11914 results about "Computer device" patented technology

This invention relates to a gait emotion recognition method, system, storage medium, and computer device based on spatiotemporal graph convolution. The method includes the following steps: S1, data augmentation by reversing the temporal direction of gait; S2, obtaining deep emotion features and prior emotion features respectively through a spatiotemporal graph convolutional network and prior feature statistical methods; S3, performing nonlinear mapping on the prior emotion features using a feature mapping layer; S4, inputting the fused features of the deep emotion features and prior emotion features into an emotion classifier to obtain the emotion category. The feature mapping layer of this invention achieves more effective feature fusion by performing nonlinear mapping on prior features; it also introduces causal temporal convolution to replace general temporal convolution, effectively extracting fine-grained temporal features by enhancing temporal correlation and cross-period feature fusion. Furthermore, a walking direction recognition auxiliary task is designed to accelerate the training and convergence speed of the model, enhancing the ability to extract temporal-dependent features and the performance of emotion recognition.

A power distribution network cable fault point location method and system, a computer device and a medium, belonging to the technical field of power distribution networks. The location method comprises: S101, when a cable has a fault, a transient signal of a wide-area frequency band is generated, a fault traveling wave containing high-frequency components; a traveling wave line mode component having a relatively small chromatic dispersion is used to perform measurement; decoupling an extracted fault traveling wave signal; S102: the traveling wave voltage and current will have a sharp change when the traveling wave generated by the fault point reaches a measurement end, and the traveling wave head can appear as a high-frequency sudden change in a time-frequency graph, the sudden change point being the position of the wave head; S103: performing EEMD decomposition on the fault traveling wave line mode component, and extracting a first IMF component for Hilbert transform to obtain a time-frequency graph thereof, a sampling moment corresponding to the position of a first frequency sudden change point on the time-frequency graph being the arrival time of the fault traveling wave head; and using a double-end fault location algorithm to calculate a distance with respect to the fault point. The result of the location method has relatively high precision, and, compared with the actual fault position, has a relative error not more than 4%.

The invention discloses a retrieval method and device based on a knowledge graph, computer equipment and a storage medium. The method comprises the steps that the knowledge graph is acquired, a node relation vector is output through a preset relation learning module, nodes related to user query content are retrieved from the knowledge graph, and an external knowledge representation result is generated; performing feature fusion on the user query content and the external knowledge representation result by using the correlation matrix to obtain a fused feature vector; performing image-text bidirectional semantic alignment on the fused feature vector based on an attention mechanism to generate a joint feature vector; and performing matching calculation based on the joint feature vector to obtain a matching score for representing the semantic correlation degree. According to the method, the user query content and the generated external knowledge representation result are fused through the correlation matrix, image-text semantics are aligned through an attention mechanism, the joint feature vector is generated, the matching score is calculated, and cross-modal retrieval is realized by using the matching score, so that the information retrieval accuracy of the community property management system is greatly improved.

The invention discloses a road bridge design method and system based on a BIM real scene model, and relates to the technical field of constructional engineering information, and the method comprises the steps: a BIM model carries out multi-objective optimization through a quantum annealing algorithm in combination with a bridge-crossing topology library, synchronously optimizes topological connectivity, structural stress and energy consumption indexes, generates a parameterized component, and feeds back the parameterized component to the BIM model; the model of the BIM model is loaded and optimized based on augmented reality equipment, the model is superposed to a reality scene through space anchoring, and the BIM model is corrected in combination with a conflict monitoring engine and a knowledge graph; the corrected BIM model is deployed to edge computer equipment, a dynamic deviation thermodynamic diagram is generated by scanning a construction surface, a lofting robot is driven to execute coordinate dotting, and meanwhile construction error data is transmitted back; according to the method, the problem that a global optimal solution is difficult to find when a traditional optimization algorithm is used for processing a complex multi-target problem is solved by utilizing an advanced quantum computing technology, and the economical efficiency and the structural safety of bridge design are greatly improved.

The invention relates to a question and answer model training method and device, a question and answer method and device, computer equipment, a computer readable storage medium and a computer program product. The method comprises the steps that a training set is constructed, and cue words of a first category with a known attack category label and cue words of a second category with an unknown attack category label are arranged in the training set; training an initial reward model according to the cue word to obtain a reward model which is used for outputting a reward signal; inputting the cue word into the large language model, outputting to obtain predicted reply data, and inputting the predicted reply data and the cue word into a reward model to obtain a reward signal for the predicted reply data; and determining an updating strategy of the large language model according to the reward signal of the predicted reply data, and updating the large language model based on the updating strategy to obtain a trained question and answer model. By adopting the method, the recognition precision of the question and answer model for known attack types can be improved, and the omission ratio for unknown attack types can be reduced.

The invention relates to the technical field of draught fan detection, in particular to a draught fan current detection method and system, computer equipment and a storage medium, and the draught fan current detection method comprises the following steps: collecting draught fan current data, analyzing the time sequence characteristics of the draught fan current data, monitoring and comparing the change trend of the distance between adjacent points, and determining the distance between adjacent points; and determining an optimal embedding dimension and time delay through a phase space reconstruction method, and generating a fan current phase space trajectory. According to the method, the time sequence characteristics of the fan current data are analyzed, the embedded dimensions and the time delay parameters are optimized by adopting the phase-space reconstruction method, and the track point distribution mode is deeply analyzed, so that the capability of capturing abnormal behaviors is effectively improved. The transient characteristic signals are classified and indexed by combining the signal decomposition and frequency band screening technology, the short-time abnormal fluctuation in the current signals is accurately identified, and the response capability to the short-time high-frequency signal change is improved.

The invention relates to the technical field of tooth modeling, and discloses a three-dimensional tooth modeling system based on computer vision, computer equipment and a readable storage medium. According to the method, mirror reflection, diffuse reflection and subsurface scattering components in an original image are separated, mirror reflection intensity is normalized in combination with a dynamic truncation algorithm, pixel saturation is eliminated, groove and nest textures are reserved, a complete point cloud is obtained based on a two-dimensional texture image and cubic spline repair, and a multi-exposure point cloud sequence is obtained through bimodal calibration. The method comprises the following steps: solving the problem of data dislocation, carrying out weight assignment and data fusion on three-dimensional points in a plurality of exposure point cloud sequences to obtain three-dimensional fusion feature data, combining layered optical modeling and photon tracking compensation deviation, fusing clinical constraints, finally dynamically adjusting parameters, feeding back and optimizing, and outputting a micron-sized precision model. The modeling defect caused by difficulty in effectively coordinating feature contribution degrees under different exposure conditions is overcome, and high-precision modeling is realized.

The invention relates to an experimental data processing method and device, an AI analysis module and computer equipment, and belongs to the field of data processing.The method comprises the steps that multi-dimensional original data are partitioned according to types, and formats are unified; generating a similarity matrix based on time and space neighborhood information, and marking abnormal fluctuation points; effective signals are separated through a time-frequency feature matching noise library; extracting multi-layer features of basic statistics, time sequence correlation and trend change; and dynamically screening core features to update the tracking type experimental model. The matched AI analysis module integrates hardware circuits of data partitioning, similarity calculation, anomaly marking, noise matching, feature extraction and model updating, and whole-process acceleration is achieved. According to the method, through multi-dimensional data compatibility processing, accurate anomaly detection, multilayer feature fusion and model adaptive optimization, the experimental data processing efficiency and conclusion reliability are remarkably improved, and the method is suitable for real-time analysis of multiple scenes such as scientific research and industry.

Disclosed in the present application are an evaluation method and apparatus for a large language model, a storage medium and a computer device. The method comprises: on the basis of a target application field of a target large language model to be evaluated, acquiring a test question bank corresponding to the target large language model, wherein the test question bank comprises objective questions belonging to the target application field, and question stems and answers of the objective questions are obtained by analyzing corpus samples of the target application field; and using the test question bank to test the target large language model, so as to obtain an output result of the target large language model for the tested objective questions; and, on the basis of answers corresponding to the tested objective questions and the output result of the target large language model for the tested objective questions, evaluating the target large language model.

The invention discloses a multi-modal data processing method and system, computer equipment and a readable storage medium, which can realize deep association and complementarity mining of multi-modal information and improve the accuracy and robustness of multi-modal understanding. The method comprises the following steps: an environment sensing module adjusts an environment sensing strategy according to feedback information transmitted by a self-adaptive decision module, and acquires multi-modal data according to the environment sensing strategy; the multi-modal encoding module encodes the multi-modal data into multi-modal feature vectors of the same dimension; a cross-modal fusion module fuses the multi-modal feature vectors to obtain fusion features; the self-adaptive decision-making module selects a decision-making network matched with the task type from a predefined network library according to the task type of the current decision-making task, inputs the fusion features into the decision-making network, and generates feedback information according to the decision-making process of the decision-making network; and the meta-learning controller evaluates the system performance of the current multi-modal data processing system and adjusts system parameters according to an evaluation result.

The embodiment of the invention provides a joint torque estimation method and device based on electromyographic signals of lower limb exoskeletons, computer equipment, a storage medium and a computer program product, and relates to the technical field of exoskeletons. The method comprises the following steps: acquiring an initial filtering electromyographic signal of a target object; obtaining an electromyographic signal envelope corresponding to the initial filtering electromyographic signal according to the initial filtering electromyographic signal; performing normalization processing on the electromyographic signal envelope to obtain an electromyographic signal value corresponding to the electromyographic signal envelope; according to the electromyographic signal value, obtaining an independent source signal corresponding to the initial filtering electromyographic signal; and obtaining a joint torque value of a target exoskeleton matched with the target object according to the independent source signal. According to the method, the accuracy and efficiency of joint torque estimation based on the electromyographic signals are improved.

The present application relates to a multi-objective scheduling method and apparatus for an urban power grid, a computer device, a storage medium, and a computer program product. The method comprises: connecting hydrogen energy vehicles to an original urban power grid operation system to obtain an updated urban power grid operation system; determining an electrical-hydrogen coupling unit from the updated urban power grid operation system; establishing a corresponding mathematical model for each subunit in the electrical-hydrogen coupling unit; determining a first objective function on the basis of operation cost parameters of respective modules of the updated urban power grid operation system, determining a second objective function on the basis of energy supply parameters of each subunit of the electrical-hydrogen coupling unit, and generating a multi-objective optimization and scheduling model on the basis of the first objective function and the second objective function; and solving the multi-objective optimization and scheduling model to obtain a target scheduling strategy.

The invention discloses a declarative knowledge construction method and device for semantic and situational knowledge collaborative modeling, computer equipment and a readable storage medium, and relates to the field of data processing.The method comprises the steps that firstly, a multi-modal document is analyzed, a chapter abstract is extracted, and structured content is obtained; entities, events and multi-modal knowledge points are extracted from the structured content, and cross-modal fusion is carried out on the entities, the events and the multi-modal knowledge points; carrying out anaphora resolution based on the fused knowledge points, and constructing a double atlas containing a knowledge atlas, a affair atlas and a four-dimensional relation triple; clustering the double maps to obtain a theme community, and performing association mapping on the community, the triple and the entity event, the chapter abstract and the multi-modal knowledge point to form association knowledge; vectorizing the associated knowledge and establishing a vector knowledge index; and carrying out compression ratio and accuracy evaluation on the knowledge through an evaluation system, and feeding back and optimizing the whole knowledge construction process. According to the method, multi-modal knowledge deep fusion and semantic scene collaborative modeling are realized, and the knowledge structuring degree and the application reliability are improved.

A broadband measurement method and apparatus for a power system, and a computer device, which relate to the technical field of broadband measurement. The method comprises: acquiring a target electrical signal; on the basis of a local mean decomposition algorithm, decomposing in a time domain the target electrical signal into a plurality of signal components having a single frequency; determining the instantaneous frequency of each signal component; on the basis of the instantaneous frequencies, determining a corresponding time-frequency transformation algorithm and a corresponding time-frequency transformation parameter; and on the basis of the corresponding time-frequency transformation parameter, using the corresponding time-frequency transformation algorithm to perform time-frequency transformation on each signal component, so as to obtain a broadband spectrum that comprises frequency-domain information corresponding to each signal component. The method is applicable to spectrum analysis performed on a voltage / current signal that includes a plurality of signal components, the calculation is quickly and precisely performed, the broadband measurement requirements of a new power system can be met, and a strong support is provided for constructing a wide-frequency-oscillation wide-area monitoring system for the power system.

A subject analysis method and apparatus, and a computer device, a storage medium and a computer program product. The subject analysis method comprises: acquiring a target image sequence, clinical data and an image processing model, wherein the target image sequence is obtained by means of performing image sequence conversion on an image to be subjected to detection, and the image processing model includes a pre-trained feature extractor and a trained classification sub-model; performing feature extraction on the target image sequence by means of the feature extractor, and performing analysis processing on a feature extraction result by means of the classification sub-model, so as to obtain a target analysis result; and on the basis of a large-scale language analysis model, performing subject analysis on the target analysis result and the clinical data, so as to obtain a subject analysis result.

The invention discloses a self-adaptive teaching strategy adjustment method based on sentiment analysis and a computer device. The method comprises the following steps: obtaining a target emotion feature data stream according to multi-modal student emotion data, and then extracting visual deep features, audio deep features and physiological deep features from the target emotion feature data stream; processing the visual attention weight, the audio attention weight, the physiological attention weight, the visual deep feature, the audio deep feature and the physiological deep feature according to a preset fusion strategy to obtain a multi-dimensional fusion feature, and analyzing the feature to obtain basic emotion recognition information of the individual student; analyzing the basic emotion recognition information through a built personalized emotion model of each student individual to obtain corresponding emotion state evaluation information; and on the basis of the constructed teaching knowledge graph and the emotional state evaluation information, generating a personalized teaching adjustment strategy so as to dynamically adjust an actual teaching scheme. According to the method, dynamic adjustment can be realized, and personalized teaching requirements are met.

The invention relates to a vehicle suspension control method and device, computer equipment and a readable storage medium. The method comprises the following steps: combining obtained vehicle body dynamics time series data, vehicle control time series data and environment perception time series data in a vehicle driving process into time series data of multiple channels, determining a self-adaptive modal number of the time series data under each channel according to the time series data under each channel, and determining the self-adaptive modal number of the time series data under each channel according to the self-adaptive modal number of the time series data under each channel; and performing modal decomposition on the time sequence data based on the adaptive modal number, extracting frequency domain characteristics of the time sequence data, integrating the frequency domain characteristics of the time sequence data of the multiple channels, inputting the frequency domain characteristics into a trained suspension control parameter prediction model to obtain target time sequence characteristics for predicting suspension control parameters, and performing prediction on the suspension control parameters based on the target time sequence characteristics. Vehicle suspension control parameters are obtained through prediction, and a suspension system of the vehicle is controlled based on the vehicle suspension control parameters. By adopting the method, the risk of rollover of the vehicle can be obviously reduced.

The invention discloses a computing power analysis method used in a cloud computing environment, a matching method and a computer device. The computing power analysis method used in the cloud computing environment comprises the steps that historical task operation data of different types of computing power nodes in a cloud computing platform are acquired, associated computing power portraits corresponding to the computing power nodes are generated through preprocessing, and then a target model is trained in combination with the historical task operation data to obtain a task-computing power mapping model; performing multi-dimensional dynamic and static analysis on a to-be-executed user calculation task to obtain task feature information; and analyzing the task feature information based on the task-computing power mapping model to obtain analysis information of each type of computing power nodes. According to the method, the required computing power resources and the analysis information of each type of computing power nodes can be accurately predicted according to the dynamic and static analysis feature information of the task before the task is executed, so that accurate computing power identification and intelligent matching are realized, the reasonability and efficiency of task allocation are improved, the resource waste rate is effectively reduced, and the adaptation accuracy in a multi-tenant environment is improved.

The embodiment of the invention discloses a medical data privacy protection method and a computer device. The method comprises the following steps: firstly, acquiring medical industry laws and regulations and target medical institution data asset information; building a logic relation graph containing data classification and security level according to laws and regulations, and importing the laws and regulations information into a first graph database to obtain a laws and regulations graph; extracting a logical relationship in the data asset information, constructing a data logical relationship graph containing a library table column structure, and importing the data logical relationship graph into a second graph database to obtain an asset graph; business process information is collected, and a data flow relation is identified to perfect an asset map; analyzing the name and content of the data assets to obtain a classification and grading analysis result; on the basis of the result, constructing a heterogeneous graph neural network model by using a graph attention network, establishing a mapping relationship between the two maps, and updating the asset map; and finally, carrying out privacy protection on the data assets according to the updated asset atlas. According to the method, the accuracy and efficiency of medical data classification and grading can be improved, and the privacy protection capability is enhanced.

The invention relates to the technical field of fault diagnosis, in particular to a fault self-diagnosis system and method for a high and low temperature aging test chamber of a storage chip and computer equipment. The temperature control abnormal index is obtained through the data of the temperature control system sensor, early warning can be performed when the temperature control system has abnormal fluctuation, the performance evaluation basis of the air circulation system is provided by the air system efficiency coefficient, the heating efficiency and the refrigeration coefficient are obtained through the data of the heating and refrigerating system sensor, and the performance of the heating and refrigerating system is evaluated. An electrical stability index is obtained through data of a test system sensor, the operation stability of an electrical system is evaluated, the stability of the electrical system is detected in time, and a fault probability index is obtained through a system entropy increase rate, the electrical stability index, a thermal coupling factor, a wind system efficiency coefficient and a temperature control abnormal index. Therefore, the stability and the reliability of the equipment can be improved, the maintenance cost can be reduced, the use efficiency of the equipment is improved, and the accuracy and the safety of test results are ensured.

An adaptive variable step-size LMS filter based on a hyperbolic tangent function and its filtering method and computer equipment belong to the field of filter technology, and solve the problem that the LMS algorithm improved based on a nonlinear function requires too many manual adjustments of parameters and that the convergence speed and convergence accuracy of the traditional LMS algorithm cannot be satisfied simultaneously. The method of the present invention includes: determining the filter tap length and the number of iterations; initializing the filter parameters; obtaining the filter input signal vector; determining the instantaneous expected signal of the filter; obtaining the instantaneous output signal; obtaining the instantaneous error signal of the filter and the accumulated amount of the error signal based on the instantaneous output signal; obtaining the instantaneous step size using a step-size update formula based on a hyperbolic tangent function; and continuously iterating the filter tap weight coefficient vector to construct an LMS filter using the filter tap weight coefficient vector obtained after the number of iterations. The present invention is suitable for improving the performance of the LMS filter, and the filter of the present invention can be widely used in various signal processing fields.

The invention relates to a two-dimensional garment making layout generation method and device and computer equipment, and the method comprises the steps: carrying out the three-dimensional reconstruction of an input image of a target garment, and obtaining a three-dimensional model of the target garment; identifying the three-dimensional model to obtain a plurality of three-dimensional curved surface cutting pieces in the three-dimensional model; mapping each three-dimensional curved surface cutting piece into a corresponding two-dimensional tiled cutting piece, and determining cutting piece parameter information of each two-dimensional tiled cutting piece; and based on each two-dimensional tiled cutting piece and the cutting piece parameter information of each two-dimensional tiled cutting piece, generating a two-dimensional layout of the target garment. According to the method and the device, the problems of low generation efficiency of the layout and insufficient precision of the layout are solved, and the generation efficiency of the layout and the precision of the layout are improved.

The invention discloses a transformer fault intelligent detection method and device based on deep learning multi-mode fusion, computer equipment and a readable storage medium. The method comprises the steps that firstly, RGB image data of a target transformer are collected by means of an image sensor, infrared thermal imaging image data are collected by means of an infrared sensor, and sound signals are collected by means of a sound sensor; and preprocessing the data, taking the preprocessed data as multi-modal detection data, and inputting the multi-modal detection data into a pre-trained multi-modal fault recognition model which comprises a sensing layer, a fusion layer, a backbone network and an output layer which are cascaded in sequence, so as to obtain a fault detection result of the target transformer. According to the method, multi-modal data are fused, the deep learning advantage is exerted, the comprehensiveness and accuracy of transformer fault detection are improved, and an effective means is provided for intelligent operation and maintenance of a power system.

The invention discloses a learning achievement prediction method based on learning behavior data mining and a computer device. The method comprises the steps of collecting multi-dimensional learning behavior data of students, performing preprocessing and feature extraction, constructing a structured learning behavior feature set, then constructing a target machine learning model suitable for different prediction tasks, inputting structured learning behavior features of the students to be predicted into the model, and obtaining a continuous cumulative risk prediction curve; converting the cumulative risk prediction curve into discrete classification labels, and generating a classification prediction result with reserved time sequence characteristics; and expanding the structured learning behavior feature set by adopting a pseudo-label semi-supervised learning algorithm, iteratively training a target machine learning model, inputting the structured learning behavior features of a to-be-predicted student into the trained model to obtain a comprehensive prediction result, and generating a visual learning achievement prediction scheme according to a classification prediction result. The method can provide a comprehensive and accurate prediction result.

The invention discloses a database question and answer model training method and device, a storage medium and computer equipment, and the method comprises the steps: associating standard structured query language statements, standard execution result answers and standard natural language questions, and generating training annotation data; and collecting simulation derivation problems possibly proposed for the database to obtain non-labeled data for training. Based on a GRPO reinforcement learning framework and a scoring reward function provided by a double-tower model, training is carried out on the scoring reward function by utilizing training labeling data, supervised fine tuning training is carried out on a database question and answer model, and non-labeling data for training, format rewards, executable rewards and scoring rewards of the scoring reward function are combined, so that the scoring reward function of the database question and answer model is obtained. And continuing to train the database question and answer model after supervised fine tuning training. Preliminary training is carried out through a small amount of annotation data, then subsequent training is carried out through non-annotation data, the reasoning ability of the model can be stimulated, the annotation cost is reduced, and the training efficiency is improved.

The present application relates to the technical field of big data processing, and discloses a risk behavior identification method and apparatus, a storage medium, and a computer device. The method comprises: acquiring access traffic data of sensitive data, and on the basis of service categories to which access users corresponding to the access traffic data belong, classifying the access traffic data to obtain sensitive data access data of a plurality of service categories; on the basis of the sensitive data access data of each service category, updating model parameters of a risk assessment model of the service category; by means of the risk assessment model of the service category and on the basis of the sensitive data access data of the service category, calculating a risk behavior assessment value of each access user under the service category; and on the basis of the risk behavior assessment value of each access user under the service category, obtaining a risk behavior identification result of each access user. The method can improve the adaptability, the practicability and the assessment accuracy of risk behavior assessment in service scenarios, and provides visual assessment results.

The invention discloses an electric power inspection robot detection and deployment method and system and computer equipment, and the method comprises the steps: obtaining high and low wire harness three-dimensional point cloud data of a to-be-detected road scene, inputting the data into a teacher model and a student model, extracting BEV features, employing a focus module to carry out the distillation processing of the three-dimensional features of the teacher-student model, and obtaining the three-dimensional point cloud data of the teacher-student model; creating foreground and background masks, and calculating the loss of foreground features; a focus relation distillation module is introduced, features of nine angular points of a foreground position are extracted, Gaussian similarity calculation is carried out, and the balance of the model in different types of foreground feature learning is improved; a global distillation module is applied to improve the overall performance, model pruning is performed after model training is completed, and the model performance is optimized; and the model after pruning optimization is converted into an ONNX format, deployment and acceleration are carried out on TensorRT, and the reasoning efficiency is further improved. The problems of low detection precision of the low-wire-harness radar and difficulty in actual deployment of the model are effectively solved.

The embodiment of the invention relates to the field of artificial intelligence, and provides a model context protocol injection attack protection method and device based on dynamic semantic analysis, computer equipment and a storage medium, and the method comprises the steps: obtaining request data corresponding to a call request initiated by a user through a model context protocol, carrying out the preprocessing of the request data, obtaining the preprocessed request data; performing semantic vectorization on request text and context historical information in the request data through a lightweight bidirectional encoder representation model to output an initial risk score; correcting the initial risk score according to context historical information carried in a model context protocol to obtain a corrected final risk score; and performing hierarchical defense decision according to the final risk score, and determining risk grading information corresponding to each piece of request data so as to execute a protection action corresponding to each piece of risk grading information. By adopting the method, the accuracy of identifying the protocol injection attack can be improved.

The present application relates to a startup / shutdown control method and apparatus for a metal reactor, a computer device, and a storage medium. The method comprises: according to the operation data of a unit to be controlled in a first historical period, acquiring a first multi-dimensional timing series corresponding to target parameters and acquiring a modal component corresponding to the first multi-dimensional timing series; according to the modal component corresponding to the first multi-dimensional timing series, constructing a first prediction model for primary loop temperature prediction and a second prediction model for reactor power prediction; acquiring a real-time timing series corresponding to the target parameters, and separately inputting the real-time timing series into the first prediction model and the second prediction model so as to acquire a primary loop temperature prediction series and a reactor power prediction series; and, according to the primary loop temperature prediction series and the reactor power prediction series, acquiring a predicted working condition of a startup separator and, according to the predicted working condition, performing startup / shutdown control on the unit to be controlled. Using the method can reduce startup / shutdown control costs of metal reactors.

The invention provides an end-to-end automatic driving track generation method and system, computer equipment and a medium, and belongs to the field of path intelligent planning, and the method comprises the steps: obtaining traffic environment three-dimensional perception information and a natural language instruction; converting the traffic environment three-dimensional perception information to obtain a structured semantic mark, and adjusting the weight of the structured semantic mark through a natural language instruction; based on the weight of the structured semantic mark, visual flow features are extracted from the structured semantic mark, and language flow features are extracted from the natural language instruction; based on a cross-modal attention mechanism, the visual flow features and the language flow features are fused, fusion features are obtained, and a simulation track is generated; real-time collision time is calculated, when the real-time collision time is larger than a collision time threshold value, the simulation track is corrected based on physical constraints and a traffic regulation library, and a corrected driving track is obtained. The automatic driving simulation track constructed by the method conforms to the intention of a user, and the system reliability and the man-machine cooperation efficiency are improved.