this embodiment utilizes a programmable logic array (PLA) to dynamically optimize the coding scheme to improve data storage efficiency. The use of the programmable logic array allows the system to adjust the coding process according to the actual data characteristics, enabling the coding efficiency and storage performance to reach the optimal state. It can be understood that the flexibility of the programmable logic array (PLA) enables the storage device to dynamically adjust its coding strategy, thus better adapting to the changing characteristics of the data stream and avoiding resource waste that may be caused by static coding.

[0154] During the data writing process, the embodiments particularly emphasize the real-time monitoring of the integrity of photon signals. Through a feedback mechanism, the system can instantaneously adjust the storage voltage and pulse width. The purpose of this design is to ensure that photon signals do not distort during the data writing process. It should be understood that the measures for adjusting the pulse width and voltage of optical signals are to adapt to the dynamic changes of the data load, thereby ensuring the high reliability of the storage unit under different working conditions.

[0155] Furthermore, this embodiment also improves the mutual conversion link between optical signals and electrical signals to ensure the stability and real-time performance of data during cross-media transmission. The real-time performance of signal conversion affects high-speed storage systems because any slight delay will cause a large time deviation under a large data flow. By using optoelectronic conversion technology, it is ensured that there is no information loss during signal conversion. It can be understood that this conversion technology enhances the anti-interference ability of the system and improves the error tolerance rate of data transmission.

[0156] It is worth noting that photon caching not only solves the speed bottleneck in traditional electronic caching but also provides a more effective solution in latency management. Through photon caching, the system can achieve dynamic management and optimization of high-speed data, especially performing well in application scenarios that require quick responses. Photon caching has superior speed advantages and lower energy consumption characteristics, enabling the system to not only accommodate large data flows but also operate with lower energy consumption when processing data.

[0157] Through the method of this embodiment, the efficient cooperation between the optoelectronic hybrid integrated circuit and the storage device interface effectively improves the performance of data processing. The system has not only improved in terms of data transmission speed and storage efficiency but also reached a new height in overall energy efficiency and processing stability. At the same time, this embodiment provides a feasible solution for realizing the diversification and high efficiency of future storage technologies.

[0158] Embodiment Eight

[0159] To solve the efficiency and accuracy problems in the data extraction and decoding processes, this embodiment conducts a refined design on the data extraction and decoding module and uses the photon inverse transformation method to parse and output the target data. Combining optical technology and algorithms provides an efficient and accurate solution for data processing.

[0160] Specifically, a request mode monitoring mechanism is introduced in this embodiment to optimize the decoding priority. By monitoring the request mode of the target data, the system can identify high-frequency and low-frequency requests and dynamically adjust the decoding priority order according to the historical request frequency. This mechanism ensures that high-priority data can be decoded and output in a timely manner, thereby reducing decoding latency and improving the response speed of the system. It should be understood that this priority configuration can effectively relieve the system load and improve the processing efficiency in a large data environment.

[0161] Furthermore, before actual decoding, the present embodiment performs pre-decoding on the target data to extract position information and structured identifiers. The pre-decoding process aims to obtain key context information through rapid analysis of the data structure, which can assist the subsequent complete decoding process in quickly locating and processing specific data blocks. It can be understood that the accuracy of this step directly affects the overall decoding quality and efficiency, and the successful implementation of pre-decoding depends on the completeness of the structured identifier library and the acquisition of accurate information.

[0162] Furthermore, to improve the decoding accuracy, the present embodiment also adopts a prediction model to enhance the decoding process. The prediction model continuously optimizes the decoding path through dynamic learning of historical data sets to ensure that the path used for each data decoding is the optimal path. It can not only effectively improve the decoding accuracy but also quickly adjust the decoding strategy when the data content changes to maintain the stability of the system. It should be understood that the improvement of the prediction model not only depends on the learning of historical data but also needs to be adjusted and updated in combination with the latest data patterns to cope with the constantly changing data environment.

[0163] During the decoding process, real-time signal gain adjustment and phase shift control can ensure the integrity and accuracy of data decoding through the gain and phase shift of optical signals. Optical signals may be affected by various environmental factors during transmission, which are likely to cause signal degradation or bit errors. This real-time adjustment mechanism can immediately respond to signal changes, correct potential deviations, and support stable data parsing and decoding in a complex signal environment.

[0164] Furthermore, a photon inverse transformation algorithm is adopted to achieve efficient data decoding and transmission. The photon inverse transformation algorithm is responsible for accurately reconstructing the decoded optical signal into an available data stream and performing optimized output. The photon inverse transformation algorithm lies in the precise reconstruction of complex waveforms, which not only ensures the integrity of the data stream but also reduces the decoding time and resource consumption through efficient algorithm design. It can be understood that this reconstruction and optimization technology enables the decoded data stream to smoothly enter the subsequent processing stage, providing an efficient solution for system data processing.

[0165] Through this embodiment, the application of the photon inverse transformation method not only improves the decoding efficiency and data processing speed but also ensures the integrity of information during the data conversion process, provides support for high-density data processing and transmission applications, and can operate stably under different data loads and complex environments to achieve more efficient data access.

[0166] Embodiment Nine

[0167] To address the issues of insufficient data processing efficiency and parallel processing capabilities in storage devices, this embodiment proposes a data processing system for storage devices, realizing a highly integrated photonic computing and data storage solution. The processing system includes an input interface module, a multimode interference splitter, a photonic computing module, an error correction code module, a data storage module, and a data extraction and decoding module, forming a coordinated and integrated system architecture.

[0168] Specifically, the input interface module, as the front-end processing unit of the system, is responsible for receiving external data streams and performing preliminary preprocessing on them. The preprocessing process not only includes the standardization of data formats but also involves the preliminary suppression of noise signals to improve the efficiency and accuracy of subsequent signal processing modules. It should be understood that the effectiveness of the preprocessing directly determines the success of the subsequent processing flow, and its primary task is to ensure that the input signal has a good signal-to-noise ratio and adaptability.

[0169] Furthermore, the data stream after preliminary preprocessing is received by the multimode interference splitter, which divides the data stream into multiple signal channels. This splitting mechanism utilizes the principle of multimode interference in optical technology, enabling the data stream to be decomposed into different paths and analyzed and processed through parallel signal processing channels. The application of this technology aims to enhance the system capacity and speed through multi-channel parallel processing, especially suitable for complex data environments that require simultaneous processing of multiple signals.

[0170] Furthermore, the photonic computing module contains a photonic transistor array and a phase modulation sub-module. These components together provide powerful parallel computing capabilities and efficient data processing paths. The photonic transistor array can use photons as the computing carriers, thus achieving high-speed computing with low energy consumption. At the same time, the optical phase modulation sub-module provides precise phase control to ensure synchronization between each signal channel and correct signal calculation. It can be understood that the photonic computing architecture of this embodiment will effectively enhance the computing power of the entire system and meet the requirements of higher data throughput.

[0171] Furthermore, to ensure accurate and consistent data, the photonic computing module is coupled with an error correction code module. The error correction code module uses a multi-step cyclic correction mechanism for data correction and can quickly detect and correct potential data errors. Errors introduced during data propagation, if not controlled, will lead to serious data distortion and information loss. Through the multi-step cyclic correction mechanism, the correction process can be dynamically adjusted to adapt to different error types and complexities.

[0172] Furthermore, as the terminal storage unit of the system, the data storage module is connected to the main storage controller to achieve data storage and retrieval. The data storage module not only has efficient data storage capabilities, but also adopts data compression algorithms to improve the storage efficiency and space utilization rate of the system. It can be understood that the design of the data storage module provides a solid foundation for large-scale data preservation and high-frequency data access, and also complements the energy efficiency strategy of the device.

[0173] To meet the parsing requirements of user requests, the data extraction and decoding module operates relying on photon inverse transformation decoding technology. The data extraction and decoding module processes user requests and converts the stored data into available output to meet immediate application requirements. The adoption of photon inverse transformation decoding not only improves the decoding speed but also has significant advantages in maintaining data quality.

[0174] Through the optimization measures and system integration in this embodiment, the data processing system provides an efficient and reliable solution for high-performance storage. The application of photon technology not only enhances the computing and transmission capabilities of the system but also lays a foundation for the development of future storage technologies, and can demonstrate superior performance in different application scenarios.

[0175] Embodiment Ten

[0176] To solve the problem of insufficient efficiency in modern data processing, this embodiment provides a computer-readable storage medium storing computer instructions for executing a data processing method. These instructions are designed to optimize the performance and accuracy of the processor when executing complex tasks, covering all key features in the data processing method.

[0177] Specifically, when these computer instructions are executed by the processor, first, the system receives and preliminarily preprocesses the data stream through the input interface module. This preprocessing step ensures that the input data is in the best state to reduce the workload in subsequent processing stages. Subsequently, the processor uses the multi-mode interference division technology to divide the preprocessed data into multiple parallel signal channels. In this way, through the parallel processing strategy, the system can process multiple signals simultaneously, thereby improving the overall data processing efficiency.

[0178] Furthermore, the photon computing module is responsible for performing efficient parallel data processing operations to ensure efficient resource utilization. The processor adjusts the state of the photon transistor according to the instructions to achieve fast and low-power consumption operations, and uses phase modulation to improve the accuracy and synchronization of signal processing.

[0179] Furthermore, the instructions also coordinate the error correction code module for data correction, adopting a multi-step cyclic correction mechanism to ensure the security and integrity of data transmission. Such an error correction mechanism can automatically detect and correct errors that may occur during the transmission process, improving the reliability of the data.

[0180] Furthermore, data extraction and user request response are performed through photon inverse transformation decoding technology. This technology is used to convert photon signals into processable electronic data streams, enabling the system to exhibit excellent decoding speed in different application scenarios.

[0181] The advantage of this embodiment is that, through computer instructions in the storage medium, the efficiency of the computer processor in performing data processing tasks is enhanced, ensuring the requirements for various complex data environments are met.

[0182] The above embodiments have further elaborated on the purpose, technical solutions, and beneficial effects of the present invention. It should be understood that the above are only specific embodiments of the present invention and are not used to limit the protection scope of the present invention. Any modifications, equivalent replacements, improvements, etc. made on the basis of the technical solutions of the present invention should be included within the protection scope of the present invention.

Claims

1. A data processing method for a storage device, characterized in that: The method comprises the following steps: Receive an input data stream, the data stream comprising a plurality of data units, and perform noise filtering and signal standardization through an initial preprocessing module to improve the integrity of the data stream; Splitting the pre-processed data stream by a multi-mode interferometric splitter, wherein the splitter performs spatial and frequency domain decomposition of the signal according to the length and wavelength difference of the optical path for subsequent parallel processing; The split data stream is introduced into a photon computing module, and the photon computing module uses a photon transistor array to perform logic operations and phase modulation calculations for synchronous processing of multiple data units to improve computing efficiency; Using an error correction code module to perform quality detection and error correction on the data stream output by the photon computing module, the error correction process includes performing data correction based on a function of dynamically adjusting parameters to correct errors introduced by parallel processing; storing the error-corrected data stream in the storage device, the storage device being combined with a high-density controller to ensure fast access characteristics of the data; In response to a user data request, the target data is retrieved from the storage device through a data extraction and decoding module, and the data extraction and decoding module uses an inverse photon transformation method to parse and output the target data to reduce latency; The steps of performing noise filtering and signal standardization through the initial preprocessing module include: Representing the data stream as a multi-dimensional matrix, wherein multiple dimensions represent signals in multiple different frequency bands; Compute the energy distribution of a signal and apply a self-adjusting filter bank , the calculation formula is: , where x represents the input signal, F i (x) is the output of a specific filter in the self-adjusting filter bank, i represents the number of the filter, and α and β are parameters obtained by iterative optimization, which are used to minimize the influence of noise signals in the frequency band; Perform point-by-point filtering and apply a recursive algorithm to update the filter parameters to enhance the fidelity of the target signal; The signal obtained after the filter processing is amplitude-adjusted by the standardization unit to ensure that the signal amplitude is within the defined range; Frequency domain analysis is performed on the normalized signal using Fast Fourier Transform to filter out interfering frequencies.

2. The data processing method of the storage device according to claim 1, characterized in that: The splitting of the pre-processed data stream by a multi-mode interference splitter includes the splitter performing a beam splitting operation based on coherent optics, the steps comprising: generating an initial light beam by using a tunable laser, wherein the initial light beam covers all signal frequencies; The initial light beam is introduced into the optical network formed by the liquid crystal adjustable phase plane, and the operation mechanism of the initial light beam is expressed as: , where γ n and θ n is the phase offset value dynamically adjusted by a preset algorithm, Δφ represents the phase change, N represents the maximum number of harmonics, n represents the harmonic order, and t represents the time variable; The initial light beam is controlled by the phase plane to obtain a phase-controlled light beam, and the phase-controlled light beam is aligned with the phase of the signal channel through a spatial light modulator to obtain a modulated light beam; The modulated light beam is spectrally dispersed through a dispersion delay line to achieve spatial encoding of the signal; The horizontal and vertical polarization states of the transmitted signal are separated using a ratiometric detection device.

3. The data processing method of the storage device according to claim 1, characterized in that: The steps of the photon computing module using the photon transistor array to perform logic operations and phase modulation calculations include: A phase-locked loop is used to detect the phase error of the input optical signal; Calling the phase modulation control function , where δ, ε and λ represent the parameters adjusted according to feedback, T(σ) represents the phase modulation control function, and σ represents the input phase, which is used to adjust the system response and signal gain; performing phase error compensation to inject an error correction signal into the modulation frequency response of the photonic transistor; After the input optical signal passes through the photon transistor array, a target phase transformation is used to make the light beam phase reach the system standard; After the calculation, the input optical signal is once again normalized in full signal amplitude, so as to stabilize the input optical signal within a limited dynamic range.

4. The data processing method of the storage device according to claim 3, characterized in that: The phase modulation mechanism in the photonic computing module includes a photon multiplication cascade suppressed oscillator network, which is configured by the following steps: Determine the relationship matrix M used to describe the topological structure of the transistor network; A signal optimization algorithm is used to optimize signal routing. The expression of the signal optimization algorithm is: , where minΦ represents the optimal signal, i,j represents the nodes of adjacent transistors, and σ i and σ j represent the phase shift at adjacent transistor nodes respectively; Adjust the oscillator frequency and feedback gain to minimize oscillation damping and maximize network stability; Enable the multiplication signal path and select the target channel with a gain higher than the preset threshold for output signal enhancement; Integrated feedback adjusts propagation delay parameters to maintain coherent integration of the output signals.

5. The data processing method of the storage device according to claim 1, characterized in that: The storage device includes a non-volatile storage unit, and the storage unit cooperates with a controller to implement the following steps: Receiving the error-corrected data stream, and performing full-process bit scaling adjustment through an encoder to adapt to storage space; The data stream enters the storage buffer and uses a priority cache mechanism to optimize access speed; Applying random access memory combined with dynamic refresh rate and power management strategies to optimize energy consumption; When the data stream is written, monitoring the writing completion and the status of the storage unit; During the data stream storage process, a redundant storage strategy is introduced to ensure data disaster recovery security.

6. The data processing method of the storage device according to claim 5, characterized in that: The storage unit is connected to the interface of the storage device via an optoelectronic hybrid integrated circuit, and the interface includes: High-speed data transmission channel, supporting transmission rates up to hundreds of megabits per second; Dynamic optimization of encoding schemes using programmable logic arrays to improve data storage efficiency; During the writing process, the integrity of the photon signal is monitored in real time, and the storage voltage and pulse width are adjusted instantly using the feedback mechanism; Perform mutual conversion between optical signals and electrical signals for stable and real-time data transmission; Photonic caching technology is used for cache and latency management.

7. The data processing method of the storage device according to claim 1, characterized in that: The data extraction and decoding module uses the photon inverse transformation method to parse and output the target data, including the following steps: Monitoring the request pattern of the target data and optimizing the decoding priority using the historical request frequency; Pre-decoding the requested target data to extract location information and structured identifiers; Adopting a prediction model to enhance decoding accuracy, the prediction model obtains the best decoding path by dynamically learning historical data sets; Real-time adjustment of signal gain and phase offset during decoding to ensure complete and accurate decoding; The decoded optical signal is reconstructed into a usable data stream using an inverse photon transform algorithm for optimized output.

8. A data processing system for a storage device, used to implement the data processing method for a storage device according to any one of claims 1 to 7, characterized in that: The system comprises: An input interface module configured to receive an input data stream and perform preliminary preprocessing; A multi-mode interference divider, used for dividing the pre-processed data stream into a plurality of signal channels; a photonic computing module, including a photonic transistor array and a phase modulation submodule, configured to perform parallel data processing; An error correction code module, coupled to the photon computing module, performs data correction using a multi-step cyclic correction mechanism; A data storage module connected to the storage controller to implement data storage; The data extraction and decoding module is used to respond to user requests and perform photon inverse transform decoding.

9. A computer-readable storage medium storing computer instructions, characterized in that: When the instructions are executed, the processor of the computer performs the data processing method according to any one of claims 1 to 7.

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