148 results about "Sequence optimization" patented technology

The invention provides an intelligent assembling and welding manufacturing method and system for an open-type pressure machine tool body, and relates to the technical field of open-type pressure machine tool body assembling and welding. The method comprises the steps that according to the structural characteristics of the open-type pressure machine tool body, the machine tool body is divided into a plurality of independent functional modules, and the functional modules are positioned and clamped; the assembled open type pressure machine tool body is obtained; the method comprises the steps that a welding sequence optimization model of welding seams is established according to a key stress structure of an open type pressure machine tool body, and the optimal welding sequence of the welding seams is obtained by solving the welding sequence optimization model; welding the assembled open-type pressure machine tool body by utilizing the optimal welding sequence, and in the welding process, identifying a welding seam type and defect characteristics by adopting a phased array ultrasonic imaging technology, outputting a welding defect ultrasonic imaging result, predicting an expansion evolution behavior of the defect according to an intelligent detection result, and forming a quantitative defect evaluation system; and multi-information fusion intelligent evaluation of the welding quality is realized.

The invention relates to the technical field of construction control, and particularly discloses a method and a system for dynamically regulating and controlling the installation precision of an inclined-span steel box arch bridge based on machine learning. Wherein the real-time data comprises real-time stress of the arch rib, real-time deformation of the arch rib, real-time environment wind speed and real-time hoisting parameters; constructing a prediction model capable of analyzing the coupling effect of the wind load and the hoisting unbalance load based on a large amount of historical construction data; inputting real-time data into the prediction model to obtain a real-time coupling effect analysis result; outputting a hoisting sequence optimization instruction based on the decision engine, the coupling effect analysis result and a preset mechanical constraint condition of the obliquely-spanning steel box arch bridge; based on the deviation between the real-time deformation amount of the arch rib and a preset installation precision threshold value, a cable force grading adjustment scheme is generated; construction efficiency of the inclined-span steel box arch bridge is effectively improved, construction safety and installation precision are guaranteed, and stability and reliability of the bridge structure are guaranteed.

The invention relates to an intelligent planning method for an assembly sequence of a high-loading-density aviation equipment product. The intelligent planning method solves the problems that the efficiency is low and errors are prone to occurring when the assembly sequence is manually formulated. Comprising the following steps: Step 1: establishing an extraction method and a standardized expression method of three types of object information of parts, an assembly process and assembly constraints in a process information model, and forming an assembly process information model integrating a product structure, an assembly process, an assembly tool and the like; 2, related assembly process knowledge and resources are carded, an interference matrix, a connection matrix and a support matrix are formed to serve as input and constraint conditions of assembly sequence planning, an assembly sequence solving space is formed, an assembly sequence planning mathematical model is finally formed, and an assembly sequence candidate set is generated; step 3, carrying out the construction of an assembly sequence evaluation index; designing an assembly sequence evaluation index calculation mode, constructing an assembly sequence evaluation index calculation method, establishing a multi-criterion assembly sequence optimization method, and supporting acquisition of an optimal assembly sequence.

The invention discloses a service starting sequence optimization method and device, electronic equipment and a storage medium, and the method comprises the steps: obtaining a multi-mode data set of at least one service in a substrate management controller, creating a target function, taking each service in the at least one service as a node to construct a target tree structure, and obtaining a target tree structure; the optimization objective of the objective function is to minimize the total start time of at least one service; and based on the target function and the target tree structure, determining a target node corresponding to the optimal evaluation index in each layer of structure, and finally starting the service corresponding to each target node in sequence according to the hierarchical relationship between the determined target nodes. By means of the method, the technical problem that in the related technology, flexibility is poor when all services in the baseboard management controller are started in a predefined static sequence is solved, and the technical effect of optimizing the service starting sequence through the self-adaptive dynamically-changed operation environment is achieved.

The invention discloses a method for aided analysis of influence of 5 'UTR on mRNA translation efficiency based on a machine learning model, and relates to the technical field of synthetic biology and computational biology, the method comprises the following steps: S1, constructing a 5' UTR plasmid library; s2, transcribing the 5 'UTR plasmid into an independent mRNA (messenger ribonucleic acid) with a luciferase-opt luciferase, and taking the luminous intensity of the luciferase-opt luciferase as the translation intensity of the mRNA; s3, analyzing characteristics related to protein expression intensity of the mRNA; and S4, constructing a random forest algorithm model on the basis of the features analyzed in the step S3 to predict the influence of the 5 'UTR on the mRNA translation efficiency. According to the method for auxiliary analysis of the influence of the 5 'UTR on the mRNA translation efficiency based on the machine learning model, a feasible technical route reference is provided for sequence optimization of mRNA in subsequent mRNA vaccine development, so that the mRNA vaccine can efficiently express target protein in a body.

The invention relates to the technical field of production management, in particular to a production scheduling optimization system based on data analysis, which comprises a process data trend analysis module, a station resource real-time acquisition module, a scheduling sequence optimization judgment module, a rhythm coordination check module and a scheduling dynamic optimization module. According to the method, the process duration trend is analyzed in real time, continuous growth is accurately recognized, rhythm parameters are dynamically corrected, the stable process rhythm is ensured, efficient resource connection is achieved based on the station task completion time and the matching task and station idle starting time, the task sequence is dynamically adjusted according to the station idle state, and task connection is optimized; the beat proportion of adjacent tasks is verified, the task interval is adjusted, the beat difference is controlled within a reasonable range, beat sudden change is prevented from interfering with production, the rapid response ability to production changes is integrally enhanced, task and resource collaborative optimization is achieved, the overall scheduling efficiency and beat consistency are improved, and the production continuity and the resource utilization rate are improved.

The invention relates to a resource allocation and process planning optimization method under complex constraint conditions, which comprises the following steps of: representing various constraint conditions of a process planning process by utilizing a double-layer directed acyclic graph, and generating and constructing an initial feasible solution set meeting all the constraint conditions; initializing a state space, wherein the state space comprises state variables of a process number, an average process length, reward value fluctuation and fitness; noise is collected and coupled to a Q network, action selection is carried out, and low-level heuristic algorithm options are output; generating a new taboo sequence by using the selected low-level heuristic algorithm, and optimizing the current feasible solution; switching a low-level heuristic algorithm according to the improvement amplitude and the execution duration; and outputting an optimal processing feature sequence and process division combination after multiple times of training. Compared with the prior art, the method has the advantages that multiple process decisions can be optimized at the same time, sub-optimal results caused by separate optimization processes are avoided, processing time and resource consumption are remarkably reduced, and production efficiency and scheme adaptability are improved.

The invention, which relates to the field of FPGA and machine learning, discloses a high-flow-degree MambaV2 hardware acceleration system with an optimized calculation sequence based on FPGA, comprising a preceding stage module, a tensor expansion module, a hidden layer state calculation module, an output state calculation module, a residual multiplication normalization module and an output linear layer module. The calculation process of the MambaV2 algorithm is divided into a plurality of pipeline levels, and a data stream is output in a pipeline form. According to the method, an original algorithm structure is not changed, the operation sequence of a hidden layer state equation in an original algorithm is changed, an algorithm for optimizing the calculation sequence is provided, the number of times of operation is reduced, cache consumption is reduced, the MambaV2 model is deployed and reasoned on an FPGA, the edge scene deployment requirement is met, customized acceleration of the FPGA is utilized, and data streams are adjusted, so that the edge scene deployment efficiency is improved. The pipeline design of the whole algorithm is realized, the utilization rate of computing resources is improved, the problem of data blocking is solved, and computing delay and cache consumption are reduced.

The invention discloses a simulation IC layout wiring sequence optimization and wiring method based on reinforcement learning, and the method comprises the steps: obtaining wiring data which comprises a wiring region, an obstacle region, a wire net set, and the starting point and terminal point of each wire net; gridding the wiring space, and constructing a multi-channel image according to the wiring data; a multi-channel image is used as a state, the problem of simulating IC line net sorting is solved based on a Markov decision process, and the method comprises the steps that a line sequence selection model is constructed, the current multi-channel image is used as input, a probability vector and a predicted value are output, and line net sorting is completed; according to the sorting result of all the wire nets, in combination with the wiring area, the obstacle area and the starting point and the ending point of each wire net, wiring is carried out on each wire net in sequence, and a wiring result is obtained; and calculating a performance index of a wiring result as an award, constructing a total loss function in combination with a predicted value, and optimizing a line sequence selection model. According to the invention, the efficiency of simulating IC wiring is effectively improved, and the performance result of the layout is improved.

The invention discloses a relation enhancement type named entity recognition method and device, a medium and a program product, and relates to the field of data processing. The method comprises the following steps: performing feature extraction on text data through a BERT model to obtain a plurality of tokens and semantic feature vector sequences; performing different-scale convolution operations by using a convolutional neural network to obtain local character combination features, and fusing the local character combination features with the semantic feature vector sequence to form a feature enhanced semantic feature vector sequence; the Transform model determines a long-distance dependency relationship of semantic feature vectors in the sequence by using an attention mechanism; generating a long entity structure model based on the long-distance dependency relationship, and then determining an enhanced semantic feature vector sequence; and performing tag sequence optimization processing on the named entity through a conditional random field layer, determining sequence labeling loss, boundary detection loss and relation prediction loss, determining total loss, and adjusting a tag sequence so as to determine the named entity. According to the method, the accuracy and integrity of named entity recognition can be effectively improved.

The invention relates to a logic comprehensive sequence optimization space exploration method and system for reinforcement learning based on reward correction, and belongs to the technical field of electronic design automation. A logic comprehensive process is embedded into a reinforcement learning algorithm based on reward correction, and the logic comprehensive process is modeled as a reinforcement learning environment; and the logic comprehensive sequence optimization space is automatically explored. A reinforcement learning algorithm is improved for a logic comprehensive scene, feature extraction and action coding are carried out, the circuit state expression precision is improved, and the search space is expanded. According to the method, by adding award correction, the exploration ability in the early stage of training is improved, a self-adaptive award function is obtained through intelligent agent training, performance reduction caused by limitation of priori knowledge on the circuit optimization direction is reduced, and a better logic comprehensive area result is obtained under the condition that delay constraints are met.

The invention discloses a discrete orthogonal code sequence optimization method based on an iterative greedy code search memetic algorithm, and belongs to the technical field of radar waveform design. The performance of the method provided by the invention is superior to that of the existing method, and the self-correlation and cross-correlation performance of the discrete orthogonal code sequence set optimized by the method provided by the invention is further close to the existing lower bound, so that the difference between the self-correlation and cross-correlation performance of the discrete orthogonal code sequence set and the existing lower bound is reduced. Compared with a greedy code search method, the iterative greedy code search strategy provided by the invention is stronger in local search capability, better in optimization effect and higher in search efficiency. And the method can improve the final search optimization result of the algorithm under the condition of not increasing the operation time of the algorithm. By simultaneously adopting crossover and mutation operators in each iteration, the search diversity of the algorithm is increased, and the search space of the algorithm is expanded, so that a discrete orthogonal code sequence set with better orthogonality can be obtained more possibly.

The invention discloses a model node execution sequence optimization method and electronic equipment. The method comprises the steps of obtaining a model structure file of a to-be-executed model; according to the model structure file, obtaining operation processes of the to-be-executed model, an intermediate data value generated by each operation process and a connection relation between the operation processes; constructing a directed acyclic graph by taking the operation process as a model node, taking the connection relationship as a connection sequence of each model node and taking the intermediate data value as an edge; the current actual memory is the minimum, the execution sequence of each model node is obtained based on the directed acyclic graph, and the current actual memory comprises the current occupied memory value subtracting the released intermediate data value. According to the method, the optimal model node execution sequence is determined in a software automatic analysis mode by taking the minimum current actual memory as a target, so that the execution sequence of each operation node in the to-be-executed model is optimized, and long-time occupation of the memory is avoided.

The invention provides a lean production-oriented self-adaptive digital intelligent scheduling method and system, and relates to the technical field of data processing, and the method comprises the steps: taking an initial resource allocation scheme as the basis of task sequence optimization, constructing a time detection line on a time axis, detecting the overlapping condition of different tasks in an execution period, and recognizing resource conflict points; generating a preliminary scheduling plan under the condition of meeting the equipment capacity and man-hour constraint; based on multi-source data of a production site, evaluating the preliminary scheduling plan, updating scheduling parameters, and generating an optimized scheduling scheme; and converting the optimized scheduling scheme into an equipment control instruction, and transmitting the equipment control instruction to a control terminal of a production unit to complete closed-loop optimization of production resources. Reasonable adaptation and efficient scheduling of production resources can be achieved, the production efficiency is improved, and resource waste and conflicts are reduced.

The invention relates to a Halton sequence optimization method and system based on RISC-V vector extension and application. The Halton sequence optimization method comprises the following steps: 1) identifying mathematical characteristics generated by a Halton sequence; a Halton sequence is generated; after continuous index values are partitioned and grouped, simultaneous processing of the multiple index values is achieved through a parallelized grouping calculation method in combination with vectorization operation; 2) carrying out vectorization optimization by using an RISC-V vector instruction; in a Halton sequence generation process, carrying out parallel processing on the grouped index values by using an RISC-V vector extension instruction; 3) packaging into a dynamic link library; and 4) integration and calling with a Hadoop framework. According to the Halton sequence generation method, the number of loop iterations is remarkably reduced, and the Halton sequence generation efficiency is greatly improved. According to the method, resource waste in traditional serial computing is avoided, meanwhile, branch jump and condition judgment expenses are reduced, and the utilization rate of hardware computing resources is increased.

The invention discloses an assembly sequence planning method based on a multi-strategy improved flower pollination algorithm, and relates to the research field of assembly sequence planning. According to the method, on the basis of assembly body information, the assembly direction transformation cost, the assembly tool transformation cost and the assembly operation difficulty cost are set as assembly cost evaluation indexes of the assembly sequence, and a sequence evaluation system is constructed through quantitative evaluation of the constructed assembly sequence cost. The improved flower pollination algorithm based on multiple strategies is provided, the convergence efficiency of assembly sequence planning optimization can be improved, the method has more advantages than a single algorithm, the problem that sequence optimization falls into a local optimal solution can be solved, and a new thought and an efficient method are provided for assembly planning research. The method aims at solving the problems that in industrial product assembly sequence planning, a flower pollination algorithm is weak in solution capacity in a discrete spatial domain, prone to falling into local optimum, low in solution efficiency and the like, the optimal assembly sequence is rapidly solved, then the production time and cost of industrial products are reduced, and the assembly quality is improved.

The invention discloses a sequence optimization method for balancing functions and yield. The sequence optimization method comprises the following steps: acquiring an initial amino acid sequence, a cell environment and a nucleic acid expression quantity; obtaining an initial nucleic acid sequence according to the initial amino acid sequence; obtaining a yield fraction through a preset yield module according to the initial nucleic acid sequence, the cell environment and the nucleic acid expression quantity; obtaining at least one function score through at least one preset function module according to the initial amino acid sequence; and simultaneously optimizing the initial amino acid sequence and the initial nucleic acid sequence through an optimization module according to the yield fraction and the at least one functional fraction to obtain an optimized amino acid sequence and an optimized nucleic acid sequence. According to the invention, the functional module and the yield module are integrated, and multi-objective optimization of protein sequences and corresponding nucleic acid sequences is realized through the optimization module.

The invention provides a building standard floor electromechanical pipeline modular arrangement sequence optimization method, and belongs to the technical field of building construction, and the method comprises the steps: firstly building a fine building BIM model, and dividing an electromechanical system into small modules; then analyzing the relationship between the modules, and determining a necessary sequence and a variable sequence; a weighted directed graph network is established for the variable sequence, and a heuristic function is defined. And searching an optimal arrangement path in the network by adopting an improved A * algorithm, and generating a detailed arrangement scheme. And finally, collision detection and simulation installation are carried out in the BIM model, and the feasibility of the scheme is verified. If problems exist, weight adjustment and optimization are returned until a final scheme is obtained. According to the optimization method, the BIM technology, the graph theory and the heuristic search algorithm are combined, electromechanical pipeline arrangement in a complex building can be effectively optimized, and the technical problem that in the prior art, modular arrangement sequence optimization of electromechanical pipelines of a building standard floor is difficult to achieve quickly and efficiently is solved.

According to the SQL query optimization method and system based on deep reinforcement learning driving, in the stage that an optimizer generates a physical query execution plan, action screening and state regression technologies are combined, and the performance of a deep Q network is improved. And a more accurate Q value can be obtained by training the neural network by using the'action-state 'pair. Besides, by balancing the weight of the Q network and the weight of the dual-depth Q network, the error of a target estimation value is effectively reduced, so that more accurate Q value estimation is realized. After the method is adopted, the following technical effects are achieved: (1) flexible connection sequence optimization: deep reinforcement learning enables the action values of the target network and the evaluation network to be optimally updated by analyzing SQL query and dynamically adjusting the connection sequence; and (2) compared with the traditional method, the method not only can dynamically adjust the connection sequence in the query, but also can perform optimal selection according to reward feedback and historical data. According to the optimization strategy, the query time is remarkably shortened in complex multi-table connection query, the query execution efficiency is improved, and generation of a non-optimal plan is avoided.

The invention relates to the technical field of information management, and discloses a talent information intelligent matching management system, which comprises a matching module, a sequence construction module and a sequence optimization module, and is characterized in that the matching module is used for constructing a demand vector matrix according to demand information, constructing an evaluation vector matrix according to talent information, and optimizing the evaluation vector matrix according to the talent information; the demand vector matrix and the evaluation vector matrix are input into a noise disturbance model generated through pre-training, the output matching degree is obtained, the sequence construction module is used for determining Q to-be-recommended targets according to the matching degree, constructing P to-be-recommended sequences based on the Q to-be-recommended targets and calculating the prejudice influence degree value corresponding to each to-be-recommended sequence, and finally, the prejudice influence degree value corresponding to each to-be-recommended sequence is obtained. The sequence optimization module is used for optimizing the P to-be-recommended sequences according to the prejudice influence degree value and the natural heuristic algorithm, the nonlinear relation between high-dimensional complex data is captured, the to-be-recommended sequences are optimized through the natural heuristic algorithm, and the problem of fairness deficiency in the recommendation process is effectively solved.

The invention provides an underground cavern intelligent construction sequencing optimization method and system, and the method comprises the steps: firstly integrating geological parameters, construction machinery parameters and historical construction cases to construct a construction method database, and then determining a layering height parameter set through a dynamic matching algorithm according to the spatial topological relation between geomechanical indexes and a three-dimensional geological model in the database, according to construction machinery and layering height parameters, a transverse partition and axial partition parameter set is generated by using an octree space subdivision algorithm, then composite coding is performed based on multiple attributes of the three-dimensional geologic model and the parameters, and a unique construction block coding set is generated; and finally, performing construction sequence optimization on the code set by using a multi-objective optimization algorithm to generate a Pareto optimal solution set containing construction period, cost and stability optimization paths, and dynamically adjusting construction sequence parameters in the solution set according to surrounding rock deformation data to realize intelligent construction sequencing optimization of the underground cavern.

The invention provides an intelligent iron tower construction process simulation and optimization method based on digital twinning, which is applied to the technical field of intelligent iron tower construction, and comprises the following steps: obtaining a human intervention event sequence and a construction execution state transition sequence corresponding to each construction stage in a construction process, the human intervention event sequence at least comprises a manual confirmation trigger moment, an unplanned pause trigger type and a manual intervention duration, and the construction execution state transition sequence at least comprises a construction step start-stop state, a state duration and a state switching result; according to the method, the causal influence of human intervention behaviors before and after construction execution state transition is quantified, the causal influence weight is constructed, and the construction sequence is optimized in combination with digital twin construction simulation, so that the problems that construction sequence optimization in the prior art lacks quantifiable constraint parameters and results are unstable are solved; the construction steps are sensitive and stable, the construction sequence can be predicted and executed, and the reliability of construction management decisions and the risk controllability of the construction process are improved.

The invention discloses a damaged road network recovery sequence optimization method with maximum toughness as a target based on user balance flow distribution, and aims to solve the problems of low recovery efficiency of a post-disaster traffic road network and lack of scientific basis for decision making. The method comprises the following implementation steps: firstly, constructing a damaged road network toughness evaluation framework, and defining a toughness index as a reciprocal of a product of recovery time and total transit time in combination with a traffic network topological structure and a flow demand; secondly, establishing damaged road network toughness optimization scene parameters, abstracting a road network into a connected graph, and defining a recovery scheme set; then, a mixed integer linear programming model with the maximum toughness as the target is constructed, a nonlinear problem is converted into a linear problem through secant approximation, and constraint conditions such as flow balance and travel requirements are set; and finally, performing global optimization solution on the model by using a high-performance solver to generate an optimal recovery scheme. According to the method, the road network toughness can be scientifically quantified, the recovery sequence is optimized, blindness of experience decision making is avoided, the passing efficiency of a post-disaster traffic system is rapidly improved, efficient and scientific decision support is provided for post-disaster traffic recovery, and the method has important practical application value.

The invention provides a large model self-attention optimization method and device, equipment and a medium, and relates to the technical field of artificial intelligence. The method comprises the following steps: obtaining a KQ large matrix obtained after an input sequence passes through a large model self-attention structure, and dividing the KQ large matrix into a plurality of KQ small matrixes; carrying out local attention calculation on each KQ small matrix, and carrying out similarity descending sorting on the KQ small matrixes and other KQ small matrixes to obtain a sorting list; and for each element in the input sequence, according to the calculated sorting list, selecting the local attention corresponding to the KQ small matrix with the similarity ranking in the first r to carry out weighted calculation, and taking the local attention as the attention representation of the current element until all the elements of the input sequence are completed, so as to obtain an attention result after the input sequence is optimized. According to the method, the calculation complexity of the model is reduced, remote context information can be captured, and the accuracy of the model is improved.

The invention discloses a test sequence optimization method and device, equipment and a storage medium. Comprising the steps of obtaining function point related data, and generating a function point test sequence based on an upper confidence bound algorithm; defining a global resource occupancy matrix, and according to the function point test sequence, sequentially importing the function points, traversing and querying the global resource occupancy matrix, and generating a parallel scheduling task; and executing the parallel scheduling task, and monitoring resource conflict conditions and test failure information in real time. By comprehensively considering related data of the function points, intelligent sorting of the function points is achieved, and the problems that in a traditional sorting method, exploration and utilization are difficult to balance and global dynamic optimization is lacked are solved. By reasonably distributing parallel tasks, resource conflicts are avoided, the utilization rate of limited test resources is improved, and efficiency reduction caused by unreasonable resource planning is reduced. Resource conflicts are found and processed in time through real-time monitoring, the conflicts are prevented from influencing the test process, test failures can be quickly responded, and the test efficiency is improved.

The invention discloses an automatic file analysis method based on a semantic evolution graph. The method comprises the following steps: generating a policy document standardized corpus; obtaining a policy document structured corpus unit set; constructing an initial semantic map snapshot containing a version stamp; establishing a time slice semantic evolution graph sequence; obtaining a multi-time sequence fusion vector; generating a multi-time sequence optimization word embedding vector matrix; performing time continuous regularization on the multi-time-sequence optimization word embedding vector matrix, outputting time continuous word embedding vectors, and mapping the time continuous word embedding vectors to nodes and edges of the time slice semantic evolution graph sequence to form a global semantic evolution graph fusion representation sequence; and taking the global semantic evolution graph fusion representation sequence as feature input to obtain a policy document analysis result set. According to the method, the timeliness, the accuracy and the interpretability of automatic analysis of the policy document are greatly improved.

The invention discloses an order-driven mixed box stacking method, system and equipment and a medium. According to the method, a stacking process is decoupled into two associated subtasks of active warehouse-out decision and real-time stacking execution; the problems of frequent stack transfer and invalid reciprocating carrying caused by scattered goods combination in a physical layout limited scene in an existing offline stacking algorithm are effectively solved, and meanwhile, the space utilization rate bottleneck caused by lack of order global view in a traditional online stacking algorithm is also overcome; and a brand-new framework support and a global optimization decision view angle are provided for efficient disassembly operation in a limited physical space. An energy-based warehouse-out sequence optimization algorithm is introduced into a warehouse-out decision-making layer, and the algorithm deeply fuses the local fine sampling capability of Langevin dynamics and the global evolution capability of a genetic algorithm, so that the technical defects that a traditional heuristic rule is difficult to capture complex geometric features of goods and is easy to fall into local optimum are overcome; and the three-dimensional warehouse is guided to realize active and efficient replenishment scheduling.

The invention provides a generator polynomial estimation method and device for a Gold sequence. The generator polynomial estimation method comprises the following steps: receiving a signal and extracting a pilot frequency position in the signal through constellation mapping; extracting each pilot symbol in the signal according to the pilot position so as to confirm a pilot sequence; if the pilot frequency sequence is a Gold sequence, calculating the order of a pilot frequency sequence polynomial according to the rank solving method matrix; generating a polynomial set of the m sequence optimal pair according to the order; generating a plurality of target Gold sequences based on the m sequence optimization pairs in the polynomial set; and determining a generator polynomial of the pilot frequency sequence according to the plurality of target Gold sequences and the pilot frequency sequence. The order of the matrix is calculated through the rank solving method, only the rank of the matrix needs to be calculated, compared with a minimum calculation length method, the method is more efficient, and complexity and redundancy in subsequent calculation are effectively reduced. In addition, the data size required by the method is small, a correlation method is adopted for processing, the method has good fault-tolerant performance for the Gold sequence containing error codes, and sequence parameters can be accurately estimated.

The present invention discloses a smart contract vulnerability detection method. First, the smart contract is used as the input of the static processing stage. Secondly, the fuzzy testing system will reversely construct the test cases containing data flow dependency facts through the suspected vulnerability code line, and assign them to the fixed test case pool. At the same time, the test cases are randomly constructed using the optimization idea and assigned to the non-fixed test case pool. Finally, the fuzzy testing system dynamically executes the test cases in the two test case pools. For the non-fixed test case pool, the optimization of the test case includes transaction sequence optimization and parameter mutation, and the test case value judgment formula with comprehensive coverage information proposed by the present invention is used to guide the optimization process of the test case pool. The present invention effectively improves the test efficiency of the smart contract fuzzy test, can explore more paths in the tested smart contract, and can discover more contract vulnerabilities.