224 results about "Priority list" patented technology

The invention relates to the technical field of engineering progress management, in particular to an engineering construction digital project management method and system, and the method comprises the steps: database building, real-time data collection, and model building: generating a visual construction progress model; progress deviation judgment: calculating progress deviation and performing judgment; deviation analysis: calculating a resource gap of an affected process, and generating a resource allocation priority list; resource adjustment: pushing an adjustment instruction to the construction terminal according to the priority list; simulation: simulating the adjusted construction progress, and if the deviation is not eliminated, executing a redistribution step; redistribution: executing the deviation analysis step again; and optimization: optimizing subsequent project progress plan generation logic. The system comprises a database building module, a real-time data acquisition module, a model building module, a progress deviation judgment module, a deviation analysis module, a simulation module, a redistribution module and an optimization module. The method and the device have the effect of facilitating fine management of the engineering project.

The invention discloses a network key node identification and optimization method, system and device, and a storage medium, and relates to the field of key node identification and dynamic optimization of an electric power Internet of Things communication network, and the method comprises the steps: collecting the original data of the electric power Internet of Things, and obtaining a clean data cube in space-time alignment; constructing a dynamic space-time map, and further generating a dynamic weight matrix; evaluating the key nodes based on the dynamic weight matrix to obtain a node priority list; establishing a multi-target optimization model based on the node priority list in combination with a real-time network state; solving the multi-objective optimization model to obtain an optimal solution set, performing strategy matching and verification to obtain a verified optimization instruction set, and identifying and optimizing the network key nodes through the verified optimization instruction set; according to the method, through multi-dimensional quality portrait construction and space-time atlas analysis, network key nodes are scientifically identified and dynamically optimized, so that the stability, the safety and the performance of the network are improved.

The invention relates to the technical field of information, and discloses an intelligent scheduling method and system for a vehicle-mounted mobile charging system, and the method comprises the steps: obtaining the emergency degree values, geographic positions and charging demand grade values of all charging demands in a current region, and building a demand priority list; acquiring real-time electric quantity states, position information and electric quantity consumption rates of all charging vehicles, and establishing a vehicle availability list; according to the two tables, combined with the target position distance, the road condition and the traffic condition, calculating the estimated time and the residual electric quantity of each charging vehicle arriving at the target position; when the estimated time is smaller than a preset time threshold value and the residual electric quantity meets the requirement, the vehicle is marked as an available vehicle; when the available vehicles are insufficient, judging whether cross-regional scheduling is carried out or not; and optimizing the global scheduling instruction by adopting a scheduling algorithm and dynamically scheduling the charging vehicles. The method can achieve the reasonable scheduling of the charging vehicles, and improves the overall efficiency.

The invention provides a large model reasoning optimization method and system for operation and maintenance technical services, and the method comprises the steps: firstly obtaining a set comprising a plurality of to-be-processed operation and maintenance service requests, each to-be-processed operation and maintenance service request carrying operation and maintenance demand description information and an equipment operation scene identifier; performing priority dynamic calibration on operation and maintenance reasoning tasks based on the information, generating a priority list, deploying computing resources and operation and maintenance knowledge resources of large model reasoning according to the priority list and reasoning link requirements, forming a collaborative scheduling scheme, configuring a reasoning process according to the collaborative scheduling scheme, and executing large model reasoning; and finally, converting the result into an operation and maintenance response instruction in combination with the equipment operation scene identifier, and sending the operation and maintenance response instruction to the execution terminal, so that accurate processing of the operation and maintenance task, reasonable allocation of resources and efficient response of the operation and maintenance service are realized.

The invention provides a database fault processing method and system based on reinforcement learning, and relates to the technical field of database management, and the method comprises the steps: collecting a performance index, an execution plan and error log data at a fault moment, constructing a causal decision tree to recognize a fault root cause, and generating a propagation link; constructing a reinforcement learning action sequence based on the priority list; dividing a master control action set and an adjustment action set according to the influence degree of the action on the node state; and the execution strategy is dynamically adjusted according to the performance improvement ratio. According to the method, the fault root cause can be accurately positioned, the processing strategy is adaptively optimized, and the fault recovery efficiency and the system reliability are improved.

The invention discloses an Internet of Things edge computing task unloading method based on a comprehensive gateway, which belongs to the technical field of computer data processing, and comprises the following steps: acquiring tasks, equipment and network data of Internet of Things equipment, and fusing to generate a unified information view; determining task priorities based on the view by applying a dynamic priority evaluation mechanism, and generating a dynamic priority list; applying the time sequence analysis model to generate short-term prediction data; in combination with the dynamic priority list and the short-term prediction data, multi-objective optimization is performed through a collaborative decision model, and a task unloading strategy is generated; generating feedback data according to the task unloading strategy; and generating an adjustment signal by using the feedback data, and updating the dynamic priority evaluation mechanism and the collaborative decision model. According to the method, the unified information view is generated by fusing the multi-source data, collaborative decision making is carried out in combination with dynamic priority evaluation and short-term prediction data, and the model is continuously optimized by utilizing feedback data, so that self-adaptive, forward-looking and efficient task unloading can be realized.

The invention discloses a network security protection management system and method based on big data, and relates to the technical field of network security management, and the method comprises the steps: collecting industrial protocol layer data, physical sensor data and process parameters, extracting network behavior features, physical features and process correlation features, and constructing an industrial security feature vector; according to the security weight coefficient and the industrial security feature vector of the current process stage, calculating a threat score of the equipment, and dynamically adjusting a threat level through a three-level verification mechanism; constructing an equipment dependency graph, performing risk diffusion calculation based on the adjacent matrix and the attenuation coefficient, and generating a risk diffusion priority list to adjust an isolation strategy; an improved multi-target genetic algorithm is adopted to solve a Pareto optimal solution set, an equipment recovery sequence and a process parameter adjustment scheme are determined, and a recovery instruction is issued to realize safe recovery, so that the dynamic perception and accurate defense capability for industrial network threats is improved, and the safety and stability of industrial production are guaranteed.

The invention discloses an intelligent charging scheduling method and system, and the method comprises the steps: collecting a real-time multi-source data set, building an LSTM charging demand prediction model based on an LSTM neural network, inputting the real-time multi-source data set into the LSTM charging demand prediction model for prediction, and defining a dynamic priority weight function according to the charging demand and the power grid schedulable margin. Generating a priority list; establishing a multi-target hierarchical optimization algorithm by utilizing MIP mixed integer programming and an NSGA-II genetic algorithm, and solving a charging scheduling scheme in a hierarchical manner by taking a priority list as a constraint based on the multi-target hierarchical optimization algorithm; active peak load shifting is performed by using a grading response strategy, the regional peak load is reduced, the capacity expansion investment of the transformer is delayed, and the service life of equipment is prolonged. And in combination with the V2G reverse power supply capability, electric vehicle energy storage resources can be called in an emergency state of a power grid, so that the local power supply reliability is improved, and the carbon emission of a traditional peak regulation power plant is reduced.

The invention discloses an intelligent logistics scheduling method based on Beidou positioning, and the method comprises the steps: obtaining the real-time position information of a vehicle, and constructing a traffic state matrix in combination with road congestion data; judging a weather influence area based on the weather change trend, and obtaining a weather risk area in combination with the traffic state matrix; obtaining inventory state data, and generating a resource demand priority list in combination with the weather risk area; adopting a multi-objective optimization algorithm, taking the resource demand priority list, the traffic state matrix and the weather risk area as input, obtaining an optimal path set, and generating a preliminary scheduling scheme; if a dynamic task insertion request is received, the task insertion feasibility is judged, the resource demand priority list is adjusted, the multi-objective optimization algorithm is operated again, and a corrected scheduling scheme is generated. Through multi-dimensional data fusion and intelligent algorithm optimization, the real-time performance, flexibility and reliability of logistics scheduling are improved, and an effective solution is provided for resource allocation in a complex environment.

The invention relates to the technical field of deep sea test task management, in particular to a deep sea test task scheduling and management method based on multi-platform collaboration, comprising the following steps: S1, generating an environmental adaptability task priority list; s2, generating a multi-platform interface configuration scheme based on the priority list in S1; s3, constructing a space-time conflict probability model, and outputting an obstacle avoidance path and a resource allocation strategy; s4, generating a data link topological graph; and S5, calculating a deep sea test efficiency index, and optimizing a subsequent test task priority list. According to the method, intelligent scheduling and efficient execution of deep sea test tasks are realized through task priority dynamic optimization, space-time conflict prediction, data link adaptive regulation and control and efficiency index feedback optimization, and the stability of multi-platform collaborative operation, the communication reliability and the resource utilization rate are improved.

The invention discloses a medical network collaborative penetration test system and method based on artificial intelligence, and belongs to the technical field of medical information security, and the method comprises the steps: integrating a hospital intranet, medical equipment, cloud data center data and historical network delay data to construct a medical network data center; analyzing association between network vulnerabilities and delay data through a graph neural network, constructing an attack path analysis model containing delay weights, and quantifying risk scores to generate a test target priority list; defining a protocol vulnerability and a delay threshold, designing a test scene and generating an attack vector, matching an attack tool to perform a simulation test, calculating a vulnerability triggering success rate and evaluating a risk; generating a penetration test case according to the test target priority list and the risk level, executing multiple rounds of attack tests and generating a report; and monitoring the change of the vulnerability triggering success rate by dynamically adjusting the delay parameter, setting an early warning threshold value, and monitoring and sending a risk early warning notification in real time.

The invention relates to the technical field of aviation safety data analysis and pilot training optimization, provides an aviation safety data and training data integrated analysis method and system, electronic equipment and a storage medium, and solves the problems of poor pertinence of pilot training and poor foresight of safety guarantee. The method comprises the following steps: automatically extracting threats, errors and unexpected aircraft states in aviation unsafe events from multi-source heterogeneous data, and judging a management state; based on the aviation safety knowledge graph, positive and negative competency and behavior indexes corresponding to the managed and unmanaged states are identified through graph calculation; the statistical frequency is analyzed, and a priority list of the security data side is generated; carrying out weighted fusion to obtain a target training list by combining a training data statistical analysis result of the same period; and in combination with the threat priority, generating a training scene configuration scheme containing threat elements and capability items, and forming a course content, evaluation standard and resource optimization configuration scheme. According to the invention, the pertinence of pilot training is improved.

The invention discloses an enterprise project database intelligent management method and system, and relates to the technical field of data management and resource optimization. The method comprises the following steps: verifying the integrity of business unit project data in real time through an intelligent cleaning engine, and structurally storing the business unit project data to a central project database; integrating the progress, the cost, the risk field, the commercial value and the strategic weight to dynamically calculate a comprehensive value index; based on the value index and the real-time resource inventory, a resource allocation scheme is generated through a hierarchical adaptation strategy, and a priority list is output; tracking progress deviation during execution, and triggering data isolation and index optimization when risk offset exceeds a threshold value; risk root causes are analyzed, value weights are dynamically adjusted, a rule base is managed in a back-feeding mode, and system self-optimization is achieved. According to the method, the problems of resource allocation rigidness, risk response lag and lack of dynamic value guidance in traditional project management are solved, the resource utilization rate is improved, and the project delay risk is reduced.

The invention discloses a weather-based HPC resource scheduling method, which comprises the following steps of: storing meteorological data blocks into a high-speed cache region on the basis of meteorological data and access frequency to form a high-frequency data cache set; the meteorological tasks in the high-frequency data cache set are analyzed and processed, and a task priority list is generated; performing resource allocation on the task priority list to generate a resource scheduling scheme; on the basis of a resource scheduling scheme, according to a preset load balancing threshold value, dynamically adjusting tasks to be distributed to computing nodes with light loads, and generating a dynamic load balancing mechanism; performing three-dimensional grid division on the meteorological data blocks in the high-frequency data cache set to form a meteorological spatial-temporal distribution coordinate system; and transmitting resource information of each meteorological block in the task priority list when the meteorological task is executed and a corresponding coordinate value of each meteorological block in the meteorological space-time distribution coordinate system to a neural network model for prediction, and performing secondary adjustment of resource configuration according to a prediction result to complete meteorological task resource scheduling.

The invention discloses a privacy leakage traceability method based on a data consanguinity map, and relates to the technical field of privacy leakage traceability, and the method comprises the steps: obtaining any node in a block chain network, and enabling the node to correspond to any enterprise; performing blood relationship analysis on the plurality of privacy data of any enterprise to generate a data blood relationship map; obtaining leaked privacy data, recording the data as a tracing starting point, and performing tracing analysis to obtain a tracing record; generating a visual traceability chart; performing prediction analysis on a first tracing path in the plurality of tracing paths to obtain a first leakage probability; and generating a troubleshooting priority list, and performing leakage troubleshooting and traceability on the private data. The technical problems that in the prior art, it is difficult to quickly locate the source for privacy data leakage, the checking process is lack of accuracy, and data security management is lack of effective means for accurately evaluating the leakage risk are solved, and the technical effects of achieving efficient tracing and accurate checking of privacy data leakage and improving the data security management capacity are achieved.

The invention relates to an AI Agent-based operation maintenance scheduling method and system. The method comprises the steps of collecting multi-modal operation state data of a plurality of industrial devices in real time through a multi-modal sensing network; key feature extraction is carried out on the multi-mode operation state data; performing fault risk assessment by using an equipment health assessment model in combination with the key features and historical working condition data; generating an optimal operation and maintenance scheme of the potential fault industrial equipment according to the evaluation result and the real-time operation state data; finally, an equipment operation and maintenance task priority list and an automatic operation and maintenance scheduling scheme are generated on the basis, the scientificity and systematicness of industrial equipment operation and maintenance scheduling can be improved, the problems that data of a traditional operation and maintenance mode is not comprehensive, and an operation and maintenance scheme is lack of planning and intelligentization are effectively solved, and the operation and maintenance efficiency and quality of industrial equipment are improved.

The invention belongs to the technical field of computers and artificial intelligence, and discloses a priority differential cache-based MoE batch reasoning method and system, and the method comprises the steps: recognizing global hot experts with higher activation frequency in each layer, enabling the global hot experts to remain in a high-priority cache during initialization, and building a priority list of the experts in each layer; in the reasoning stage, selecting an expert set needing to be activated in the current layer; the expert priority score is dynamically updated according to the activation and reuse behaviors of the experts, and the experts are migrated and replaced among the high-level cache, the middle-level cache and the low-level cache according to the priority score and a cache threshold value; according to a current layer gating result and a historical activation track, predicting an expert set which may be activated in a next layer, and asynchronously loading the expert set to a low-priority cache during current layer calculation so as to realize time overlapping of calculation and communication. According to the method, the expert migration frequency can be reduced, the cache hit rate and the throughput rate are improved, the video memory utilization rate is remarkably optimized, and good expansibility and universality are achieved.

The invention discloses a communication power supply load measurement and calculation and air switch control method and system based on service priority, and relates to the technical field of communication power supply intelligent management. Comprising the following steps: initializing and acquiring a load service priority list and a time threshold; monitoring an AC input state, switching to a standby power supply mode when the AC input state fails, and recording a power-off moment; a hybrid algorithm of ampere-hour integration and dynamic voltage calibration is adopted to measure the residual capacity of the storage battery in real time and with high precision, and residual power supply time is dynamically predicted; when the waiting time exceeds a first threshold value and the remaining power supply time is smaller than a second safety threshold value, a turn-off instruction is generated; and dynamically generating a turn-off sequence from low to high according to the service priority list, and iteratively executing turn-off and state re-evaluation until the endurance safety requirement of the core service is met. According to the invention, the problems of low control precision, incapability of distinguishing service priorities and low resource utilization rate in the prior art are solved, and accurate, intelligent and service-aware power management is realized.

The invention relates to an intelligent agent automatic detection system and method based on a large model, and the system comprises a data collection module which collects multi-source heterogeneous data monitored in real time in an automatic detection process; the quantification module is used for carrying out confidence coefficient coding on the preprocessed multi-source heterogeneous data by utilizing a pre-trained large model to obtain a confidence coefficient score of an abnormal event; the matching module is used for carrying out multi-dimensional priority ranking on the abnormal events based on the confidence score; the strategy generation module is used for simulating a strategy selection scene in a dynamic environment by adopting a reinforcement learning algorithm according to the sorted abnormal priority list so as to obtain an optimized dynamic strategy set; and the detection processing module is used for selecting the highest matching item from the optimized dynamic strategy set and carrying out corresponding processing on the detected abnormity. According to the method, the accuracy, efficiency and adaptability of intelligent agent automatic detection are effectively improved, and a reliable anomaly detection and response mechanism is provided for complex systems such as network security and industrial monitoring.

The invention relates to a dynamic data grouping method and system based on time delay perception under an Flink framework, and the method comprises the steps: carrying out the statistics of key value frequencies in a data stream, and dividing high and low frequency key values according to the key value frequencies; calculating the number of candidate instances allocated by high and low frequency key values, and calculating the completion time delay of each candidate instance to obtain a priority list; according to the time delay priority list, traversing a set of current to-be-distributed data, and selecting data of instances needing to be distributed; traversing the time delay priority list, and preferentially distributing the data to the instance with the minimum completion time delay for execution; updating the time delay information of the selected task instance by using a time delay sensing load model according to the runtime data of the selected task instance in the task execution process; and repeatedly executing the steps S2-S6 to execute the data packet. According to the method, the load balancing degree of the cluster when the data packet of the Flink cluster is executed in the heterogeneous cloud environment is effectively improved.

A system and method for providing virtual desktops to client devices of users is disclosed. The system includes available cloud regions that each can provide virtual desktops and associated resources. A desktop pool resource management engine is coupled to the available cloud regions and collects constraint data from the available cloud regions as well as operational data from desktop clients and desktop agents. A cloud usage profile is created for a subset of the users from the operational data. A priority list of available cloud regions for a user in the subset is created based on the usage profile and the constraint data of the available cloud regions. The highest priority cloud region from the priority list is recommended for the user requesting a desktop. A control plane selects the highest priority cloud region according to the priority list to provide the desktop to the client device.

The application is suitable for the technical field of data storage, and provides a data disaster recovery method and device, comprising the following steps: determining a fault type in a distributed cluster system and a range of data to be recovered; acquiring state parameters of multiple normal nodes in the distributed cluster system, and service sensitivity features and data block access features of the data to be recovered; performing data preprocessing operations; respectively adopting a service sensitivity evaluation model and a node state evaluation model to determine a data recovery priority list and a node priority list; determining a fault processing strategy according to the data recovery priority list and the node priority list; and executing the fault processing strategy. Through the above method, the real-time evaluation algorithm of the heterogeneous big data node is increased on the basis of dynamically updating the data recovery priority based on the service sensitivity evaluation model, and compared with the traditional technology, the fault perception of the business system can be reduced while the data disaster recovery efficiency is improved.

The invention relates to a dynamic scheduling method and device of a household energy system, computer equipment, a computer readable storage medium and a computer program product. The method comprises the following steps: predicting a household load power prediction value and a photovoltaic power generation power prediction value after a preset time length based on real-time collected operation condition data of a household power distribution system; based on the household load power prediction value, the photovoltaic power generation power prediction value and the operation condition data, establishing a multi-target dynamic optimization scheduling model; solving the multi-target dynamic optimization scheduling model to obtain an optimization scheduling strategy; real-time dynamic comprehensive priority scores of the load devices are obtained, the real-time dynamic comprehensive priority scores of different load devices are sorted, and a load device priority list is obtained; and according to the optimized scheduling strategy and the load equipment priority list, generating a real-time scheduling control instruction and issuing the real-time scheduling control instruction. By adopting the method, the autonomous adjustment capability of the system can be effectively improved.

A system and method for providing virtual desktops to client devices of users is disclosed. The system includes available cloud regions that each can provide virtual desktops and associated resources. A desktop pool resource management engine is coupled to the available cloud regions and collects constraint data from the available cloud regions as well as operational data from desktop clients and desktop agents. A cloud usage profile is created for a subset of the users from the operational data. A priority list of available cloud regions for a user in the subset is created based on the usage profile and the constraint data of the available cloud regions. The highest priority cloud region from the priority list is recommended for the user requesting a desktop. A control plane selects the highest priority cloud region according to the priority list to provide the desktop to the client device.

The invention relates to the technical field of trauma rescue, in particular to a trauma rescue intelligent decision-making and priority distribution system which comprises the steps of collecting original rescue data; preprocessing the original rescue data to obtain preprocessed rescue data; processing the pre-processed rescue data through a deep learning model to obtain shock probability, injury type and environmental physiology associated data; calculating ISS total score priority by using shock probability, injury type and environmental physiology associated data association; and generating a patient priority list according to the ISS total score priority, and preferentially carrying out resource allocation on the patient with the highest score in the patient priority list. According to the method, the injury condition scores of the patients are dynamically generated through the deep learning model, the medical resources are automatically allocated according to the high-low sequence of the scores, the optimal ambulance route is generated in combination with the GIS map, it is ensured that the critical patients can be treated preferentially, and the fairness and timeliness of rescue are improved.

The invention discloses a learning assistance method, device and equipment based on a big language model and a storage medium, and the method comprises the steps: collecting historical interaction data of a user and the big language model, after desensitization processing and structured storage, extracting keywords and knowledge points through a natural language processing module, clustering similar problems and knowledge points, and carrying out the clustering of the similar problems and knowledge points; constructing a user knowledge graph containing knowledge point relevance and mastery score; generating a learning priority list based on the map score, and matching a preset knowledge base to generate a personalized learning plan; pushing the plan to a user side, and obtaining a learning and testing result to determine the knowledge mastery degree; and comparing the mastering degree with a preset threshold value, if the mastering degree is lower than the threshold value, regenerating the targeted plan, and if the mastering degree is not lower than the threshold value, marking the knowledge points as mastered and updating the graph. According to the scheme, knowledge blind spots in dialogues are dynamically tracked, conversion from passive question answering to active learning is achieved, and the knowledge mastering ability of a user is systematically improved.

The invention relates to the technical field of rehabilitation medical treatment, and discloses a multi-modal data analysis method for rehabilitation decision support. The method comprises the steps that current physiological index data of a patient are collected and compared with historical samples, and individual difference evaluation values are obtained; analyzing current physiological index data and training intensity related branch paths through a tree decision model, and determining a risk prediction score; action priorities are calculated in combination with the risk scores and the physiological data, and a preliminary action priority list is generated; if the calculation is overtime, optimizing the structure of the decision model to obtain a simplified priority list; fusing the change trend of the physiological state of the patient with the dynamic adjustment model to obtain an updated action sorting sequence; and finally generating and outputting a personalized rehabilitation training scheme and outputting the personalized rehabilitation training scheme to a display device. According to the method, through multi-modal data fusion, tree-shaped decision risk assessment and real-time dynamic adjustment, the problems of insufficient personalized precision of a rehabilitation scheme and system response lag are solved, and the safety and suitability of training are improved.

The invention relates to the technical field of index structure recombination, in particular to a nursing process management database index optimization method, which comprises the following steps of: performing cost calculation on multiple indexes by adopting a gradient lifting regression tree, comparing the cost calculation with a threshold value, and forming a recombination candidate priority list under the support of a numerical result; a potential recombination object has a quantifiable judgment standard, predicted consumed time is calculated in combination with the low-ebb IO capacity and compared with the time slice length, the screening process can correspond to the system resource bearing condition, a B + tree algorithm is adopted for calculating a loading factor and the continuous scanning length, and through merging, splitting and redistribution operation, the screening efficiency is improved. A node organization structure is finely adjusted, it is guaranteed that an index structure and an access mode are kept coordinated, information entropy is utilized to sort and screen a field sequence, the field arrangement with high distinction degree is preferentially used for constructing a new index, atomic switching operation is executed to replace an old index, and the field sequence has adaptability in a dynamic data environment.

The invention relates to the technical field of energy management, in particular to a micro-grid-based heat energy management method and system, which comprises the following steps: acquiring temperature and power data and establishing a path corresponding relation, extracting heat response and load change parameters to generate a priority list, embedding a topological structure chart to adjust a path starting sequence, and establishing a path starting sequence. And identifying phase change to form a conflict node group, and writing a control table to generate a path distribution structure. According to the method, path mapping of temperature and power is constructed, node response characteristics are extracted to generate a priority sequence, the path state is updated in combination with a topological structure, interference nodes are recognized based on phase change comparison, a control sequence is set, heat energy distribution is promoted to keep the regulation rhythm, and the recognition capacity of path dynamic behaviors is enhanced; the coordination level between nodes and the resource utilization efficiency are improved, the heat energy dispatching process is promoted to tend to be smooth in the environment of frequent load fluctuation, the fluctuation influence caused by path conflicts and regulation and control lagging is reduced, and the supporting foundation of stable operation is strengthened.

Provided in the present application is a scheduling method. In the method, during the determination of a target scheduling action, action modules are traversed one by one on the basis of the current states and an action priority list corresponding to the action modules, an output is provided with a target scheduling action as the granularity, and it is not necessary to traverse all possible scheduling sequences, compare the scheduling sequences, and then output an optimal scheduling sequence, thereby greatly reducing the depth of traversal and search, and thus facilitating a reduction in time consumed for determining the target scheduling action and an improvement in the timeliness of the scheduling method, and ameliorating the problem of the degree of matching between a scheduling instruction, which is output by means of the scheduling method, and the actual state of a semiconductor process device being relatively low due to a relatively long period of time being consumed. Moreover, in the scheduling method, traversing action modules one by one to determine a target scheduling action can not only be applied to the scheduling of action modules for transport, e.g., robots, but can also be applied to the scheduling of action modules, e.g., spraying mechanisms, thereby facilitating an improvement in the applicability of the scheduling method.