Data processing method and system of storage device and storage medium

Abstract

The invention relates to the technical field of storage data processing, in particular to a data processing method and system of a storage device and a storage medium. The method comprises the following steps that an input data stream is received, the data stream comprises a plurality of data units, and noise filtering and signal standardization are executed through an initial preprocessing module so that the integrity of the data stream can be improved; and splitting the preprocessed data stream through a multi-mode interference divider, and performing spatial and frequency domain decomposition of the signal by the divider according to the length and wavelength difference of the optical path for subsequent parallel processing. According to the invention, photon calculation and storage technologies are combined, and a traditional electronic calculation part is replaced by an efficient photon signal processing module. Specifically, a photon calculation module is utilized, the speed of data processing is increased, and particularly, performance improvement by several times can be provided when large-scale and high-concurrency data requests are processed.

Description

Technical Field

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

[0002] The data processing of storage devices is gradually unable to meet the requirements of high efficiency, high throughput and low latency in the big data era due to the physical limitations and architectural design of storage devices. 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 microelectronics design. In addition, with the exponential growth of the amount of information, storage devices gradually expose bottlenecks in data access speed, parallel processing capabilities and energy consumption. Therefore, the technical problem raised 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 of 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: In one aspect, a method for processing data of a storage device is provided, the method comprising 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, 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.

[0005] Furthermore, the step of performing noise filtering and signal standardization through the initial preprocessing module includes: 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, α and β are parameters obtained through iterative optimization, which are used to minimize the impact 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.

[0006] Furthermore, the splitting of the pre-processed data stream by the 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; 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.

[0007] Furthermore, 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, 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.

[0008] Furthermore, 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 for describing 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 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.

[0009] Furthermore, the storage device includes a non-volatile storage unit, and the storage unit cooperates with the 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.

[0010] Furthermore, the storage unit is connected to the interface of the storage device through 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; Monitor photon signal integrity in real time during the writing process, and use feedback mechanisms to instantly adjust storage voltage and pulse width; 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.

[0011] Furthermore, 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.

[0012] In another aspect, a data processing system of a storage device is provided, the system comprising: 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.

[0013] On the other hand, a computer-readable storage medium is provided, which stores computer instructions. When the instructions are executed, the processor of the computer executes the data processing method as described above.

[0014] The beneficial effects of the present invention are: The present invention combines photon computing and storage technologies to replace the traditional electronic computing part with an efficient photon signal processing module. Specifically, the use of the photon computing module speeds up the data processing speed, especially when processing large-scale, high-concurrency data requests, and can provide several times the performance improvement. At the same time, the data stream splitting achieved by the multi-mode interference divider can decompose the data space and frequency domain 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 to detect and correct errors, thereby improving the accuracy of data processing. The large-scale use of storage units ensures the fast access capability of data, so that both storage and retrieval operations can be performed with lower latency and higher reliability.

[0015] The above description is only an overview of the technical solution of the present invention. In order to more clearly understand the technical means of the present invention and implement it according to the contents of the specification, the following is a detailed description of the preferred embodiments of the present invention in conjunction with the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 is a flow chart of a data processing method in one embodiment of the present invention; Figure 2 A schematic diagram of noise elimination of a self-adjusting filter bank in one embodiment of the present invention; Figure 3 It is a schematic diagram of the principle of a multi-mode interference divider in one embodiment of the present invention; Figure 4 An optical path diagram of a multi-mode interference divider in one embodiment of the present invention; Figure 5 A schematic diagram of a phase modulation diagram of a photon transistor array in one embodiment of the present invention; Figure 6 A schematic diagram of a multi-dimensional matrix for data stream preprocessing in one embodiment of the present invention; Figure 7 This is a diagram showing the effect of noise suppression in data stream preprocessing according to an embodiment of the present invention; Figure 8 Schematic diagram of optoelectronic hybrid integration in one embodiment of the present invention. DETAILED DESCRIPTION

[0017] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all of the embodiments.

[0018] In the embodiments of the present invention, words such as "exemplary" or "for example" are used to indicate examples, illustrations or explanations. Any embodiment or design described as "exemplary" or "for example" in the embodiments of the present invention should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "exemplary" or "for example" is intended to present related concepts in a specific way.

[0019] The present invention provides the following preferred embodiments: Embodiment 1 In order to solve the shortcomings of traditional storage devices in data processing speed and accuracy, this embodiment proposes a data processing method for a storage device to improve processing efficiency and data integrity. Figure 1 As shown, the method comprises the following steps: S100, receiving an input data stream, the data stream including a plurality of data units, and performing noise filtering and signal standardization through an initial preprocessing module to improve the integrity of the data stream.

[0020] S200, splitting the pre-processed data stream by a multi-mode interference 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.

[0021] S300, the split data stream is imported into the photon computing module, and the photon computing module uses the photon transistor array to perform logic operations and phase modulation calculations for synchronous processing of multiple data units to improve computing efficiency.

[0022] S400, using an error correction code module to perform quality detection and error correction on a data stream output by the photon computing module, wherein the error correction process includes performing data correction based on a function of dynamically adjusting parameters to correct errors introduced by parallel processing.

[0023] S500: storing the error-corrected data stream in a storage device, wherein the storage device is combined with a high-density controller to ensure fast access to data.

[0024] S600, responding to a user data request, retrieving target data from a storage device through a data extraction and decoding module, and the data extraction and decoding module parses and outputs the target data using a photon inverse transformation method to reduce latency.

[0025] In this embodiment, after the input data stream enters the processing system, it first passes through the initial preprocessing module. In the preprocessing module, adaptive filtering technology is used to filter out noise. The selection of filtering technology can be fine-tuned according to the characteristics and quality requirements of the input data. The specific mechanism mostly uses a dynamically adjusted adaptive filter group to deal with signal interference in different frequency bands, such as Figure 2 As shown. After noise filtering is completed, the data stream is normalized. During this process, the signal amplitude is adjusted to ensure that the data remains within a specific range during subsequent processing, so as to facilitate processing by the computing module. It should be understood that the optimization and selection of the initial preprocessing module affects the efficiency of the entire data processing chain.

[0026] Furthermore, the pre-processed data is split by a multi-mode interference divider. Figure 3 and Figure 4 As shown. In addition, the splitting is based on the length of the optical path and the difference in wavelength, so that the signal is decomposed in the space and frequency domain in an optimized form. It can be understood that the selection and adjustment of the optical path largely determines the efficiency of subsequent parallel processing. The multi-mode interference divider is designed to take into account different optical path length ratios to ensure minimum phase error and maximum signal strength when the signal passes through.

[0027] The next process involves the use of photonic computing modules, which perform synchronized logic operations and phase modulation calculations through photonic transistor arrays, such as Figure 5 As shown. Photonic transistors are usually selected with stable performance and fast response to cope with the complex operation requirements of parallel data units. In photonic computing, phase modulation calculation is a complex task, which is based on the detection and adjustment of 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 transistor is finely adjusted to process multiple input signals while ensuring the controllability and efficiency of synchronous computing.

[0028] After the photon computing module completes the operation, the quality detection and error correction of the data stream use the error correction code module to perform data correction, which uses a function based on dynamically adjusted parameters for real-time correction to solve 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 inspection to ensure the reliability of the output data, and on the other hand, the high accuracy of the data is ensured by continuous adjustment and optimization of the function parameters. The design of the error correction mechanism is real-time and adaptive, which gives the module a strong fault recovery capability.

[0029] Furthermore, the data after error correction 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 coordinated working relationship of the controller affect the fast access time. The high-density controller optimizes the data writing and reading paths and reasonably allocates storage resources, thereby shortening the data access time.

[0030] Furthermore, in the process of responding to user data requests, the data extraction and decoding module uses the photon inverse transformation method to parse and output the target data to reduce system latency. In this link, the application of the photon inverse transformation algorithm improves the accuracy of decoding and the efficiency of data output, ensuring that users can obtain high-accuracy data with a lower waiting time after issuing a request.

[0031] Through this embodiment, performance improvement and reliability enhancement of each stage of data processing are achieved, especially in the integrated optimization of noise filtering, signal standardization, photon calculation and error correction. It not only improves the overall processing speed of the system, but also improves the accuracy of data processing and the effectiveness of access, providing a practical path for efficient data processing in complex computing environments.

[0032] Embodiment 2 In order to solve 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 spectrum complexity of the signal often cause troubles for stable data processing. For this reason, in this embodiment, the input data stream is represented as a multidimensional matrix, such as Figure 6 As shown, each dimension corresponds to signals in different frequency bands, so as to perform detailed analysis and processing of signal energy.

[0033] Furthermore, in order to improve the efficiency and accuracy of noise filtering, a self-adjusting filter group is used in this embodiment. Specifically, by calculating the energy distribution of the input signal, the signal characteristics of each frequency band are identified, and the multivariate filter group matching it is activated. The specific implementation of filtering is based on the following calculation formula: (1) It should be noted that the meaning of each parameter in formula (1) is: X represents the input signal. In practical applications, x represents the signal value that needs to be filtered. In other words, x can represent the signal amplitude or power at a point in time.

[0034] F i (x) is the output of a specific filter in the self-adjusting filter bank, which represents the result of 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 together on the input signal x, different filters focus on filtering signal noise in different frequency bands.

[0035] α is used to control the sensitivity and response curve of the filter. It is obtained through iterative optimization and affects the gain change of the filter to the input signal. When the input signal amplitude changes, α determines the rate of change of the filter output.

[0036] β is used to adjust the signal offset to optimize the performance of the filter so that the filter can be more effectively focused on the target frequency region.

[0037] e is the natural exponential constant, approximately 2.718, used in the calculation of the exponential function. The exponential function form helps describe the nonlinear adjustment process of the filter response to signal strength, especially when the input signal deviates from the set range.

[0038] The overall purpose of the calculation formula (1) of this embodiment is to provide a nonlinear filtering mechanism. By adjusting the α and β parameters, the filter can be adapted to different signal conditions and minimize the influence of noise signals in a specific frequency band.

[0039] Formula (1) filters the signal based on multiple frequency bands, so that each frequency band is processed by an adaptive filter. This filter can dynamically respond to changes in the signal and 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, such as Figure 7 It is applicable to complex and diverse signal processing environments, requiring the filter to be able to autonomously adapt to the changing input signal.

[0040] Furthermore, in the process of performing point-by-point filtering, this embodiment introduces a recursive algorithm to update the filter parameters so that the filter can achieve the best effect in improving the fidelity of the target signal. The recursive algorithm relies on the information of previous input and output to adjust the filter parameters in real time.

[0041] The filtered signal enters the standardization unit for amplitude adjustment. It should be emphasized that standardization is not only to ensure that the signal amplitude is within the set range, but also to provide a unified standard signal input for subsequent signal detection and processing. The standardization 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 risk of distortion introduced by nonlinear gain operations.

[0042] After the signal is standardized, the Fast Fourier Transform (FFT) is applied to perform frequency domain analysis on the standardized signal to further filter out the interfering frequencies outside the effective spectrum of the signal. This frequency domain analysis relies on FFT to quickly decompose the spectral characteristics of the signal, allowing the system to identify and remove noise components in the frequency domain in real time. This processing ensures that the signal exists in the purest and most efficient state before entering the subsequent calculation module.

[0043] The benefit of this embodiment is that by optimizing the signal preprocessing steps, combined with the representation of multidimensional matrices, self-adjusting filter banks, and fast 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, multi-stage signal preprocessing method effectively solves the problem of data noise filtering and standardization under different background signal conditions, and can significantly improve the scope of application and stability of the system when applied in practice.

[0044] Embodiment 3 In order to accurately and orderly realize 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 combining phase modulation with spatial light modulation to improve the accuracy and efficiency of data processing.

[0045] Specifically, a tunable laser is used to generate an initial beam of light, which is set to cover all signal frequencies. This light source selection can ensure that a wide-band optical carrier is provided for the entire system in the initial stage. This initial beam is further transmitted to the optical network formed by the liquid crystal adjustable phase plane to achieve specific optical regulation. It should be understood that the role of the liquid crystal adjustable phase plane in this network is to finely adjust the phase of the light beam, and the process is represented by a calculation formula: (2) It can be understood that the calculation in formula (2) means dynamically adjusting the phase offset value 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 meaning of each parameter is: Δφ represents the amount of phase change and is used to describe the result of phase adjustment, which depends on the superposition of multiple harmonic signals.

[0046] N represents the maximum number of harmonics, defines the number of harmonics used in phase modulation, and is used to describe more complex waveforms. n represents the specific nth-order harmonic.

[0047] γ nRepresents the amplitude coefficient corresponding to the nth order harmonic. It is an adjustable parameter that determines the contribution of each harmonic. n Adjusts the contribution of each harmonic to the overall phase change.

[0048] t represents the time variable, which is the parameter of the signal change over time, usually a continuously changing quantity, so that the phase produces continuous modulation in time.

[0049] Furthermore, the initial light beam is converted into a phase-controlled light beam after being controlled by the phase plane. In this stage, the signal channel is phase-aligned through the spatial light modulator, 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, and to form interactive feedback with the liquid crystal phase plane to achieve phase synchronization.

[0050] After the modulated beam is formed, the spectrum is dispersed through the dispersion delay line, and the spatial encoding of the signal is maintained. The application of the dispersion delay line ensures the complete diffusion of the signal in the spectrum space, so that each signal can be accurately identified and accessed in subsequent processing. It should be understood that this spectrum dispersion is not a simple frequency division, but a distance encoding of the signal through structured dispersion.

[0051] Furthermore, the modulated light beam obtained based on the above operation uses a ratio detection device to separate the horizontal and vertical polarization state transmission signals. The configuration of the ratio detection device lies in its highly sensitive polarization separation capability, which can reliably separate polarization signals on a micro scale. 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.

[0052] 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, showing a new beam splitting mode with multi-layer optimization and precise control. The most significant benefit is that through the close combination of phase control and spatial light modulation, efficient data separation and processing are achieved, and the overall stability and adaptability of the system are improved. Not only does it enhance the device's compatibility with diverse input signals, but it also improves the signal processing efficiency in complex optical environments.

[0053] Embodiment 4 In the photonic computing module, in order to solve 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, which is specifically used to detect the phase error of the input optical signal. The phase-locked loop not only improves the ability to control the initial phase of the input signal, but also provides a reliable error feedback source for subsequent phase modulation.

[0054] In this embodiment, the phase modulation control process uses an optimized control function, and its calculation formula is: (3) In the calculation formula (3), δ, ε, and λ are parameters based on system feedback adjustment. It should be understood that σ, as a representative of the input phase, can better respond to system requirements and optimize signal gain through adjustment of this function. The square term and sine term in the function provide means for secondary and harmonic adjustment respectively, and the λ term is used to set the reference phase level to ensure stable operation.

[0055] Furthermore, the phase error compensation part accurately injects the detected error signal into the system through the modulation frequency response of the photon transistor. This process can quickly correct the phase error by controlling the response characteristics of the modulation frequency. The configuration of the photon transistor ensures the reliability of high-frequency modulation, and the selection of its modulation frequency response depends on the specific signal frequency bandwidth and dynamic range requirements.

[0056] Furthermore, the input optical signal after passing through the photonic transistor array will perform a target phase shift so that the phase of the signal beam meets the standard set by the system. The "target phase shift" here refers to the phase standardization of the signal through a series of preset phase shifts and compensations. In this process, the photonic transistor is not only a simple signal channel, but also acts as a dynamic regulator of the signal phase to ensure that the output signal meets the needs of specific application scenarios.

[0057] Furthermore, after the calculated signal, this embodiment also considers the signal amplitude standardization processing. Finally, the standardization of the full signal amplitude ensures that the system operates stably within a limited dynamic range. Through this embodiment, the excessive fluctuation of the signal amplitude can be effectively suppressed, avoiding signal distortion and processing failure caused by an excessively large dynamic range.

[0058] Through the specific method design of this embodiment, the photonic computing module not only realizes 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 greatly improves the adaptability and stability of the photonic computing module in high-speed, big data processing environments, and at the same time provides an optical signal processing solution that can effectively replace the deficiencies of traditional electronic devices in multi-tasking processing. The implementation of this method has laid a solid foundation for the development of future high-performance optical computing systems, and has also proposed new ideas and directions for the further development of phase modulation technology.

[0059] Embodiment 5 In order to solve the accuracy and stability problems of phase modulation in the photon computing module, this embodiment optimizes the configuration of the photon multiplication cascade suppression oscillator network. Specifically, the network improves the reliability of phase modulation and the stability of signal output by establishing an optical signal processing mechanism.

[0060] Specifically, this embodiment determines a relationship matrix M to describe the topology 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 and signal transmission paths between transistor nodes can be optimized. For different topologies, the influence of the relationship matrix needs to be verified and fine-tuned through simulation and experiments.

[0061] Furthermore, this embodiment uses a signal optimization algorithm to optimize signal routing. The target calculation formula of the signal optimization algorithm is: (4) 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. Where minΦ represents the optimal signal, i,j represents the nodes of adjacent transistors, and σ i and σ j The fine phase control and reduction of differences enable the network to achieve a high degree of phase synchronization, respectively, for the phase offset of adjacent transistor nodes. The algorithm also achieves efficient energy transmission by adjusting the phase of each node to minimize the overall phase offset of the network. It can be understood that this algorithm is designed to ensure the coherence and stability of the multi-node network, providing support for the subsequent frequency adjustment of the oscillator.

[0062] In the oscillator configuration, the embodiment pays special 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 to determine the appropriate oscillator operating frequency. The optimized oscillator not only improves the signal amplification effect, but also ensures that the system operates near the stable point, so that the phase modulation efficiency of the network is guaranteed.

[0063] Furthermore, the function of enabling the multiplication signal path is to enhance the output signal by selecting channels with gains above a preset threshold. The multiplication signal path strengthens those signal channels with excellent gain performance 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 are selectively adjusted to reduce their impact on the overall signal processing efficiency.

[0064] Furthermore, the integrated feedback adjusts the propagation delay parameters to maintain the coherent integration of the output signal. The propagation delay parameters are used for the synchronization of multi-node signals in the system, and the adjustment process needs to consider the actual propagation characteristics and potential delay changes of each signal path. Through accurate delay parameter setting, the system can effectively compensate for each signal path to ensure that the overall output signal is phase consistent in the time domain and frequency domain.

[0065] Through the configuration optimization and phase control of this embodiment, the photon computing module can not only improve its signal processing capabilities in complex networks, but also show enhanced stability in high-frequency and high-density data applications. This method of combining optical network topology optimization and phase modulation lays a solid technical foundation for further improving the efficiency of photon computing.

[0066] Embodiment 6 In order to solve 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 is ensured that data remains efficient, safe and energy-efficient throughout the entire storage process.

[0067] In this embodiment, the encoder will perform full-process bit scaling adjustment on the error-corrected data stream when it enters the storage device. The goal of bit scaling adjustment is to optimize the space utilization efficiency of data in the storage unit. The accuracy in the operation directly affects the compression effect and access speed of the data, so the setting of the encoder parameters must be cautious and dynamically adjusted according to real-time storage requirements. This adjustment mechanism needs to be understood as that in different storage scenarios, the corresponding bit scaling strategy should also change accordingly to adapt to the differentiated characteristics of the storage space.

[0068] Furthermore, after the data stream enters the storage buffer, the priority cache mechanism optimizes the access speed. The priority cache mechanism relies on the cache algorithm to ensure that high-frequency access data is processed quickly through judgment and scheduling, reducing access latency and improving system responsiveness. It should be understood that priority caching not only considers the frequency of data access, but also intelligently optimizes potential access patterns based on prediction algorithms, thereby improving access efficiency overall.

[0069] In terms of energy management, this embodiment uses random access memory combined with dynamic refresh rate and power management strategy 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 can not only extend the service life of the storage device, but also provide an optimized energy efficiency performance under various operating conditions. Such a design can ensure that the device can save energy to the maximum extent without affecting performance.

[0070] At the same time, during the process of writing data streams to storage units, the system monitors the integrity of the writes and the working status of the storage units in real time. By introducing a real-time monitoring mechanism, the system can capture any potential errors or abnormal conditions and quickly take appropriate corrective measures to ensure the high integrity of data writes. The implementation of storage unit status monitoring also includes dynamic detection of physical wear to help prevent potential hardware failures.

[0071] In addition, in the process of data storage, a redundant storage strategy is introduced in the embodiment to ensure the security of data disaster backup. The redundant strategy copies key data to distributed storage units so that when a single storage unit fails, the data will not be lost. The implementation of this strategy relies on a complex redundancy algorithm to ensure the reasonable allocation of data backup in space and time without increasing storage costs.

[0072] Through this embodiment, the non-volatile storage unit in the storage device not only obtains improved storage efficiency, but also achieves 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 suitable for applications in various storage environments.

[0073] Embodiment 7 In order to solve the efficiency and reliability problems of storage devices during high-speed data transmission, this embodiment realizes the optimized connection between the storage unit and the storage device interface through the optoelectronic hybrid integrated circuit, which not only improves the data transmission speed, but also enhances the overall processing capability of the system. Figure 8 shown.

[0074] 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 the current information system for data transmission speed, the transmission performance of large data volume and low latency is achieved through signal transmission technology and optimized line structure. It is understandable that at such a high transmission rate, the signal integrity and bit error rate need to be controlled, so anti-interference and signal reinforcement measures are required at the physical level.

[0075] Furthermore, this embodiment uses a programmable logic array (PLA) to dynamically optimize the encoding scheme to improve data storage efficiency. The use of a programmable logic array allows the system to adjust the encoding process according to actual data characteristics so that the encoding efficiency and storage performance are optimal. It can be understood that the flexibility of the programmable logic array (PLA) enables the storage device to dynamically adjust its encoding strategy to better adapt to the changing characteristics of the data stream and avoid the waste of resources that may be caused by static encoding.

[0076] During the data writing process, the embodiments particularly emphasize the real-time monitoring of the integrity of the photon signal. Through a feedback mechanism, the system can adjust the storage voltage and pulse width in real time. The purpose of this design is to ensure that the photon signal is not distorted during the data writing process. It should be understood that the pulse width and voltage adjustment measures for the optical signal are to adapt to the dynamic changes of the data load, thereby ensuring the high reliability of the storage unit under different working conditions.

[0077] Furthermore, this embodiment also improves the mutual conversion link between optical signals and electrical signals, ensuring 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 large data flows. By using optoelectronic conversion technology, it is ensured that there is no information loss during the signal conversion process. It can be understood that this conversion technology enhances the system's anti-interference ability and improves the fault tolerance of data transmission.

[0078] It is worth noting that photon caching not only solves the speed bottleneck in traditional electronic caching, but also provides a more effective solution for latency management. Through photon caching, the system can dynamically manage and optimize high-speed data, especially in application scenarios that require fast response. Photon caching has superior speed advantages and lower energy consumption characteristics, allowing the system to not only accommodate large flows when processing data, but also operate with lower energy consumption.

[0079] Through the method of this embodiment, the efficient collaboration between the optoelectronic hybrid integrated circuit and the storage device interface effectively improves the performance of data processing. The system is not only improved in data transmission speed and storage efficiency, but also reaches a new height in overall energy efficiency and processing stability. At the same time, this embodiment provides a feasible solution for achieving the diversification and efficiency of future storage technology.

[0080] Embodiment 8 In order to solve the problem of efficiency and accuracy in the process of data extraction and decoding, this embodiment refines the design of 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, it provides an efficient and accurate solution for data processing.

[0081] Specifically, a request pattern monitoring mechanism is introduced in this embodiment to optimize the decoding priority. By monitoring the request pattern 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 delays and improving the response speed of the system. It should be understood that this priority configuration can effectively alleviate system load and improve processing efficiency in a big data environment.

[0082] Furthermore, before the actual decoding, the present embodiment pre-decodes the target data to extract the position information and structured identifier. The pre-decoding process aims to obtain key context information by quickly analyzing the data structure, which can help the subsequent complete decoding link to quickly locate and process 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.

[0083] Furthermore, in order to improve the decoding accuracy, this embodiment also uses 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 accuracy of decoding, 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 model to cope with the ever-changing data environment.

[0084] During the decoding process, real-time signal gain adjustment and phase offset control ensure the integrity and accuracy of data decoding through the gain and phase offset of the optical signal. Optical signals may be affected by a variety of environmental factors during transmission, which can easily lead to signal degradation or bit errors. This real-time adjustment mechanism can instantly respond to signal changes, correct potential deviations, and support stable data parsing and decoding in complex signal environments.

[0085] Furthermore, the photon inverse transformation algorithm is used to achieve efficient data decoding and transmission. The photon inverse transformation algorithm is responsible for accurately reconstructing the decoded optical signal into a usable data stream and optimizing the output. The photon inverse transformation algorithm is to accurately reconstruct 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.

[0086] 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.

[0087] Embodiment 9 In order to solve the problem of insufficient data processing efficiency and parallel processing capability in storage devices, this embodiment proposes a data processing system for storage devices, which realizes a highly integrated photon computing and data storage solution. The processing system includes an input interface module, a multi-mode interference divider, a photon computing module, an error correction code module, a data storage module, and a data extraction and decoding module, forming a coordinated integrated system architecture.

[0088] 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 the data format, 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 preprocessing directly determines the success of the subsequent processing process, and its primary task is to ensure that the input signal has a good signal-to-noise ratio and adaptability.

[0089] Further, the data stream that has undergone preliminary preprocessing is received by a multi-mode interference divider, which divides the data stream into multiple signal channels. This division mechanism uses the principle of multi-mode interference in optical technology to enable the data stream to be decomposed into different paths and parsed and processed through parallelized signal processing channels. The application of this technology is intended to improve system capacity and speed through multi-channel parallel processing, especially for complex data environments that need to process multiple signals simultaneously.

[0090] Furthermore, the photonic computing module contains a photonic transistor array and a phase modulation submodule. These components together provide powerful parallel computing capabilities and efficient data processing methods. The photonic transistor array can use photons as computing carriers, thereby achieving high-speed computing at low energy consumption. At the same time, the optical phase modulation submodule provides precise phase control to ensure synchronization and correct signal calculation between each signal channel. It can be understood that the photonic computing architecture of this embodiment will effectively enhance the computing power of the entire system and adapt to higher data throughput requirements.

[0091] Furthermore, to ensure data accuracy and consistency, 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, which can quickly detect and correct potential data errors. If errors introduced during data propagation are not controlled, they 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.

[0092] Furthermore, the data storage module, as the terminal storage unit of the system, is connected to the main storage controller to realize 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 of the system. It can be understood that the design of the data storage module provides a solid foundation for large-scale data storage and high-frequency data access, and also complements the energy efficiency strategy of the device.

[0093] In order to meet the parsing needs of user requests, the data extraction and decoding module relies on the photon inverse transform decoding technology to operate. The data extraction and decoding module converts the stored data into usable output by processing user requests to meet the immediate application needs. The use of photon inverse transform decoding not only improves the decoding speed, but also has significant advantages in maintaining data quality.

[0094] 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 photonic technology not only improves the computing and transmission capabilities of the system, but also lays the foundation for the development of future storage technology, and can show superior performance in different application scenarios.

[0095] Embodiment 10 In order to solve the problem of insufficient efficiency in modern data processing, this embodiment provides a computer-readable storage medium, which stores computer instructions for executing a data processing method. These instructions are intended to optimize the performance and accuracy of the processor when performing complex tasks, covering all key features in the data processing method.

[0096] 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 of the subsequent processing stage. Subsequently, the processor uses multi-mode interference partitioning 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 at the same time, thereby improving the overall data processing efficiency.

[0097] Furthermore, the photonic computing module is responsible for performing efficient parallel data processing operations to ensure efficient use of resources. The processor adjusts the state of the photonic transistor according to the instructions to achieve fast and low-energy operations, and uses phase modulation to improve the accuracy and synchronization of signal processing.

[0098] Furthermore, the instruction also coordinates 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 data.

[0099] Furthermore, data extraction and user request response are performed through photon inverse transform decoding technology, which is used to convert photon signals into processable electronic data streams, so that the system exhibits excellent decoding speed in different application scenarios.

[0100] The benefit of this embodiment is that, through the computer instructions in the storage medium, the efficiency of the computer processor in executing data processing tasks is enhanced, ensuring that it is applicable to the needs of various complex data environments.

[0101] The above embodiments further illustrate the purpose, technical solutions and beneficial effects of the present invention. It should be understood that the above are only specific implementation methods of the present invention and are not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made on the basis of the technical solutions of the present invention should be included in the scope of protection 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, 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.

2. The data processing method of the storage device according to claim 1, characterized in that: 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, α and β are parameters obtained through iterative optimization, which are used to minimize the impact 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.

3. 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; 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.

4. 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, 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.

5. The data processing method of the storage device according to claim 4, 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 for describing 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 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.

6. 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 the 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.

7. The data processing method of the storage device according to claim 6, 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.

8. 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.

9. A data processing system for a storage device, 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.

10. 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 8.

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