468 results about "Finite-state machine" patented technology

The invention provides a heat storage heat pump system control method based on a physical information neural network, and belongs to the technical field of heat storage pump system intelligent control. Aiming at the problems that in the prior art, an algorithm is difficult to adapt to dynamic energy consumption requirements, engineering application of a model is difficult due to building space heterogeneity, high-order RC model prediction credibility is weak, engineering feasibility is poor and the like, a solution combining a physical information sequence to sequence neural network technology and a finite-state machine control strategy is provided. On the model level, a 2R2C resistance-capacitance RC model of building temperature change is established, and then a PI-Seq2seq prediction model is proposed based on the physical model. On the control flow optimization level, on the basis of an industrial and commercial time-of-use electricity price policy, an FSM control model is designed, a system state set is defined, parameters and a transfer function are input, and a control rule is constructed in combination with the working period of a building heat pump and the characteristics of a heat storage tank. And finally, energy consumption cost optimization and indoor temperature stabilization under the peak-valley electricity price are realized.

The invention discloses a secure transmission system based on a multimedia short message, and relates to the technical field of communication and network security, and the system comprises a protocol stack security reinforcement module which carries out the hierarchical reconstruction of an MMS protocol stack, comprises a preprocessing layer, is used for intercepting and filtering illegal characters, and verifies the legality of a message structure through a finite-state machine; the analysis layer is used for pre-judging memory requirements based on message types and lengths by adopting a dynamic memory allocation strategy; the execution layer is integrated with a null pointer checking mechanism, and the validity of a pointer is forcibly verified before a protocol stack calls a function; the encryption and signature module is used for carrying out end-to-end encryption on a message body and metadata by adopting a hybrid encryption algorithm and carrying out message integrity verification through a lightweight hash tree; and the vulnerability real-time monitoring module is embedded into an abnormal behavior detection model based on machine learning and is used for dynamically identifying memory occupation abnormity and illegal instruction calling high-risk operation when the protocol stack runs.

The invention discloses a robot image-free visual target docking method and system based on multi-modal perception, and the method comprises the steps: carrying out the recognition calculation of a preset target in the image data of the environment in front of a robot through a target detection model YOLOv4-tiny, and constructing a robot control feedback quantity; radial distance information of an object in the environment is obtained, a scanning range is divided through a regional strategy, straight line fitting calculation is conducted, and a deviation evaluation value of the robot relative to the yaw angle is constructed; and the robot accumulative mileage is obtained, finite-state machine hierarchical decision making and mistaken stop prevention judgment are conducted in combination with the robot control feedback quantity and the robot relative yaw angle deviation evaluation value, and robot graph-free visual target docking is achieved. According to the invention, full-process autonomous control of the robot from target discovery, navigation on the way, obstacle avoidance to end fine alignment can be realized. The robot graph-free visual target docking method and system based on multi-modal perception can be widely applied to the technical field of graph-free navigation.

The invention provides a disease course record generation method and system based on a mobile terminal and multi-modal data, and the method comprises the following steps: firstly constructing a directional collection channel and a ward round session associated with a specific patient through the signal interaction between the mobile terminal and a sickbed; in the session, multi-modal data streams with timestamps, such as voice, images and operation instructions, are obtained in real time, and audio semantic anchor points and visual semantic anchor points are extracted from the multi-modal data streams. And constructing an image-text semantic unit by calculating and associating the audio and video anchor points of which the semantic similarity exceeds a threshold. And taking the unit as a current observation state, and verifying the legality of the state transition through a finite-state machine model. And based on the image-text semantic units passing the verification, template slot filling and term standardization are carried out, and a structured disease course record data packet is generated and written back to the HIS system. The method has the effect of improving the working efficiency and quality of clinical documents.

An end-side-cloud three-in-one active chat robot system for high-emotional merchants comprises an end side, a local edge server and a cloud end, and the end side is used for collecting multi-modal data of a user and performing local lightweight real-time processing and response execution; the local edge server is used for receiving and fusing the multi-modal features and the context information from the end side, and carrying out sentiment calculation, dialogue management and active trigger decision making with medium complexity; the cloud end is used for operating a super-large-scale model and providing global knowledge management, long-term user portrait storage and model training optimization; and the end side, the local edge server and the cloud end carry out cooperative communication through an encrypted channel to form a distributed intelligent processing architecture. According to the method, global optimization is realized by integrating end-side lightweight sensing, edge multi-modal fusion and cloud long-term memory. A composite finite state machine (FSM) active questioning mechanism is combined with sentiment calculation, and is different from traditional rule type triggering. Off-line and on-line fusion scheduling and multi-agent role playing are combined to be applied to a high-emotional-quotient interaction scene, and the simulation is improved. Multi-modal emotion perception circulation is introduced, and the problem of single text emotion misjudgment is solved.

The invention relates to the technical field of network security awareness, in particular to a security awareness method and system for a GPU cluster, and the method comprises the steps: collecting multi-source security situation data, and providing basic information for subsequent security analysis; by constructing and updating a cluster security situation map in real time, information timeliness is updated and ensured in real time, and powerful support is provided for risk assessment; a dynamic risk assessment model is utilized to calculate vertex and edge scores, high-risk vertexes and key risk conduction paths are identified, and a basis is provided for resource isolation and scheduling; the targeted instruction is generated according to the security risk score and the preset security policy library, different security risks are effectively handled, and safe and stable operation of the cluster is guaranteed; by converting the instruction into a specific control command and distributing and executing the specific control command, effective implementation of security measures is ensured, and the cluster security protection capability is improved; by setting a finite-state machine monitoring instruction execution state, a recovery mechanism is automatically triggered, and continuous and safe operation of the cluster is guaranteed.

The invention relates to the technical field of vehicle control, and discloses a parking start-stop battery intelligent module management system, which comprises a data acquisition and preprocessing module, a driving intention inference module, a self-adaptive transient power capability evaluation module and a multi-dimensional fusion decision and control module, discrete driving intention states such as congestion crawling and intersection waiting are recognized through a finite state machine; according to the intention state, adaptively selecting an evaluation sub-model from an evaluation sub-model library so as to evaluate the transient power capability of the battery, and outputting a transient power capability score and a corresponding confidence coefficient; and the system fuses the driving intention, the transient power capability score and the confidence coefficient, corrects logic through the preliminary decision matrix and the confidence coefficient, performs rechecking in combination with the whole vehicle level constraint condition, and generates and issues a final start-stop control instruction. According to the method, the problems of single decision and poor situation adaptability in the prior art are solved, the intelligence and reliability of start-stop decision are remarkably improved, and the driving experience is optimized.

The invention relates to the technical field of IPv6 self-configuration, in particular to an IPv6 network mode self-adaptive configuration method and system based on an RA message. In the method, a finite-state machine establishes a state transition path according to the combination relation of M and O flag bits in an RA message cache set and prefix field content, converts a network mode recognition process into a quantifiable state judgment sequence, and sends the state judgment sequence to the RA message cache set; configuration ambiguity caused by flag bit fluctuation in traditional static judgment is eliminated, pattern recognition under network environment changes has continuity and self-consistency, the classification regression tree constructs a branch judgment path with the duration, the advertisement interval and the prefix consistency of an RA source as characteristic variables, an optimal node is selected through a hierarchical splitting algorithm, and the judgment accuracy is improved. The dynamic mapping of stateful and stateless configuration paths is realized, the misjudgment rate of configuration branches is reduced, and the address allocation delay is optimized, so that the IPv6 self-configuration has a structured decision-making capability and a parameter mapping self-optimization characteristic, and the connection reliability, the address generation speed and the configuration accuracy are improved.

The invention discloses a cigarette cut tobacco batch production circulation control system and method based on single-point condition judgment, and the system comprises a work order parameter verification module which is used for receiving a work order issued by a scheduling system, and carrying out the work order information integrity verification, process parameter compliance verification and equipment suitability verification; the regular expression configuration module is used for providing a visual interface and configuring a regular judgment rule set of equipment starting conditions, ending conditions and abnormal ending conditions; and the single-point judgment execution mechanism is embedded into the PLC control layer, acquires equipment parameters in real time, dynamically evaluates regular conditions based on a finite-state machine model, and triggers an equipment start-stop instruction. According to the system, the cigarette cut tobacco batch switching time is remarkably shortened, and the work order circulation efficiency is improved; the manual intervention frequency is further reduced, and the misoperation rate is further reduced; and the problem of production interruption caused by disconnection of the equipment state and the process parameters is solved.

Techniques for constraining a language model's generation / decoding process using a finite state machine are described. A finite state machine may represent information corresponding to APIs, arguments and argument values that are available / supported by the system. In some embodiments, a language model (LM) is constrained to generate tokens representing valid API calls based on the finite state machine. The system may enable generation of argument values from a defined set or free-form generation of argument values. The system may also enable unconstrained generation of a response by the LM. In some embodiments, a trie data structure is used to determine the possible next tokens that the LM can generate from.

The invention provides a non-deep traffic analysis streaming matching search analysis method, which comprises the following steps of: constructing a deep data packet detection architecture on the basis of a data platform development kit (DPDK); aiming at the non-encrypted traffic, establishing a multi-mode recognition mechanism combining a regular expression and a feature bit stream mode; aiming at the encrypted traffic, constructing an encrypted traffic feature library according to the statistical features, the protocol features and the behavior features; real-time risk detection of network traffic is realized by adopting a streaming matching search technology; and constructing an intelligent decision and response mechanism, and integrating flow analysis and identification results. According to the non-deep traffic analysis streaming matching search analysis method provided by the invention, real-time analysis, risk identification and supervision of network non-encrypted traffic and encrypted traffic are realized by taking a data platform development kit (DPDK) as a basis and combining a high-performance streaming regular expression engine and a finite-state machine principle; the method can be widely applied to scenes of network communication supervision, data security protection, malicious traffic monitoring and the like.

The invention discloses a hanging basket motion control method and system based on operation state recognition, particularly relates to the technical field of bridge construction automation, and is used for solving the problem of poor synchronous control of hanging basket movement. According to the method, working conditions are accurately recognized through multi-source sensing data fusion and a finite-state machine, servo system parameter self-adaptive setting is achieved based on working condition parameter mapping, and an anti-interference movement track is generated by adopting a model prediction control algorithm fusing pose feedback and wind load feedforward; walking synchronization and platform stability are achieved through cross coupling control and posture closed-loop fine adjustment, emergency response is provided through the independent safety guarding thread, control and protection double closed loops are formed, and therefore the motion precision, the anti-interference capacity and the intrinsic safety level of the hanging basket under the variable-load and strong-disturbance working conditions are effectively improved, and the safety of the hanging basket is improved. And the energy consumption and the mechanical vibration frequency in the moving process of the hanging basket are reduced.

The invention relates to the technical field of unmanned aerial vehicle control and management, in particular to an unmanned aerial vehicle parameter consistency configuration method based on template management, which adopts a structured parameter configuration template in a JSON format, comprises four blocks of metadata, parameter definition, constraint rules and digital signatures, establishes an MAVLink communication link with an unmanned aerial vehicle flight controller through a template management terminal, and provides an unmanned aerial vehicle parameter consistency configuration method. And executing an automatic configuration process based on a finite-state machine, and ensuring parameter logic consistency by using a difference comparison and constraint verification mechanism. According to the method, temporary storage, batch writing and atomic submission of parameter changes are achieved through the atomization transaction submission protocol, the integrity of the configuration process is guaranteed, the digital signature auditing log containing the timestamp, the device identifier, the template hash and the change details is generated after configuration is completed, whole-process tracing is supported, and the method is high in practicability and easy to popularize. Headless mode operation, parameter white list control and hardware abstraction layer adaptation are supported, and high automation, standardization and safety of unmanned aerial vehicle parameter configuration can be achieved.

The invention discloses an interpretable end-to-end automatic driving method based on a multi-modal large model, and belongs to the technical field of data processing, and the method comprises the following steps: S1, obtaining a driving environment image, carrying out the feature extraction and coding, and generating a visual representation; s2, generating scene semantic representation by using the semantic answer; s3, determining a state by using a finite state machine; s4, generating a final action according to the visual representation and the scene semantic representation, and constraining the final action through the state of the finite-state machine; and S5, based on the final action, generating a language instruction. According to the method, navigation information and human natural language instructions are encoded into prompts, and large model behavior generation is guided.

The invention belongs to the technical field of data processing. The invention discloses a matrix multiplication calculation task processing system, method and device, a storage medium, a program product and a chip, and the system comprises a storage module which is used for storing a preset weight matrix in advance; the linear transformation control module comprises a finite-state machine used for matrix multiplication operation scheduling and is used for generating an input request signal so that the matrix multiplication calculation task processing system can receive an input matrix; the operation module is used for executing matrix multiplication operation on the input matrix and a preset weight matrix to obtain a matrix multiplication operation result and generate an output matrix; and the data flow control module is used for controlling a plurality of groups of data flows formed by the input data in the input matrix according to rows to sequentially enter the operation module according to the input request signal until all the data in the input matrix execute and complete the matrix multiplication operation. The problems that in the prior art, a matrix multiplication accelerator lacks flexibility in a linear layer implementation task of artificial intelligence hardware, the data migration cost is high, and the energy efficiency ratio is low are solved, and the method is suitable for self-defined extension based on an RISC-V instruction set architecture, can be used as a coprocessor of an RISC-V processor, and can be used as a coprocessor of the RISC-V processor. And linear layer calculation in the neural network is accelerated, especially in an artificial intelligence task, the calculation efficiency can be remarkably improved, the power consumption can be reduced, and the real-time processing requirement can be met.

A method for converting unstructured data. The method includes receiving a custom-defined data schema that is constructed according to a structured data syntax. The method includes converting the custom-defined data schema into a language model modifier that restricts outputs based on preceding outputs and integrating the language model modifier with an autoregressive machine-learned language model (LLM) to modify output scores of the autoregressive LLM. When receiving a data file that includes unstructured data, the method includes generating a first output from the autoregressive LLM and receiving a set of tokens representing candidates of a second output succeeding the first output. Each token is associated with a score. The method further includes identifying a rule in the language model modifier using the first output, modifying scores of the tokens that violate the rule and selecting one of the tokens as the second output based on the modified scores.

The invention discloses a book-based subject knowledge system construction method and device, and the method comprises the steps: representing a directory as a directed tree, and dividing the directed tree into a skeleton module and a plurality of chapter modules; carrying out the association and expansion of a title through a large language model, and generating an abstract; knowledge concepts, subject terms and structural relations in titles in all the modules are extracted and subjected to double correction to obtain a plurality of knowledge system trees, and then a mother tree obtained after the skeleton module is processed and a sub-tree obtained by the chapter module are spliced; and finally, giving a plurality of knowledge trees obtained according to a plurality of groups of book directories in the same subject field, and combining the knowledge trees into a unified knowledge system tree based on a finite-state machine principle. According to the method, the knowledge system is constructed and decoupled into an extraction stage and a fusion stage based on LLM, the problem of a pain point of drifting between knowledge concepts and attributes when a simple chapter title is faced is solved, and strict control on semantic consistency and hierarchical rationality in a path merging process is ensured based on a state machine.

The invention provides a constraint method and device for model structured output based on a finite-state machine, and relates to the technical field of network security, and the method comprises the steps: converting a structured constraint rule into a finite-state machine, constructing a shared vocabulary finite-state machine in combination with a model vocabulary, and dynamically limiting an output token range and a field sequence in a reasoning process. It is ensured that model output strictly conforms to preset field definition, value range and grammar rules, the problems of field missing and format disorder in a traditional mode are thoroughly solved, and results can be directly analyzed and utilized by a downstream system. According to the method, low resource occupation, high reusability and quick response are realized while output accuracy and structuring are guaranteed, and key technical support is provided for large-scale application of a large model in security scenes such as attack detection and vulnerability recognition.

The invention discloses an unmanned aerial vehicle relay tracking system and method for cloud side-end cooperation in an urban scene, and belongs to the technical field of intelligent traffic and unmanned aerial vehicle cooperative control. In order to solve the problems of insufficient endurance and limited sight distance of existing single-machine tracking in an urban large-range scene, a physically distributed and logically unified cloud-side-end three-level architecture is constructed, wherein a cloud end is responsible for natural language instruction analysis and global road network monitoring; the edge end serves as a regional scheduling hub and is responsible for operating a high-precision detection model, extracting a target Re-ID feature fingerprint and calculating a cross-machine relay time window; and the terminal unmanned aerial vehicle only operates a lightweight tracking model and a visual servo control law. According to the method, the urban area is divided into a plurality of honeycomb grids, seamless locking and relay of a target among unmanned aerial vehicles in different grid responsibility areas are realized by using a cross-view feature relay protocol and a collaborative finite-state machine, and the engineering problem of long-time-sequence and large-range vehicle tracking is effectively solved.

Provided is a method for controlling virtual robots. The method includes: starting a plurality of virtual robots constructed by using a finite state machine, wherein the virtual robots are configured to simulate, in a plurality of loops, a user operating an application program, and the finite state machine comprises a plurality of states, a plurality of events occurring at the plurality of states, and actions triggered by the plurality of events; determining, in each loop, a view in which the virtual robots are located in the application program, and determining the view as the state; simulating, in each loop, the user to select a service in the view, and determining the selected service as the event; and driving, in each loop, the virtual robots to execute, in the view, a user operation configured to trigger the service, and determining the user operation as the action.

The invention provides a WebSocket communication reconnection recovery method and related equipment. The method comprises the following steps: managing the life cycle of WebSocket connection through a finite-state machine; the method comprises the following steps: monitoring a WebSocket connection state by adopting a dual-channel heartbeat mechanism, configuring a client to send a PING frame of a current timestamp at a dynamic heartbeat interval for an uplink channel, and configuring a server to return a PONG frame of the current timestamp within a preset response time for a downlink channel; when the connection is interrupted, non-sent messages are cached through a non-standard LRU strategy combined with time sequence priorities, and reconnection interval time is calculated based on an exponential back-off algorithm; and finally, the WebSocket connection is reconnected based on the reconnection interval time, and the unsent message is retransmitted through a non-standard LRU strategy after the connection is recovered. According to the method, a finite-state machine is used for managing the WebSocket, a two-channel heartbeat mechanism and exponential backoff are used for rapid reconnection, a non-standard LRU strategy is used for guaranteeing that key information is not lost, and the defects that original WebSocket management is disordered, and connection is not reliable are overcome.

An active chatbot system with behavioral awareness and on-demand conversation and a method thereof are disclosed. In the active chatbot system, a client-end host continuously senses a client behavior state, and transmits the sensed client behavior state and an on-demand conversation setting to a server-end host to generate a rough question message having a natural language structure, and input the rough question message to a plurality of finite state machines to generate a precise question message. The server-end host transmits the precise question message to an artificial intelligence platform to obtain a corresponding answer message, stores the answer message to an answer list, filters out an answer message matching the on-demand conversation setting as an on-demand conversation message, transmits the on-demand conversation message to the client-end host for output. Therefore, the technical effect of improving human-computer interaction and initiative of chatbot can be achieved.

The invention provides a finite-state machine extraction method and device for logic system design and a storage medium, and the method comprises the steps: determining a signal node based on a condition trigger signal and an assignment signal of a process block in a compiling partition, so as to construct a first finite-state machine tree; associating a condition assignment statement for assigning the condition trigger signal to the corresponding signal node in the first finite-state machine tree to obtain a second finite-state machine tree; determining a signal value of the signal node based on a conditional statement corresponding to the conditional trigger signal and the conditional assignment statement, and updating the second finite-state machine tree based on the signal value to obtain a third finite-state machine tree; updating corresponding state transition information between the signal values of the signal nodes in the third finite-state machine tree to obtain a fourth finite-state machine tree; and combining the fourth finite-state machine trees to obtain a global finite-state machine result.

The invention relates to an exoskeleton robot control method and device and an exoskeleton robot. The method comprises the following steps: determining an activity parameter according to a signal collected by a sensor; identifying a plurality of motion intentions according to the activity parameters, judging motion identifiers under the motion intentions, and determining a plurality of motion identifiers; performing state transition according to the plurality of action identifiers by utilizing a finite-state machine to obtain a target sub-state; a plurality of motion modes are preset in the finite-state machine, and each motion mode corresponds to at least one sub-state; and controlling the power unit according to the control instruction corresponding to the target sub-state. According to the method, the motion intention recognition and the finite-state machine are combined to complete the operation state transition, so that the misjudgment rate of state recognition can be reduced, and the robustness of the finite-state machine is improved; the multiple preset motion modes of the finite-state machine are divided into the sub-states, the situation of wrong assistance can be eliminated to a certain degree, misoperation of the exoskeleton robot is avoided, and the assistance control accuracy is improved.

The invention discloses a finite state inquiry method and system based on a large model and a knowledge graph, and relates to the field of business handling, when there is a business handling intention, a finite state machine sets an initial business state, and a large language model analyzes, inputs and extracts keywords and demands to determine a current state; the user naturally converses with the big language model, and the big model is guided to complete the process according to the knowledge graph and the current state; when the user interrupts questions, the large model answers the questions according to the knowledge graph, and then the finite-state machine records the state to help the user to seamlessly return and continue to handle the questions; according to the method, the problem of large model illusion is effectively solved, and the accuracy and stability of user intention judgment are improved; intelligent autonomous business handling without manual intervention is realized; the seamless recovery process after the user interrupts questions at any time is supported, and the flexibility and coherence of business handling are enhanced; comprehensive knowledge coverage is ensured by means of a knowledge graph, and user questions are accurately answered; and meanwhile, the dependence on manual business personnel is reduced, and the labor cost is reduced.

The invention discloses an exoskeleton robot abnormal working condition automatic response and risk avoiding decision-making method, and relates to the technical field of exoskeleton, and the method comprises the steps: deploying a plurality of types of sensors to synchronously collect environment data, human physiological data and exoskeleton operation data, and carrying out the preprocessing to remove interference; through fusion of abnormal harmonic detection and a finite-state machine, dangerous working conditions such as wind speed abrupt change are identified, and grades are judged; monitoring eye movement and heart rate variability data in real time, quantifying cognitive load and grading; a decision rule base is constructed, and joint locking and other adaptive risk avoiding decisions are generated in combination with working condition types, grades and cognitive loads; the controller drives the execution mechanism to execute a decision, continuously monitors data at a frequency, dynamically adjusts the decision or recovers a normal mode. According to the method, dangerous working conditions are accurately identified through multi-source data fusion, adaptive risk avoiding measures are automatically triggered, a power assisting strategy is dynamically adjusted in combination with cognitive loads, and operation flexibility and physical output are balanced.

The embodiment of the invention discloses a finance and taxation service request processing method and device, and the method comprises the following steps: receiving a finance and taxation service request, carrying out the semantic analysis through a BERT model trained based on a finance and taxation field corpus, and extracting a user intention and a key service entity; based on a finite-state machine model, managing a dialogue flow of a business scene, triggering state transition according to a current dialogue state, a user intention and a key business entity, and orderly promoting an invoicing, checking or diagnosing business process; scheduling a corresponding AI capability module to execute a task according to the user intention and a service scene defined by the key service entity; an execution result of the AI capability module is fed back to a front end in real time through a server pushing technology and is displayed in linkage with information input by a user; based on key business entities and operation combinations recorded in a user operation log, the key business entities and the operation combinations are automatically abstracted and packaged into independent reusable functional components. According to the embodiment of the invention, finance and tax business requirements can be adapted through multi-step and scenarized dialogue response.

The invention relates to the technical field of robot autonomous decision-making, and discloses an underwater robot behavior decision-making method based on a behavior tree, which describes and controls the specific behavior of an underwater robot based on the behavior tree, organizes and manages complex behavior logic through a tree structure, and analyzes a specific task to determine the behavior of the underwater robot. And decomposing into behavior sub-trees, and combining control nodes and action nodes in the behavior sub-trees to complete a specific logic switching function. Compared with a traditional finite-state machine, the method has the advantages of modularization, expandability and high readability, the coupling degree between modules in the sub-tree is low, the sub-tree can be transplanted to multiple positions of the behavior tree, the problem that the workload of the reuse state of the finite-state machine is large is effectively solved, and the method has good practical application value.

A system and method are disclosed for identifying malicious activity on a target device based on behavior analysis of the target device. The system includes a behavioral analyzer run on a virtual machine connected to the target device. The virtual machine collects system events and parameters from the target device and run a script, independent of the target device, to detect a threat. The script is a set of instructions executed to analyze behavior of an object by processing and correlating the events. The script includes a rule structure which stores signatures and expressions of the known malwares. By correlating the selected event parameters with known malware parameters, it is determined whether the event imposes a threat or not. A finite state machine is used for the state transition table.

The invention relates to the technical field of hoisting control, in particular to a hoisting control system and method for modularized fast-installed electrical equipment, which forcibly limits the sequence relation of the whole clamping and propelling process through a finite-state machine, so that the propelling direction, segmented propelling, syn-position recording and propelling and maintaining switching of a clamping component are all controlled by state constraint; through Markov decision, subsequent state transfer is associated only according to current state information in a holding stage, so that reverse displacement change in the holding stage is not judged by a single-point threshold value any more, but is continuously associated in a state sequence form, the conditions of syn-position jumping, advanced holding and repeated advancing in clamping action are avoided, and the clamping efficiency is improved. The development trend of reverse displacement is described through inter-state transfer records, so that formation of a keeping interval is established on the basis of continuous state consistency, continuous transfer association based on the current state is formed in the keeping stage, and the clamping process has controllability in time dimension and space dimension at the same time.