248 results about "Sorting algorithm" patented technology

The invention relates to the technical field of photovoltaic systems, and particularly discloses a fault identification method and system for a photovoltaic system, and the method comprises the steps: collecting data in real time through environment, electrical parameters and an equipment state monitoring sensor, carrying out the cleaning and standardization, extracting time domain, frequency domain and time frequency features, and screening a feature subset through a correlation analysis and feature importance sorting algorithm; detecting and classifying faults by using a hybrid model composed of an isolated forest algorithm and a random forest classifier; positioning a fault subsystem and analyzing a root cause by means of a hierarchical diagnosis strategy, a graph neural network and a Bayesian reasoning algorithm; and periodically updating the model based on the new data. The method can quickly and accurately identify and position faults, adapts to a complex environment, reduces the operation and maintenance cost, and improves the operation reliability and operation and maintenance efficiency of a photovoltaic system.

The invention provides a household appliance knowledge question-answering method and system based on retrieval enhancement generation. The method comprises the following steps: acquiring household appliance field multi-modal data from a multi-format document library; extracting text information, table information and chart information in the multi-modal data; performing domain term injection processing on the extracted information, and constructing a packet domain enhancement index; receiving a natural language question input by a user; the natural language problem is analyzed through a query optimizer, and semantic retrieval and keyword retrieval are executed in parallel; carrying out fusion processing on the semantic retrieval result and the keyword retrieval result; selecting matched document fragments by adopting a relevancy sorting algorithm; inputting the matched document fragments into a large language model to generate candidate answers; verifying the compliance and traceability of the candidate answers through a credibility evaluation module; outputting a final answer with a reference source; and storing the high-frequency questions and the final answers into a cache library to solve the problems that the answer accuracy of a knowledge question-answering system is reduced and the response efficiency is limited.

The invention relates to the technical field of embedded system construction, in particular to a software dependency relationship automatic integration method and system based on meta packaging, and the method comprises the steps: S1, creating a meta packaging framework in an embedded construction system, and defining and constructing a stage hook through a Makefile; s2, automatically deploying the pre-compiled software package and the shared dependency library to a target directory of a construction system through a resource injection script; s3, analyzing a metadata file of the pre-compiled software package, extracting dependency relationship information, and generating a global dependency relationship list based on a directed acyclic graph topological sorting algorithm; and S4, inversely writing the global dependency relationship list into the dependency declaration configuration of the meta-packaging by utilizing the dependency aggregation script. By implementing the scheme, the problems of dependency sequence disorder and metadata auditing failure during embedded system integration are solved, the reliability of system integration is improved, the system construction efficiency is optimized, the safety of system construction is enhanced, and the maintainability and expansibility of system construction are improved.

The invention discloses a fast charging station planning method based on urban dynamic traffic demand prediction and correction, and relates to the technical field of electric vehicle fast charging station planning, and the method comprises the steps: S1, constructing a traffic topology connection matrix according to urban road network topology, and recognizing traffic hub nodes and long-distance nodes as candidate nodes; s2, proposing three dynamic factors including social demands, user public praise and demand elasticity, and correcting the electric vehicle traffic demand prediction value of each path unit; s3, constructing a multi-target fast charging station capacity optimization model taking the minimum planning total cost and prediction error as targets, wherein the multi-target fast charging station capacity optimization model comprises fast charging station self constraints and power grid adaptation constraints; s4, solving the model by adopting a non-dominated genetic sorting algorithm to obtain a Pareto solution set, and selecting a planning result according to the demand of a decision maker; according to the method, the problems of blind site selection and static demand prediction in traditional planning are solved, economy and power grid safety are considered, planning scientificity and decision-making flexibility are improved, and supply and demand of charging resources can be effectively balanced.

The invention provides a user-defined script dynamic editing and executing method and system of a Unity engine. The method comprises the following steps that codes are input and preprocessed; executing text cleaning, word segmentation and annotation preprocessing according to the line feed as a code line set; grammatical analysis and semantic understanding; constructing an abstract syntax tree, identifying a syntax structure, and combining context analysis semantics to generate a symbol table; processing errors in real time; positioning an error line through a multi-stage matching strategy, highlighting an error position through a visual interface, and generating a repair suggestion in combination with a line number mapping table, error propagation analysis and a multi-evidence fusion mechanism; executing dynamic codes; explaining and executing the corrected script, and feeding back an execution result; through interaction with a Unity engine, operation control of a script on a game object and a scene is realized; through three core technologies of semantic weight dynamic matching, an anti-interference navigation state machine and a hybrid sorting algorithm, the long-standing problems of low matching precision, poor interaction experience and the like in the field of code completion are solved.

The invention discloses a production control method and system based on multi-source data fusion and an intelligent algorithm, and relates to the technical field of automatic control. Multi-dimensional production state information is collected through a sensor, multi-source heterogeneous data is integrated into a data set in a unified format through a data fusion technology, and the multi-dimensional production state information is obtained; a long short-term memory network is used for trend analysis and prediction, a multi-objective optimization algorithm is used for calculating an optimal control parameter, multi-loop cooperative adjustment is realized, an adjustment instruction is issued through a distributed control protocol, a priority ranking algorithm is introduced to ensure system stability, and a game theory negotiation algorithm is used for realizing dynamic resource allocation among production units. The production scheduling plan is optimized through the genetic algorithm, continuous monitoring and optimization can be achieved, control parameters can be adjusted in a self-adaptive mode, the production efficiency and the resource utilization rate are effectively improved, and intelligent automatic control under the complex production environment is achieved.

The invention discloses a multi-dimensional natural language reasoning method based on a safety emergency knowledge graph. The method comprises the following steps: S1, collecting multi-source heterogeneous data, and carrying out standardized cleaning, entity recognition and relationship extraction; s2, constructing an emergency knowledge graph, performing storage and indexing by adopting a graph database, and designing an incremental updating mechanism; s3, intention classification and keyword recognition are conducted on natural language questions input by a user through the deep learning model, and the questions are mapped into initial entity nodes in the graph; s4, performing multi-hop reasoning around an initial entity in the knowledge graph, screening out a most relevant and logically coherent Top-K path in combination with reinforcement learning and a graph sorting algorithm, and realizing deep correlation analysis and answer candidate generation of the question; s5, generating an answer in a structured and natural language mixed form based on a reasoning result, and providing a visual reasoning path for display; a user feedback mechanism is introduced, and continuous optimization of the system and iterative updating of the model are supported.

The invention provides a distributed photovoltaic and energy storage load collaborative optimization operation method and system. The method comprises the following steps: acquiring multi-source data and processing the multi-source data by adopting an improved wave sorting algorithm; constructing a CNN and LSTM hybrid model to predict photovoltaic output; applying a single-index time FPT algorithm and combining with a cactus shrinkage principle to predict load demand and electricity price fluctuation; establishing a collaborative optimization model taking economy and photovoltaic consumption rate as double targets; carrying out distributed iterative optimization by adopting a single-index time FPT algorithm based on a cactus shrinkage principle; and generating a multi-time-scale control instruction and issuing the multi-time-scale control instruction to each device to realize cooperative control. Through the innovative data processing method and optimization algorithm, the efficient cooperative operation of the distributed energy system is realized, the photovoltaic consumption rate and the economic benefit are improved, and the practical value is relatively high.

The invention provides a lightweight configurable cue word labeling method and system for a Web system, and relates to the technical field of Web front-end development and man-machine interaction, and the system comprises a front-end labeling plug-in module, a cue word content service module, a cue word management module and an intelligent recommendation module. The front-end labeling plug-in captures text content selected by the user by monitoring a global shortcut key, and allows the user to add prompt words of a text, a rich text or a link type; the prompt word content service module provides an API (Application Program Interface) to realize persistent storage of data; the prompt word management module provides a unified management interface for an administrator; and the intelligent recommendation module automatically judges the prompt word display priority through a candidate recall and sorting algorithm based on the user click behavior data, and continuously optimizes the recommendation result by using an online learning mechanism. The method is integrated to an existing Web system in a non-intrusive mode, business core codes do not need to be modified, the use threshold of the system is remarkably lowered, and precipitation and sharing of business knowledge are promoted.

The invention relates to the technical field of mechanical arm obstacle avoidance, and particularly discloses a mechanical arm obstacle avoidance method and system based on vision. The method comprises the following steps: collecting environmental visual data to identify auxiliary potential obstacles and construct an attribute matching list; in combination with characteristics of a task real-time execution stage, an ideal solution sorting algorithm is improved to construct an auxiliary adaptation degree evaluation model, an auxiliary target obstacle is determined after sorting, an auxiliary scheme is designed, and a path planning core constraint is extracted; auxiliary function nodes are positioned, a path is initialized, and a collaborative motion track is output through quadratic programming optimization; and calculating a failure judgment coefficient based on the auxiliary adaptation degree and the demand achievement rate, and carrying out track local reconstruction or updating according to scenes. The system comprises an obstacle recognition module, a path planning module, a trajectory adaptation module and a trajectory updating module. The obstacle is converted into the auxiliary resource from the obstacle avoidance object, and the task efficiency of the mechanical arm can be improved.

The invention discloses a hybrid expert model structured pruning and acceleration method and system based on micro expert sorting, belongs to the technical field of large language models, and solves the problems that an existing MoE pruning method cannot consider the requirements of fine-grained pruning, reasoning acceleration and structural analysis generalization at the same time, coarse-grained expert-level pruning damages the performance of a model, and the reliability of the model is poor. And fine-grained compression lacks speed increase and lacks a unified micro-analysis method. The method comprises the following steps: splitting each expert network in a hybrid expert model into a plurality of micro experts, and modeling the plurality of micro experts, so that the micro experts in different expert networks have comparability; sorting all the micro experts according to the energy indexes of the micro experts by adopting a micro expert sorting algorithm; and processing the sorted micro-experts by adopting a pruning algorithm, selecting a core micro-expert for reservation, and directly deleting the rest of the micro-experts. The method is suitable for application scenes such as edge calculation and multi-task learning.

The invention provides a performance test and evaluation method, system and device for a power grid credential environment and a medium, and the method comprises the steps: obtaining real-time data of each node through employing a distributed test probe and a data flow distribution strategy, and constructing a digital twinborn body of the power grid credential environment; the digital twin body comprises twin nodes corresponding to the nodes; extracting multi-dimensional performance test data of each twin node under the performance test load by adopting a classification dimension convergence method; and based on the performance test prediction data of each twin node, performing risk assessment on each twin node by adopting an entropy weight-comprehensive sorting algorithm. According to the method, dynamic load balance of each node is ensured through a data traffic distribution strategy, normal operation of the power grid credential environment is prevented from being influenced during test evaluation, and the method is suitable for a complex scene in which the power grid credential environment is easily impacted by a high load; data evaluation is carried out through performance test prediction data, data exception can be captured, and potential performance risk bottlenecks of a power grid credential environment can be found in advance.

The invention discloses a content abstract generation method based on chapter structure analysis, and belongs to the technical field of natural language processing. The method comprises the steps that firstly, an original text is preprocessed, then text structure deep analysis is carried out, the text type of the text is recognized, an explicit / implicit text relation is extracted, and special symbols are introduced through a Prompt normal form for implicit text relation extraction to strengthen logic semantics; secondly, scoring sentences by adopting a double-path scoring mechanism in combination with a deep neural network model of chapter structure features and an optimized text sorting algorithm, and fusing scores through a logistic regression model; then, screening target sentences based on a chapter relation weighted secondary modulus function and a greedy algorithm, and finally, carrying out post-processing to generate an abstract. According to the abstract generation method, the chapter structure logic is deeply utilized, so that the problems of logic unsmoothness, information redundancy or key relation missing in the existing abstract generation are solved, and the semantic coherence and information integrity of the abstract are improved.

The invention discloses a multi-constraint-based multi-objective optimization design method for a hexapod robot, and aims to solve the problem of task failure possibly caused by driving overload, structural interference or foot end slipping when a foot robot moves and operates in a field environment due to the fact that a real field topographic condition is not considered in a foot robot optimization method. According to the method, the optimal body size of the hexapod robot meeting the terrain trafficability constraint is calculated by utilizing the terrain trafficability constraint in combination with the to-be-executed terrain conditions, terrain indexes and to-be-optimized geometric parameters of the hexapod robot, and then the optimal body size of the hexapod robot meeting the terrain trafficability constraint is calculated according to the leg size constraint of the hexapod robot; taking the minimum value of the joint peak value stress and the joint peak value power under the leg size constraint as an optimization target, and obtaining the optimal leg size by using a non-dominated sorting algorithm. Therefore, the optimal structure of the hexapod robot is obtained. According to the method, the body size and the leg structure size of the hexapod robot are optimized through terrain adaptability constraints and multiple real physical constraints, and the method belongs to the field of robot design.

The invention discloses a panel detection and recognition method, device and equipment and a computer readable storage medium, and relates to the technical field of panel detection, optical character recognition and the like, the method combines panel detection and optical character recognition technologies, analysis and extraction of panel expression content information and service logic information are achieved, and the method has the advantages of being high in practicability and high in practicability. Structured processing of identification result data is realized by utilizing a hierarchical sorting algorithm, and meanwhile, definition and analysis of processing configuration items are introduced, so that display and analysis of identification contents and data analysis are facilitated, the identification effect is improved, an expected identification effect can be achieved especially when a page with complex business logic is identified, and the identification efficiency is improved. And panel detection applications are simpler and more convenient.

The invention relates to the technical field of livestock measurement, in particular to a beef cattle body size intelligent measurement device and method, in the beef cattle body size intelligent measurement device and method, hoof joint point pixel coordinates are converted and angle differences are calculated in combination with a lexicographical sequence block reordering algorithm, identity binding codes are accurately generated, uniqueness and stability of beef cattle individual recognition are ensured, and the accuracy of beef cattle body size measurement is improved. The method effectively reduces the identification confusion risk when multiple beef cattle are measured at the same time, generates a simulation sample through local pixel block rendering and generative adversarial network, screens a sample according with a threshold according to authenticity scores, improves the sample richness, optimizes the coverage of a model training set, extracts body size fields and position coding values, and calculates correlation coefficients between fields. Low-correlation field pairs are removed, only high-correlation fields are reserved for cross combination, a modeling sample pool is constructed, the representativeness of samples and variable combination diversity are enhanced, and the fitting precision and popularization capability of a body size estimation model are improved.

Methods, systems, and computer storage media for providing security incident prioritization management using an adaptive incident prioritization engine of a security management system are described. The adaptive incident prioritization engine provides security incident prioritization based on an adaptive incident prioritization (AIP) framework built using a ranking algorithm. In particular, the adaptive incident prioritization framework employs a Best Matching 25 (BM25) algorithm and strategically and programmatically adapts the algorithm (e.g., an adaptive incident prioritization model) to rank security incidents based on a local security incident relevance metric (an adaptation of Term Frequency—TF—in BM25) and a global security incident rarity metric (an adaptation of Inverse Document Frequency—IDF—in BM25) associated with security incidents. A prioritization score for a security incident is calculated based on aggregating weighted frequencies of security incident ranking components (e.g., security incident metadata) within a security incident to determine an overall significance of the security incident.

The invention relates to the technical field of field bridge scheduling, in particular to a partial order and multi-index evaluation-based field bridge scheduling method, device and equipment and a medium, and the method comprises the steps of obtaining a task queue which comprises a plurality of container management tasks; analyzing a dependency relationship among all the container management tasks in the task queue, generating a partial order relationship based on the dependency relationship, and traversing all the tasks according to the partial order relationship to generate a directed acyclic graph; generating a plurality of scheduling sequences based on the directed acyclic graph by using a topological sorting algorithm; calculating a plurality of core indexes of each scheduling sequence according to a moving route planned by a field bridge for completing the task queue in each scheduling sequence; and on the basis of each core index and the preset weight corresponding to the core index, the comprehensive evaluation score of each scheduling sequence is calculated, and the scheduling sequence with the lowest comprehensive evaluation score is a pre-selected scheduling sequence. The method can optimize the dispatching decision of the yard bridge, and improves the operation efficiency of the yard equipment.

According to the charge and discharge control method and system for the energy storage battery system, a state space model is constructed based on a Thevenin equivalent circuit theory, voltage, current and temperature data collected in real time are fused, and the charge state and the available power state of each battery cluster are estimated with high precision by adopting an extended Kalman filtering algorithm; according to the operation mode, a dynamic sorting algorithm is adopted to calculate the charging and discharging dynamic weight of each battery cluster, the power of the low-SOC battery cluster is preferentially improved in the charging mode, and the power of the high-SOC battery cluster is preferentially called in the discharging mode; in combination with a multi-objective optimization function which aims at improving economical efficiency and power grid supporting capacity, target power of each battery cluster is determined through iterative allocation under the condition that condition constraints are met, and independent control is executed by a string converter. Accurate sensing of the battery state and dynamic optimization and balance control of the charging and discharging strategy are achieved, the system operation economical efficiency and the power grid supporting capacity are remarkably improved, and the service life of the battery is remarkably prolonged.

The invention belongs to the technical field of intelligent education and personalized learning, and provides a personalized learning path recommendation method based on a course knowledge point sequence. The method aims at solving the problems that existing learning path recommendation logic association is insufficient, the personalized matching degree is low, and the optimization efficiency is not high. The method comprises the following steps: constructing a knowledge point sequence model by adopting a topological sorting algorithm on the basis of a curriculum knowledge point pre-modification and post-modification relationship, a difficulty level and dependence intensity, and generating a structured knowledge relationship graph; a learner feature model and a knowledge resource model are established, learner features are described from the dimensions of learning targets, cognitive levels, learning styles and the like, and the matching degree with knowledge points is calculated; and constructing an optimization model taking learning cost minimization, mastering degree maximization and path continuity optimization as targets, performing iterative optimization by using an improved intelligent optimization algorithm, and generating a learning path with logic rationality and personalized adaptability. According to the method, the scientificity and learning efficiency of learning path recommendation can be effectively improved.

The invention discloses an industry knowledge graph query and retrieval interface system capable of being quickly customized, and relates to the field of computer software, and the system comprises a data and representation unit which is used for generating a semantic vector and storing the semantic vector into an open source vector database; the mixed retrieval engine is used for carrying out word segmentation processing on a query statement input by a user and obtaining a search result by combining an extended search mechanism in the knowledge graph and utilizing a fusion sorting algorithm; the reasoning and optimizing unit is used for reasoning the retrieval result and realizing adaptation of different industries in combination with a meta-learning-based field adaptive engine; and the interaction and service unit is used for optimizing user interaction experience through a dynamic hot word recommendation mechanism and realizing system deployment and customized configuration according to the micro-service architecture. According to the method, the structured element indexes are extracted from the corresponding industry files by constructing the industry standard knowledge graph, so that the accuracy and the specialty of data sources are ensured.

The embodiment of the invention provides a three-dimensional Gaussian sputtering scene rendering method and device based on an unreal engine. The method comprises the following steps: analyzing point cloud data of a three-dimensional Gaussian sputtering scene based on a graphics processor, and obtaining structured point cloud attribute data; mapping each Gaussian point in the point cloud attribute data into a quadrilateral instance, and determining a mapping result; according to the point cloud attribute data and a preset double-tone sorting algorithm, determining a sorting result of Gaussian point depths corresponding to the quadrilateral instance; according to the point cloud attribute data, the mapping result, the sorting result and a preset physical rendering model, processing the obtained light source data in the three-dimensional Gaussian sputtering scene, and determining an illumination calculation precision standard; and according to the distance between the preset camera and the point cloud data in the three-dimensional Gaussian sputtering scene, performing scene rendering by using the point cloud attribute data, the mapping result and the illumination calculation precision standard. According to the scheme, the engine compatibility problem is solved, and the rendering performance is optimized.

This invention discloses an online editing and parallel execution method and system for vehicle network test cases, relating to the field of vehicle network testing technology. It establishes an online editing environment via a web interface, enabling automatic generation of protocol code and providing testers with a convenient and efficient development experience. Regarding test case execution, it accurately analyzes the dependencies of test cases, uses a topology sorting algorithm to construct a reasonable execution plan, and adaptively switches between sequential and parallel execution modes. Combined with containerization isolation technology, it allocates an independent execution container for each test case, ensuring the isolation and stability of the testing environment. Simultaneously, it initializes a unified test execution context for each test case and establishes a communication connection with the vehicle network bus simulation engine through a standardized interface, achieving standardization and normalization of operations such as message sending and receiving, fault injection, and assertion verification. In variable management, it adopts a copy-on-write mechanism to achieve isolated sharing of project variables, avoiding data crosstalk.

This invention discloses a ciphertext K-nearest neighbor classification method based on fully homomorphic encryption, relating to the fields of data security and privacy computing. The method includes: acquiring a training sample set with known categories, preprocessing and performing fully homomorphic encryption to generate a ciphertext training set; performing fully homomorphic encryption on the client side on the feature vectors of the samples to be classified to generate ciphertext test samples; calculating the Euclidean distance between the ciphertext test samples and each training sample in the ciphertext training set to obtain a set of ciphertext distances; selecting the K smallest ciphertext distances using a ciphertext sorting algorithm; subtracting each ciphertext distance from each ciphertext distance in the ciphertext distance set, and packaging it with the category label of the corresponding training sample; and determining the final category of the sample to be classified based on the difference information after decryption on the client side. By performing K-nearest neighbor classification operations in a fully ciphertext state, it can obtain accurate classification results consistent with plaintext calculations while ensuring that the privacy of the original data is not leaked.

The invention provides a data-driven forward material selection method and system for an automobile multi-material body, and relates to the technical field of automobile research and development, and the method comprises the steps: obtaining a material selection target constraint condition and candidate material data of the automobile body; on the basis of the candidate material data, vehicle candidate material schemes are generated, multiple data-driven vehicle target material selection models are called to select the vehicle candidate material schemes in sequence, and vehicle feasible material selection schemes meeting material selection target constraint conditions are obtained; a non-dominated sorting algorithm is called to screen the feasible material selection schemes of the whole automobile, and candidate recommendation schemes of automobile body material selection are obtained; and multi-criterion decision making is carried out on the candidate recommendation schemes to obtain a final recommendation scheme of automobile body material selection. The method is used for solving the technical problem of low material selection process efficiency caused by complex vehicle body materials, material selection constraints and numerous research and development requirements when forward material selection is carried out by depending on manual experience in the prior art in automobile research and development.

The invention relates to the technical field of vehicle lamp assembly, in particular to a vehicle lamp assembly method based on a body-equipped intelligent agent, which comprises the following steps: calibrating a camera on a manipulator and a hand-eye in an Eye-in-Hand mode; obtaining a vehicle lamp material image collected by a camera, obtaining the position of each material area of the vehicle lamp through the constructed segmentation network, and determining the priority of each material area through a sorting algorithm; the positions and priorities of all the material areas are obtained, the motion path of the mechanical arm is planned through the calibrated coordinate conversion relation, and the mechanical arm is controlled to grab the materials according to the motion path and the self-adaptive grabbing strategy; the robot with the body is driven to move to a preset discharging position through the sliding rail; a multi-modal end-to-end large model is introduced, and a dynamic coordinate conversion relation is generated in real time by combining multi-sensing fusion feedback of tactile sense, tactile sense and visual sense to control a manipulator to place materials. According to the invention, high-precision, high-efficiency and high-flexibility integrated assembly of the vehicle lamp can be realized.

The embodiment of the invention discloses a position optimization method based on multipath scattering parameter fusion, and relates to the field of passive positioning in a complex environment. For signals interrupted due to shielding, path relevance of multi-path parameters before and after signal interruption is re-established through a multi-path component grading sorting algorithm of different time window scales, multi-path component tracking is achieved, and therefore high-precision robust positioning is achieved. According to the method, multipath parameter information of multipath signals is utilized to realize path order identification and scatterer matching of the signals, joint optimization is carried out based on multipath parameters and a geometric model, and high-precision positioning of a target and a scatterer is realized. And high-precision target positioning under strong shielding and strong multipath environments can be realized by using radio multipath signals.

The invention discloses an interactive safe medication suggestion method and system oriented to medicine-diagnosis cross check, and belongs to the technical field of medical artificial intelligence and clinical decision support (CDSS). According to the method, an intelligent probe is implanted into a writing interface of an electronic medical record (EMR), so that full-dimensional context information such as structured diagnosis, unstructured texts (such as chief complaint) and past medicine history of a patient is sensed in real time. At the moment when a doctor inputs a prescription, the system concurrently executes'drug-disease 'taboo verification and'drug-drug' interaction verification by using a pre-constructed heterogeneous medical knowledge graph. Once a potential medication risk is detected, the system immediately triggers a blocking mechanism, and automatically generates compliant alternative drug suggestions which are pharmacologically equivalent and conform to the physiological characteristics of the current patient based on graph embedding vector similarity and a multi-objective optimization sorting algorithm.

The invention discloses a Byzantine consensus method and device, and relates to the technical field of block chains, and the method comprises the steps: allowing transactions of each replica node and the sequence information of the transactions to be perceived by other replica nodes in advance, and achieving the sorting and submission of all transactions in a consensus round in combination with a weight-recursive transaction sorting algorithm, the block chain consensus method has the advantages that acknowledgement delay is remarkably reduced, consistency and fairness of transaction sequences are improved, transaction verification and communication overhead is reduced by compressing transaction hash and simplifying transaction sorting based on the transaction ID mapping table, the whole consensus process has the advantages of high performance and low communication overhead, and consensus reliability in the block chain is improved.

The invention relates to a method and a system for measuring a curl index of a corn leaf and evaluating a drought stress degree. According to the method, a foundation laser radar is used for collecting point cloud data of a corn plant in the morning and the afternoon under different drought stress degrees in a large bell mouth period; threshold segmentation and stem leaf segmentation are carried out; performing optimization preprocessing operation on the blade point cloud; cutting the point cloud of the blade by using a gradually expanded cylinder, and calculating the vein point of the blade; carrying out equidistant plane segmentation on the corn leaf from the tip to the base of the leaf; calculating the Euclidean distance between two points which are farthest from each other in each section of blade section curve point cloud of the two sample sets; fitting the blade section curve point cloud by using a greedy sorting algorithm based on the shortest distance and a cubic spline curve, and calculating a spline curve fitting length; calculating a blade curl index and an average actual blade curl index of the blade; and finally performing drought stress degree grading evaluation.