41 results about "Matrix sequence" patented technology

The invention belongs to the technical field of robot control, and discloses a foot-arm multi-task control method and system based on time-varying terminal cost, and the method comprises the steps: generating a whole-body reference trajectory of a robot offline, and pre-calculating a time-varying terminal cost matrix sequence based on the reference trajectory; based on the time-varying terminal cost matrix sequence, constructing and solving a nonlinear model predictive control optimization problem to obtain an optimization sequence of a system state and control input; and the optimization sequence serves as a reference instruction and is input into a layered whole-body controller for multi-task priority optimization, and a joint control instruction is generated and output to a robot execution mechanism. According to the method, the problem of short vision of a traditional NMPC in a long-time task is effectively relieved, and the operation precision of the robot is remarkably improved.

The invention discloses an RMFE-based distributed matrix multiplication implementation method. The method comprises the following steps: initializing parameters, constructing an RMFE function, splitting a to-be-processed matrix sequence into sequence vectors, sequentially carrying out phi function operation, obtaining a matrix distributed multiplication result through distributed matrix multiplication, and recovering through psi function operation to obtain a multiplication result of a to-be-processed matrix. According to the method, a distributed multiplication problem of a plurality of matrixes on a small domain / Galois ring is converted into a distributed multiplication problem of a single matrix on a large domain / Galois ring, and the calculation overhead on an expansion domain is allocated to the plurality of matrixes, so that the communication overhead required by distributed matrix multiplication is effectively reduced; the method has important application value for a high-speed communication system.

The application discloses a kind of stability sustained analysis method of underwater structure tensor information based on memory characteristics;It relates to underwater measurement technical field, the application is measured to underwater structure in N time points, obtains N three-dimensional height difference tensor matrix sequence;With the dimension of one of three-dimensional height difference tensor matrix as benchmark and as benchmark matrix, matrix alignment operation is carried out to other N-1 three-dimensional height difference tensor matrix, and alignment matrix is obtained;Difference is obtained between alignment matrix and benchmark matrix, and point difference matrix is obtained;For each point difference matrix, select edge irregular area and calculate effective selected area value;Based on the three-dimensional height difference tensor matrix obtained in current stage measurement, current memory length and historical effective selected area value set, current effective selected area value is updated by linear transformation and nonlinear activation processing function, the stability change trend of underwater structure is analyzed and early warning is carried out, and the accuracy of underwater structure tensor information stability analysis is effectively improved.

This invention discloses a method and device for controlling the lifting and lowering of sweeping discs in a sweeper truck, relating to the field of sanitation vehicle control technology. The method includes: S1, periodically collecting sweeping disc linkage monitoring data and preprocessing the data; S2, generating a sweeping disc linkage state matrix sequence, extracting matrix shift markers, and generating sweeping disc linkage judgment data based on the sweeping disc motion state; S3, matching the matrix shift markers to corresponding sweeping disc lifting action templates, performing timer combination matching, calculating the sweeping disc action duration value, and generating linkage strategy data; S4, generating corresponding side sweeping disc lifting control commands based on the linkage strategy data, and executing timing control according to the sweeping disc action duration value. This invention solves the problem of insufficient linkage between operation mode switching and side operation switching in existing sweeper truck sweeping disc lifting control, resulting in delayed sweeping disc lifting response, uncoordinated actions, and difficulty in dynamically adjusting the control duration.

The invention discloses an underwater structure tensor information stability continuous analysis method based on memory characteristics. The method relates to the technical field of underwater measurement, and comprises the following steps: measuring an underwater structure at N time points to obtain N three-dimensional elevation difference tensor matrix sequences; by taking the dimension of one three-dimensional elevation difference tensor matrix as a reference and a reference matrix, carrying out matrix alignment operation on the other N-1 three-dimensional elevation difference tensor matrixes to obtain an alignment matrix; performing subtraction on the alignment matrix and the reference matrix to obtain a point difference matrix; for each point difference matrix, selecting an edge irregular area and calculating an effective selected area value; based on a three-dimensional elevation difference tensor matrix obtained through measurement at the current stage, the current memory length and a historical effective selected area value set, a current effective selected area value is updated through linear transformation and a nonlinear activation processing function, the stability change trend of the underwater structure is analyzed, and early warning is carried out. And the accuracy of underwater structure tensor information stability analysis is effectively improved.

The invention relates to a key identification method based on a millimeter wave radar. The method comprises the following steps: S1, obtaining distance angle spectrograms corresponding to different time frames; s2, obtaining an effective RA matrix corresponding to the distance angle spectrogram; s3, sorting the effective RA matrixes of the time frames, carrying out peak value detection based on the sum of the energy intensities of the effective RA matrixes, taking the effective RA matrix corresponding to each peak value as a seed matrix of single-bond data, and constructing a matrix sequence by using a single seed matrix and certain effective RA matrixes before and after the seed matrix; s4, respectively extracting spatial-temporal features based on each matrix sequence, and obtaining a key classification result based on the extracted spatial-temporal features and a first classifier; s5, extracting a continuous number part as a key sequence corresponding to one word according to a continuous key classification result; s6, generating a plurality of candidate words based on each key sequence; and S7, based on the candidate words of each key sequence, obtaining a text inference result based on a large language model. Compared with the prior art, the method has the advantages of high accuracy and the like.

This invention discloses a bird sound recognition method based on voiceprint recognition, comprising the following steps: collecting bird sound signals from a natural environment and preprocessing them to obtain a sound frame sequence; extracting acoustic features and calculating a frame feature vector sequence; performing segmented processing to calculate a structure consistency score and comparing it to obtain a frame weight sequence, constructing a weighted covariance matrix sequence; performing symmetric positive definite matrix constraint processing and mapping it to a symmetric positive definite matrix manifold space to obtain a manifold representation matrix sequence; combining the manifold representation matrix sequences to construct a covariance manifold trajectory; calculating shape invariants and constructing voiceprint feature vectors; inputting an improved supervised metric learning model to calculate similarity and determine the bird species category or individual bird voiceprint information corresponding to the bird sounds; and outputting the bird sound recognition result. This invention achieves bird sound recognition through a covariance manifold trajectory voiceprint recognition method, possessing the advantage of high recognition accuracy.

The invention provides a power node load prediction method and device, and relates to the technical field of data processing, and the method comprises the steps: obtaining a power grid weighted directed graph of a target regional power grid, and carrying out the prediction of a plurality of continuous historical time steps before a prediction time point; respectively constructing a node feature matrix and an adjacent weight matrix of the power grid weighted directed graph corresponding to each time step; stacking the node feature matrixes of the plurality of historical time steps along the time dimension to form a node feature matrix sequence, and stacking the adjacent weight matrixes of the plurality of historical time steps along the time dimension to form an adjacent weight matrix sequence; and inputting the node feature matrix sequence and the adjacent weight matrix sequence into a pre-trained space-time diagram neural network model to obtain a power load prediction value of each power grid physical node in the target regional power grid in one or more time steps in the future.

The invention discloses a vehicle interaction decision-making method based on an unprotected intersection and a related device, and the method comprises the steps: obtaining a system state matrix, an own vehicle control matrix and an other vehicle control matrix in linear system state equations of an own vehicle and an other vehicle, first to fourth positive semi-definite matrixes and first and second positive definite matrixes are arranged in the optimization target of the linear quadratic differential game problem of the own vehicle and the other vehicle; taking the first positive semidefinite matrix and the third positive semidefinite matrix as a first intermediate matrix and a second intermediate matrix at the Nth moment respectively; according to the first intermediate matrix, the second intermediate matrix, the first positive definite matrix, the second positive definite matrix, the system state matrix, the self-vehicle control matrix, the other-vehicle control matrix, the second positive definite matrix and the fourth positive definite matrix at the Nth moment, reverse recursion is carried out on an optimal control gain matrix equation set of the two vehicles and a coupling Riccati equation of the first intermediate matrix and the second intermediate matrix at the kth moment; and obtaining an optimal control gain matrix sequence of the vehicle, and controlling the vehicle according to a control quantity sequence determined based on the optimal control gain matrix sequence.

This invention relates to an energy production forecasting method using a partial grey model with fractional derivatives, belonging to the field of energy production forecasting. It first selects the current monthly production values ​​of different energy sources as a database to construct an original matrix sequence X. (0) As input to the model; secondly, fractional derivatives and fractional accumulation operators are introduced when constructing the model to dynamically predict energy output under the grey effects of exponential and sine functions; finally, the simulated value X of the model is calculated. (r) , Restore value X (0) Furthermore, the model was compared with a control model in various indicators; the particle swarm optimization algorithm was used to find the optimal parameter vector that minimizes the MAPE value; finally, the new model was applied to energy production forecasting. This invention introduces exponential and trigonometric functions, giving the model's time response function oscillatory characteristics, thus accurately capturing and effectively mapping data volatility, significantly improving adaptability and flexibility; the integration of fractional derivatives and fractional accumulation operators into the model significantly improves prediction accuracy.

The invention relates to the technical field of spectral measurement and data correction technologies, in particular to a reflectivity reconstruction method based on adaptive weighted multi-core support vector regression, which comprises the following steps: calling cross-aperture spectral measurement data, aligning and normalizing, comparing channel response vector direction differences, calculating neighborhood spacing and combining, and calculating the reflectivity of the cross-aperture spectral measurement data. According to the method, dynamic analysis of spectrum form indexes and local structure indexes is combined, kernel function weights are adjusted in a self-adaptive mode, spectrum form differences and local changes between different samples are accurately captured, and the reflectivity prediction curve sequence is obtained through dynamic analysis of the spectrum form indexes and the local structure indexes. According to the method, a dynamic weighting kernel function based on similarity between samples and neighborhood density is utilized to solve the problem that local complexity cannot be effectively incorporated into modeling, so that under different optical sampling conditions, the reconstruction result of spectral data is more stable and accurate and adapts to the change of complex spectral data, and the accuracy and consistency of reflectivity reconstruction are improved.

The invention provides an environment monitoring data evidence storage method based on a block chain, and relates to the technical field of data processing. The method comprises the following steps: firstly, performing matrix processing on target environment monitoring data to form an environment data matrix sequence; secondly, performing multi-angle semantic mining on the environment data matrix sequence to form a plurality of semantic mining vectors, and forming an environment data global vector based on the plurality of semantic mining vectors; then, performing anomaly recognition based on the global vector of the environmental data to form an environmental anomaly analysis result; on one hand, when an environment anomaly analysis result reflects that an anomaly exists, encrypted environment monitoring data and the environment anomaly analysis result are stored in a target block chain; and on the other hand, when the environment anomaly analysis result reflects that no anomaly exists, the target environment monitoring data and the environment anomaly analysis result are stored in the target block chain. Based on the method, the problem that data privacy and accessibility are difficult to effectively balance in the prior art can be improved.

This invention relates to a data concatenation method, computing device, medium, and program product. The method includes: acquiring multiple query matrix sequences and multiple key matrix sequences; generating a first index matrix and a second index matrix to indicate the concatenation method of the sequences based on a preset concatenation size, the size of the multiple query matrix sequences, and the size of the multiple key matrix sequences. A first dimension of the first index matrix represents the number of groups of the multiple query matrix sequences, and a second dimension represents the sequence range of the query matrix sequences contained in the same group. The method further includes performing attention calculations on multiple input data using an attention operator based on the first and second index matrices to obtain attention results corresponding to the multiple input data. This method can increase the upper limit of the number of concatenated query matrices and key matrices, and improve the concatenation efficiency of query matrices or key matrices.

This invention discloses a communication security detection method and system based on deep learning, belonging to the field of deep learning technology. The method includes: collecting communication data from a communication network to generate a communication session data set; performing field encoding and normalization processing to generate a communication behavior matrix sequence; constructing an improved PixelCNN to generate a communication field conditional probability matrix; calculating and generating session anomaly scores to generate a long-term weak anomaly association sequence; generating an abnormal communication topology map; executing the METIS algorithm based on the abnormal communication topology map to generate a candidate lateral penetration subgraph set; calculating penetration area scores based on the candidate lateral penetration subgraph set to generate a communication security detection result. This invention, by introducing an improved PixelCNN and METIS algorithm, achieves high-precision automatic detection of concealed lateral penetration behavior and anomaly propagation area localization in complex communication networks.

Provided is an image-to-video generation method. A source image including a target object is inputted into a first video generation model to obtain a material video. An interframe transform matrix sequence is determined according to the material video. An object masked image corresponding to the target object is obtained from the source image. The interframe transform matrix sequence is applied to the object masked image to obtain a masked image sequence including a plurality of masked images. The interframe transform matrix sequence is applied to the source image to obtain a target object image sequence including a plurality of target object images. Target input data is determined according to the source image, the masked image sequence and the target object image sequence. The target input data is inputted into a second video generation model supporting local redrawing to obtain a target video.

This invention discloses a deep learning-based method and system for determining the state of surface electromyography (EMG) signals, relating to the field of signal processing technology. The method includes the following steps: S1, generating a multi-channel signal matrix sequence; S2, outputting a dynamic spatial adjacency matrix; S3, by improving the RGCN model, dividing neighbor features based on relation indices, triggering weight competition to rearrange and solidify the block diagonal submatrices through index perturbation, and then relying on its exclusive shielding projection to block gradient backpropagation of dissimilar relations, finally aggregating the isolated projection and its own features to output a spatial feature map sequence; S4, outputting a spatiotemporal joint temporal feature tensor; S5, outputting a discriminative feature vector; S6, outputting a probability distribution vector; S7, outputting the state discrimination result. This invention overcomes the limitations of traditional methods, such as static mapping distortion, single feature channel recalibration, and neglect of temporal dynamic constraints, providing an efficient solution for the accurate decoding of continuous non-stationary EMG signals.

The invention relates to the technical field of space optical communication, in particular to a space optical communication-oriented lithium niobate waveguide array adaptive phase shaping method, which specifically comprises the following steps of: extracting a space phase matrix set and packaging to form an original phase sequence; calculating a phase change rate matrix sequence, and performing reconstruction processing on an original phase sequence to obtain a slowly-changed phase sequence; establishing a space mapping relation with a waveguide channel; calculating a target compensation phase sequence of each waveguide channel to obtain a channel compensation phase sequence of the calibrated array; forming a shaping light field time sequence by combining the light field amplitude sequence of each channel; and constructing a slowly varying reference phase matrix, calculating a phase deviation matrix of each period and setting a convergence index, performing convergence judgment in combination with an allowable phase deviation threshold, and outputting the shaping light field passing the convergence judgment as a final shaping light field. According to the invention, the problem that the physical response speed of the array is mismatched with the disturbance change rate in a slow turbulence scene in the prior art is solved.

The application relates to the technical field of underwater robot control, in particular to an underwater robot state analysis system based on deep learning, which comprises a data acquisition module used for acquiring a sonar image sequence, a three-axis acceleration sequence, a three-axis velocity sequence, a thruster feedback current value sequence and a thruster instruction value sequence; a data preprocessing module used for obtaining a sonar image tensor matrix sequence, a first motion state vector sequence, a second motion state vector sequence, a first running state vector sequence and a second running state vector sequence; a deep learning module used for calculating a water flow disturbance characteristic value vector when it is judged that an underwater robot deviates from an expected motion state; and an identification module used for judging whether the underwater robot deviates from the expected motion state due to water flow disturbance according to the water flow disturbance characteristic value vector. The underwater robot external water flow disturbance state identification is realized through deep learning.

The invention discloses a dynamic traffic prediction method, device and equipment, a medium and a product, and the method comprises the steps: obtaining traffic matrixes of a plurality of time steps among all nodes in a communication network, and forming a traffic matrix sequence; according to the traffic matrix sequence, determining an initial spatio-temporal representation feature and a dynamic mode sensing adjacency graph; and obtaining a flow prediction result according to the dynamic mode perception adjacency graph, the initial space-time representation characteristics and the stacked space-time modeling module. The method comprises the following steps of: constructing a dynamic mode sensing adjacency graph based on a traffic matrix sequence under a plurality of real-time time steps, describing local spatial correlation between adjacent traffic, mining global spatial correlation between non-adjacent traffic, and carrying out space-time modeling on the dynamic mode sensing adjacency graph and the traffic matrix sequence based on a stacked space-time modeling module to obtain a space-time model; and the contribution of different flows to the prediction result is adaptively adjusted according to the real-time change of the flows, so that the prediction stability and generalization ability in a non-stable or burst flow scene are improved.

The invention provides a semiconductor wafer manufacturing AMHS logistics state intelligent prediction method and system, and the method comprises the steps: carrying out the multi-scale time sequence feature extraction and hierarchical spatial relation modeling through an encoder, and capturing the short-term, middle-term and long-term dynamic characteristics through a multi-scale attention mechanism in the time dimension, a graph attention network GAT and an improved space Transform are adopted in parallel in the spatial dimension to model a local neighborhood and global dependency relationship respectively, and cross-scale spatial-temporal feature fusion is realized through connection of a gating fusion mechanism and a residual error; and decoding the fused complex spatial-temporal features by using a decoder including double-layer convolution decoding and reverse normalization processing, and predicting a logistics state matrix sequence of a plurality of time steps in the future. According to the method, through deep coupling of the multi-scale spatial-temporal features, the problems of spatial-temporal feature separation, incomplete multi-scale information capture and difficult modeling of a complex topological structure in a traditional method are effectively solved, and the accuracy and stability of AMHS logistics state prediction are remarkably improved.

The application provides an environment monitoring data storage method based on a block chain, and relates to the technical field of data processing.In the application, firstly, target environment monitoring data is subjected to matrix processing to form an environment data matrix sequence; secondly, the environment data matrix sequence is subjected to multi-angle semantic mining to form a plurality of semantic mining vectors, and based on the plurality of semantic mining vectors, an environment data global vector is formed; then, based on the environment data global vector, abnormality identification is performed to form an environment abnormality analysis result; on one hand, when the environment abnormality analysis result reflects that there is abnormality, the encrypted environment monitoring data and the environment abnormality analysis result are stored into a target block chain; on the other hand, when the environment abnormality analysis result reflects that there is no abnormality, the target environment monitoring data and the environment abnormality analysis result are stored into the target block chain.Based on the above method, the problem that data privacy and accessibility are difficult to effectively balance in the prior art can be improved.

The application relates to an engine scheduling method for a mixed-granularity digital model, which comprises the following steps: step S1, model sequencing, constructing a model interaction relationship matrix DSM, grading and sequencing the simulation model, and adjusting the matrix sequence to determine the model execution priority; step S2, designing a task scheduling strategy, adopting an improved centralized scheduling strategy, globally controlling task distribution by a scheduling host, and actively reporting the load by a node and executing part of the task scheduling; and step S3, based on the frame period of system simulation and a multi-rate distributed simulation node synchronization mechanism, executing a mixed task scheduling algorithm based on the priority, and the priority sequence is as follows: periodic task > occasional task > background task. The application can realize the engine scheduling of the digital model under the mixed granularity.

The invention belongs to the technical field of wireless signal behavior perception, and discloses a wireless signal action recognition method based on fragment enhancement, which comprises the following steps of: acquiring a wireless radio frequency signal, preprocessing the wireless radio frequency signal, and converting the wireless radio frequency signal into time sequence matrix sequence data; constructing a wireless signal action recognition model based on fragment enhancement, extracting global context features and local context features based on attention guidance from time sequence matrix sequence data, and fusing the global context features and the local context features; and training the wireless signal action recognition model based on fragment enhancement, recognizing to-be-recognized sample data by using the trained wireless signal action recognition model, and outputting a prediction result of an action category. According to the method, feature expression of key action segments can be adaptively concerned and enhanced, and effective modeling is carried out on internal dynamic and external contexts of the segments, so that the capability of distinguishing similar complex actions is improved.

The invention discloses an audio and video equipment automatic inspection method for realizing fault reporting, and relates to the technical field of monitoring equipment maintenance, and the method comprises the steps: obtaining a plurality of monitoring video frames of target equipment in a preset sampling period when monitoring that a video stream enters a macroscopic static state; constructing a bit consumption matrix sequence based on the bit consumption of each monitoring video frame; based on the center-of-gravity shift trajectory of each matrix in the bit consumption matrix sequence, generating a residual feature vector sequence, the residual feature vector sequence being used for representing the center-of-gravity change degree when the screen display information of each monitoring video frame is refreshed; and if the sequence fluctuation feature of the residual feature vector sequence meets a preset condition, reporting a device freezing fault signal or circularly playing the fault signal. The technical effect of accurately identifying the fault condition of the audio and video equipment is achieved.

The application discloses a graph learning method, comprising: sampling original space-time information of nodes in a graph to obtain a set of space-time sequences of the nodes in the graph; encoding each node in the set of space-time sequences according to a space-time sequence in the set of space-time sequences to obtain an encoding matrix of each node; obtaining an encoding matrix sequence of each space-time sequence in the set of space-time sequences according to the encoding matrix of each node; and fusing the encoding matrix sequences corresponding to the space-time sequences with the same target node in the set of space-time sequences to obtain space-time attribute information of the target node. The technical scheme provided by the application solves the problem in the prior art that only the space information of nodes is collected in graph learning, and rich data information cannot be provided for downstream specific services, and the model performance of the downstream specific services is improved.

The application discloses a pose-driven controllable video generation method based on a diffusion model, which comprises the following steps: acquiring a reference appearance image set and a pose sequence, generating an observation tensor and establishing a skeleton template; calling a differentiable SoftPOSIT to obtain a soft assignment matrix, a frame-level pose initial result, a visibility weight and a matching confidence; alternately updating and applying a bone length invariance, joint amplitude limiting and motion chain consistency constraint to output a frame-level pose estimation; organizing an initial pose sequence according to the matching confidence to generate a visibility weight sequence and a soft assignment matrix sequence; establishing a cross-frame factor graph to output a time-consistent pose sequence and generate a factor graph state; integrating the foregoing quantities into a video conditional sequence; synchronously adjusting a matching temperature parameter and a noise intensity in diffusion denoising, calculating a pose consistency guide and incrementally updating the factor graph state; and generating a video frame sequence according to the pose consistency guide. The application improves pose consistency and time sequence stability and enhances controllability.

The application provides a kind of collaborative office encryption method and system based on non-commutative group transformation and perturbation mapping, method includes: extracting the feature matrix of the heterogeneous data to be encrypted, and encoding as initial group element sequence after quantization processing;From the discrete Gaussian distribution sampling error polynomial matrix superimposed to the sequence to introduce perturbation;Conjugate transformation is carried out using the system public key to obtain ciphertext polynomial matrix sequence;Random mask matrix embedding group transformation attribute is generated;Blind processing is implemented by group multiplication, and the accumulated noise norm is limited within the decryption fault boundary by cooperating with the module switching mechanism;Dynamic routing identification is generated by analyzing network path state, and encrypted transmission message is constructed;Secondary blind is carried out on load header using algebraic operator, and network layer plaintext resolvable anti-quantum encryption data is generated.The application can effectively resist quantum computing attack and flow statistical analysis, and protect the efficient and safe flow of heterogeneous data in office environment.

The application discloses a wave height prediction model, which comprises a first LGE module, a feature encoder, an encoder, a second LGE module and a decoder. The first LGE module is used for encoding a marine multi-element time sequence related to wave height. The input of the encoder is connected with the output of the first LGE module, and the encoder is used for outputting a high-dimensional feature of the marine multi-element. The second LGE module is used for encoding a matrix sequence spliced by a latter half sequence of the marine multi-element time sequence and a zero matrix sequence. The decoder takes the output of the second LGE module and the high-dimensional feature output by the encoder as input, and outputs a prediction result of the wave height. The encoder is composed of N (N=4) encoding layers. Each encoding layer comprises a first hollow causal convolution self-attention layer, a first residual connection and a normalization layer, a first forward propagation layer and a second residual connection and a normalization layer in sequence.

This invention discloses a method and apparatus for generating video from images. In this embodiment, a source image containing a target object is input into a first video generation model to obtain a source video. An inter-frame transform matrix sequence is determined based on the source video. Then, an object mask image corresponding to the target object is obtained from the source image. Applying the inter-frame transform matrix sequence to the object mask image yields multiple mask images, forming a mask image sequence. Applying the inter-frame transform matrix sequence to the source image yields multiple target object images, forming a target object image sequence. Target input data is determined based on the source image, the mask image sequence, and the target object image sequence. This target input data is then input into a second video generation model supporting local redrawing to obtain the corresponding target video. By generating video through two models, intelligent end-to-end image-to-video generation is achieved. It can achieve diverse motion trajectories while maintaining the target object's non-diffusing state without introducing preset motion parameters.

Embodiments of the present application provide an agent task execution method, which can be applied to the field of artificial intelligence technology. The agent task execution method comprises: generating dictionary programming data corresponding to user task information through a preset logical form analysis rule; analyzing the dictionary programming data to generate a logical form operator graph according to a preset logical form analysis operator library; and generating an AND / OR matrix sequence according to the logical form operator graph, the AND / OR matrix sequence being used to implement a task execution process corresponding to the user task information. Embodiments of the present application also provide an agent task execution device, equipment, a storage medium and a program product.