495 results about "Signal mapping" patented technology

The invention discloses an intelligent multi-modal virtual digital human interaction system based on an AI language large model. The system comprises a high-authenticity face generation module; the high-authenticity face generation module uses an AdaAN network, based on adaptive feature fusion and voice driving and time sequence modeling of voice features, feature information related to voice is extracted, the extracted voice features are processed through a deep neural network, it is ensured that the voice and facial expressions are highly aligned in time and space, and the face recognition accuracy is improved. Collecting a bio-electricity signal, mapping the signal to facial muscle movement, generating a final facial expression, and interacting with a user; the system further comprises an intelligent interaction module, a training optimization and efficient generation module, an efficient integration module, a multi-modal data acquisition module, an AI large model core processing module, a digital human image generation and driving module, an interaction scene adaptation module and a feedback optimization module. The invention further discloses a multi-mode digital human interaction method which has wide application value.

The invention provides a display panel defect detection method and system based on multi-modal data fusion, and the method comprises the steps: obtaining multi-type representation data generated in a detection process of a display panel, and carrying out the feature mapping of the multi-type representation data; respectively mapping the panel appearance characterization data, the panel signal characterization data and the panel interlayer characterization data to a preset feature space to generate a cross-type mapping feature set, and performing feature association enhancement on the cross-type mapping feature set to obtain a cross-type mapping feature set; establishing an association relationship among the appearance mapping features, the signal mapping features and the interlayer mapping features, generating an association enhanced feature set, performing defect feature reasoning on the association enhanced feature set, extracting a feature combination conforming to a defect feature mode, and generating a defect feature reasoning result; and outputting a defect detection result of the display panel. According to the invention, the accuracy and reliability of the defect detection result of the display panel can be improved.

The invention discloses a pump station maintenance-oriented dismounting path practical training method and system, and relates to the technical field of pump station dismounting. The method comprises the following steps: constructing a virtual-real simulation environment of pump station equipment, carrying out dynamic dismounting monitoring identification on pump station parts in the pump station equipment based on the virtual-real simulation environment, and determining a dismounting path; the method comprises the following steps: acquiring operation track data of a training person in real time through a motion capture device, obtaining a somatosensory interaction motion signal, mapping the somatosensory interaction motion signal to a virtual-real simulation environment for disassembly and assembly analysis, and generating a disassembly and assembly motion deviation parameter set; and performing dynamic optimization based on the disassembly and assembly action deviation parameter set in combination with disassembly and assembly multi-dimensional feedback data, generating multi-stage practical training difficulty, performing adaptation on practical training personnel according to a disassembly and assembly path according to the multi-stage practical training difficulty, and determining a multi-stage practical training scheme. The technical problems that in the prior art, pump station maintenance dismounting practical training lacks real scene simulation, and efficient skill training is difficult to achieve are solved, and the technical effect of improving the practical training effect is achieved.

Disclosed are a method and device for repeatedly transmitting an uplink channel in a wireless communication system. A method for a terminal to repeatedly transmit an uplink channel in a wireless communication system according to an embodiment of the present disclosure comprises: an uplink channel mapping step for mapping one uplink channel to a plurality of transmission occasions (TO) associated with a plurality of transmission units (TU) that are fewer in number than the plurality of TOs, wherein each of one or more specific TUs among the plurality of TUs includes two or more contiguous TOs; a step for mapping a spatial relation reference signal (RS) to each of the plurality of TOs; and a step for transmitting the one uplink channel to a base station at each of the plurality of TOs on the basis of the spatial relation reference signals, wherein the spatial relation reference signals to be mapped to the two or more contiguous TOs included in the one or more specific TUs may be identical or different.

The invention provides an automatic operation process management method and device based on multi-sensor fusion, and relates to the technical field of automatic processes, and the method comprises the steps: constructing an instrument pose flow and operation field geometric model under a unified coordinate system through unified timestamp alignment and spatial geometry solution of multi-source sensor data; semantic segmentation and three-dimensional reprojection are combined to generate a shielding probability graph, cross-channel compensation reconstruction is carried out, and the image integrity is improved. And forming a conflict risk set through voxel field discretization and short-time prediction, and performing combined solution with standardized operation process constraints to generate a scheduling instruction and a safety time window. And finally, mapping the image, the pose and the physiological signal to a knowledge graph, and outputting consistency judgment and deviation correction suggestions in combination with a time sequence attention recognition process node. According to the method, the problem that the operation process management is not accurate enough due to the problems of view overlapping and the like of multi-source sensor data can be solved.

The invention discloses a real-time fatigue monitoring system based on an EEG signal, and relates to the technical field of neural regulation and control, and the system comprises a chaos reconstruction module which collects an original EEG signal, carries out the phase-space reconstruction of the original EEG signal through a chaos attractor processor, generates a chaos trajectory tensor, and carries out the phase-space reconstruction of the original EEG signal when the Lyapunov index of the chaos trajectory tensor exceeds a preset index threshold value, outputting a low-speed frequency band oscillation component and a high-speed frequency band oscillation component; and the closed-loop termination module is used for terminating the physical light field projection when the continuous detection periods of the updated alertness drop probability value, cognitive overload probability value and physiological fatigue probability value are all lower than a preset safety threshold value. Through multi-scale recursion phase space reconstruction, an original EEG signal is mapped to a three-dimensional chaotic trajectory tensor space, and dynamic frequency band decoupling is triggered by using a Lyapunov exponent threshold.

The invention relates to the field of chip testing, in particular to a chip signal integrity test analysis method, device and equipment and a storage medium. The method comprises the following steps: extracting a chip performance index based on a chip design requirement document; performing saturation frequency excitation setting based on the chip performance index to generate an excitation test signal; performing a multi-port signal excitation test and parallel signal sampling based on the excitation test signal to generate a high signal-to-noise ratio response data set; obtaining a chip layout design file; performing chip communication link analysis on the chip layout design file, and constructing communication line layout data; performing signal mapping and waveform quality evaluation on the communication line layout data based on the high signal-to-noise ratio response data set to obtain a node signal distortion rate; and performing signal integrity quantization and theoretical signal integrity score comparison based on the node signal distortion rate, and marking a fault node. According to the invention, the chip testing efficiency is improved, and the signal abnormal part of the chip is accurately and efficiently detected.

The invention relates to the technical field of electrical measurement and power system monitoring, in particular to a cable insulation fault detection method. The method comprises the following steps: measuring an electric field signal and constructing an electric field distribution model in a cable to obtain a signal mapping result of a cable insulation state; the signal mapping result of the cable insulation state is mapped to a frequency domain, smoothing processing is carried out, multi-scale analysis is carried out on the signal after smoothing processing, fault features of the cable are extracted, the insulation state of the cable is quantified, the probability that the cable has an insulation fault is calculated, and a basis is provided for fault early warning and maintenance. The problems that most of traditional insulation fault detection methods depend on single electrical or spectral characteristic detection, so that detection information is incomplete; the stability is poor, and misjudgment or missed judgment is easily caused; in some methods, power failure or direct contact with a cable is needed for measurement, and continuous operation of the cable is affected. And complex electric field distribution and dynamic change thereof in the cable are difficult to accurately describe, and the fault development trend is difficult to predict.

The invention relates to the technical field of medical treatment, in particular to a multi-parameter collaborative infusion process intelligent monitoring and priority calling method, which comprises the following steps of: acquiring four-dimensional monitoring data through a liquid level pressure sensor array, an infrared dripping speed detection module, a flexible temperature patch and a photoelectric heart rate sensor, and combining an LSTM (Long Short Term Memory) anomaly prediction model, and outputting the pipeline state score, the allergy risk index and the infusion progress prediction value in real time. And generating a real-time risk value through a weight configuration mechanism and stage identification logic, judging a risk level according to a dynamic threshold value, and outputting a corresponding early warning level signal. And the system further maps the early warning level signals into common, emergency and critical call instructions, and distributes the common, emergency and critical call instructions to a multi-channel execution terminal to construct a multi-level linkage response mechanism of closed-loop feedback. According to the invention, the accuracy and response efficiency of infusion risk identification can be significantly improved, and the method is suitable for intelligent reconstruction and automatic early warning response scenes of a clinical nursing information system.

The invention provides a multi-channel one-core multi-detection integrated detection method, which is applied to the technical field of in-vitro diagnosis and comprises the following steps: acquiring time sequence original signals of blood routine, liver function and kidney function channels; obtaining a baseline stable primary signal through cross-channel baseline trend coupling and adaptive correction; adopting a machine learning model to identify interference components and performing component offset to obtain a decoupling signal; mapping the decoupling signal into a physiological parameter value and carrying out rationality test; when the low credible parameter exists, a rechecking mechanism is triggered; performing weighted fusion on the basis of the rationality test result and the recheck signal to obtain a comprehensive parameter set; and finally generating a comprehensive detection report containing the credibility score. Cooperative processing and fusion analysis of multi-channel detection signals are realized, and the accuracy and reliability of a detection result are remarkably improved through interference identification and a dynamic recheck mechanism.

The invention provides a helicopter cable fault detection system and method, and belongs to the technical field of helicopter electrical system detection. The system comprises a positioning and matching device and a multi-modal signal analysis device, the positioning and matching device determines the relative position with a to-be-detected aviation plug through a dual-spectrum camera, generates a contact coordinate matrix and a contact and probe mapping table, injects a test signal to identify the contact material so as to obtain a topology result, and detects the contact pressure; and the multi-modal signal analysis device is used for matching parameters based on the topology result and the mapping table, setting a detection signal, positioning the contact, applying a signal in combination with pressure, acquiring a response signal, establishing a mapping table, and analyzing the response signal to generate a contact and fault result mapping table. According to the invention, the whole process automation from aviation plug positioning, signal detection to fault analysis is realized, the system can complete the core links of contact coordinate generation, signal mapping, fault determination and the like without manual intervention, and the detection efficiency is greatly improved.

The invention belongs to the technical field of communication, and particularly relates to a high-robustness multi-channel frequency shift Chirp modulation signal generation method and system for a time-frequency doubly selective channel. The method comprises the following steps of: 1, calculating robust code digits based on statistical characteristics of time delay and Doppler frequency shift of an ionized layer channel; 2, based on the number of robust code bits in the step 1, setting an address code, and realizing RO-MFSCM signal mapping of binary information bits; 3, modulating and transmitting the RO-MFSCM signal based on the information mapping in the step 2, thereby realizing the transmitting process of the RO-MFSCM signal; step 4, based on the emission signal in the step 3 and the robust code digit in the step 1, the receiver carries out robust domain energy aggregation on the RO-MFSCM signal; and step 5, based on the multi-channel robust domain energy aggregation signal in the step 4, adopting a multi-channel parallel demodulation algorithm and de-mapping to realize the receiving process of the RO-MFSCM signal under the time-frequency doubly selective channel. The method aims at the transmission fading problem of the FSCM signal in the ionosphere time-frequency doubly selective channel.

The invention relates to the technical field of thunder and lightning signal sampling, and discloses a GAN-based thunder and lightning very-low-frequency electromagnetic wave signal up-sampling method, a terminal and a medium. The method comprises the following steps: preprocessing a lightning very-low-frequency low-sampling signal and a real high-sampling signal; constructing a generative adversarial network; the generator is used for extracting initial features from the low-sampling signals and performing residual enhancement, then mapping low-sampling features after residual enhancement to a high-sampling dimension through a feature-guided dynamic interpolation mechanism, and performing correction and signal mapping on intermediate features after interpolation to obtain reconstructed signals matched with a target high-sampling rate; the discriminator is used for reconstructing the authenticity probability of the signal; after a generator and a discriminator are jointly trained by adopting an alternative updating mode of discriminator loss and generator loss, a newly input low sampling signal is preprocessed, and a corresponding reconstruction signal is output through the generator. According to the invention, accurate reconstruction from a lightning very-low-frequency low-sampling signal to a high-sampling signal is realized.

The invention relates to the technical field of radar signal processing, and discloses a complex ground reflection environment-oriented dual-path target detection method, which comprises the following steps of: performing quantile truncation and discretization processing on original range profile data, mapping a processed signal to a D-channel discrete feature space and generating a D-dimensional discrete feature matrix; the D-dimensional discrete feature matrix is input into a dual-channel parallel processing architecture, a CFAR adaptive detection module is adopted in a first path to suppress strong clutters to generate a first response matrix, and a false attention mechanism is adopted in a second path to enhance weak targets to generate a second response matrix; performing spatial alignment and weighting processing on the first response matrix and the second response matrix, and performing cross-domain confidence fusion to output a comprehensive response matrix; and carrying out fixed threshold binarization processing on the comprehensive response matrix based on a global threshold to obtain a target detection result. The method has both local gain compensation and global adaptive suppression, and can realize more robust detection of a weak target under a complex background.

The invention discloses a spiral line three-dimensional positioning method for buried tap water pipeline distributed optical fiber temperature measurement. The method comprises the following steps: firstly, winding a sensing optical fiber on the outer wall of a pipeline in a cylindrical spiral line form; then, the temperature of the pipeline is monitored in real time through a distributed optical fiber Raman temperature measurement system; when the pipeline leaks to cause temperature abnormity, the system accurately positions the one-dimensional arc length L of an abnormal point along the optical fiber by using the optical time domain reflection principle; finally, the geometric parameters of the cylindrical spiral line and the measured arc length L of the optical fiber are creatively combined, and the coordinates of the leakage point in the three-dimensional space are calculated through a parameter equation. According to the method, one-dimensional temperature measurement signals are successfully mapped into three-dimensional space coordinates, the axial position of the leakage point along the pipeline and the radial direction of the leakage point on the cross section of the pipeline can be accurately determined at the same time, and the problem that accurate three-dimensional positioning cannot be carried out on the leakage point of the buried pipeline in the prior art is solved. The maintenance efficiency is greatly improved; and the maintenance cost is reduced.

The invention discloses a rolling bearing fault time-varying instantaneous feature enhancement and extraction method. The method comprises the following steps: acquiring an original bearing fault signal; decomposing an original bearing fault signal based on an adaptive noise complete set empirical mode decomposition method to obtain a series of intrinsic mode function components; component properties are distinguished based on a correlation coefficient hopping criterion, and denoising is carried out; mapping the bearing fault signal after noise reduction to a time-frequency chirp rate space based on a local maximum frequency chirp rate synchronous compression chirp transformation method; and extracting a time-frequency chirp rate coefficient reflecting fault time-varying instantaneous characteristics from the extruded time-frequency chirp rate space, reconstructing a bearing fault impact component, performing Hilbert envelope analysis on the bearing fault impact component, and identifying the characteristic frequency of the bearing fault. According to the method, the fault time-varying instantaneous characteristics of the rolling bearing can be clearly and accurately extracted, and the method has a good engineering application prospect.

A wearable electroencephalography device includes an ear cuff around an auricle of an ear comprising a curved structure conforming to contours of the ear, an electroencephalography electrode positioned on the ear cuff and contacting skin for detecting brain activity signals, a wireless controller integrated with the ear cuff and processing the brain activity signals from the electroencephalography electrode, and a wireless communication module transmitting the processed brain activity signals to an external device. The wearable electroencephalography device includes a mobile application receiving the transmitted brain activity signals from the wireless communication module, analyzing the brain activity signals using machine learning algorithms to determine emotional states of a user, and providing real-time feedback regarding the emotional states through a user interface. The mobile application comprises a self-report interface allowing the user to input subjective state data and a live-feed interface displaying real-time subjective state data derived from the brain activity signals.

The invention discloses an integrated optical signal receiver and method, particularly relates to the field of optical communication, is used for solving the problem of video stream transmission quality reduction caused by optical signal distortion in a low optical signal-to-noise ratio environment, and aims to eliminate cross coupling caused by a birefringence effect by constructing a dynamic polarization state transfer matrix. Generating a clear independent polarization state electric signal sequence; time-frequency domain processing is carried out on the electric signal sequences, linear phase noise caused by laser linewidth and phase deviation caused by an optical fiber nonlinear effect are compensated, and signals with higher phase accuracy are output; then, the signals are mapped to a four-dimensional constellation space, a clustering method is used for generating an adaptive judgment boundary, symbol judgment is optimized, and the anti-noise capability is improved; through cooperative work of an inner loop and an outer loop, system parameters are dynamically adjusted according to signal characteristics, and processing stability and adaptability are enhanced. And finally, performing soft decision decoding on the signal, improving the decoding accuracy in combination with phase residual correction, and outputting a reconstructed video data stream.

The invention relates to an analog quantity selection method and system based on a Boolean switch, in particular to the field of industrial automation, and aims to directly realize signal mapping by adopting a pure hardware combinational logic circuit and thoroughly eliminate instruction execution time, so that the state change of a Boolean switch signal can be instantaneously reflected on analog quantity output; according to the method, real zero-delay switching is achieved, meanwhile, program adjusting algorithms in any form are avoided, disturbance phenomena such as overshoot or undershoot are completely eradicated from the principle, and high stability and accuracy of output signals are ensured. Finally, according to the technology, the control instruction can be accurately and synchronously issued without delay, and the response speed and the synchronization precision of the whole control system in a high dynamic response industrial scene are remarkably improved.

The invention discloses an automatic configuration method for dispatching access of plant station equipment, and the method comprises the steps: A, obtaining communication data from the plant station equipment, and enabling the communication data to comprise a frame structure field; b, based on the frame structure field, identifying a communication protocol type by comparing an initial identifier, a function code field and a frame length field; c, in combination with the communication protocol type and the equipment attribute field, an access configuration strategy template is matched, and the template comprises a communication parameter set, an interface adaptation rule and a signal mapping relation; d, generating a structured configuration data file according to the configuration strategy template; and E, deploying the configuration data file to a scheduling system to complete equipment communication registration and data interaction initialization.

The invention discloses a progressive migration training decoding method based on GPT, and belongs to the field of optic nerve decoding. According to the method, a GPT-based category and text decoding model is constructed based on functional magnetic resonance signal data recorded when a subject watches a large number of visual perception images, and the method comprises an image encoder used for embedding image feature vectors extracted by adopting a pre-training model into a hidden feature space; prompt containing task information and position codes are embedded into a hidden feature space; the visual information encoder is used for mapping a brain visual region voxel signal to a hidden feature space by using a GRU; the whole-brain information encoder is used for guiding the selection of voxel signals of other regions of the brain by taking a voxel signal of a visual region of the brain as a context, and mapping global information to a hidden feature space; and the language generator fuses all the hidden features and selects the Token with the highest probability to generate the text of the corresponding content. According to the method, the decoding accuracy is improved, and the generalization ability of the model is enhanced.

The invention provides a radar signal anti-interference processing method and system based on a space-time attention mechanism. The method comprises the following steps: mapping an original horizontal polarization channel signal and an original vertical polarization channel signal into an interference suppression space, and generating a three-dimensional feature tensor comprising an amplitude component, a polarization phase difference component and an interference suppression residual component; according to the amplitude component, dividing a radar observation area into multiple layers of target blocks in a spatial dimension, and calculating a space-time association weight between adjacent layers of target blocks through a space-time attention mechanism; and according to the space-time correlation weight, screening out a plurality of target blocks meeting a preset continuous motion condition, and decoding a motion mode of the plurality of target blocks by using a long short-term memory network to generate an anti-interference target trajectory coordinate and a velocity vector. According to the method, the polarization characteristics and the space-time attention mechanism are fused, the anti-interference effect of radar signal processing in a strong interference environment is improved, and then the detection precision of the multi-target motion trail is improved.

The invention provides a method and a device for transmitting data, which can be applied to an OTN (Optical Transport Network) system, reduce the processing cost during service data mapping and achieve the purpose of improving the system performance. The method comprises the steps that firstly, i paths of first optical transport network (OTN) signals are mapped to second OTN signals, and the second OTN signals comprise n paths of third OTN signals; and then generating a fourth OTN signal from the second OTN signal based on the X-byte interleaving granularity, and sending the fourth OTN signal. Wherein the mapping granularity adopted for mapping the jth path of first OTN signal in the i paths of first OTN signals to the second OTN signal is nj * X bytes, the rate of the jth path of first OTN signal is nj times of the reference rate, i is an integer greater than or equal to 1, j is equal to 1, 2, 3... i, nj is an integer greater than or equal to 1, and n and X are integers greater than 1.

The invention discloses a method and an apparatus in a node for wireless communication. A node receives a first information block indicating a plurality of PRACH opportunities; the node receives a second information block indicating a target sub-band and at least one full duplex symbol; the first PRACH opportunity is one of the plurality of PRACH opportunities indicated by the first information block, and the first PRACH opportunity is overlapped between a time domain and at least one full duplex symbol indicated by the second information block; a synchronous broadcast signal is mapped to a valid PRACH opportunity of the plurality of PRACH opportunities, the validity of the first PRACH opportunity depending on a relationship of the first PRACH opportunity between the frequency domain and the target sub-band. The random access capacity is improved.

The invention discloses a blade flutter monitoring method and device, a monitoring system, a storage medium and a computer program product, and the method comprises the steps: carrying out the modal feature analysis of a blade to determine a flutter monitoring region, laying a flexible sensing element in the flutter monitoring region, and enabling the flexible sensing element to be used for collecting the strain signal of the blade; in the running process of the blade, real-time running parameters of the blade and real-time strain signals collected by the flexible sensing element are obtained; determining a reference strain signal corresponding to the inertial centrifugal force of the blade according to the real-time operation parameters and a preset mapping relation; the mapping relation is the mapping relation between the operation parameters of the blade and the reference strain signal; deducting the reference strain signal from the real-time strain signal to obtain a target strain signal corresponding to the aerodynamic force; and according to the target strain signal and a preset formula, calculating pneumatic work of the blade in a set vibration period, and according to the pneumatic work, judging whether the blade flutters or not. According to the scheme, the accuracy and the real-time performance of flutter judgment are improved.

The present disclosure proposes a concept of training a machine learning network for use with a ToF camera. Based on a predefined synthetic scene, it is simulated a ground truth time resolved illumination return signal for a light pulse emitted from the ToF camera to the synthetic scene and scattered back from the synthetic scene to the ToF camera. The synthetic scene comprises a plurality of scene points with known distances between each of the scene points and the ToF camera. Based on the ground truth time resolved illumination return signal and a simulation model of the ToF camera, it is then simulated an output signal of at least one ToF pixel capturing the synthetic scene. Based on the simulated output signal of the ToF pixel and the ground truth time resolved illumination return signal, weights of the machine learning network are adjusted to cause the machine learning network to map the simulated output signal of the ToF pixel to an output time resolved illumination return signal approximating the ground truth time resolved illumination return signal.

A method may obtain a failure propagation model, wherein the failure propagation model comprises: a model representation of a plurality of hardware components, a set of hardware failure probabilities; and a logic that maps signals to the hardware components. A method may receive an alert signal from the aircraft. A method may map the alert signal to the plurality of hardware components. A method may perform a root-cause diagnosis of the alert signal that has been mapped to the plurality of hardware components comprising: determining via the failure propagation model, one or more combinations of hardware failures associated with the alert signal; and determining a probability of occurrence associated with the combinations of hardware failures. A method may display a report that includes combinations of hardware failures associated with the alert signal and the probability of occurrence associated with the combinations of hardware failures.

The invention relates to the field of virtual debugging, and particularly discloses a control program and a virtual debugging method of a cold roll forming machine shearing system, and the method comprises the steps: S1, building a virtual model comprising a feeding mechanism, a shearing mechanism, a servo driver and a photoelectric sensor in an NX MCD environment, outputting an accumulated feeding length and a sensor signal by the model, and receiving the position setting of a cutter; s2, a trimming procedure is written for an S7-1200 PLC (programmable logic controller) in a TIA Portal (TIA Portal); s3, two-way communication between the NX MCD and the PLC is established through OPC, the accumulated feeding length and a sensor signal are mapped into PLC input, and the cutter position set output by the PLC is mapped into NX MCD servo input; and S4, the two are simulated synchronously, control logic and parameters are optimized based on model feedback, and the target is to minimize fixed length error and motion instability. According to the invention, the problems of long field debugging period, high cost, inaccurate shearing triggering and unstable return in the prior art are solved, and the shearing precision and the operation reliability are improved.

The invention discloses a network data intelligent management system and method based on artificial intelligence, and relates to the technical field of network data management, original sensor data is projected to a sparse domain through an auto-encoder, whether the sparsity of the original sensor data meets a compressed sensing condition is verified, if yes, a Gaussian random matrix is constructed, and if not, a Gaussian random matrix is constructed; the Gaussian random matrix maps sparse signals to a low-dimensional space, a server side uses a basis tracking reconstruction algorithm to recover original sensor data according to the Gaussian random matrix, and after performance data in actual deployment are collected, the performance score of the deployment is calculated and obtained according to the evaluation model. And a corresponding optimization strategy is generated according to a performance score calculation result. According to the invention, in an application scene with limited bandwidth and limited equipment energy consumption, the data quality is ensured, the cost of data acquisition and transmission is significantly reduced, and the overall efficiency and intelligent level of network management are improved.

Provided in the embodiments of the present application are a signal transmission method, a communication device and a storage medium. The communication device comprises at least one first conversion unit, a first crossing unit, at least one second crossing unit and at least one second conversion unit. In the embodiments of the present application, the signal transmission method comprises: by means of the first conversion unit, mapping a client signal to a first-type signal container and sending the first-type signal container to the first crossing unit; by means of the first crossing unit, scheduling the first-type signal container to the second crossing unit; by means of the second crossing unit, demapping the first-type signal container to a second-type signal container and cross-scheduling the second-type signal container to the first crossing unit; and by means of the first crossing unit, synchronously cross-scheduling the second-type signal container to a corresponding second conversion unit, which can demap the second-type signal container to a fifth signal container and demap the fifth signal container to the client signal.