30 results about "Sequence structure" patented technology

The invention relates to the technical field of propagation effect evaluation, in particular to a propagation effect evaluation system and method based on multi-platform data fusion. In the propagation effect evaluation system and method based on the multi-platform data fusion, through normalization of forwarding frequency and position mapping with a user sorting index, behavior data is converted into a structured unit; the fusion capability of different platform data in the time dimension and the frequency dimension is enhanced, a sequence vector of a propagation path is established after a sequence difference value and a frequency difference value between nodes are processed through a graph neural network, and a coding structure is formed in combination with direction judgment and weight combination to reflect the seriality and the propagation intensity in the node propagation path, so that the propagation speed of the nodes is improved. Through continuous matching operation of a path sequence structure, similarity judgment is carried out on a track sequence, a core path with actual diffusion continuity is effectively screened out, aggregation processing is carried out according to platforms by combining node strength, a path bit sequence and a weighted relation among repeated indexes, regional difference values are extracted, and a propagation strength index of each platform is obtained.

The invention relates to a method, a device and equipment for identifying ancient characters of a war bamboo slip, and a medium. The method comprises the following steps: acquiring a to-be-identified war bamboo slip image containing a plurality of ancient characters; flattening a local feature graph containing local detail information of ancient characters in a to-be-recognized war national bamboo slip image output by the OfficientNetV2 convolutional neural network into a sequence structure, and splicing the sequence structure with a feature sequence containing global semantic information and long-range dependency information of the ancient characters output by a Transform encoder in a channel dimension to form a joint feature tensor; layer normalization processing is carried out; and inputting the combined feature tensor subjected to layer normalization processing into a classification module of an ancient character recognition model so as to output a single character recognition result corresponding to a single ancient character or character sequence recognition results corresponding to a plurality of continuous ancient characters. Compared with the prior art, the method has better supplementing and repairing capacity when processing defective and broken strokes, and the recognition capacity for characters with incomplete structures or complex forms is remarkably improved.

The invention relates to the technical field of electromagnetic transient simulation, in particular to an electromagnetic transient simulation system of a power system containing multiple electronic power switches, which is characterized in that numbering and topological serial number analysis are performed on multi-switch node, resistance, inductance and capacitance parameters, and a connection mapping matrix is formed by adopting impedance conversion and admittance construction modes; a sequence structure is formed by coding control variables through a genetic algorithm, a variable sequence is constructed through switching time points and topological connection, error and duration combined evaluation is executed, an optimal combination is screened, time matrix construction is carried out after energy storage voltage and filter current are collected, and a principal component vector and a covariance projection coefficient are extracted; meanwhile, an input matrix is constructed, harmonic information is fused in a unified time step, a residual network structure is introduced to carry out time-step-by-step iterative updating processing on an initial weight, dynamic adjustment is completed in combination with residual offset calculation and local error compression in each step, and a continuous and complete reconstructed waveform is output.

The invention discloses a large language model XPath generation method based on hierarchical composite reward reinforcement learning, and the method specifically comprises the following steps: 1, obtaining HTML (Hypertext Markup Language) source codes and page element information of a target webpage, carrying out the data cleaning, and obtaining structured data containing a DOM (Document Object Model) hierarchical sequence structure and an element attribute value; performing data annotation on the structured data after data cleaning to obtain an annotated data set; 2, selecting a basic model, performing supervision and fine tuning on the basic model by using the annotation data set, and taking the basic model subjected to supervision and fine tuning as a strategy model; constructing a layered composite reward function to perform reinforcement learning fine tuning, so that the output hierarchy of the strategy model is aligned with the input DOM hierarchy, and obtaining a final model subjected to two-stage fine tuning; and 3, generating a standard XPath character string, and outputting structured data matched with the input DOM hierarchy to display layer-by-layer construction logic of the standard XPath character string. According to the method, the stable XPath can be generated, and the generation process is completely transparent and traceable.

The invention discloses a method and a system for constructing a collision-zone-free frequency hopping sequence set in a '1 + M' form, and mainly solves the problems that in the prior art, frequency point collision is generated when time delay deviation is large, and new collision is introduced in a secondary mapping or expansion process. The implementation scheme of the method comprises the following steps: generating two frequency hopping sequence set sums with different collision-free regions by adopting a collision-free region construction method; combining the two sequence sets to obtain 1 + M frequency hopping sequences; segmenting 1 + M frequency hopping sequences according to a set length to obtain sequence segments of the first sequence; setting random numbers which are uniformly distributed between 0 and 1, and constructing a zeroth dynamic frequency slot set and a first dynamic frequency slot set by utilizing the sum of the frequency slot sets, the size of the total frequency slot set and the sum of frequency slot elements in the total frequency slot set; and performing frequency slot mapping by using the sum sequence segment to obtain a collision-zone-free frequency hopping sequence set consisting of 1 + M frequency hopping sequences. According to the method, effective expansion of the collision-free area can be realized while the consistency of the sequence structure is kept, and the method can be used for orthogonal frequency hopping networking when the relative time delay difference between users is relatively large.

The invention discloses a hierarchical control domain-based test flow arrangement method and system, and the method comprises the steps: setting a control node, facilitating the introduction of complex branch, cycle, parallel, event and other control flows, generating a control domain of a hierarchical recursive structure based on the control node, and generating a bounding box based on the region range of the control domain, according to the method, the whole flow arrangement diagram is of a simple sequence structure, one node is connected with one node or a bounding box, and the interior of each bounding box is of a tree-shaped branch structure, so that complex control flows such as branches, circulation, parallelism and events can be accurately and clearly represented; and all control flows in one control domain uniformly flow out from the output port of the bounding box, and subsequent nodes only need to be connected with the output port of the bounding box by one connecting line, so that the number of connecting lines in a flow arrangement diagram is greatly reduced, the overall structure is simplified, the test flow logic is clearer, and the test efficiency is improved. And the flow arrangement diagram is easier to understand and maintain.

The invention discloses a behavior sequence structured coding method based on high-order nonlinear dependency, which comprises the following steps of: firstly, performing time sequence perception embedding on an original behavior sequence; then, hierarchical coding is carried out on the embedded sequence by stacking a network formed by a plurality of structured coding units, each coding unit comprises parallel processing paths which are respectively used for processing first-order linear features and high-order nonlinear interaction features, and the features of all orders are weighted and combined through a learnable transformation module; and finally, carrying out aggregation and projection on the output of the network, and generating a coding representation suitable for a downstream task. According to the method, a network structure containing a multi-order processing path is constructed, so that a neural network can more effectively encode and utilize a hidden structured high-order nonlinear dependency relationship in sequence data, and feature representation with higher expressive power is provided for a sequence modeling task.

The application relates to the technical field of key generation, in particular to a data encryption and key generation method for online payment, which comprises the following steps: extracting a touch rhythm to generate a sequence, performing round encoding to construct a bit structure, and combining amount information to generate a key. In the application, user touch behavior characteristics are converted into a rhythm sequence, a dynamic encryption basis is constructed through sequence rotation and bit mapping mode, a user behavior level parameter is introduced in the key generation process, payment behavior and transaction data are deeply bound by fusing amount section labels, the key generation no longer depends on fixed rules or external key negotiation process, independent key output on the terminal side is realized through sequence structure reorganization and template parameter matching, the individuality and unpredictability of the key are improved, the identification ability for imitated behavior and the protection effect for man-in-the-middle attack are enhanced, and the problems of static characteristics and response lag of the traditional key mechanism in actual deployment are effectively alleviated.

The present application relates to the technical field of electromagnetic transient simulation, in particular to an electromagnetic transient simulation system of a power system containing multiple power electronic switches, in which the parameters of multiple switch nodes, resistors, inductors and capacitors are numbered and topological serial numbers are analyzed, an impedance conversion and admittance construction method is used to form a connection mapping matrix, a genetic algorithm is applied to form a sequence structure by coding control variables, a variable sequence is constructed by switching time points and topological connections, and a combination is selected by performing a combined evaluation and screening of errors and time length, after collecting energy storage voltage and filter current, a time matrix is constructed, an element vector and a covariance projection coefficient are extracted, an input matrix is constructed, harmonic information is fused in a unified time step, a residual network structure is introduced to perform step-by-step iterative updating of initial weights, dynamic adjustment is completed by combining residual offset calculation and local error compression at each step, and a continuous and complete reconstructed waveform is output.

The invention discloses a roadbed freezing damage diagnosis method based on satellite remote sensing data enhancement and SCNN-BN. The method comprises the following steps: firstly, acquiring PS-InSAR settlement time sequence data of a railway roadbed, and preprocessing the data to eliminate noise and improve data quality; then, performing time sequence alignment and enhancement on the original data by adopting an FDTW algorithm, and generating a training data set with higher representativeness and balance; then, a convolutional neural network model based on a sequence structure is designed, and the convergence speed, stability and diagnosis precision of the model are remarkably improved in combination with a batch normalization technology; and finally, through experimental verification, the performance of the proposed method is comprehensively evaluated. Experimental results show that the method has a high accuracy rate of 96.76% in railway roadbed freeze injury diagnosis, has excellent performance in a noise environment, and is significantly superior to that of a traditional model. The method provides an efficient and reliable solution for intelligent monitoring of the railway infrastructure.

A knowledge injection-based fine-grained entity classification method and an interactive system are disclosed. The method comprises: inputting a sentence, a mentioned entity and knowledge graph triple information of the mentioned entity into a sentence-level encoder; constructing a feature representation of a word, a mentioned entity and triple information output by the sentence-level encoder into a graph structure and inputting the graph structure into a graph-level encoder; and inputting knowledge-enhanced sentence feature representation output by the graph-level encoder into an entity classification model for fine-grained entity classification of a target phrase in the sentence. On the basis of injecting fact knowledge in a knowledge base into a sentence in a sequential encoding manner and encoding entities based on a sequence structure, the application further constructs a graph structure using the fact knowledge and the sentence and encodes the graph structure based on GNN. Thus, the entity classification model can jointly learn entity representation learning in the sentence and in the knowledge graph structure and perform fine-grained entity classification with higher accuracy.

The application relates to the field of artificial intelligence, in particular to a big data information analysis method and system based on artificial intelligence, which comprises the following steps: acquiring a jump section direction vector and classifying label nodes with consistent directions, splitting a path lacking a mapping relationship to form an interrupted section, identifying nodes with continuous directions as bridge section entrances, rearranging path sections to generate a sliding connection sequence, and combining semantic fields to sort behavior trajectories to generate a focused analysis set; in the application, the jump section direction is classified according to node order and channel number, the direction aggregation boundary is determined, the semantic field is combined with the path position to identify an interruption, the positioning accuracy of the node sequence structure and the rationality of the paragraph division are improved, the bridge section connection is based on direction consistency screening, the path continuation logic is maintained, the behavior trajectory and the aggregation number are sorted according to the sequence and the level, the behavior aggregation sequence of the semantic chain is constructed, and the coherence of path identification in the channel aggregation area and the stability of node attribution are enhanced.

The invention discloses a file optimization method and device, a storage medium and electronic device.The file optimization method comprises the steps that a first preset interface is called to obtain all work area folders in a development environment, all source files meeting a preset extension name requirement are screened out from files in each work area folder, and a source file set is obtained, the source file set comprises a first type of files meeting a first extension name requirement and a second type of files meeting a second extension name requirement; the text content of each source file in the source file set is read, an analysis engine corresponding to a second type file in the source file set is used for converting a character string representing the text content into a structured data object, and the data object is a data set containing multiple data structures; the plurality of data structures comprise at least one of the following structures: a structure consisting of key value pairs and a sequence structure consisting of a plurality of elements; and sorting the data objects, and writing a sorting result into the target file.

The invention belongs to the technical field of sequence recommendation and forgetting learning, and discloses a sequence recommendation system forgetting learning method based on structure perception, which comprises the following steps: aiming at polluted interaction data in a sequence recommendation model, evaluating influence characteristics of each polluted item through a double-index quantitative diagnosis mechanism; wherein the sequence influence range index SIS quantifies the pollution through predicting the propagation degree of a dependency relationship and the sequence structure dependency index SSD, and evaluates the structural importance of a project on maintaining the sequence time sequence coherence from global and local levels; an adaptive fine tuning strategy is executed based on the SIS and the SSD obtained through diagnosis, influence decoupling suppression pollution propagation and intensity are controlled by the SIS, adaptive interpolation is carried out between isolation and restoration strategies according to an SSD value in structure compensation, and further, the generalization ability of a model on clean data is kept by keeping loss; according to the method, targeted intervention is achieved, the sequence structure integrity is kept while effective pollution is eliminated, and recommendation performance and credibility are maintained.

The invention relates to the technical field of learning progress feedback, in particular to a dancing motion learning progress self-adaptive adjusting method and system, and the method comprises the following steps: analyzing a joint three-dimensional posture based on posture capturing equipment, obtaining a joint rotation sequence structure, calculating the rotation angle change of continuous frames, and judging a rhythm transition positioning point; and extracting a center label of the sequence structure of the analysis section, judging the distance between the center label and the transition point, adjusting the analysis section and correcting the node. According to the method, the jump position in the action is judged by comparing the direction and breadth of rotation change between continuous frames, the center mark number of the structure is extracted in the local frame segment according to the relative relation of angle change, then the offset parameter is obtained according to the interval relation between the center mark number and the jump position, and the analysis section is readjusted when the offset exceeds the range. The node recognition is established on the basis of a continuous rotating structure, so that the jump position has a dynamic reference basis, and the problem that a static node mode cannot adapt to continuous action changes is solved.

The application discloses a sedimentary mineral prediction analysis method and system based on sedimentary microfacies and a knowledge graph, relates to the technical field of mineral prediction analysis, and can recognize the time sequence evolution process of microfacies from lithological profiles, logging data, sequence structures and other multi-source data through a sedimentary evolution sequence extraction module, and construct a structured evolution matrix, thereby reconstructing the evolution logic of a sedimentary environment in the time dimension. In the original method, parameters such as porosity and permeability are usually processed separately in reservoir evaluation, and are discontinuous in space and not coupled between attributes. However, the system matches key reservoir parameters in seismic inversion and logging interpretation with a sedimentary evolution structure through a multi-dimensional reservoir attribute coding module, generates a three-dimensional attribute tensor RS, abstracts sedimentary evolution from artificially divided sequence units into an evolution structure matrix Mac, avoids subjective dependence of traditional qualitative description, establishes an evolution model in a data-driven manner, and enhances the restoration capability of a dynamic paleoenvironment.

The invention relates to the technical field of key generation, in particular to a data encryption and key generation method for online payment, which comprises the following steps: extracting a touch rhythm generation sequence, executing rotation coding to construct a bit structure, and generating a key in combination with amount information. According to the method, the user touch behavior characteristics are converted into the rhythm sequence, a dynamic encryption basis is constructed through a sequence rotation and bit mapping mode, user behavior level parameters are introduced in the secret key generation process, and payment behaviors and transaction data are deeply bound through fusion of the amount section labels; key generation does not depend on a fixed rule or an external key negotiation process any more, independent key output of the end side is achieved through sequence structure recombination and template parameter matching, the individuation and unpredictability of the key are improved, the recognition capacity for counterfeit behaviors and the protection effect for man-in-the-middle attacks are enhanced, and the key generation efficiency is improved. And the problems of static characteristics and response lag of a traditional key mechanism in actual deployment are effectively relieved.

The invention discloses a scientific calculation script generation method and system. The method comprises the following steps: receiving scientific calculation task demand information described by a natural language; an encoder in a pre-trained deep learning model is adopted to encode the demand information to obtain a context vector matrix, the context vector matrix is used for representing semantic information of the demand information, and the deep learning model adopts a sequence-to-sequence structure; and decoding the context vector matrix by adopting a decoder in a pre-trained deep learning model to obtain a decoding result, and determining a scientific calculation script of the demand information based on the decoding result. The method provided by the invention at least solves the technical problem that the accuracy of the generated script is relatively low due to the fact that the script is generated based on the preset rule template in related technologies.

The invention discloses a capacitance energy storage frequency modulation control method, system and device and a medium, and the method comprises the steps: decomposing a frequency modulation sequence into a plurality of subsequences through VMD to extract the features of different frequency scales, enabling the features of a complex frequency modulation instruction to be clearer, obtaining the prediction error corresponding to each subsequence, measuring the similarity degree between the sequences through attraction, and obtaining the prediction error corresponding to each subsequence; an iterative merging mode of'merging-substitution-recalculation 'is adopted, sub-sequences with the maximum similarity degree are selected for merging each time, the number of the sub-sequences is gradually reduced, and it is guaranteed that sequence features are optimized in each step of merging. The finally generated combined sequence features are more concentrated, the prediction model can be better adapted, the model input quality and the prediction effect are improved, redundant information is reduced, the sequence structure input into the prediction model is optimized, the prediction precision is improved, and finally the capacitor energy storage frequency modulation control is more accurate and the response is more efficient.

The invention provides a voltage quality treatment method and system based on multi-scene data, and the method comprises the steps: carrying out the timestamp matching of a multi-scene record, and extracting a continuous jump point set, so as to enable the voltage node difference, reactive power regulation action change and gear switching points to have a unified sequence structure; consistency mapping of multiple types of records on a time axis is realized; performing grade reconstruction on multiple variable quantities through a convolutional neural network response sorting mechanism, and enabling sequence differences to have a scalable processing capability; introducing an extreme value retrieval and average value sequence comparison mechanism, so that a fluctuation section can form a stable fragment set according to a difference value distribution rule; a deep neural network is adopted to calculate difference value importance, continuous large area recognition of a difference value time sequence is achieved, action conflicts are clearly expressed in a chain structure on a time axis, and a traceable algorithm-level result is provided for voltage anomaly positioning.

The invention discloses a multi-biological sequence model unified management method, system and device, a medium and a product. The method comprises the steps that in response to a biological sequence reasoning requirement, biological sequence task information is obtained, and the biological sequence task information comprises multiple pieces of sequence information; analyzing each piece of sequence information to obtain a corresponding biological sequence structure feature and a sequence type thereof; obtaining a biological sequence target model corresponding to each sequence type; grouping the multiple pieces of sequence information to obtain a plurality of subgroup sequence sets; memory requirements corresponding to each subgroup sequence set are obtained and sorted, a required memory sorting result is obtained, and batch strategies corresponding to the multiple pieces of sequence information are determined according to the current actual memory; and dynamically determining a model loading strategy according to the batch strategy, and executing a reasoning task according to the model loading strategy. According to the method, when a large amount of diversified biological sequence data is processed, appropriate models can be efficiently and intelligently selected and managed, and effective utilization of resources and efficient execution of tasks are achieved.

The invention relates to the technical field of high polymer material detection, in particular to an analysis method of a copolymer sequence structure and application thereof. According to the method, copolymer powder is subjected to high-temperature cracking under the action of a cracking catalyst, and a cracking product is subjected to gas chromatography-mass spectrometry, so that the sequence structure of the copolymer is obtained. According to the invention, by optimizing the separation conditions of gas chromatography, the determination and good separation of multiple sequence structures in the copolymer are realized. According to the method, 26 sequence structures after polyformaldehyde cracking and six sequence structures after styrene-acrylonitrile copolymer cracking can be measured and separated, the measured sequence structures are comprehensive in variety and high in separation degree, and the accuracy of sequence distribution analysis is effectively improved.

The invention discloses a simulation sequence generation method, system and equipment based on double genetic transfer matrixes. The method comprises the following steps: step 1, converting sequence data into state relation data; step 2, constructing a double transfer matrix; step 3, genetic algorithm optimization; 4, simulation data are generated based on the double matrixes; 5, outputting and applying data; according to the method, the object transfer matrix and the operation transfer matrix are constructed to realize separated modeling of the object and the operation, the genetic algorithm is combined to optimize the double matrixes generation by generation, and the object-operation legality mapping table is matched, so that the flexibility and controllability of the simulation data generation process are greatly enhanced; the generated simulation data effectively keeps the statistical characteristics and sequence structure details of the original data, the diversity of the generated data is improved, the consistency of object-operation combinational logic is guaranteed, the generated simulation data is ensured to be reasonable, large-scale real data does not need to be depended on, and the computing resource requirement is reduced.

The present application relates to the technical field of propagation effect evaluation, in particular to a propagation effect evaluation system and method based on multi-platform data fusion, in the present application, the behavior data is converted into a structured unit by normalizing the forwarding frequency and mapping the position with the user ranking index, the fusion ability of different platform data in the time dimension and the frequency dimension is enhanced, the sequence vector of the propagation path is established by processing the order difference and the frequency difference between nodes through the graph neural network, the direction judgment and the weight combination are combined to form the coding structure, which is used to reflect the sequence and the propagation strength in the node propagation path, the similarity of the trajectory sequence is judged through the continuity matching operation of the path sequence structure, the core path with actual diffusion continuity is effectively screened out, the weighted relationship among the node strength, the path position sequence and the repeated index is combined, the aggregation processing is carried out according to the platform, the regional difference value is extracted, and the propagation strength index of each platform is obtained.

The invention relates to a man-machine collaborative education resource recommendation method and system for large-scale online learning, and belongs to the technical field of intelligent education. The method comprises the following steps: firstly, modeling video watching interaction behavior information of a learner in a learning process, and respectively converting video interaction data of the learner into a graph structure and a sequence structure so as to describe long-term learning preference and short-term learning attention change of the learner; secondly, through a man-machine cooperation mechanism based on multiple rounds of dialogues, embedded representation of the learner and teaching resources is dynamically updated by utilizing real-time interaction feedback of the learner in the learning process, and continuous perception and adjustment of learning requirements are realized; and finally, in combination with professional background information of the learner, a recommendation strategy is adaptively optimized through a synergistic effect among different recommendation agents, so that a teaching video recommendation result which better conforms to the learning progress and personalized requirements of the learner is provided for the learner.

The application discloses a behavior sequence structured coding method based on high-order nonlinear dependence. First, a time sequence sensing embedding is performed on an original behavior sequence. Then, a network formed by stacking multiple structured coding units is used to perform hierarchical coding on the embedded sequence. Each coding unit comprises parallel processing paths, which are respectively used for processing first-order linear features and high-order nonlinear interaction features, and each order of features is weighted and combined through a learnable transformation module. Finally, the output of the network is aggregated and projected to generate a coding representation suitable for a downstream task. The application constructs a network structure comprising multiple order processing paths, so that the neural network can more effectively encode and utilize the structured high-order nonlinear dependence relationship hidden in the sequence data, and provide a more expressive feature representation for sequence modeling tasks.

The invention relates to the technical field of variable frequency regulation, in particular to a variable frequency speed regulation energy-saving control method and system based on load dynamic identification, and the method comprises the steps: constructing a multi-dimensional time sequence structure for data collected in a current operation period of a system, and identifying the dynamic trend of a current load based on the sequence; establishing a candidate frequency set, outputting a power prediction value corresponding to the candidate frequency set through a power estimation model, calculating a unit energy-saving potential after the current frequency is adjusted to the candidate frequency through an energy-saving potential algorithm, and inputting the candidate frequency corresponding to the unit energy-saving potential greater than a threshold value into a feasible frequency set; on the basis of the feasible frequency set and the corresponding unit energy-saving potential, establishing a comprehensive scoring function, and selecting an optimal target frequency through the comprehensive scoring function; and inputting the optimal target frequency and the current operation frequency into a feedback correction function, outputting an execution frequency of the frequency converter through the feedback correction function, and converting the execution frequency into a corresponding control instruction.

Contemporary gene sequencing techniques, including “Next Generation Sequencing” techniques, can include sequencing a plurality of fragments of a target polynucleotide. However, tire limitations of existing sequencing techniques, and the often repetitive or otherwise difficult-to-sequence structure of natural polynucleotides, means that it can be difficult and / or expensive to generate accurate sequences. Methods provided herein include inserting dual polynucleotide ‘barcodes,’ along with neighboring primer sequences, into a target polynucleotide prior to other sequencing processes. These inserted barcodes can improve the accuracy of sequences generated for the target by adding ‘noise’ into the target, allowing subsequent sequencing techniques (e.g., alignment, stitching, etc.) to more accurately estimate the target-plus-barcodes sequence. The primers can cause the fragments to begin at points within the target that correspond to the beginning of other sequences, facilitating the stitching of sequence ends together. The barcodes can then be removed to provide the sequence of the target polynucleotide.

The invention discloses a load spectrum block ordering method considering an order effect, and relates to the technical field of aero-engine structural member load spectrum compilation, and the method comprises the steps: carrying out the rain flow counting of an actual measurement load spectrum, extracting all load cycles, and determining the order attribute of each cycle based on hysteresis loop analysis; dividing a front cycle and a rear cycle by using a maximum load point of a flight profile, and taking a 0-max-0 cycle as a first-stage reference cycle; step-by-step back insertion is carried out according to the load grades from high to low; multiple sub-blocks at the same level are sorted according to the mean value of the spectral blocks, the minimum value is arranged at the tail end of a parent loop, and the rest are inserted into frequency midpoints; according to the method, the nesting hierarchy of the flight load is accurately reconstructed, it is ensured that the program spectrum is highly consistent with the original flight spectrum in the sequence structure, sequence accumulation is avoided, and the influence of actual load interaction on the fatigue mechanism is finely simulated. Program spectrum compilation requirements under deep nesting and variable working conditions can be flexibly met, and a solid support is provided for efficient generation and fatigue test standardization of a large flight load spectrum.

The invention provides a data processing method and device, equipment, a storage medium and a program product, which can be applied to the technical field of financial science and technology. The method comprises the following steps: determining a rendering point number in response to a click zooming operation of a user; based on the data values, screening in a priority queue set according to a preset sequence structure to obtain a first mark point set, the number of mark points in the first mark point set being equal to the number of rendering points, and the priority queue set comprising a plurality of mark points having respective corresponding data values, the data values are pre-determined based on the business values and the spatial distances of the mark points, and the sequence structure indicates a storage sequence obtained by arranging the plurality of mark points in a descending order based on the corresponding data values; the first mark point set is traversed based on the real-time position of the user, a second mark point set used for rendering is obtained, and the position range covered by the second mark point set is smaller than the position range covered by the first mark point set.