A digital signal self-adjusting and switching path optimization system

Abstract

The application discloses a kind of digital signal self-regulation and switching path optimization system, including signal feature perception unit, modulation coding decision unit, path state monitoring unit, path optimization decision unit, signal transmission execution unit and feedback closed-loop correction unit.Signal feature perception unit acquires digital signal multidimensional basic parameter and is converted into feature vector;Modulation coding decision unit is based on adaptive modulation and coding algorithm, and modulation coding mode is selected according to feature vector;Path state monitoring unit monitors the multidimensional parameter of transmission path and constructs correlation matrix, and path optimization decision unit utilizes multi-path optimization model to evaluate path and output selection instruction;Signal transmission execution unit handles and transmits signal according to instruction;Feedback closed-loop correction unit compares parameter before and after transmission, and deviation data is fed back to optimize subsequent processing.The system realizes adaptive modulation coding and transmission path dynamic optimization of digital signal, and improves transmission efficiency and stability.

Description

Technical Field

[0001] This invention relates to the field of digital signal conditioning, and more particularly to a digital signal self-adjustment and switching path optimization system. Background Technology

[0002] With the continuous innovation of communication technology, the efficient transmission and processing of digital signals has become crucial. As the Internet of Things (IoT), 5G, and even future 6G technologies develop, digital signal transmission scenarios are becoming increasingly complex, demanding ever-increasing stability, speed, and reliability. From real-time data interaction in smart home devices to high-definition image transmission in telemedicine, and instant information processing in autonomous vehicles, all rely on the precise transmission and processing of digital signals. This requires systems capable of dynamically adjusting modulation and coding modes and transmission paths based on signal characteristics and transmission path conditions.

[0003] However, existing digital signal processing systems have limitations in modulation and coding scheme selection. Traditional systems typically employ fixed modulation and coding schemes, failing to adaptively select the most suitable scheme based on real-time changes in the digital signal, such as amplitude fluctuations, phase shifts, frequency variations, and symbol rate adjustments. This fixed approach can easily lead to signal distortion and low transmission efficiency in situations with poor signal quality or complex transmission environments.

[0004] Meanwhile, existing technologies also have shortcomings in transmission path optimization. Most systems consider only one or a few factors, such as link delay or bandwidth, when selecting a path, without comprehensively evaluating the overall state of the transmission path and neglecting important parameters such as packet loss rate, bandwidth utilization, and the correlation between paths. This makes it difficult for the system to switch to the optimal transmission path in a timely manner when facing complex and ever-changing network environments, hindering the stable and efficient transmission of digital signals. Summary of the Invention

[0005] In order to overcome the shortcomings and deficiencies of the existing technology, the present invention provides a digital signal self-adjustment and switching path optimization system.

[0006] The technical solution adopted in this invention is a digital signal self-adjustment and switching path optimization system, comprising:

[0007] The signal feature sensing unit is used to collect the basic parameters of digital signal amplitude, phase, frequency and symbol rate in real time, and convert the parameters into multi-dimensional feature vectors.

[0008] The modulation and coding decision unit is connected to the signal feature sensing unit, receives the multi-dimensional feature vector, and performs dynamic matrix transformation in the multi-dimensional feature vector space through a preset adaptive modulation and coding algorithm to generate a modulation and coding mode selection instruction.

[0009] The path status monitoring unit monitors the link delay, packet loss rate, and bandwidth utilization parameters of the transmission path in real time, and constructs a path topology correlation matrix.

[0010] The path optimization decision unit is connected to the path status monitoring unit, receives the path topology correlation matrix, uses a multi-path optimization model, and comprehensively evaluates each transmission path through weight iteration to output a path selection instruction.

[0011] The signal transmission execution unit is connected to the modulation and coding decision unit and the path optimization decision unit respectively. According to the modulation and coding mode selection instruction and the path selection instruction, the digital signal is modulated and coded, and then transmitted through the selected transmission path.

[0012] The feedback closed-loop correction unit is connected to the signal feature sensing unit, the path status monitoring unit, and the signal transmission execution unit. It receives the actual parameters after signal transmission and the parameters before transmission, generates parameter deviation data, and feeds the parameter deviation data back to the signal feature sensing unit and the path status monitoring unit for use in the next signal processing and path selection.

[0013] Furthermore, in the modulation and coding decision unit, the adaptive modulation and coding algorithm selects the modulation and coding mode using the following formula: M = α·f(V) + β·g(SNR), where M is the modulation and coding mode, V is the multidimensional feature vector from the signal feature sensing unit, f(V) is the mode pre-screening function based on the multidimensional feature vector, α is the influence weight of the multidimensional feature vector on the modulation and coding mode selection, SNR is the signal-to-noise ratio of the digital signal, g(SNR) is the mode adjustment function based on the signal-to-noise ratio, β is the influence weight of the signal-to-noise ratio in the modulation and coding mode selection, and α and β satisfy α + β = 1 and α, β ∈ [0, 1].

[0014] Furthermore, in the path optimization decision unit, the multi-path optimization model uses the following formula for path evaluation: Among them, P score Here are the evaluation scores for each transmission path, where B represents the path bandwidth. Let ω1 be the average path delay, ω2 be the weighting coefficient of bandwidth in path evaluation, and ω1+ω2=1, where ω1, ω2∈[0,1]. This represents the average packet loss rate along the path.

[0015] Furthermore, during the feedback process of the feedback closed-loop correction unit to the signal feature sensing unit, a feature vector correction formula is constructed: V ' =V + ΔV, where V 'V is the original multidimensional feature vector, ΔV is the feature vector correction amount calculated based on the parameter deviation data, ΔV=γ·ΔP, where γ is the correction coefficient matrix and ΔP is the parameter deviation vector before and after signal transmission.

[0016] Furthermore, in the modulation and coding decision unit, when performing dynamic matrix transformation, a transformation matrix T is used, and the transformation process satisfies the formula: M' = T·V, where M' is the transformed modulation and coding mode vector, T is the dynamic transformation matrix constructed based on historical modulation and coding modes and statistical characteristics of digital signal parameters, and V is a multidimensional feature vector.

[0017] Furthermore, when the path optimization decision unit performs weight iteration using the multi-path optimization model, it adopts the following weight update formula: in, This refers to the i-th weight coefficient after the (n+1)-th iteration. Let η be the weight coefficient after the nth iteration, and η be the iteration step size. Let be the partial derivative of the path evaluation score with respect to the i-th weight coefficient.

[0018] Furthermore, in the dual-state buffer scheduling, the signal transmission execution unit adopts the buffer capacity allocation formula: C1=θ·C total C2=(1-θ)·C total Where C1 is the buffer capacity for storing the signal to be modulated and coded, and C2 is the buffer capacity for storing the modulated and coded signal to be transmitted. total θ represents the total buffer capacity, and θ is the allocation coefficient that is dynamically adjusted according to the modulation and coding processing speed and the signal transmission speed, where θ∈[0,1].

[0019] Furthermore, the path status monitoring unit uses the following correlation degree calculation formula when constructing the path topology correlation matrix: Among them, A ij ρ represents the degree of association between path i and path j, and is an element constituting the path topology association matrix A. ij Let ρ be the parameter correlation coefficient between path i and path j. ii and ρ jj Let be the variances of the parameters of path i and path j, respectively.

[0020] Furthermore, the data interaction between the modulation and coding decision unit and the path optimization decision unit adopts a collaborative decision-making formula: D joint =λ·M+(1-λ)·P path , where D joint For the collaborative decision-making result, M is the modulation and coding mode selection instruction output by the modulation and coding decision unit, and P... pathλ is the path selection instruction output by the path optimization decision unit, and λ is the collaborative weight of modulation coding decision and path optimization decision, λ∈[0,1].

[0021] Beneficial Effects: This invention proposes a digital signal self-adjustment and switching path optimization system. The system utilizes a signal feature sensing unit to comprehensively collect parameters such as amplitude, phase, frequency, and symbol rate of the digital signal, transforming them into multi-dimensional feature vectors to provide accurate basis for modulation and coding decisions. The modulation and coding decision unit, based on an adaptive modulation and coding algorithm, dynamically selects the optimal modulation and coding mode according to the real-time characteristics of the signal, avoiding signal distortion and low transmission efficiency caused by fixed modes in complex environments. For transmission path optimization, the path status monitoring unit monitors multi-dimensional parameters such as link delay, packet loss rate, and bandwidth utilization in real time, and constructs a path topology correlation matrix to comprehensively reflect the path status. The path optimization decision unit, combined with a multi-path optimization model, comprehensively considers various parameters to evaluate the path, dynamically adjusting the path selection strategy through weight iteration, overcoming the limitations of traditional systems that only consider single or a few factors. Furthermore, the feedback closed-loop correction unit generates deviation data by comparing parameters before and after signal transmission and feeds it back to the front-end unit, achieving continuous system optimization. The signal transmission execution unit, based on the decision instructions, ensures stable modulation, coding, and transmission of the signal through dual-state buffer scheduling, ultimately achieving efficient and stable transmission of digital signals in complex environments. Attached Figure Description

[0022] Figure 1 This is a system unit composition diagram of the present invention;

[0023] Figure 2 This is a flowchart illustrating the system operation of the present invention. Detailed Implementation

[0024] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. The application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0025] like Figure 1 As shown, a digital signal self-adjustment and switching path optimization system includes:

[0026] The signal feature sensing unit is used to collect the basic parameters of digital signal amplitude, phase, frequency and symbol rate in real time, and convert the parameters into multi-dimensional feature vectors.

[0027] Specifically, during the process, it can accurately acquire fundamental parameters of digital signals, such as amplitude, phase, frequency, and symbol rate. These parameters are key characteristics of digital signals: amplitude reflects the strength of the signal, phase reflects the waveform deviation, frequency determines the rate of signal change, and symbol rate is closely related to the data transmission rate. By acquiring these parameters, the initial state of the digital signal can be fully understood. To facilitate subsequent processing, this unit transforms these acquired fundamental parameters using multidimensional vector mapping technology, converting them into multidimensional feature vectors to provide a standardized data format for subsequent signal processing.

[0028] During implementation, the signal feature sensing unit typically employs high-precision sensors and data acquisition modules. These sensors can quickly and accurately detect changes in various parameters of the digital signal and convert analog signals into digital signals. The data acquisition module periodically acquires data from the digital signal according to a set sampling frequency, ensuring that no critical information is missed. The acquired data undergoes preliminary filtering and noise reduction processing to remove interference signals and ensure the accuracy of the acquired parameters. Then, using specific algorithms and mapping rules, the processed data is converted into a multi-dimensional feature vector to facilitate analysis and decision-making by subsequent units.

[0029] The modulation and coding decision unit is connected to the signal feature sensing unit, receives the multi-dimensional feature vector, and performs dynamic matrix transformation in the multi-dimensional feature vector space through a preset adaptive modulation and coding algorithm to generate a modulation and coding mode selection instruction.

[0030] Specifically, the modulation and coding decision unit (MCD) plays a core decision-making role in the entire system. Its function is to select the most suitable modulation and coding mode based on the characteristics of the input signal. It is connected to the signal feature sensing unit and receives multi-dimensional feature vectors from that unit. After receiving the multi-dimensional feature vectors, the unit uses a preset adaptive modulation and coding algorithm to perform in-depth analysis in the multi-dimensional feature vector space. By comprehensively considering various parameters of the signal, it determines the characteristics of the current signal and transmission requirements, and then performs dynamic matrix transformation operations. This transformation maps the multi-dimensional feature vectors to different modulation and coding modes, ultimately generating a modulation and coding mode selection instruction, providing a clear direction for the signal modulation and coding processing.

[0031] In practical implementation, this unit integrates a high-performance processor and storage module. The processor is responsible for running the adaptive modulation and coding algorithm, performing complex calculations and analyses on multi-dimensional feature vectors. The storage module pre-stores various modulation and coding modes and their corresponding parameters and performance indicators, as well as various rules and models required for algorithm operation. During dynamic matrix transformation, the processor performs linear or nonlinear transformations on the multi-dimensional feature vectors according to the algorithm's logic, continuously adjusting the matrix parameters and structure to find the most suitable modulation and coding mode for the current signal. After generating the modulation and coding mode selection instruction, this unit accurately transmits the instruction to the subsequent signal transmission execution unit, ensuring that the signal can be modulated and coded in the optimal way.

[0032] The path status monitoring unit monitors the link delay, packet loss rate, and bandwidth utilization parameters of the transmission path in real time, and constructs a path topology correlation matrix.

[0033] Specifically, the main task of the path status monitoring unit is to monitor the operational status of the transmission path in real time. It continuously monitors important parameters such as link delay, packet loss rate, and bandwidth utilization. Link delay reflects the time delay experienced by the signal during transmission, packet loss rate reflects the proportion of data lost during transmission, and bandwidth utilization indicates the usage of path bandwidth resources. These parameters directly affect the transmission quality and efficiency of digital signals. Through real-time monitoring of these parameters, the unit can comprehensively understand the operating status of each transmission path. Based on this, it uses topology correlation analysis technology to study the relationships and mutual influences between paths, constructing a path topology correlation matrix to provide detailed data support for subsequent path optimization decisions.

[0034] In practice, this unit collects data on the transmission path using hardware such as network probes and traffic monitoring devices. These devices are distributed across key nodes in the network to acquire various path parameters in real time. The collected data is transmitted to the unit's internal processing module, which uses specialized algorithms to clean, organize, and analyze the data. When constructing the path topology correlation matrix, the processing module determines the values ​​of each element in the matrix based on the correlation and mutual influence between the path parameters. Simultaneously, the unit periodically updates the collected data and the constructed matrix to adapt to dynamic changes in the network environment, ensuring that the latest status of the transmission path is reflected in a timely and accurate manner.

[0035] The path optimization decision unit is connected to the path status monitoring unit, receives the path topology correlation matrix, uses a multi-path optimization model, and comprehensively evaluates each transmission path through weight iteration to output a path selection instruction.

[0036] Specifically, the core function of the path optimization decision unit is to comprehensively evaluate and optimize the selection of transmission paths. It is connected to the path status monitoring unit and receives the path topology correlation matrix from that unit. After receiving the matrix, the unit uses a multi-path optimization model to conduct a comprehensive and in-depth analysis of each transmission path. By introducing a weighted iteration method, it dynamically adjusts and balances multiple factors affecting path selection. Different factors, such as link delay, packet loss rate, and bandwidth utilization, have different levels of importance under different transmission scenarios and signal requirements. Through weighted iteration, the unit can assign appropriate weights to each factor according to the actual situation, thereby conducting a more accurate comprehensive evaluation of each transmission path and ultimately outputting the optimal path selection command to ensure that digital signals can be transmitted through the best path.

[0037] During implementation, this unit relies on powerful computing capabilities and complex algorithms to achieve its functions. The internal computing module calculates and processes the data in the path topology correlation matrix according to the rules of the multi-path optimization model. During weight iteration, the algorithm continuously adjusts the weight values ​​of each factor based on historical transmission data and current signal requirements. For example, when real-time requirements are high, the weight of link delay increases accordingly; when the amount of transmitted data is large, the weight of bandwidth utilization is increased. Through multiple iterative calculations, the optimal weight combination is gradually found, thus obtaining an accurate path evaluation result. Finally, this unit sends path selection instructions to the signal transmission execution unit to guide the selection of the signal transmission path.

[0038] The signal transmission execution unit is connected to the modulation and coding decision unit and the path optimization decision unit respectively. According to the modulation and coding mode selection instruction and the path selection instruction, the digital signal is modulated and coded, and then transmitted through the selected transmission path.

[0039] Specifically, the signal transmission execution unit is the key link in realizing the actual transmission of digital signals. It is connected to both the modulation and coding decision unit and the path optimization decision unit, receiving modulation and coding mode selection instructions and path selection instructions from these two units. Upon receiving the instructions, this unit first performs corresponding modulation and coding processing on the digital signal according to the modulation and coding mode selection instruction. Modulation and coding processing converts the original digital signal into a format suitable for transmission on the selected path, improving the signal's anti-interference capability and transmission efficiency. After processing, the unit transmits the modulated and coded digital signal through the selected transmission path according to the path selection instruction. Simultaneously, to ensure the stability and smoothness of signal transmission, this unit employs a dual-state buffer scheduling mechanism for effective buffering and scheduling management of the signal.

[0040] In its implementation, the unit has two internal buffer areas, one for storing the signal to be modulated and the other for storing the modulated and encoded signal to be transmitted. A dual-state buffer scheduling mechanism dynamically adjusts the capacity allocation of the two buffer areas based on the modulation and coding processing speed and the signal transmission speed. When the modulation and coding processing speed is high, the capacity of the buffer area storing the signal to be modulated and encoded is increased; when the transmission speed is high, the capacity of the buffer area storing the modulated and encoded signal to be transmitted is increased. This dynamic buffer scheduling method prevents signal backlog or loss during processing and transmission. Furthermore, the unit is equipped with a high-speed data transmission interface and a signal processing chip to ensure fast and accurate modulation and coding processing and transmission of signals.

[0041] The feedback closed-loop correction unit is connected to the signal feature sensing unit, the path status monitoring unit, and the signal transmission execution unit. It receives the actual parameters after signal transmission and the parameters before transmission, generates parameter deviation data, and feeds the parameter deviation data back to the signal feature sensing unit and the path status monitoring unit for use in the next signal processing and path selection.

[0042] Specifically, the feedback closed-loop correction unit plays a role in optimization and calibration within the system. It is connected to the signal feature sensing unit, path status monitoring unit, and signal transmission execution unit. Its main function is to collect the actual parameters of the transmitted signal after transmission and compare them with the parameters before transmission. This comparison reveals changes in the signal during transmission, such as amplitude attenuation, phase shift, and increased delay, generating parameter deviation data. This deviation data reflects problems in the system's signal processing and path transmission. This unit feeds this parameter deviation data back to the signal feature sensing unit and the path status monitoring unit, enabling these units to adjust and optimize subsequent signal processing and path selection based on the feedback information, forming a complete feedback closed-loop system.

[0043] During implementation, the feedback closed-loop correction unit employs a dedicated data acquisition and analysis module. The data acquisition module obtains the actual parameters after signal transmission from the signal transmission execution unit, and simultaneously obtains the original parameters before transmission from the signal feature sensing unit and the path status monitoring unit. The analysis module uses precise calculation and comparison algorithms to perform detailed comparison and analysis of the two sets of parameters, calculating the deviation values ​​for each parameter. To ensure the accuracy and timeliness of the feedback, this unit sets a reasonable feedback cycle, periodically comparing and analyzing the signal parameters and providing feedback. After feeding back the parameter deviation data to the signal feature sensing unit and the path status monitoring unit, this unit also tracks and evaluates the feedback effect, further optimizing its workflow and algorithms based on the evaluation results to continuously improve the overall performance and stability of the system.

[0044] Preferably, in the modulation and coding decision unit, the adaptive modulation and coding algorithm selects the modulation and coding mode using the following formula: M = α·f(V) + β·g(SNR), where M is the modulation and coding mode, V is the multidimensional feature vector from the signal feature sensing unit, f(V) is the mode pre-screening function based on the multidimensional feature vector, α is the influence weight of the multidimensional feature vector on the modulation and coding mode selection, SNR is the signal-to-noise ratio of the digital signal, g(SNR) is the mode adjustment function based on the signal-to-noise ratio, β is the influence weight of the signal-to-noise ratio in the modulation and coding mode selection, and α and β satisfy α + β = 1 and α, β ∈ [0, 1].

[0045] Specifically, by comprehensively considering two factors—multidimensional feature vectors and signal-to-noise ratio (SNR)—and setting influencing weights and performing function calculations, the system achieves refined selection of modulation and coding modes. The multidimensional feature vectors reflect the inherent characteristics of the signal, while the SNR reflects the quality of the transmission environment; both together determine the final mode. This two-factor weighted decision-making approach enables the system to dynamically adjust the modulation and coding strategy based on signal characteristics and environmental changes, improving transmission efficiency and reliability. During implementation, the system needs to pre-establish a mapping table between multidimensional feature vectors and modulation and coding modes, and dynamically adjust the weight allocation based on the real-time SNR to adapt to different transmission scenarios.

[0046] Preferably, in the path optimization decision unit, the multi-path optimization model uses the following formula for path evaluation: Among them, P score Here are the evaluation scores for each transmission path, where B represents the path bandwidth. Let ω1 be the average path delay, ω2 be the weighting coefficient of bandwidth in path evaluation, and ω1+ω2=1, where ω1, ω2∈[0,1]. This represents the average packet loss rate along the path.

[0047] Specifically, the evaluation method for the path optimization decision unit uses bandwidth, average latency, and average packet loss rate as core evaluation indicators. Weighting coefficients balance the influence of each indicator to calculate the path evaluation score. The ratio of bandwidth to average latency reflects the path's transmission efficiency, while the packet loss rate reflects transmission reliability. This comprehensive evaluation method avoids the limitations of single-indicator evaluation, enabling the system to comprehensively consider path performance. In practical applications, the system dynamically adjusts the weighting coefficients based on real-time monitoring data, prioritizing paths with high bandwidth, low latency, and low packet loss rate to ensure the stability and efficiency of signal transmission.

[0048] Preferably, during the feedback process of the feedback closed-loop correction unit to the signal feature sensing unit, a feature vector correction formula is constructed: V ' =V + ΔV, where V' V is the original multidimensional feature vector, ΔV is the feature vector correction amount calculated based on the parameter deviation data, ΔV=γ·ΔP, where γ is the correction coefficient matrix and ΔP is the parameter deviation vector before and after signal transmission.

[0049] Specifically, the feedback correction mechanism of the feedback closed-loop correction unit to the signal feature sensing unit dynamically adjusts the feature vector by calculating the correction amount between the original multidimensional feature vector and the parameter deviation after transmission. The correction coefficient matrix is ​​dynamically generated based on historical feedback data and system performance to ensure the accuracy of the correction amount. This feedback correction mechanism enables the system to continuously optimize the acquisition and analysis of signal features based on actual transmission effects, improving the accuracy of subsequent modulation and coding decisions. During implementation, the system periodically updates the correction coefficient matrix to adapt to the constantly changing transmission environment.

[0050] Preferably, in the modulation and coding decision unit, when performing dynamic matrix transformation, a transformation matrix T is used, and the transformation process satisfies the formula: M' = T·V, where M' is the transformed modulation and coding mode vector, T is the dynamic transformation matrix constructed based on historical modulation and coding modes and statistical characteristics of digital signal parameters, and V is a multidimensional feature vector.

[0051] Specifically, the dynamic matrix transformation process of the modulation and coding decision unit involves constructing a transformation matrix based on historical modulation and coding modes and the statistical characteristics of digital signal parameters. Through linear transformation of multi-dimensional feature vectors, a modulation and coding mode vector more suitable for the current signal characteristics is generated. This dynamic transformation method based on statistical characteristics enables the system to learn from historical experience and optimize the decision-making process. In practical applications, the system continuously collects and analyzes historical data to update the transformation matrix, thereby improving the accuracy and adaptability of mode selection.

[0052] Preferably, when the path optimization decision unit performs weight iteration using the multi-path optimization model, it adopts the following weight update formula: in, This refers to the i-th weight coefficient after the (n+1)-th iteration. Let η be the weight coefficient after the nth iteration, and η be the iteration step size. Let be the partial derivative of the path evaluation score with respect to the i-th weight coefficient.

[0053] Specifically, the weight iteration mechanism of the path optimization decision unit determines the iteration step size by calculating the partial derivative of the path evaluation score with respect to the weight coefficients, thus enabling dynamic updates of the weight coefficients. This iterative optimization method allows the system to adjust the importance of each evaluation indicator based on the real-time path status, gradually finding the optimal weight combination. During implementation, the system sets reasonable iteration termination conditions to ensure both evaluation accuracy and improved decision-making efficiency.

[0054] Preferably, in the dual-state buffer scheduling, the signal transmission execution unit adopts the buffer capacity allocation formula: C1=θ·C total C2=(1-θ)·C total Where C1 is the buffer capacity for storing the signal to be modulated and coded, and C2 is the buffer capacity for storing the modulated and coded signal to be transmitted. total θ represents the total buffer capacity, and θ is the allocation coefficient that is dynamically adjusted according to the modulation and coding processing speed and the signal transmission speed, where θ∈[0,1].

[0055] Specifically, the dual-state buffer scheduling strategy of the signal transmission execution unit dynamically adjusts the capacity allocation of the two buffer regions based on the dynamic changes in modulation and coding processing speed and signal transmission speed. When the processing speed is faster than the transmission speed, the buffer capacity of the signal to be modulated and coded is increased; conversely, the buffer capacity of the modulated and coded signal to be transmitted is increased. This dynamic allocation method effectively balances the speed difference between processing and transmission, avoids signal backlog or loss, and improves the overall stability and transmission efficiency of the system.

[0056] Preferably, the path status monitoring unit uses the following correlation degree calculation formula when constructing the path topology correlation matrix: Among them, A ij ρ represents the degree of association between path i and path j, and is an element constituting the path topology association matrix A. ij Let ρ be the parameter correlation coefficient between path i and path j. ii and ρ jj Let be the variances of the parameters of path i and path j, respectively.

[0057] Specifically, the path status monitoring unit constructs a path topology correlation matrix by calculating the parameter correlation coefficients between paths and performing normalization to obtain the path correlation degree matrix. This correlation analysis method can reveal the mutual influence relationships between different paths, providing more comprehensive information for path optimization decisions. In practical applications, the system periodically updates the correlation matrix to reflect dynamic changes in the network topology, ensuring the accuracy of path selection.

[0058] Preferably, the data interaction between the modulation and coding decision unit and the path optimization decision unit adopts a collaborative decision-making formula: D joint =λ·M+(1-λ)·P path , of which F joint For the collaborative decision-making result, M is the modulation and coding mode selection instruction output by the modulation and coding decision unit, and P... path λ is the path selection instruction output by the path optimization decision unit, and λ is the collaborative weight of modulation coding decision and path optimization decision, λ∈[0,1].

[0059] Specifically, the collaborative decision-making mechanism between the modulation and coding decision unit and the path optimization decision unit generates a final collaborative decision result by setting collaborative weights and comprehensively considering the modulation and coding mode and path selection instructions. This collaborative decision-making approach enables the two core units to cooperate and jointly optimize based on signal characteristics and path status, avoiding suboptimal results that might result from individual decision-making. During implementation, the system dynamically adjusts the collaborative weights according to different transmission requirements to balance transmission efficiency and reliability.

[0060] like Figure 2 As shown, a digital signal self-adjustment and switching path optimization system is described. The system operation includes the following steps:

[0061] Step S1: Acquire digital signals using a signal feature sensing unit and convert the basic parameters of the digital signals, such as amplitude, phase, frequency, and symbol rate, into multidimensional feature vectors.

[0062] Step S2: The multidimensional feature vector is transmitted to the modulation and coding decision unit. The modulation and coding decision unit performs dynamic matrix transformation in the multidimensional feature vector space through an adaptive modulation and coding algorithm to generate a modulation and coding mode selection instruction.

[0063] Step S3: Use the path status monitoring unit to monitor the link delay, packet loss rate, and bandwidth utilization parameters of the transmission path in real time, and construct the path topology correlation matrix;

[0064] Step S4: The path topology correlation matrix is ​​transmitted to the path optimization decision unit. The path optimization decision unit uses a multi-path optimization model to comprehensively evaluate each transmission path through weight iteration and outputs a path selection instruction.

[0065] Step S5: The modulation and coding mode selection instruction and the path selection instruction are transmitted to the signal transmission execution unit. The signal transmission execution unit performs modulation and coding processing on the digital signal and transmits it through the selected transmission path.

[0066] Step S6: The feedback closed-loop correction unit receives the actual parameters after signal transmission and the parameters before transmission, generates parameter deviation data, and feeds the parameter deviation data back to the signal feature sensing unit and the path status monitoring unit to provide data basis for the next signal processing and path selection.

[0067] A digital signal self-adjustment and switching path optimization system effectively overcomes the shortcomings of traditional technologies in modulation coding and path optimization through multi-unit collaboration and innovative mechanisms, demonstrating significant advantages. In the modulation coding stage, traditional systems employ fixed modes, unable to adapt to real-time signal changes. In contrast, the signal feature sensing unit of this system can collect multi-dimensional fundamental parameters of the digital signal, such as amplitude, phase, frequency, and symbol rate, in real time and convert them into multi-dimensional feature vectors. Based on this precise data, the modulation coding decision unit uses adaptive modulation and coding algorithms to perform dynamic matrix transformations in the multi-dimensional feature vector space, flexibly selecting the optimal modulation coding mode according to the actual signal state, avoiding signal distortion and significantly improving transmission efficiency.

[0068] In transmission path optimization, existing technologies often struggle to adapt to complex network environments due to their limited consideration of single factors. This system's path status monitoring unit not only monitors key parameters such as link latency, packet loss rate, and bandwidth utilization in real time, but also constructs a path topology correlation matrix to comprehensively present the status of each path and their interrelationships. The path optimization decision unit, utilizing a multi-path optimization model, comprehensively evaluates each transmission path through weighted iterative evaluation, incorporating factors such as bandwidth, latency, and packet loss rate, and dynamically adjusts the path selection strategy to ensure that digital signals are always transmitted through the optimal path in complex and ever-changing network environments.

[0069] Furthermore, the feedback closed-loop correction unit in the system generates deviation data by comparing various parameters before and after signal transmission and feeds it back to the signal feature sensing unit and path state monitoring unit, achieving continuous system optimization and self-calibration. The signal transmission execution unit, based on decision instructions, uses dual-state buffer scheduling to modulate, encode, and transmit the signal, ensuring the stability and smoothness of signal processing. The close collaboration among these units gives the system greater flexibility, stability, and efficiency in digital signal processing and transmission, comprehensively addressing the shortcomings of traditional systems.

[0070] In the description of this invention, it should be noted that, unless otherwise explicitly specified and limited, the terms "set," "install," "connect," "link," and "fix" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal communication between two components. Those skilled in the art will understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0071] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various equivalent changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A digital signal self-adjusting and switching path optimization system, characterized by, The application relates to a signal transmission method and device. The application comprises the following parts: A signal feature sensing unit is used for collecting amplitude, phase, frequency and symbol rate basic parameters of a digital signal in real time, and converting the parameters into a multi-dimensional feature vector; A modulation and coding decision unit is connected with the signal feature sensing unit, receives the multi-dimensional feature vector, and generates modulation and coding mode selection instructions through a preset adaptive modulation and coding algorithm and dynamic matrix transformation in the multi-dimensional feature vector space; A path state monitoring unit is used for monitoring link delay, packet loss rate and bandwidth utilization rate parameters of a transmission path in real time, and constructing a path topology correlation matrix; A path optimization decision unit is connected with the path state monitoring unit, receives the path topology correlation matrix, uses a multi-path optimization model, and outputs path selection instructions through weight iteration and comprehensive evaluation of each transmission path; A signal transmission execution unit is connected with the modulation and coding decision unit and the path optimization decision unit, and is used for modulating and coding the digital signal according to the modulation and coding mode selection instructions and the path selection instructions, and transmitting the digital signal through the selected transmission path; 2. The digital signal self-adjusting and switching path optimization system of claim 1, wherein, A feedback closed-loop correction unit is connected with the signal feature sensing unit, the path state monitoring unit and the signal transmission execution unit, receives actual parameters after signal transmission and parameters before signal transmission, generates parameter deviation data, and feeds back the parameter deviation data to the signal feature sensing unit and the path state monitoring unit for next signal processing and path selection.

3. The digital signal self-adjusting and switching path optimization system of claim 1, wherein, The path optimization decision unit, the multi-path optimization model adopts the following formula to evaluate the path: Wherein, P score is the evaluation score of each transmission path, B is the path bandwidth, is the path average delay, ω1 is the weight coefficient of bandwidth in path evaluation, ω2 is the weight coefficient of the comprehensive influence of delay and packet loss rate, and ω1+ω2=1, ω1, ω2∈[0, 1], is the path average packet loss rate.

4. The digital signal self-adjusting and switching path optimization system of claim 1, wherein, In the modulation and coding decision unit, the adaptive modulation and coding algorithm selects a modulation and coding mode through the following formula: M = alpha * f (V) + beta * g (SNR), wherein M is the modulation and coding mode, V is the multi-dimensional feature vector from the signal feature sensing unit, f (V) is a mode pre-screening function based on the multi-dimensional feature vector, alpha is the influence weight of the multi-dimensional feature vector on the modulation and coding mode selection, SNR is the signal-to-noise ratio of the digital signal, g (SNR) is a mode adjustment function based on the signal-to-noise ratio, beta is the influence weight of the signal-to-noise ratio in the modulation and coding mode selection, alpha and beta satisfy alpha + beta = 1 and alpha, beta [0, 1].

5. The digital signal self-adjusting and switching path optimization system of claim 1, wherein, In the feedback process of the feedback closed-loop correction unit to the signal feature sensing unit, a feature vector correction formula is constructed: V' = V + AV, wherein V' is the corrected multi-dimensional feature vector, V is the original multi-dimensional feature vector, AV is the feature vector correction amount calculated according to the parameter deviation data, AV = gamma * DP, gamma is a correction coefficient matrix, and DP is a parameter deviation vector before and after signal transmission. In the modulation and coding decision unit, a transformation matrix T is used for dynamic matrix transformation, and the transformation process satisfies the formula: M' = T * V, wherein M' is the transformed modulation and coding mode vector, T is a dynamic transformation matrix constructed based on historical modulation and coding modes and statistical characteristics of the digital signal parameters, and V is the multi-dimensional feature vector.

6. The digital signal self-adjusting and switching path optimization system of claim 1, wherein, The path optimization decision unit adopts a weight updating formula when performing weight iteration by using a multipath optimization model: wherein, is the ith weight coefficient after the n+1th iteration, is the ith weight coefficient after the nth iteration, and η is an iteration step size, is a partial derivative of the path evaluation score with respect to the ith weight coefficient.

7. The digital signal self-adjusting and switching path optimization system of claim 1, wherein, In the dual-state buffer scheduling, the signal transmission execution unit adopts the buffer capacity allocation formula: C1=θ·C total C2=(1-θ)·C total Where C1 is the buffer capacity for storing the signal to be modulated and coded, and C2 is the buffer capacity for storing the modulated and coded signal to be transmitted. total θ represents the total buffer capacity, and θ is the allocation coefficient that is dynamically adjusted according to the modulation and coding processing speed and the signal transmission speed, where θ∈[0,1].

8. The digital signal self-adjusting and switching path optimization system of claim 1, wherein, The path state monitoring unit adopts a correlation degree calculation formula when constructing the path topology correlation matrix: Wherein, A ij is the correlation degree of path i and path j, an element of the path topology correlation matrix A, ρ ij is the parameter correlation coefficient of path i and path j, ρ ii and ρ jj are the variances of the parameters of path i and path j respectively.

9. The digital signal self-adjusting and switching path optimization system of claim 1, wherein, The data interaction between the modulation and coding decision unit and the path optimization decision unit adopts a collaborative decision formula: D joint = λ · M + (1 - λ) · P path , wherein D joint is the collaborative decision result, M is the modulation and coding mode selection instruction output by the modulation and coding decision unit, P ath is the path selection instruction output by the path optimization decision unit, and λ is the collaborative weight of the modulation and coding decision and the path optimization decision, λ ∈ [0, 1].

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