105results about How to "Improve scheduling efficiency" patented technology

The invention belongs to the technical field of intelligent manufacturing and industrial scheduling, and provides a high-altitude platform multi-test task production scheduling method based on a clustering algorithm and multi-objective optimization, and the method comprises the steps: automatically calculating the exclusive resource demands of all working condition points in a plurality of aero-engine test tasks based on a multi-parameter coupling model; for each task, dividing a plurality of work packages by adopting a clustering algorithm based on the multi-dimensional features containing exclusive resource requirements in the task; a multi-objective model of multiple optimization objectives is constructed, all the work packages are jointly sorted globally, and the optimization objectives comprise the equipment utilization rate; and finally, under the condition that constraint of resource capacity, mutual exclusion of test cabins and task continuity is satisfied, allocating an available tester and a continuous time window to each work package, and generating an initial production scheduling plan. According to the method, the production scheduling efficiency and the resource utilization rate are effectively improved, the plan robustness is enhanced, and the method is suitable for high-complexity aero-engine test scheduling scenes.

The present application relates to elevator dispatching technical field, disclose a kind of elevator dispatching arbitration method, comprising the following steps: step 1: receiving multi-source call request, corresponding identity fingerprint is assigned to each call request and monitors the waiting time of each call request;Step 2: obtain the current load of target elevator and car remaining space, whether the current load of target elevator and car remaining space meets corresponding call request is judged;When the current load of target elevator or car remaining space does not meet corresponding call request, then cancel the call request;When the current load of target elevator and car remaining space both meet corresponding call request, then carry out step 3;Step 3: using dispatching score model to calculate call request, obtain the score of call request;Step 4: using shadow dispatcher to send shadow instruction to target elevator, shadow instruction triggers the corresponding stop layer action of target elevator.Simultaneously, a kind of elevator dispatching arbitration system is also disclosed.

The application belongs to the field of workshop scheduling, and particularly discloses a semiconductor wafer probe test scheduling method and system based on an improved genetic algorithm, which adopts double-layer process-machine coding, and combines auxiliary resource taboo coding as an individual, effectively converts a complex scheduling problem into a coding form through an innovative coding and decoding strategy, and avoids traversal search on all selectable auxiliary resources; global-local hybrid search is carried out through the improved genetic algorithm, on the basis of machine level exchange with the traditional critical path-key block, search on the auxiliary resource immediately preceding work is increased, and neighborhood exploration is carried out based on a load balancing strategy, so that the search efficiency and solution quality are improved. The application can quickly and efficiently solve the semiconductor wafer probe test scheduling scheme, effectively improve the equipment utilization, speed up the scheduling efficiency, and break through the bottleneck of traditional scheduling.

This invention relates to the field of intelligent transportation technology, and more particularly to an artificial intelligence-based intelligent traffic command and dispatch system, comprising: a traffic command module for adjusting and setting timing parameters such as phase overflow buffer duration coefficient, phase interception pre-activation coefficient, and intersection phase clearing margin coefficient; a duration statistics module for selecting vehicles passing through a specific road segment within a preset duration to determine the monitored vehicles and obtain the average road network travel delay time; an intelligent analysis module for determining whether traffic command and dispatch meet standards based on the average road network communication delay time and generating corresponding processing instructions; and a parameter adjustment module for adjusting the preset duration, baseline travel duration, phase overflow buffer duration coefficient, phase interception pre-activation coefficient, and intersection phase clearing margin coefficient. This invention effectively achieves precise monitoring of traffic command and significantly improves the efficiency of traffic command and dispatch.

The application provides a kind of intelligent scheduling method, device and server of multi-hole gate, it is related to the technical field of water conservancy engineering automation and artificial intelligence optimization scheduling, comprising: using the historical operation data and simulation data of multi-hole gate, determine target input characteristics, and through light gradient boosting tree model, target input characteristics are carried out nonlinear regression training processing, determine the target flow prediction model of current time;Through multi-constraint search model, the gate opening combination is carried out hierarchical expansion processing, and the gate expansion scheduling scheme is obtained, and the target flow prediction model is used to carry out batch prediction processing and scoring processing on the gate expansion scheduling scheme, to obtain the gate candidate scheduling scheme set;Upper limit detection screening processing and error tolerance screening processing are carried out for the gate candidate scheduling scheme set, to determine the target scheduling scheme of multi-hole gate.The application can significantly improve the prediction accuracy of gate water outlet and the scheduling efficiency of multi-hole gate.

This invention provides a method and system for generating multi-level detail (LOD) individual models based on BeiDou grid codes. The method includes: acquiring and parsing BeiDou grid coding information to obtain all maximum-level grid codes; utilizing the hierarchical prefix inheritance characteristic of BeiDou grid codes, deriving a hierarchical tile index file with a multi-scale LOD structure based on the prefix of each maximum-level grid code; for each sub-tile, transforming the WGS84 geodetic coordinates within the sub-tile's coverage area into coordinates compatible with the target 3D rendering engine via a coordinate transformation link including a local tangent plane coordinate system to obtain the sub-tile's local vertex data; constructing a transformation matrix for parent-child hierarchical connection; batch generating B3DM tile files based on the sub-tile's local vertex data and the transformation matrix, assigning a batch ID attribute to each individual model within the file under a given individualization style, and storing the individual model metadata corresponding to the batch ID in a batch table.

This invention belongs to the field of port operation and management technology. It provides a port project full-process decision management system based on big data analysis, comprising: verifying and analyzing multiple historical estimated arrival times using a hypothetical moving window; determining the optimal moving window using the moving window averaging method and Euclidean algorithm; obtaining a model correction factor based on historical estimates and actual times; obtaining the estimated arrival time; comparing it with the waiting-to-departure time; if a mismatch is found, removing the original stable connecting vessels; reselecting vessels based on connection matching degree and re-evaluating; until a vessel with a time-dimensional connection match is found within the connecting vessel sequence. By optimizing the moving window and introducing correction factors, the accuracy of arrival time prediction is improved, avoiding delays due to time mismatches and effectively improving port vessel scheduling efficiency. This invention solves the problem of how to accurately predict the arrival time of stable connecting vessels and determine whether it matches their waiting-to-departure time.

The application provides a multi-platform cooperative control system and method based on adaptive cruise, comprising a state monitoring platform, an intelligent ticketing platform, a digital twin platform, a dispatching platform and a response review platform; the state monitoring platform obtains real-time operation data of a target power grid through a distributed sensor network and outputs a state diagnosis result; the intelligent ticketing platform automatically generates an operation ticket based on a power grid knowledge graph; the digital twin platform constructs an adaptive dynamic simulation model using the real-time operation data and the state diagnosis result, and simulates and verifies the operation ticket; the dispatching platform dispatches network orders to power grid equipment according to the operation ticket and the simulation verification result; and the response review platform reviews and verifies the state after the dispatching. Through the construction of a multi-platform cooperative control mechanism and the establishment of an adaptive dynamic simulation model, the simulation accuracy and speed are improved, and the safety of the power grid and the efficiency of power grid dispatching are further improved.

This invention discloses a heterogeneous acceleration system and method for analog modulation recognition of large-scale multi-channel signals, comprising: a CPU host processing end and a GPU device computing end; the CPU host processing end is used for asynchronous pipelined processing of data reading, task scheduling, and result distribution, realizing parallel execution of data reading, GPU computing, and result distribution on the time axis; the GPU device computing end completes the entire process of signal preprocessing and deep learning model inference within the GPU memory after the original signal is transmitted to the video memory via the PCIe bus, and does not exchange data with the CPU host processing end until the recognition result is generated. This invention eliminates data transport and I / O blocking through a three-stage asynchronous pipeline architecture and a closed-loop processing link in the entire video memory, and combines parallel preprocessing operators, inference acceleration engines, multi-stream scheduling mechanisms, and dynamic video memory pools to achieve high throughput and low latency recognition of large-scale multi-channel analog modulation signals.

The present application relates to a kind of based on improved SPEA2 logistics park pickup vehicle scheduling method.Based on the multi-objective optimization algorithm of SPEA2, logistics park scheduling time and pickup vehicle operation time are optimized simultaneously.First, by finding the placement of target goods, according to the running speed of yard crane and the placement of goods, the pickup time of pickup vehicle in each yard is calculated.Secondly, the insertion decoding method is used to insert the pickup vehicle into the idle time of yard.In addition, the neighborhood search strategy of exchanging critical path is also used to select the pickup vehicle operation on the critical path for exchange in the iteration process of algorithm.This method solves the pickup vehicle scheduling problem of logistics park with mixed storage structure, effectively utilizes the idle time of yard by insertion decoding, and avoids falling into local optimal solution by critical path neighborhood search, thereby reducing the running time of logistics park and pickup vehicle operation time, and improving the efficiency of pickup vehicle of logistics park.

The invention relates to the technical field of industrial data scheduling and intelligent manufacturing, and provides a big-data-driven intelligent information resource management platform which adopts a closed-loop architecture composed of five modules, namely a data perception and characterization module, a knowledge cognition and constraint modeling module, an online dynamic optimization decision module, a resource execution and coordination module and a meta-cognition and evolution module. Real-time quantitative evaluation of data quality is realized through a triple quality vector; automatically converting the text business rule into a computable mathematical constraint and knowledge graph; carrying out dynamic scheduling decision making by adopting a knowledge enhancement reinforcement learning agent fused with a double reward mechanism; node-level real-time fine tuning is realized through an edge actuator; and closed-loop self-evolution of the scheduling model and the knowledge base is realized by means of a meta-cognitive mechanism. According to the method, the scheduling efficiency, the business compliance and the system adaptive capacity are cooperatively improved, and the method is suitable for industrial quality inspection data scheduling scenes with high real-time performance and high compliance requirements.

The application discloses a phased multi-strategy instruction scheduling method and system for a VLIW architecture, and the method comprises the following steps: a base step S1, receiving a symbolic assembly structure SAS; S2, configuring three types of scheduling view interfaces and registering corresponding scheduling strategies, the three types of scheduling view interfaces comprising a global view interface, a loop view interface and a linear view interface; S3, performing loop view scheduling, traversing all loop blocks, and calling the loop view interface in parallel to generate a candidate scheduling scheme; S4, performing linear view scheduling, traversing remaining unscheduled basic blocks after the scheduling of all loop blocks is completed, and calling the linear view interface in parallel to generate a candidate scheme; S5, performing competitive selection and register allocation on the candidate scheduling schemes generated in each phase; and S6, outputting an optimized SAS. The application can efficiently adapt to various VLIW processor architectures, and improve instruction-level parallelism and code execution efficiency.

The application discloses a kind of decentralized distributed multi-node cooperative task scheduling methods, it is related to task scheduling technical field;According to the running state data of each node corresponding to determine the ability evaluation coefficient of each node corresponding;According to the ability evaluation coefficient of each node corresponding to determine the cooperative scheduling strategy set of each node in distributed system corresponding;Obtain the task characteristic data of each to-be-executed task in distributed system corresponding, according to the task adaptation evaluation coefficient of each to-be-executed task corresponding;According to the task adaptation evaluation coefficient of each to-be-executed task corresponding and the ability evaluation coefficient of each node corresponding, determine the task scheduling result of each to-be-executed task.The running state data of each node of distributed system is acquired in real time and the corresponding ability evaluation coefficient is calculated, then the characteristic data of each to-be-executed task is combined, finally the scheduling result is determined by the accurate matching of node capability and task demand, improve the efficiency of multi-node cooperative task scheduling.

This invention discloses a data transmission method, apparatus, and system based on hierarchical port domains. The method includes dividing a switch port into several port switching units and switching domains. Each port switching unit includes several ports and first and second buffers. Each switching domain includes several port switching units and a temporary buffer. Each port has a pre-set internal routing number including a domain identifier, a unit selection bit, and a port index number. Data packets received by the receiving port are parsed to obtain the target port address and converted into a target internal routing number. If the unit selection bit of the target internal routing number is the same as that of the source internal routing number, the data packet is transmitted to the first buffer for reception by the target port. If the unit selection bits are different, the data packet is transmitted to the second buffer, and it is determined whether the domain identifiers are the same. If they are the same, the data packet is stored in the temporary buffer; if they are different, the data packet is placed in the out-of-band transmission queue for transmission to the target port. This invention offers high data transmission efficiency and good port scalability.

The application relates to a service processing method, system, device and medium based on a dual real-time kernel. A real-time Linux kernel and a real-time virtual machine monitor kernel are constructed, and a message sharing channel is established between the real-time Linux kernel and the real-time virtual machine monitor kernel; according to a preset multidimensional task scheduling strategy and a preset resource allocation and isolation strategy, the real-time Linux kernel is responsible for system service process scheduling and resource allocation at an operating system level, and the real-time virtual machine monitor kernel is responsible for scheduling, device simulation and resource allocation of a virtual machine service. Through the dual real-time kernel, task scheduling is separated, and the efficiency of task scheduling can be effectively improved. Through the optimized multidimensional task scheduling strategy and the resource allocation and isolation strategy, task scheduling is controlled, and the scheduling delay and jitter of real-time tasks in system services and / or virtual machine services are improved in combination with hardware optimization. The system device and the medium based on the method provided by the application can also achieve the above effects.

The application discloses a kind of visual algorithm self-training methods based on multi-agent collaborative optimization, comprising the following steps: constructing multi-agent system architecture, including user interaction layer, intelligent scheduling layer, A2A protocol communication layer and professional agent cluster layer;User interaction layer analyzes user task intention and generates execution plan;Scheduling agent calls professional agent to complete data processing, model building, training, testing and deployment;Task flow is coordinated through standardized communication mechanism, combined with MCP tool set, knowledge base module and memory system support task execution;When task fails, automatically execute rescheduling operation, finally output self-training result.The application significantly improves development efficiency, adaptability and intelligent level, and is suitable for industrial detection, intelligent security, autonomous driving and other computer vision tasks.

The application provides an AI supervision system, device and method for high security requirements of a restricted network environment, and belongs to the technical field of cross-over of artificial intelligence auditing, edge computing and trusted security system.The method comprises the following steps: constructing an offline intelligent agent resource cabin in a reinforced carrier, pre-packaging auditing field knowledge, rules and tools into a local encrypted database, generating a structured knowledge container and a lightweight associated network; synchronously deploying an edge rule engine and an embedded tool set to form a basic resource layer; and through the construction of the offline intelligent agent resource cabin and the scene-based intelligent engine, the overall breakthrough of AI auditing in the restricted network environment is realized.

The embodiment of the invention discloses a warehouse elevator scheduling method, computer equipment and a computer readable storage medium. The warehouse elevator scheduling method comprises the following steps: constructing a scheduling algorithm to process received task information so as to determine a reservable object; determining a reservation mode according to the task information, and obtaining a reservation form; processing the reservation form according to the reservation mode so as to determine a target object from the reservable objects; and generating and outputting a scheduling queue based on the target object. Therefore, the reservable object can be determined firstly through the scheduling algorithm, so that the workload of the multiple elevators in the warehouse is balanced, the load balance of the elevators is optimized, local congestion and resource idleness are avoided, and the overall scheduling efficiency is improved. The real-time state and resource occupation condition of each connection point can be dynamically reflected by using the reservation form, channel resources are reserved in advance in combination with a pre-locking mechanism, dynamic reservation scheduling and path cooperative control of the elevator are realized, and the response speed and stability of the system are remarkably improved.

The application discloses a task offloading method for improving task execution efficiency and edge node resource utilization, and relates to the technical field of mobile communication, comprising the following steps: constructing a task offloading model under an edge cooperation environment, and acquiring total task execution time; taking minimizing the total task execution time as a target, jointly optimizing resource allocation decisions, user and edge server association decisions, task offloading proportions, and execution location decisions according to an improved teaching optimization algorithm, and acquiring the minimum total task execution time. The application jointly optimizes task offloading location decisions, task offloading proportions, computing resource allocation, and user and server association strategies, comprehensively optimizes multiple key decision variables, effectively reduces the complexity of the task offloading problem, and improves scheduling efficiency.

The invention discloses a composite material hot press molding production scheduling method based on a man-hour uncertainty dynamic transmission mechanism. In order to solve the problems that in the hot press molding process of the composite material, the working hours have uncertainty and are easily transmitted and amplified among multiple batches and multiple devices, a working hour uncertainty dynamic transmission mechanism is constructed, and the propagation rule of the workpiece machining time uncertainty between a batch layer and a machine layer is described; on this basis, constraints including an equipment capacity constraint and a workpiece and equipment matching constraint are comprehensively considered, a scheduling optimization model with minimization of the maximum completion time as an optimization target is established, and an optimization adjustment strategy is deduced; and finally, designing a mixed meta-heuristic algorithm to solve the scheduling optimization model in combination with an optimization adjustment strategy, thereby realizing efficient search of an optimal scheduling scheme. According to the method provided by the invention, the influence of uncertainty on the design of the production scheduling scheme can be effectively reduced, and the scheduling efficiency and stability of the composite material hot press molding process are improved.

The invention relates to the technical field of vehicle scheduling, in particular to an online car-hailing cross-platform transport capacity cooperative scheduling method and system. According to the method, semantic ablation and protocol escape of underlying data can be realized by acquiring parameters such as self-developed interface protocols, data formats and positioning precision of all platforms and building decentralized middleware, and abnormal data can be found and isolated in time by adopting double-link cross check and a multi-stage abnormal data judgment model; the dynamic weight calibration scheduling model is combined, more flexible and accurate transport capacity scheduling is provided based on real-time order data and factors such as positioning accuracy and data transmission stability of each platform, optimal matching between user requirements and optimal platform drivers is realized, and the scheduling efficiency and user experience of the whole system are improved.

The invention relates to the technical field of intelligent logistics, in particular to an integrated scheduling system of an automatic guided vehicle, and the integrated scheduling system is connected with an upper business system and comprises a protocol adaptation unit, an information summarization unit, a capability matrix construction unit, a task analysis unit and a scheduling unit. The protocol adaptation unit is used for carrying out unified access on each automatic guided vehicle to realize two-way communication with each automatic guided vehicle; the information gathering unit is used for continuously collecting capability parameters and real-time operation state information of each automatic guided vehicle and storing the capability parameters and the real-time operation state information to a unified information data pool; the capability matrix construction unit is used for constructing a capability matrix table of each automatic guided vehicle based on the information data pool; the task analysis unit is used for receiving a logistics task issued by an upper-layer business system and disassembling the logistics task into a plurality of sub-tasks; and the scheduling unit is used for selecting the automated guided vehicle based on the capability matrix table and the subtasks, planning a task execution path in combination with factory map information, and issuing a control instruction to complete the corresponding subtasks.

The embodiment of the invention relates to a processing method and device for setting a traffic scheduling mark based on SDN traffic prediction. The method comprises the following steps: confirming an exchange layer topological graph of a hardware exchange layer; configuring a flow collection library for each switching node, continuously sampling the flow of each node, and sampling data based on the flow collection library; constructing a traffic prediction model, and regularly constructing a latest data set based on all traffic collection libraries to train the model; and using the traffic prediction model to predict the traffic of the exchange node set in the future period to obtain a node prediction set, and identifying whether traffic scheduling needs to be started based on the node prediction set to obtain a corresponding traffic scheduling mark and a scheduling node set. According to the invention, the risk of network congestion can be reduced, and the scheduling precision, efficiency and timeliness of traffic scheduling can be effectively improved.

The application provides a stamping line tail AGV scheduling system, relates to the field of intelligent manufacturing logistics scheduling, adopts a hierarchical centralized collaborative control architecture, sets a business layer module, a scheduling core layer module, an execution control layer module and a digital twin simulation layer module, internally arranges three customized strategies of main and auxiliary position dynamic priority distribution, flexible partition scheduling and double anti-locking traffic control in the scheduling core layer, realizes task differentiated priority distribution, operation area logical isolation control, space and time collaborative active anti-locking control, and completes strategy preposition verification and running state visual monitoring through digital twin simulation. The application can effectively adapt to the rigidity production rhythm of the stamping line tail, improve the multi-AGV collaborative scheduling efficiency and long-term operation stability, reduce the engineering landing debugging cost, and is suitable for the intelligent material carrying scene of the stamping workshop line tail.

The invention relates to the technical field of satellite communication, and discloses a logistics transportation position monitoring method and system assisted by satellite communication. The method comprises the following steps: calculating a satellite-ground distance and a line-of-sight unit vector according to a satellite position coordinate and a terminal estimation position coordinate, and generating a baseband signal based on a three-axis speed component and the line-of-sight unit vector; calculating a target peak value based on the baseband signal, and when the target peak value exceeds a noise threshold, extracting a pseudo-range measurement value and solving a three-dimensional space positioning solution with the satellite position coordinates; calculating a path constraint position from the three-dimensional space positioning solution to the optimal path section; and accumulating a reference mileage from a starting point to the optimal path section, calculating an accumulated driving mileage, converting the path constraint position, packaging the path constraint position and the accumulated driving mileage into a monitoring data packet, and reporting the monitoring data packet to a logistics monitoring platform. According to the invention, high-precision logistics position monitoring in a mobile scene is realized, and the monitoring precision and scheduling efficiency of logistics transportation are effectively improved.

The application discloses a kind of ship in and out port scheduling method considering tidal constraint, which comprises the following steps: 1) according to the real-time water depth of channel and the draft of ship, the tidal window of ship is obtained, to ensure that the bottom of ship keeps a safe gap from the bottom of channel;2) obtain the berthing time window and the departure time window of ship;3) establish scheduling optimization model;4) set constraint condition to model;5) model solution, and scheduling is carried out according to the solution result.This application considers tidal constraint and tidal current constraint simultaneously, proposes a mixed linear integer programming model, which considers the change of tidal level and the specific influence of tidal current on ship berthing and departure, and the proposed model and constraint can obtain scheduling scheme under the premise of meeting safety requirements, reduce the overall delay time of ship, and effectively improve the efficiency of port scheduling.

The invention provides a heterogeneous resource pooling management-oriented equipment virtualization orchestration method, and relates to the technical field of equipment virtualization orchestration, and the method comprises the steps: carrying out the equipment virtualization of to-be-orchestrated equipment, unifying the to-be-orchestrated equipment into a same resource pool, obtaining a production resource formula of each production plan, and obtaining a production resource formula of each production plan; establishing a resource control atlas according to a historical production resource formula, obtaining a production resource formula of a future production plan as a demand resource, and arranging resources in a resource pool through a resource allocation algorithm according to the real-time digital label and the demand resource of each virtual device type, the method comprises the steps of arranging virtual devices, outputting a logic connection relation and arranged virtual device resources through a resource control atlas, marking timestamps on the arranged virtual device resources, obtaining the usage amount of resource types of each device type through the same time interval, and calculating a historical resource allocation equilibrium coefficient as an optimization target. And optimizing the single arrangement ratio, the weight adjustment coefficient and the fitting coefficient.

The application discloses a bronchoscope supply-demand dynamic matching method based on multi-source data fusion, comprising: collecting multi-source heterogeneous data and performing fusion processing to obtain a device feature matrix, a demand feature matrix and an environment feature vector; determining a score calculation strategy according to a comparison result of a historical matching data accumulation amount and a preset threshold, determining a device availability score and a demand urgency score based on the device feature matrix and the demand feature matrix according to the score calculation strategy; constructing a device queue and a demand queue, attaching an initial scene label to each demand and performing identification, and outputting a standardized scene type; outputting an optimal matching scheme through a reinforcement learning intelligent agent based on the device queue, the demand queue, the standardized scene type and the environment feature vector; performing intelligent scheduling based on the optimal matching scheme, updating the queue based on external real-time events, and if both queues are non-empty at the same time, obtaining a new optimal matching scheme, otherwise, keeping a waiting state. The application can improve matching efficiency and accuracy.

The application discloses a kind of power grid intelligent scheduling system and method based on digital twinning and deep learning, it is related to power grid intelligent scheduling technical field, a kind of power grid intelligent scheduling method based on digital twinning and deep learning, specific steps include: step one, by fusing multi-source heterogeneous data and dynamic parameter updating mechanism, construct and physical power grid synchronous mapping power grid digital twinning, step two, adopt federal learning framework aggregation multi-area power grid data, establish equipment degradation model, perfect priority data channel mechanism, step three, based on power grid digital twinning determines real-time power grid state and equipment risk coefficient, designs deep reinforcement learning dynamic decision-making framework, realizes the load scheduling optimization of multi-objective cooperation, the application is based on power grid digital twinning determines real-time power grid state and equipment risk coefficient, designs deep reinforcement learning dynamic decision-making framework, can realize the load scheduling optimization of multi-objective cooperation.

The invention discloses an opening and closing control method and system for a water conservancy project sluice, and the method comprises the following steps: collecting water conservancy environment parameters and sluice operation parameters in real time, and obtaining parameter data; preprocessing the parameter data to obtain effective monitoring data; constructing a water gate opening and closing model, inputting the effective monitoring data into the water gate opening and closing model, and outputting target control parameters of the water gate; comparing the sluice operation parameter with a target control parameter of the sluice to obtain a difference value; generating a sluice adjustment instruction based on the difference value, driving the sluice to a target position according to the sluice adjustment instruction, and feeding back the sluice position state information terminal in real time; the conversion from single parameter control to multi-feature collaborative decision is realized through multi-dimensional parameter acquisition, the operation parameters of the water gate are accurately controlled by constructing the water gate opening and closing model, the accurate control of the opening degree of the water gate is realized, the model output is more accurate, and the water resource scheduling efficiency is improved.