307 results about "Topological sorting" patented technology

The invention discloses a report verification batch parallel execution optimization method based on a pluggable rule engine framework, and relates to the technical field of report verification, and the method comprises the following steps: receiving compliance rules, converting the compliance rules into a uniform data structure model, analyzing to generate metadata indexes, and supporting business rule set configuration; loading the matched business rule set based on the meta-information of the to-be-verified report, and registering the matched business rule set to an execution container; s3, receiving a to-be-verified report, analyzing a report structure, and generating a report field index table; performing field dependency analysis based on the report field index and the loaded business rule set, and constructing a rule field dependency graph; graph partition optimization and topological sorting are executed according to the dependency graph, and rule batches capable of being executed in parallel are generated; and uniformly collecting and outputting all rule execution results, hit states, abnormal records and performance indexes, and generating a structured verification report and an audit chain log. The financial statement compliance check execution efficiency can be improved.

The invention belongs to the field of natural language processing, and provides a task complexity driven graph semantic multi-agent collaborative decision-making method and system.The task complexity driven graph semantic multi-agent collaborative decision-making method comprises the steps that a task text is obtained and subjected to semantic coding to obtain a task semantic vector, evaluation is conducted based on the task semantic vector to obtain a complexity vector, and a task complexity score of the complexity vector is calculated; the task semantic vector and the complexity vector are fused to obtain a task representation vector, an agent capability relation graph is constructed, the participation probability of each agent node is obtained according to the task representation vector and the agent capability relation graph, and a dynamic agent combination scheme is formed; and performing task decomposition according to the agent combination scheme, constructing a sub-task dependency graph, scheduling the execution sequence of the sub-tasks through topological sorting, realizing cooperative execution of the agents, and generating a task result. According to the method, precise matching and efficient cooperation of the agent combination are realized, and the capability of processing complex tasks and the resource utilization efficiency of the multi-agent system are remarkably improved.

The invention provides an intelligent analysis and execution method and system for database cascading operation, and relates to the technical field of database management.The intelligent analysis and execution method comprises the steps that a target table and an association table are extracted through a recursive query algorithm, field mapping analysis is carried out to generate a dependency weight matrix, cyclic dependency is recognized based on the matrix and decomposed into directed acyclic subgraphs, and the directed acyclic subgraphs are analyzed; node depth values and breadth values are calculated for topological sorting to generate an execution sequence; performing node classification on the execution sequence, organizing the execution sequence into a batch processing task group, calculating an optimal execution path through an adaptive path optimization algorithm, generating a distributed transaction control instruction, and allocating the task group to an execution thread pool for execution by using a consistent Hash algorithm; monitoring an execution process, dynamically adjusting thread pool resources according to a performance prediction value, executing cascade rollback when an exception occurs, and reconstructing a transaction execution path; according to the method, the execution efficiency and stability of the database cascade operation can be effectively improved, and the system resource consumption is reduced.

The invention discloses a sensing data fusion-based radiator abnormal state detection method, relates to the technical field of radiator state detection, and is used for solving the problem of poor radiator state detection efficiency. According to the method, multiple types of sensors are arranged in combination with a radiator structure, acquisition time alignment is completed, segmentation and de-noising processing is performed on multi-modal data based on a sliding time window, and a frequency domain texture feature is constructed by extracting a frequency spectrum information entropy and a high-frequency energy ratio; constructing the feature values into graph nodes, establishing a complete connection graph, compressing the complete connection graph into a skeleton graph through conditional independence test, generating a directed acyclic graph in combination with an intersection structure and topological sorting, and calculating a causal weight to realize structure updating; and extracting a current window node state, comparing the current window node state with a prediction state, identifying an abnormal node, executing path backtracking, calculating path cost, tracing to a root cause node, extracting path evaluation information, and generating a response signal, thereby improving the abnormality diagnosis precision and scheduling linkage responsivity of the radiator.

The invention relates to an automatic derivation method, system and device for an automatic driving test scene and a medium, and the method comprises the steps: analyzing a test demand of an automatic driving system, and mapping the test demand to a multi-dimensional scene element hierarchical architecture, so as to construct a multi-layer road scene; reasoning a scene condition dependency relationship by using a dual conditional probability, and generating an initial scene fragment set in combination with probability distribution sampling; establishing a multi-objective optimization function based on the scene type distribution weight, the element redundancy penalty factor, the physical consistency constraint and the security rule, screening and recombining the multiple initial scene fragment sets, and outputting an optimal scene set; extracting a causal logical relationship of the optimal scene set according to the directed acyclic graph, and generating a scene evolution path in combination with topological sorting; and executing multiple safety verification on the scene evolution path, and determining the optimal scene passing the verification as the automatic driving test scene. The technical problems that an existing test scene is low in generation efficiency, poor in authenticity and unreasonable in event chain logic are solved.

The invention relates to the technical field of automatic deployment of cloud computing platforms, in particular to a self-adaptive deployment method and system oriented to a credential heterogeneous environment. Aiming at three technical bottlenecks of low multi-CPU architecture adaptation efficiency, frequent software dependence conflicts and complex security baseline configuration in a localization process, a heterogeneous computing resource intelligent scheduling engine and a dynamic security policy generation mechanism are innovatively provided. The method comprises the following steps: constructing a heterogeneous resource portrait through a hardware feature automatic identification technology, and realizing component installation sequence optimization based on a DAG dependency relationship analysis algorithm and Kahn topological sorting; and creating an adaptive network security policy, and sensing the firewall state of the target system in real time through a probe. Compared with a traditional deployment mode, the method supports cross-architecture compatibility, solves the problem of dependency conflicts, improves deployment efficiency, and guarantees consistency and safety of the system.

The invention relates to a high-fidelity cloud rendering cluster scheduling method and system, and the method comprises the steps: receiving a rendering task, generating a multi-level priority queue according to the task complexity, timeliness and resource demand classification, and automatically optimizing a scheduling strategy; monitoring the resource state of the heterogeneous computing node in real time; allocating tasks by using preemptive and round-robin scheduling strategies, and adjusting and coping with resource fluctuation in combination with a dynamic code rate; the tasks are decomposed by adopting a spatial blocking and time framing strategy, and an execution sequence is controlled according to a topological sorting algorithm; abnormal nodes are identified through heartbeat detection, and affected subtasks are migrated through incremental task updating. According to the method, efficient resource allocation, scheduling algorithm and idle key frame scheme can be realized, the GPU directly outputs the video stream, the rendering speed is remarkably improved, resource waste and operation cost are reduced through a dynamic resource allocation mechanism, a fault-tolerant mechanism is provided, the task is ensured to be normally completed under the condition of node fault, and the task efficiency is improved. And distributed rendering and synthesis of large-scale high-resolution images are supported.

The invention provides an industrial data online migration method, medium and system of a productivity middle platform, and belongs to the technical field of electrical digital data processing.The method comprises the steps that firstly, historical access records of an industrial data source are collected, and a deep neural network model comprising a multi-head attention layer, a time sequence feature extraction layer and a residual connection layer is constructed; and the prediction of the data change rate is realized. And based on a prediction result, dynamically calculating a data change rate demarcation point by utilizing an optimization equation set, and dividing a data table into different change frequency categories. And then constructing a data dependency relationship matrix, and determining a migration sequence by adopting an improved topological sorting algorithm. Batch migration, incremental migration and real-time synchronization mechanisms are adopted for different types of data, and model updating is triggered through real-time monitoring, so that dynamic optimization of a migration strategy is ensured. Finally, data consistency verification is carried out, migration accuracy is guaranteed, and the technical problem that the online migration efficiency of industrial data is low due to real-time dynamic change of the data change rate is solved.

The invention discloses a calculation task allocation method and device, equipment, a storage medium and a product, and relates to the technical field of artificial intelligence, and the method comprises the steps: obtaining a to-be-executed calculation task, and extracting the features of the to-be-executed task to obtain a feature label; decomposing the to-be-executed calculation task based on the feature labels to obtain sub-tasks; constructing a directed acyclic graph based on the dependency matrix of each sub-task, and performing topological sorting on each sub-task to obtain a priority and an execution sequence; according to the method, a to-be-executed calculation task is decomposed into a plurality of sub-tasks through feature labeling, a directed acyclic graph is constructed according to the dependency relationship among the sub-tasks, and the sub-tasks are distributed according to the reference execution time, the priority and the execution sequence. The priority and the execution sequence of each sub-task are determined by utilizing topological sorting, and the sub-tasks are allocated to each GPU for execution in combination with the reference execution time of each sub-task on different GPUs, so that the calculation efficiency of the calculation task is effectively improved.

The invention discloses a voice interactive console based on artificial intelligence, and relates to the technical field of intelligent consoles, a large language model is combined with context tracking and logic analysis technologies, a composite instruction is disassembled into independent tasks, a task chain is generated, a semantic weight is added to each task through a weight regression model, and the intelligent consoles are obtained. The task execution priority and the logic dependency relationship are identified, the limitation of a traditional system on the complex semantic analysis capability is overcome, a composite instruction can be accurately understood, a logic tree analysis method based on rules is adopted, a task chain is converted into a directed graph structure, the priority is dynamically adjusted, and a task execution sequence table is generated. In the scheduling process, multi-task parallel processing is carried out on tasks without logic dependence, and the resource utilization rate is increased; tasks with a dependency relationship are gradually scheduled by adopting topological sorting, so that the logic consistency is ensured.

The invention discloses a service-based calculation task dynamic abstraction method and system, and relates to the technical field of calculation task scheduling. The service-based calculation task dynamic abstraction method comprises the following steps: receiving and analyzing a to-be-executed task, and dividing the to-be-executed task into a plurality of independent service units; constructing a directed acyclic graph representing the dependency relationship between the service units, and executing topological sorting based on the directed acyclic graph to determine an execution sequence; and according to the resource demand of each service unit and the current system equipment state. According to the method, the technical problems that a traditional task scheduling method comprises but is not limited to the following technical problems that resource allocation is rigid, a heterogeneous computing environment cannot be dynamically adapted, and the hardware utilization rate is low; parallel arrangement is low in efficiency, depends on manual definition of an execution sequence, lacks an automatic arrangement capability and is difficult to deal with a complex task process; the real-time performance is insufficient, the task execution process is solidified, and the resource allocation and execution path cannot be dynamically adjusted according to the runtime state.

The invention discloses a deployment optimization method and system for multi-type collaborative construction, and relates to the technical field of intelligent scheduling, and the method comprises the steps: collecting the pre-construction data of each type of work, and carrying out the hierarchical splitting of a construction task according to the type of work and a working stage, and obtaining construction sub-tasks; constructing a dependency relationship of construction sub-tasks by utilizing topological sorting and a critical path method, and defining a sub-task execution constraint and a work type scheduling constraint by using a constraint planning method; generating a preliminary scheduling scheme by using a heuristic algorithm, and optimizing sub-task execution sorting according to the sub-task execution constraint and the work type scheduling constraint; and dynamically adjusting the execution sorting of the subtasks according to the dependency relationship of the subtasks by using a parallel scheduling algorithm to obtain a scheduling scheme of collaborative construction. According to the method, resource conflicts and delay risks are effectively avoided, high parallelism and load balancing of multiple work types of tasks are achieved, and the construction efficiency and reliability can be remarkably improved in a complex construction environment.

The invention relates to the technical field of robot scheduling, and discloses a robot task management framework system based on asynchronous communication. The system comprises a task asynchronous analysis module for performing time sequence decoupling on an original task instruction stream of a heterogeneous robot terminal by means of a multi-channel buffer queue to separate out independent executable task units; the resource dynamic mapping module is used for establishing a variable granularity resource allocation mapping table according to cluster real-time load and task resource requirements, and recording various resource dynamic binding relationships; the task priority reconstruction module is used for constructing a directed acyclic graph scheduling topology based on task logic dependence and deadline constraint, and generating a weighted execution sequence through topological sorting; the exception isolation processing module captures exceptions during operation and triggers an isolation strategy; and the cross-node cooperation module guarantees the multi-robot cooperation task state consensus through a distributed consistency protocol. The system is suitable for heterogeneous robot cluster complex task scenes.

The invention discloses a vehicle-mounted controller firmware upgrading method, which relates to the technical field of intelligent networked automobiles, and comprises the following steps of: accurately describing a dependency relationship between versions by introducing a directed acyclic graph, providing a clear topological sorting basis for an upgrading process, and improving the upgrading efficiency. The system function conflict and disorder caused by disordered upgrading sequence are fundamentally avoided; a distributed consensus protocol is utilized to ensure that each controller achieves state coordination before upgrading, so that the overall robustness of a multi-controller cluster is effectively improved when the multi-controller cluster faces network delay or individual node abnormity, and upgrading failure or version bifurcation caused by the fact that part of nodes are not ready is avoided; through the combination of the group-level binding token and the atomic switching mechanism, the controller group with a strong function dependency relationship can be ensured to be effective and updated as a unified transaction unit, and the safety and reliability of cross-domain function linkage are remarkably enhanced.

The invention relates to a data cleaning method based on rule data driving and a data cleaning engine, and belongs to the technical field of data processing. The data cleaning method comprises the following steps: receiving original data streams of a plurality of heterogeneous service systems; extracting a multi-source data feature and a cross-system field association feature, and constructing to obtain a data feature fingerprint; similarity matching is carried out on the data feature fingerprints, rules with the matching degree higher than a preset threshold value are screened, and a candidate rule set is obtained; converting the candidate rule set into a directed acyclic graph; marking conflict rule pairs to obtain a conflict early warning list; a virtual isolation layer is inserted into the directed acyclic graph, and a path identification table is generated; performing topological sorting according to the directed acyclic graph, generating a rule execution sequence, and performing cleaning processing on the original data stream; distributing a data execution channel according to the path identification table; and outputting the cleaned clean data stream and the cleaning log containing the rule execution path. According to the invention, data from different business systems can be efficiently and accurately processed.

The invention discloses a multi-scene multiplexing-oriented VUE dynamic component interface configuration and rendering method, which comprises the following steps of: receiving a multi-scene configuration file, and extracting and coding to generate a scene feature vector; component layout parameters, resource priorities and rendering strategies are automatically generated through the two-stage generative adversarial network, and application parameters are output after the generated parameters are optimized; on the basis of application parameters, asynchronous component loading and priority queue scheduling are adopted, and on-demand loading of components is achieved in combination with dependency topology sorting; dynamically dividing a rendering area by using an adaptive Gaussian algorithm, and rejecting invalid rendering through a boundary box mechanism; the DOM operation frequency is reduced by adopting an optimization Diff algorithm; and constructing a component cache pool through an elimination strategy. According to the method, intelligent configuration and efficient rendering of the interface component under multiple scenes are realized, the development efficiency is improved, and the performance is optimized.

The invention discloses a self-adaptive dynamic task unloading method and system for an edge computing power network, and the method comprises the steps: carrying out the modeling of a computing task of a user, obtaining a directed acyclic graph of the computing task, and generating a task execution sequence according to the topological sorting of the tasks in the directed acyclic graph; features of tasks in the task execution sequence are coded into high-dimensional vectors, sequence information is embedded through position coding, and a task matrix is obtained; the task matrix is input into a trained Transform model, and a task related information sequence is generated; inputting the task related information sequence into the trained dual Q network model to obtain an optimal unloading decision sequence; carrying out local calculation or edge server unloading on the calculation task based on the optimal unloading decision sequence; according to the method, efficient, low-delay and low-energy-consumption task scheduling optimization can be realized, and the task unloading efficiency is improved.

The invention relates to the technical field of constructional engineering informatization and data security, and particularly discloses a BIM and block chain-based whole-process engineering cost data collaborative management system, which comprises a data analysis and dependency graph construction module, which is used for analyzing BIM model component attributes and association relationships and constructing a directed dependency graph; the loop dependency detection and resolution module is used for identifying and resolving a loop dependency structure in the graph; the topological sorting processing module is used for generating an ordered calculation sequence of the components; the cryptographic abstract calculation module is used for generating a unique abstract value of each component in sequence; the global abstract generation module is used for aggregating and generating a global cryptographic abstract representing the overall logic state of the model; and the block chain evidence storage module is used for combining the global abstract and the related metadata to perform uplink evidence storage.

The invention relates to the technical field of power system protection, in particular to a high-temporal-spatial-resolution power carbon emission factor calculation method and system and a readable storage medium. The method comprises the following steps: extracting entities and relationships from an electricity-carbon document to form an electricity-carbon triple, designing an electricity-carbon table data extraction method based on a regular expression for an electricity-carbon table, and constructing an electricity-carbon knowledge graph according to the obtained entities, relationships and attributes; then regarding a given question and an answer corresponding to the question as a sequence, designing an electric power carbon emission data retrieval method by combining distance measurement query based on a sequence matching idea and a bidirectional attention mechanism, and realizing discovery of a target electric power carbon entity and an electric power carbon attribute from an electric power carbon field knowledge graph; further generating a corresponding calculation graph, and according to the topological sorting of the calculation graph, calling related formulas in an electric carbon formula library to complete calculation, so as to obtain a regional direct carbon emission factor; and finally, according to an electric power calling-in relation in the electricity-carbon knowledge graph, performing correction on the basis of the regional direct carbon emission factor to obtain a regional average carbon emission factor considering electric power calling-in. The invention aims to solve the problem of how to improve the accuracy of power carbon emission factors.

The invention relates to a calculation task execution method and device, equipment and a storage medium. According to the main technical scheme, the method comprises the steps of obtaining to-be-processed computing tasks, a preset mapping table and computing resource available information of computing nodes, performing topological sorting on the to-be-processed computing tasks according to a dependency relationship among the to-be-processed computing tasks to obtain a priority execution sequence of the to-be-processed computing tasks, and based on the priority execution sequence, a preset mapping table and the computing resource available information, determining a real-time execution sequence of the to-be-processed computing tasks, and executing the to-be-processed computing tasks through the first group of computing chips or the second group of computing chips according to the real-time execution sequence of the to-be-processed computing tasks. According to the method, high-complexity heterogeneous computing of computing chips of different types and different sources can be realized, high-performance computing and intelligent computing service scenes are supported, and compatibility of a high-performance computing related service system and a hyper-converged computing cluster is facilitated.

The invention provides a stacked cell hierarchy judgment method based on contour concavity and convexity. The method comprises the following steps: performing contour extraction on a cell binary image; calculating a convex hull for the contour point set of each cell, and taking the convex hull as an ideal form reference of the cell in an unshielded state; detecting the convexity defect between the contour point set of each cell and the corresponding convex hull, and quantifying the characteristic parameter of each convexity defect; screening the convexity defects according to the characteristic parameters to determine significant recesses, marking cells with at least one significant recess as concave cells, and marking cells without significant recesses as convex cells; screening candidate cell pairs according to the spatial position relationship between the cells; according to the convex-concave attributes of the two cells in the candidate cell pair and the position relation between the coordinates of the center of mass and the remarkable concave area, the shielding relation between the two cells is judged; and constructing an attribute graph by taking each cell as a node and taking the occlusion relationship as an edge, and generating a cell stacking hierarchy sequence through topological sorting.

The invention provides a framework migration method and system for a heterogeneous micro-service architecture, and relates to the technical field of micro-service architecture.The framework migration method comprises the steps that migration priorities are determined through topological sorting based on service calling features, a unified service interface description file is generated, a link tracking probe is injected, and service adaptation layer codes are generated; flow control is carried out in combination with a variation gradient token bucket algorithm, and service fusing and degradation are realized by adopting an exponential backoff algorithm and a sliding time window mechanism. According to the method, the problems of complex service dependence, difficulty in flow regulation and control and the like in heterogeneous architecture migration are solved, the security and stability of framework migration are improved, and the operation and maintenance cost of the system is reduced.

The invention relates to a computational graph processing method and device, electronic equipment and a readable storage medium. The method comprises the following steps: traversing a computational graph in the process of topological sorting of nodes, and carrying out ring formation detection on the computational graph based on in-degree of an object to obtain a ring formation detection result; the in-degree represents the forward dependency number of the object; in the process of traversing the computational graph, performing address repeated writing detection on the nodes based on the in-degree of the parameters to obtain an address repeated writing detection result; performing head node detection on the node set after topological sorting to obtain a head node detection result; and verifying the computational graph based on a ring formation detection result, an address repeated writing detection result and a head node detection result. By adopting the method, the time complexity can be reduced.

The invention provides a Hadoop cluster incremental backup recovery method based on data block optimization, and relates to the technical field of data management, and the method comprises the following steps: obtaining distribution state information and historical change records of data blocks in a distributed file system, extracting space-time correlation characteristics, and constructing a data block dependency graph containing dependency direction and dependency intensity; identifying the changed data blocks, performing topological sorting, and identifying key path data blocks and independent data block clusters; calculating a distribution entropy value according to a copy distribution position, and determining a backup granularity type; a backup execution sequence is generated according to a topological sorting result, key path data blocks are backed up preferentially, independent data block clusters are backed up in parallel, and backup metadata containing dependency relationship identifiers and distribution entropy values are generated; and performing dependency consistency verification after recovery based on the dependency relationship identifier. According to the method, the backup recovery efficiency is improved, the data consistency is guaranteed, and the storage overhead is reduced.

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 relates to the technical field of distributed data processing systems, and discloses a data processing system supporting full localization deployment and a compliance guarantee method thereof.The data processing system supporting full localization deployment comprises an audit event collection module used for collecting and storing processing event hash; the event organization module is used for realizing logic time sequence management of events through topological sorting and causal relationship marking; the hash chain construction module is used for constructing a local-to-global double-layer hash chain by utilizing the constructed directed acyclic graph and event hash; the increment synchronization module is used for executing increment synchronization based on Hash tree difference identification; the conflict solving module is used for detecting and solving event conflicts in the distributed environment by applying a version vector technology aiming at data differences found in the incremental synchronization process; according to the method, the lightweight linear hash chain is used for replacing a traditional binary tree structure, high-efficiency and low-cost audit chain maintenance is realized, and the data integrity is guaranteed in an unstable network environment.

The invention discloses a GIS-based cross-section diagram real-time synchronous display method and system, and relates to the technical field of geological modeling, and the method comprises the steps: importing multi-source heterogeneous geographic data, carrying out the standardized preprocessing, and generating a geographic information data set in a unified format; carrying out multi-dimensional feature extraction on the geographic information data set by adopting a feature level fusion algorithm, and constructing a multi-dimensional geographic feature model fusing geological structure, terrain elevation and earth surface coverage information; performing data optimization by adopting a spatial interpolation algorithm based on the multi-dimensional geographic feature model to generate a continuous geographic surface model; and performing parallel contour tracking and topological sorting on the discrete section line segment set, and generating a user-defined section diagram supporting interactive parameter adjustment in real time. According to the method, the multi-dimensional geographic feature model is constructed through the feature level fusion algorithm, semantic level association of the geologic structure, the terrain elevation and the earth surface coverage information is realized, the problem of pixel level fusion is solved, and the data space consistency error is reduced.

The invention relates to the technical field of business management, in particular to an artificial intelligence business intelligent docking service platform, in the invention, task dependency relationships are rearranged by adopting topological sorting, and waiting time and resource waste caused by improper dependency relationships among tasks are reduced through scientific execution sequence adjustment, so that the service efficiency is improved. The method comprises the following steps of: extracting a business semantic structure chart, mining tasks which can be executed in parallel, recombining an execution path, reducing redundant nodes and unnecessary delay of a process, improving the execution efficiency of the whole process, extracting key operation rules through natural language processing, establishing the business semantic structure chart by adopting a support vector machine, accurately reflecting the hierarchy and association relationship of the operation nodes, and improving the execution efficiency of the whole process. Visualization and operability of complex business logic are achieved, flow optimization is carried out through the greedy algorithm, node priorities are defined, adaptive execution paths are matched, high efficiency and execution accuracy of flow design are ensured, and the response speed, the resource utilization rate and the execution accuracy of the business flow are improved.

The invention discloses a business process construction method and system, and the method comprises the steps: generating a directed acyclic graph in response to the dragging operation and connection operation of a user on a component on a graphical interface; according to the types of the components and the dependency relationship among the components, topological sorting is conducted on the components in the directed acyclic graph, a component execution sequence list is generated, and the components in the component execution sequence list are added into a weighted priority queue in sequence; calculating a weighted priority of each component according to each component type, component complexity, component execution frequency and component resource consumption; the component execution sequence is adjusted according to the weighted priority of each component, and a final component execution sequence is generated; and sequentially connecting and configuring the components according to the final component execution sequence to generate an executable business process. The business process construction method is more efficient and more flexible.

The invention relates to the field of integrated circuit design, and discloses an SAR ADC full-automatic size optimization design method based on formula constraint, which is used for solving the problems that the traditional manual design is low in efficiency, depends on experience and is difficult to optimize high-dimensional parameters. The method comprises the following steps: 1) establishing an analytic mapping relation between system-level indexes and sub-circuit specifications, and decomposing performance indexes to each sub-circuit module through a mathematical equation; 2) constructing a directed acyclic dependency graph, representing design variables by nodes, and representing constraint relationships by edges; 3) carrying out topological sorting on the dependency graph, and generating an execution sequence containing simulation verification, knowledge calculation and size optimization; and 4) adopting a double-layer optimization mechanism: verifying the performance of the sub-circuit and the system through circuit simulation, and performing iterative optimization until a device size scheme meeting all indexes is output when the performance does not reach the standard. According to the method, the full-automatic optimization design of the SAR ADC from the system index to the transistor size is realized.