A flexible array sensor signal transmission system

Abstract

The present application relates to the technical field of signal transmission, in particular to a flexible array sensor signal transmission system, which comprises a sensing signal acquisition module, a signal conditioning and preprocessing module, an adaptive noise suppression module, a dynamic signal compression and coding module and a multi-channel high-frequency transmission module. The system acquires original sensing voltage data on the flexible array, dynamically reduces noise by combining multi-domain collaborative filtering and environment characteristic adaptive parameters, realizes compression and coding by using wavelet transform and entropy threshold algorithm, and realizes multi-channel high-speed data transmission and reconstruction feedback by optimizing channel scheduling and error code reconstruction mechanism. The system has the advantages of good structural flexibility, high data compression ratio, strong signal-to-noise ratio and strong transmission robustness, and is widely applicable to wearable electronics, biological signal acquisition, intelligent human-computer interaction and other high-density flexible sensing scenarios.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of signal transmission, in particular to a flexible array sensor signal transmission system, which is especially suitable for high-density data acquisition, noise reduction, compression encoding and multi-channel high-speed transmission in large-area deformable sensing arrays. BACKGROUND

[0002] Driven by the rapid development of wearable devices, medical monitoring, intelligent robots and flexible electronic systems, flexible array sensors, as the core devices for continuous and high-resolution physical information acquisition, have gradually become the key technical foundation for multi-dimensional human perception, tactile interaction and physiological signal detection. Such sensors are usually constructed with flexible conductive materials and multi-point array structures, and have the advantages of bendability, conformability and strong biocompatibility, enabling high-density data acquisition in complex curved surfaces or dynamic scenarios.

[0003] However, due to the simultaneous presence of signal interference, array point performance drift, transmission error rate and other problems in large-area deployment of flexible arrays, traditional single-point reading and centralized acquisition-based solutions cannot meet the real-time and stability requirements of multi-channel signals. At the same time, the inherent physical properties of flexible materials easily introduce nonlinear noise and low-frequency drift, posing a great challenge to subsequent data processing. In addition, with the increase in the number of sensing nodes, if there is no effective compression mechanism, the transmission of massive data through limited bandwidth will seriously affect the overall response efficiency of the system.

[0004] Although some basic filtering algorithms or compression strategies have been introduced in the prior art, there is a general lack of multi-module collaborative design, especially in the aspects of signal conditioning, noise reduction control, frequency domain compression and channel scheduling, which have not been deeply integrated, resulting in poor system robustness, high power consumption and insufficient reliability. Therefore, there is an urgent need for a comprehensive signal transmission system with multi-domain adaptive processing capability to support efficient compression and stable transmission of flexible arrays, in order to solve the problems of signal acquisition scattering, large data redundancy and low transmission efficiency in existing flexible sensing networks. SUMMARY

[0005] In order to achieve the above-mentioned application purposes, the present application provides the following technical solutions: a flexible array sensor signal transmission system, comprising the following modules:

[0006] A sensing signal acquisition module for acquiring original sensing voltage data sets under the action of a target based on a multi-point array structure constructed of flexible materials and a timestamp group ;

[0007] A signal conditioning and preprocessing module for normalizing, level shifting and sampling synchronization processing of the original sensing voltage data sets and outputting conditioned signals ;

[0008] an adaptive noise suppression module for generating a noise-reduced signal from the conditioned signal by applying a multi-domain filtering mechanism , the adaptive noise suppression module comprising:

[0009] an environment feature mapping unit for mapping predefined environment feature parameters to a space-time domain filtering weight vector , the environment feature parameters including root mean square of signal noise , standard deviation of signal fluctuation in spatial direction , standard deviation of signal fluctuation in temporal direction , the space-time domain filtering weight vector having filtering parameters and determined by the following environment adaptive formula:

[0010] ;

[0011] ;

[0012] in the formula, , is an empirical weight parameter, is a small quantity to avoid division by zero, is the standard deviation of signal fluctuation in spatial direction , and is the standard deviation of signal fluctuation in temporal direction;

[0013] a multi-domain collaborative filtering unit for filtering the conditioned signal by jointly applying a spatial smoothing operator and a temporal recursive operator to generate the noise-reduced signal , satisfying the following optimization objective function:

[0014] ;

[0015] a dynamic gating adjustment unit for dynamically adjusting the weight proportion of spatial filtering and temporal filtering according to real-time changes of the space-time domain filtering weight vector to enhance the robustness of the system under different noise scenarios;

[0016] a dynamic signal compression encoding module for encoding and compressing the noise-reduced signal into a compressed signal according to signal frequency domain feature parameters and compression ratio strategies ;

[0017] Multi-channel high-frequency transmission module for transmitting compressed signals According to a time division multiplexing mechanism, the signals are transmitted in parallel through several channels to a data receiving terminal.

[0018] Preferably, the sensing signal acquisition module comprises:

[0019] A flexible array layer for outputting corresponding raw sensing voltage data sets in response to external pressure, deformation and charge effects , wherein the raw sensing voltage data set satisfies:

[0020] ;

[0021] , wherein, represents the array point at time , the raw sensing voltage data, represents the sensitivity coefficient of the flexible unit of the array point , and represents the external force received by the array point at time , which is measured in real time by the pressure sensor, represents the device bias noise, which is derived from the device difference and the disturbance introduced by wiring;

[0022] The flexible array layer is composed of conductive polymer composite film and microelectrode grid interlacing, and the sensitivity coefficient of the array point satisfies the following distribution function:

[0023] ;

[0024] , wherein, , represents the array center coordinates, is the spatial dispersion standard deviation of the array point , and is the film thickness normalization coefficient;

[0025] A sampling synchronization unit is used to time mark the signals according to a set frequency and a full-array scanning strategy to obtain a time stamp set , ensuring that the subsequent data can be aligned according to time-space sequence consistency in the preprocessing stage.

[0026] Preferably, the signal conditioning and preprocessing module comprises:

[0027] An amplitude normalization unit is used to normalize the raw sensing voltage data set The center drift signal is mapped to [0, 1] and normalized to generate the center drift signal :

[0028] ;

[0029] Wherein, is the maximum dynamic range threshold of the array point , is the minimum dynamic range threshold of the array point ;

[0030] The level shift unit is used for shifting the center drift signal to the zero mean space, and outputs the conditioned signal :

[0031] ;

[0032] Wherein, is the historical mean level of the array point .

[0033] Preferably, the dynamic signal compression encoding module comprises:

[0034] The frequency domain feature extraction unit is used for performing fast wavelet transform on the noise reduction signal to obtain the frequency domain feature parameter :

[0035] ;

[0036] Wherein, is the frequency domain feature parameter of the array point , is the noise reduction signal of the array point , is the fourth order wavelet transform algorithm

[0037] The compression control unit dynamically selects the reserved coefficient set according to the feature sparsity and the compression ratio strategy , and satisfies the following formula:

[0038] ;

[0039] Wherein, is the feature dimension of the wavelet transformed feature coefficient, is the frequency domain feature parameter of the array point , is the coefficient set selected dynamically, is the feature sparsity of the array point . ​​

[0040] the set of reserved coefficients An entropy threshold control algorithm is adopted to satisfy the following decision function:

[0041] ;

[0042] wherein, denotes an entropy threshold control function, is the frequency domain feature parameter of the first feature dimension of the array point , and is a dynamic entropy threshold value;

[0043] The reversible coding unit applies a lossless entropy coding algorithm to the set of reserved coefficients to generate compressed signals .

[0044] Preferably, the multi-channel high-frequency transmission module comprises:

[0045] The sub-channel multiplexing scheduling unit is configured to perform multiplexing scheduling based on channel capacity and compressed flow rate by using an optimal allocation algorithm, and to perform frame packing, wherein the optimal allocation algorithm is as follows:

[0046] ;

[0047] wherein, denotes the throughput gain of the first sub-channel when it is allocated to the first data stream, denotes an allocation matrix, and satisfies , wherein indicates whether the signal is allocated to the channel , and finally determines that the compressed signal data is allocated to the physical transmission channel ;

[0048] The error code reconstruction unit performs error code detection and repair on the received data by using a parity check matrix , reconstructs the compressed signal , and feeds back the abnormal frame index;

[0049] The error code reconstruction unit adopts an improved convolution check reconstruction algorithm, and the reconstruction rate satisfies:

[0050] ;

[0051] wherein, denotes the total number of frames, ​​The parity check matrix is provided, the reconstruction algorithm can realize recovery of less than or equal to 3bit continuous error, and the feedback signal is transmitted back to the sub-channel multiplexing scheduling unit to adjust the dynamic channel weight.

[0052] Compared with the prior art, the application has the following beneficial effects:

[0053] Realize high-fidelity low-delay transmission of multi-dimensional signals: the application effectively reduces the spatial and temporal noise caused by the non-ideal characteristics of flexible materials through adaptive noise suppression and multi-domain collaborative filtering mechanism, and cooperates with dynamic compression and efficient scheduling transmission module to realize real-time transmission and dynamic error recovery of high-density array signals, and improves the stability and response speed of the overall system.

[0054] Enhance the robustness and adaptability of the flexible sensing system in complex scenarios: the system introduces environment adaptive parameter mapping and gating regulation mechanism, which can dynamically adjust the filtering weight and compression ratio according to the current noise distribution and environmental disturbance, significantly improving the robustness and self-regulation ability of the system in complex application environments such as wearing, medical treatment and robot.

[0055] Significantly improve the signal compression efficiency and transmission resource utilization: the dynamic compression algorithm based on frequency domain sparse feature extraction and entropy threshold control is adopted, so that the compression rate can be adaptively adjusted according to the feature energy, effectively reducing the amount of redundant data; at the same time, the multi-channel scheduling module integrates the optimization allocation model, improves the channel throughput and fault tolerance, reduces the system power consumption, and expands the engineering usability and long-term deployment ability of the system. BRIEF DESCRIPTION OF DRAWINGS

[0056] Figure 1 The system module schematic diagram provided by the application is provided;

[0057] Figure 2 The sensing signal acquisition module schematic diagram provided by the application is provided;

[0058] Figure 3 The signal conditioning and preprocessing module schematic diagram provided by the application is provided;

[0059] Figure 4 The adaptive noise suppression module schematic diagram provided by the application is provided;

[0060] Figure 5 The dynamic signal compression encoding module schematic diagram provided by the application is provided;

[0061] Figure 6 The multi-channel high-frequency transmission module schematic diagram provided by the application is provided. DETAILED DESCRIPTION

[0062] In order to make the person skilled in the art better understand the present application, the technical solutions in the embodiments of the present application will be described clearly and completely in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, not all. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor should belong to the protection scope of the present application.

[0063] Reference Figures 1-6 The embodiments of the present application provide a flexible array sensor signal transmission system, comprising the following modules:

[0064] The sensing signal acquisition module realizes high-resolution acquisition of external action signals through the cooperative work of the flexible array layer and the sampling synchronization unit.

[0065] The flexible array layer is composed of conductive composite film and microelectrode grid, forming a two-dimensional dot matrix distribution structure. After each array point responds to external action, a corresponding original sensing voltage will be generated, and the mathematical modeling relationship is:

[0066] ;

[0067] Among them, is the sensitivity coefficient of the array point , representing the response efficiency of the point unit material to external pressure; is the force received by the point per unit time , determined by the real-time charge migration and micro-strain response of the flexible material; is the system bias noise of the sensor, derived from the device difference and the disturbance introduced by wiring.

[0068] The sampling synchronization unit sets the sampling frequency , and through the scanning control logic, all units in the array are read in turn and marked with a time stamp , ensuring that the subsequent data can be aligned according to the time-space sequence consistency in the preprocessing stage.

[0069] The sensitivity coefficient is not a fixed constant, but follows the following two-dimensional Gaussian distribution model:

[0070] ;

[0071] Among them, is the array center coordinate, is the array sensitivity spatial diffusion standard deviation, is the film thickness normalization factor.

[0072] In the signal conditioning and preprocessing module, the input signal is first processed... Amplitude normalization is performed to uniformly map the response levels of each array point to the standard interval [0,1]. The normalization formula is as follows:

[0073] ;

[0074] in, and Representing array points respectively The maximum and minimum historical response thresholds, which can be dynamically updated through the system's self-learning mechanism, ensure the relative accuracy of the normalization results. This represents the normalized signal level, and its center may be offset.

[0075] Next, the module performs a level shift operation on the normalized signal, transforming it to a zero-mean reference frame to eliminate long-term bias interference. The processing method is as follows:

[0076] ;

[0077] in, For array points The average level within a certain time window. This step effectively enhances the signal's response sensitivity in subsequent filters, preventing the filtering results from deviating due to excessive bias.

[0078] In addition, the sampling synchronization mechanism will ensure that each frame The data maintains consistency over time, that is All exist There is valid data at all times, which meets the needs of subsequent spatiotemporal collaborative processing.

[0079] In the adaptive noise suppression module, the system first collects and presets a set of environmental characteristic parameters. Including root mean square of signal and noise Spatial directional fluctuation standard deviation Standard deviation of fluctuation in the time direction Mapped to space-time weights :

[0080] ;

[0081] ;

[0082] in, , For empirical weight parameters, For minute quantities, avoid division by zero. The standard deviation of signal fluctuation in the spatial direction, is the standard deviation of signal fluctuation in time direction;

[0083] Then, the conditioned signal The space-time domain joint filtering operator is applied. The spatial filtering adopts the Gaussian weighted neighborhood average method, and the time filtering adopts the recursive smoothing mechanism. The two are fused to construct the following optimization objective:

[0084] ;

[0085] The objective function simultaneously suppresses local spatial fluctuations and time jitter signals, and the optimization solution is the denoised signal.

[0086] In addition, the dynamic gating adjustment unit will monitor changes in real time and automatically adjust the space-time weight ratio to adapt to the environment noise fluctuation scene, such as the deformation of the fitting part, user motion, etc.

[0087] The output matrix is the two-dimensional induced voltage signal after denoising, maintaining high signal-to-noise ratio and multi-dimensional structure consistency, ensuring that the subsequent compression algorithm has good sparsity performance in the structure domain.

[0088] In the dynamic signal compression encoding module, the module first performs a fourth-order wavelet transform on each signal to extract the frequency domain feature parameters:

[0089] ;

[0090] Its output is a plurality of wavelet coefficient levels, capturing the energy distribution characteristics of the signal in different frequency bands. Then, according to the compression strategy and feature sparsity the optimal reserved coefficient set is dynamically selected:

[0091] ;

[0092] wherein is the selected optimal reserved coefficient subset. In order to achieve maximum information retention, the reserved set needs to meet the entropy threshold criterion:

[0093] ;

[0094] is the adaptive entropy threshold, is the information entropy function, used to filter the frequency domain components with strong meaning information.

[0095] Finally, the selected coefficients are compressed to generate data stream through lossless entropy coding.

[0096] In the multi-channel high-frequency transmission module, the sub-channel multiplexing scheduling unit constructs a scheduling optimization model according to the compressed signal flow rate and the available physical channel capacity

[0097]

[0098] , indicates whether the signal is allocated to the channel . The scheduling algorithm adopts a weighted maximum matching strategy to realize dynamic frame packaging, reducing channel conflict and delay.

[0099] The error code reconstruction unit at the receiving end detects and recovers errors based on the parity check matrix for the received data stream . An improved convolutional check algorithm is used, and its reconstruction rate is defined as:

[0100]

[0101] If an abnormal frame is found (does not satisfy the parity check matrix check), the system can automatically recover continuous errors of less than 3 bits, and feedback the abnormal information to the scheduling unit to adjust the channel priority weight in real time, enhancing the reliability of the system.

[0102] Finally, the module transmits the compressed signal to the central processing unit or the remote receiving end in a complete and lossless manner, realizing low-delay and high-precision transmission of the flexible array sensor signal.

[0103] A complete application scheme of a flexible array sensor signal transmission system, the system includes a sensing signal acquisition module, a signal conditioning and preprocessing module, an adaptive noise suppression module, a dynamic signal compression and encoding module, and a multi-channel high-frequency transmission module. The modules are sequentially and closely connected, process and transmit signal data layer by layer, and realize high-quality signal acquisition and transmission of flexible sensors in complex environments.

[0104] First, the sensing signal acquisition module is composed of a flexible array layer and a sampling synchronization unit. The flexible array layer is built on a conductive polymer composite film, and a plurality of response units under two-dimensional array coordinates sense external forces and output corresponding raw sensing voltage signals. The response characteristics of each array unit are determined by the sensitivity parameter and the device offset. The system sets a uniform sampling frequency, cooperates with the array timing control logic, labels each data point with a timestamp, generates a time tag group, and provides a basis for data time domain alignment.

[0105] ​​​​Secondly, the signal conditioning and preprocessing module receives the original voltage data output by the acquisition module, and performs amplitude normalization, level shifting and space-time alignment operations on the original voltage data to form a conditioned signal. In the normalization process, the maximum and minimum historical voltage values of each unit are used to construct a standard interval, and the level shifting is performed by calculating the average level in a sliding time window and performing reference correction. The output signal has a unified amplitude scale and zero mean characteristic, providing standardized input for subsequent noise reduction processing.

[0106] Thirdly, the adaptive noise suppression module takes the conditioned signal as input, and combines environmental characteristic parameter set including signal noise root mean square, spatial fluctuation standard deviation, time fluctuation standard deviation and temperature factor, to obtain space-time filtering weight through weighted mapping mechanism. The module combines spatial neighborhood weighted average and time recursive smoothing strategies to cooperatively suppress spatial fluctuation noise and time jitter disturbance existing in the array signal. The dynamic gating mechanism adjusts the filtering weight in real time according to the environmental parameters, realizing multi-environment adaptive performance optimization. The output is a denoised signal matrix with high signal-to-noise ratio and structural consistency.

[0107] Fourthly, the dynamic signal compression and encoding module performs frequency domain feature extraction operation on the denoised signal to extract a set of key feature coefficients. According to the compression strategy parameters, feature sparsity index and information entropy threshold control variables, the optimal reserved frequency domain coefficient subset is dynamically selected. The module combines the sparse fidelity principle, adjusts the signal compression strength through the variable compression ratio mechanism, and generates the final compressed data stream using the entropy encoding mechanism. The output data volume of the module is significantly lower than that of the original signal, providing efficient load data for the transmission system.

[0108] Finally, the multi-channel high-frequency transmission module distributes the compressed data to multiple physical channels through the scheduling unit, and performs real-time channel allocation according to the sub-channel capacity parameters and scheduling control matrix. The scheduling strategy aims to maximize the channel throughput, and constructs an optimized scheduling structure by combining the channel capacity matrix and the scheduling state matrix. The receiving end is equipped with an error code reconstruction unit, which identifies and repairs small-range error code frames through the parity check mechanism, and the reconstruction accuracy is measured by the error code recovery rate index. The system has dynamic feedback capability, which can adjust the channel priority and resource allocation strategy in real time to ensure transmission stability and robustness.

[0109] It should be noted that the embodiments in the present application and the features and technical solutions in the embodiments can be combined with each other without conflict.

[0110] Obviously, the above-described embodiments are only some embodiments but not all the embodiments of the present application, the preferred embodiments of the present application are shown in the drawings, but do not limit the patent scope of the present application. The present application can be implemented in many different forms, and conversely, the purpose of providing these embodiments is to make the disclosure of the present application more thorough and comprehensive. Although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions recorded in the foregoing specific embodiments, or make equivalent replacements to some technical features therein. Any equivalent structure made by using the content of the present application specification and drawings, directly or indirectly applied to other related technical fields, is also within the patent protection scope of the present application.

Claims

1. A flexible array sensor signal transmission system, characterized by, The application comprises the following modules: The sensor signal acquisition module is used for acquiring an original induction voltage data set under the action of the target based on a multi-point array structure constructed by flexible materials and a timestamp group ; A signal conditioning and preprocessing module is used to normalize, level shift and sample synchronize the raw induction voltage data set and output conditioned signals ; an adaptive noise suppression module for suppressing noise in the signal based on the predefined environmental characteristic parameter and conditioning the signal a multi-domain filtering mechanism is applied to generate a noise-reduced signal the adaptive noise suppression module comprises: an environmental feature mapping unit for mapping predefined environmental feature parameters , the environmental feature parameters including a root mean square of signal noise , a standard deviation of signal fluctuation in spatial direction , a standard deviation of signal fluctuation in temporal direction , to a space-time domain filter weight vector , the filter weight vector in the space-time domain filter weight vector and is determined by the following environmental adaptive formula: ； ； In the formula, , is an empirical weight parameter, is a small quantity to avoid division by zero, is the standard deviation of signal fluctuation in the spatial direction, is the standard deviation of signal fluctuation in the time direction; The multi-domain collaborative filtering unit adopts a spatial smoothing operator and a time domain recursive operator to jointly process the conditioned signal The filter processing is performed to generate a noise-reduced signal , and satisfies the following optimization objective function: ； The dynamic gating adjustment unit is used to adjust the filter weight vector in the spatiotemporal domain. The weight ratio of spatial filtering and temporal filtering is dynamically adjusted in real time to enhance the system's robustness to different noise scenarios. A dynamic signal compression encoding module is configured to encode a signal into a compressed signal according to a signal frequency domain characteristic parameter and a compression ratio strategy , a noise reduction signal is encoded into a compressed signal ; A multi-channel high frequency transmission module for transmitting compressed signals According to a time division multiplexing mechanism, the data is transmitted in parallel through several channels to a data receiving terminal.

2. The flexible array sensor signal transmission system of claim 1, wherein, The signal collection module comprises: The flexible array layer is used to output corresponding original sensing voltage data set in response to external pressure, deformation and charge effect Wherein, the original sensing voltage data set Satisfies: ； wherein, represents the array point at time , the original induced voltage data, represents the array point sensitivity coefficient of the flexible unit, represents the external force received by the point at time , measured in real time by the pressure sensor, represents the device bias noise, derived from the device difference, the disturbance introduced by wiring; A sampling synchronization unit is configured to sample the signals at a set frequency The signals are time-stamped with a full array scanning strategy to obtain a set of time stamps , ensuring that subsequent data can be aligned in time-spatial sequence consistency in the preprocessing stage.

3. A flexible array sensor signal transmission system according to claim 2, wherein, The flexible array layer is composed of conductive polymer composite film and microelectrode grid interlaced, array points Sensitivity coefficient Satisfy the following distribution function: ； wherein , denotes the array center coordinate, is the spatial spread standard deviation of the array points , is the film thickness normalization factor.

4. The flexible array sensor signal transmission system of claim 1, wherein, The signal conditioning and preprocessing module comprises: an amplitude normalization unit for normalizing the raw induction voltage data set to [0,1] and generating a center-shifted signal : ； wherein, is a maximum dynamic range threshold for the array point is a minimum dynamic range threshold for the array point is a maximum dynamic range threshold for the array point is a minimum dynamic range threshold for the array point a level shifting unit for shifting a center of a signal to a zero mean space, output conditioning signal : ； wherein is the historical mean level of the array point of the array point.

5. The flexible array sensor signal transmission system of claim 1, wherein, The dynamic signal compression encoding module comprises: The frequency domain feature extraction unit is configured to extract frequency domain features from the noise-reduced signal performing a fast wavelet transform to obtain frequency domain feature parameters : ； wherein, is a frequency domain feature parameter of the array point , is a noise reduction signal of the array point , is a fourth order wavelet transform algorithm; Compression control unit, according to characteristic sparsity and compression ratio strategy, dynamically select the reserved coefficient set , satisfy the following formula: ； wherein, is a feature dimension of the feature coefficient after wavelet transform, is a feature dimension of the feature coefficient after wavelet transform, is a feature dimension of the feature coefficient after wavelet transform, is a feature dimension of the feature coefficient after wavelet transform, is a feature dimension of the feature coefficient after wavelet transform, is a feature dimension of the feature coefficient after wavelet transform, is a feature dimension of the feature coefficient after wavelet transform, Reversible coding unit, to a set of coefficients The coefficients are applied to a lossless entropy coding algorithm to generate a compressed signal .

6. A flexible array sensor signal transmission system according to claim 5, wherein, the set of reserved coefficients With the entropy threshold control algorithm, the following decision function is satisfied: ； wherein, denotes an entropy threshold control function, is the frequency domain feature parameter of the array point in the i-th feature dimension, is the i-th feature dimension of the array point, is a dynamic entropy threshold.​ 7. The flexible array sensor signal transmission system of claim 1, wherein, The multi-channel high-frequency transmission module comprises: A sub-channel multiplexing scheduling unit is configured to perform multiplexing scheduling based on channel capacity and compression flow rate by using an optimal allocation algorithm, and perform frame packing, wherein the optimal allocation algorithm formula is as follows: ； wherein, represents the throughput gain when the th sub-channel is allocated to the th data stream, represents an allocation matrix, and satisfies represents whether the signal is allocated to the channel , and finally determines that the compressed signal data is allocated to the physical transmission channel ; Error reconstruction unit, by parity check matrix Error detection and repair on received data, compressed signal Reconstruction and feedback of abnormal frame index.

8. A flexible array sensor signal transmission system according to claim 7, wherein, The error code reconstruction unit adopts an improved convolution check reconstruction algorithm, and the reconstruction rate is 100% satisfies: ； wherein, represents the total number of frames, is a parity check matrix, the reconstruction algorithm can realize recovery for less than or equal to 3bit continuous error, and feedback signals are transmitted back to the sub-channel multiplexing scheduling unit for dynamic channel weight adjustment.

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