105 results about "Order scheduling" patented technology

The invention relates to the technical field of equipment maintenance scheduling, in particular to an equipment fault repair management system, which comprises a dependency identification and modeling module, a fault state analysis module, a task priority evaluation module, a work order scheduling generation module and a resource scheduling execution module. According to the method, a multi-level dependency topology between devices is constructed through a directed graph traversal algorithm, a trigger relation between physical connection and an operation process is quantitatively analyzed, a classification marking model is established in combination with dynamic parameters such as a performance degradation rate, a fusion influence range and a dependency factor are calculated through scalar superposition, and a priority scoring matrix is dynamically generated. A task queue structure is optimized through a sorting algorithm, a data-driven maintenance decision mechanism is formed, the fault positioning precision is improved, response delay caused by manual intervention is reduced, key node equipment maintenance lag is avoided, the resource configuration efficiency is optimized, the collaboration of fault processing and a production system is strengthened, and formulation of a preventive maintenance strategy is supported.

The invention discloses an electric unmanned motorcade multi-task joint scheduling method based on multi-agent reinforcement learning. The method comprises the following steps: predicting a travel demand; constructing a state; task candidates are generated, wherein charging candidates, order sending candidates and scheduling candidates are generated; the reward function design comprises local instant reward design and global reward construction; generating a value function; strategy improvement and Actor updating are carried out; and performing environment execution and iterative training. According to the invention, three tasks of electricity supplement, order scheduling and relocation are regarded as a joint optimization problem. A task value learning mechanism based on an Actor-Critic architecture in reinforcement learning is designed, and optimal allocation of tasks and vehicles is realized in combination with a KM algorithm. The algorithm can continuously optimize the value evaluation of various tasks in different states, and the KM algorithm ensures the global optimality of task matching. Through the combination, the system not only has the learning ability, but also can make an optimal task allocation decision in real time.

The invention relates to the technical field of intelligent work order scheduling platform design, and discloses an intelligent work order scheduling system, method and device and a medium, and the method comprises the steps: the system achieves the efficient and intelligent scheduling of a device fault work order through integrating a plurality of function modules. Firstly, the work order access module receives equipment fault information of different channels, and it is ensured that the information is comprehensive and timely. Then, the portrait construction module constructs a multi-dimensional ability portrait by using the historical service data of the maintenance personnel, and provides a data basis for intelligent matching; and then, the intelligent distribution module accurately matches and associates the most suitable maintenance personnel with the work order according to the portrait data and the work order parameters. And the order grabbing interaction module generates a to-be-grabbed work order list and an order grabbing interface, so that the scheduling flexibility is enhanced, and the scheduling efficiency is improved. The execution tracking module monitors the work order processing progress in real time, and transparent and controllable scheduling is ensured. And finally, the knowledge base question and answer module provides professional knowledge support for the maintenance personnel and helps the maintenance personnel to quickly solve the work order problem.

The invention relates to the technical field of digital data processing, in particular to an electric power communication network field operation and maintenance work order scheduling method and device, and the method comprises the steps: receiving and summarizing electric power communication fault information from a plurality of regions; classifying the fault information by adopting a support vector machine and generating an operation and maintenance work order; matching maintenance personnel with different maintenance skill levels and in a maintenance state based on personnel constraint conditions, and sending a scheduling notification; matching a solution corresponding to the fault information by using a historical database, and pushing the solution to a dispatched maintainer; and evaluating the scheduling process based on the fault processing time and the fault processing completion degree of the maintenance personnel. Therefore, the response speed and the processing efficiency of power communication network maintenance can be improved, the scheduling logic can be continuously optimized through continuously accumulated empirical data, and the overall service quality and customer satisfaction are improved.

The invention relates to the technical field of intelligent manufacturing and production optimization scheduling, and particularly discloses a thermal fuse flexible production-oriented order scheduling resource scheduling optimization method, which comprises the following steps of: generating a structured vector by extracting inherent and dynamic characteristics of an order, and constructing a production scheduling dynamic environment model by fusing a device resource state time sequence; the order comprehensive process switching cost and the resource constraint tightness are calculated, order clusters are divided through multi-target constraint clustering, and a pre-arrangement sequence is generated; key resource conflict early warning is completed through time sequence prediction; constructing a multi-target scheduling model by taking the order cluster as a unit, and generating a look-ahead window scheme by combining a rolling optimization solidification plan baseline; and finally, on the basis of a real-time execution data updating model, through dynamic priority preemption and local rescheduling adjustment when abnormity occurs, the problems of high cost of small-batch mixed arrangement, conflict identification lagging, easy damage of a thermosensitive process steady state and poor compliance constraint adaptation are solved, and collaborative optimization of the scheduling efficiency, the production yield and the compliance is realized.

The invention discloses a supply chain logistics flexible scheduling system based on multi-source data fusion, relates to the technical field of logistics scheduling, and solves the technical problems of extensive order scheduling and path optimization and insufficient dynamic response capability. Comprehensive integration of order, transport capacity, inventory and external environment data is realized, an order emergency degree classification standard is quantified, an improved algorithm and a K-means clustering + genetic algorithm are combined, and an optimal basic scheduling path considering timeliness and cost is generated; meanwhile, collaborative optimization of inventory and transport capacity is achieved based on resource characteristics such as the inventory turnover rate and the transport capacity load rate, the daily average delivery order amount of a single vehicle is increased, a double dynamic adjustment mechanism of timed refreshing and event triggering is adopted, refreshing intervals are set according to different transport scenes in a differentiated mode, and the efficiency is improved. The method can quickly respond to abnormal conditions such as sudden congestion and address deviation, can automatically adjust the path, and can reduce the delay rate caused by congestion.

The invention discloses a hierarchical priority task orderly scheduling method based on a virtual thread, which comprises the following steps: S10, receiving task parameters transmitted by a business layer, performing task packaging setting, outputting packaged task objects, and transmitting the packaged task objects to a multi-dimensional calibrator; s20, performing task expiration verification, task fusing verification and dependency verification in the multi-dimensional verifier, returning a passing or failing identifier and a failing reason, and if the passing or failing identifier does not pass, triggering a monitoring alarm to record a discard log; s30, the verified tasks enter a two-dimensional queue manager to be subjected to hierarchical enqueue processing and capacity dynamic adjustment, and the capacity dynamic adjustment uses a dynamic resource allocator to achieve elastic allocation of queue capacity and virtual threads based on load awareness; and S40, performing task execution and task retry management on the tasks in the two-dimensional queue.

The invention relates to the technical field of deep learning, and discloses a fine-grained pipeline scheduling method and device based on cluster node memory difference perception. The method comprises the following steps: estimating the ratio of the peak memory occupancy to the memory capacity of each GPU node, comparing the ratio with a preset threshold value, and selecting a re-calculation strategy and a back propagation segmentation strategy: by taking minimization of the end-to-end training time of a model as a target, scheduling and modeling micro-batch data as a flow shop problem, analyzing the dependency constraint of each flow line stage, and calculating the flow shop problem; comprising forward-back propagation sequence dependence, stage dependence, operation dependence and memory limitation, and generating a micro-batch operation sequence scheduling scheme meeting the memory capacity limitation; according to the scheduling scheme, an execution sequence queue is created by executing sorting, and the execution time sequence of calculation blocks, communication blocks and re-calculation operation is dynamically coordinated. According to the method, the GPU equipment utilization rate can be remarkably improved, and the end-to-end training completion time of the model is shortened.

The invention relates to the technical field of data processing, and discloses a manufacturing industry data intelligent analysis method and system based on deep reinforcement learning. The method comprises the following steps: carrying out time sequence processing on manufacturing industry equipment state, order characteristics, inventory level and quality index data to obtain a four-dimensional production data matrix, carrying out strategy learning through an LSTM-Actor-Critic algorithm to obtain a manufacturing decision strategy network, carrying out classification processing according to a production cycle to obtain a hierarchical data set, constructing an intelligent experience playback buffer area, and carrying out intelligent experience playback. And carrying out collaborative optimization on order scheduling, inventory replenishment and equipment task allocation decisions to obtain a manufacturing industry data intelligent analysis result. The technical problem that an existing manufacturing industry data analysis method lacks adaptive learning ability and cannot process multi-domain collaborative decision optimization is solved.

The invention relates to the technical field of artificial intelligence and government affair services, and discloses a government affair work order scheduling system based on deep reinforcement learning, which comprises an agent scheduling module for configuring deep reinforcement learning scheduling agents for a plurality of government affair service areas respectively, and outputting execution work order allocation and priority ranking; the fairness monitoring module is used for defining and monitoring fairness indexes; the prejudice analysis module analyzes an attention mode in the scheduling decision, identifies potential prejudice features associated with fairness index changes, and quantifies the influence of the potential prejudice features on the scheduling fairness; the agent optimization module is used for optimizing the scheduling agent; the collaborative scheduling and auditing module is used for constructing a collaborative scheduling system and recording a scheduling process to form an auditable log; according to the invention, by integrating the interpretable artificial intelligence module, the originally opaque deep reinforcement learning decision process becomes understandable, and the transparency of the scheduling decision is improved.

The invention relates to the technical field of intelligent work order scheduling, and discloses an intelligent work order scheduling method, system and device based on the creative technology and a medium, and the method comprises the steps: obtaining a target domain device fault work order and a preset maintenance personnel first capability parameter for a target domain; performing a second analysis operation on the target domain equipment fault work order to obtain a second work order parameter; performing third matching operation on the first capability parameter and the second work order parameter; work order scheduling is carried out according to the matching result of the third matching operation; the first capability parameter of the preset maintenance personnel is obtained through the first analysis operation, accurate scheduling is realized, matching is performed according to the actual capability of the maintenance personnel, and maintenance delay or failure is avoided. And the second analysis operation deeply analyzes the target field equipment fault work order to obtain a second work order parameter, and provides data support for subsequent matching. And the third matching operation adopts priority and common matching strategies, flexible selection is performed according to the second work order parameters, and the scheduling accuracy and efficiency are improved.

The invention discloses a self-optimizing intelligent work order scheduling method and system, belongs to the technical field of work order scheduling, and solves the problems that a traditional scheduling method is poor in dynamic adaptability due to static rules, single in optimization target and dependent on artificial experience, and a model cannot be self-optimized online. According to the technical scheme, the method comprises the steps that work order features and resource state data are collected in real time and input into a pre-trained deep reinforcement learning model to generate a scheduling scheme, a scheduling deviation value is calculated based on actual feedback data to trigger model iterative optimization, and dynamic updating of a scheduling strategy is achieved; the method is mainly used for work order automatic distribution and scheduling optimization in scenes such as a grid command center, a logistics distribution platform and a customer service center.

The invention discloses an intelligent logistics order scheduling method and system, and the method comprises the steps: obtaining a parameter set, the parameter set comprises solid state parameters and dynamic parameters, the solid state parameters are inherent attribute parameters of each order in a to-be-scheduled order queue, and the dynamic parameters are parameters determined according to the dynamic changes of the order queue and an external environment; obtaining a plurality of weight coefficients, wherein the weight coefficient is an importance degree value preset for each parameter; according to the parameter set and the weight coefficient of each parameter, calculating a scheduling priority weight value corresponding to each order through a preset weighting algorithm; ordering the orders in the order queue to be scheduled according to the calculated scheduling priority weight value; and allocating appropriate deliverymen to the orders according to the sequenced order and generating a scheduling instruction. According to the invention, the problems of single logistics order scheduling strategy and difficulty in adapting to real-time dynamic change scenes in the prior art are solved. The distribution efficiency and the resource utilization rate can be improved.

One embodiment of the present invention sets forth a technique for encapsulating compute task state that enables out-of-order scheduling and execution of the compute tasks. The scheduling circuitry organizes the compute tasks into groups based on priority levels. The compute tasks may then be selected for execution using different scheduling schemes. Each group is maintained as a linked list of pointers to compute tasks that are encoded as task metadata (TMD) stored in memory. A TMD encapsulates the state and parameters needed to initialize, schedule, and execute a compute task.

The invention provides a computing device and method based on RISC-V extension instructions, the computing device supports approximate computation of mixed precision according to approximate computation instructions in an extended approximate computation instruction set, and the computing device comprises an out-of-order scheduling and register reading module used for executing instruction dependency analysis and operand preloading, scheduling the non-approximate calculation instruction and the approximate calculation instruction to different transmitting queues respectively; the first instruction transmitting queue is used for temporarily storing a to-be-transmitted non-approximate calculation instruction; the second instruction transmitting queue is used for temporarily storing approximate calculation instructions to be transmitted; the precise calculation module is used for completing precise calculation related tasks according to the instruction from the first instruction transmitting queue; and the approximate calculation module is used for completing approximate calculation related tasks according to the instructions from the second instruction transmitting queue, and supports approximate calculation of various precisions.

This invention discloses an artificial intelligence-based operation and maintenance work order management method and system, relating to the fields of artificial intelligence and operation and maintenance management technology. The method includes constructing a CNN model, optimizing the policy network and value network using the PPO algorithm, optimizing model parameters through multi-task learning and gradient updating, outputting device failure probabilities and recommended actions, generating JSON work orders, and using a quantum decision tree based on the classical GBDT framework for split gain calculation. The quantum Monte Carlo method is used to optimize the loss function, iteratively construct a quantum decision tree, and output candidate scheduling solutions. The method uses a CNN model combined with the PPO algorithm to optimize work order generation, enhancing the accuracy and timeliness of fault prediction and action recommendations. It also introduces quantum machine learning into work order scheduling modeling, and improves the multi-objective adaptability of scheduling through the quantum decision tree and quantum Monte Carlo optimization mechanism.

The invention provides a take-out order scheduling method and system, a storage medium and a program product, and relates to the field of personnel or project management, and the method comprises the steps: generating a path network according to the coordinates of a pickup point and a delivery point of a new order; determining a group of geographic grid indexes covering the path network based on a preset grid cell size to obtain a new order path index set; calculating an intersection index set of the new order path index set and the future path index total set of the candidate rider; and when the number of the unique identifiers of the geographic grid indexes in the intersection index set exceeds a preset path matching threshold value, adding the new order into the order sequence of the target candidate rider with the highest comprehensive score, and adjusting the planned path corresponding to the target candidate rider to complete the scheduling of the new order. By implementing the method, the problem of misjudgment of the smoothness caused by path simplification can be relieved, and the matching accuracy of the order and the rider is improved, so that the overall delivery efficiency is improved.

The invention discloses a printing order intelligent production scheduling optimization method and system based on an equipment operation and maintenance state, and relates to the technical field of the manufacturing industry, and the method comprises the steps: obtaining a multi-source operation and maintenance parameter set of equipment and work order attribute information during printing, and recognizing a same-type work order interval; on the basis of the same type of work order intervals, dividing loss levels to generate an adaptation result which is used for providing an adaptation basis for equipment and work order distribution; on the basis of the adaptation result and the work order attribute information, the interference influence of work order switching on equipment production is identified by analyzing the correlation strength of the same process work order and the equipment part loss, and disturbance strength data is obtained; identifying a to-be-distributed node through the disturbance intensity data, and obtaining a feasibility result through a redistribution operation of work order switching; and constructing a production scheduling network according to the feasibility result, and obtaining a distribution optimization result after path traversal and path re-planning so as to realize dynamic matching of production scheduling and equipment operation and maintenance states.

The invention provides a medical supply chain multi-level collaborative intelligent order scheduling and tracking method and system. The method comprises the following steps: acquiring node information, inventory batch information and to-be-scheduled order information of a supply chain; the method comprises the following steps: constructing an inter-node material scheduling tendency potential energy matrix, calculating demand potential energy by using an S-type gain function according to the order demand quantity of a medical institution, calculating a node state disturbance value in combination with node historical operation interruption frequency and storage environment compliance data, and calculating the timeliness value of each batch through an exponential decay function according to the stock batch material validity period; synthesizing the three to generate a potential energy matrix; generating order candidate scheduling paths based on the potential energy matrix gradient, accumulating path node state disturbance values to obtain path comprehensive disturbance values, and selecting the path with the minimum value as a scheduling path; scheduling is executed according to the path, the logistics information is tracked in real time, and when the logistics information deviates, the historical operation interruption frequency of related nodes is updated, the node state disturbance value is adjusted, and subsequent order scheduling is optimized.

The application provides a medical supply chain multi-level collaborative intelligent order scheduling and tracking method and system, acquires supply chain node information, inventory batch information and to-be-scheduled order information; constructs a material scheduling tendency potential energy matrix between nodes, calculates demand potential energy by using an S-shaped gain function according to the quantity of medical institution order demand, calculates node state disturbance values in combination with historical operation interruption frequency and storage environment compliance data of the nodes, calculates the time value of each batch by an exponential decay function according to the shelf life of the inventory batch, and generates a potential energy matrix by comprehensively integrating the three; generates an order candidate scheduling path based on the gradient of the potential energy matrix, accumulates the path comprehensive disturbance value of the path node state disturbance value, and selects the minimum value as the scheduling path; performs scheduling according to the path, tracks logistics information in real time, updates the historical operation interruption frequency of the related nodes when the logistics information deviates, adjusts the node state disturbance value, and optimizes subsequent order scheduling.

This invention relates to the field of intelligent manufacturing and industrial automation technology, specifically to an intelligent order scheduling and material management system for a milling and turning workshop. It includes a task parsing module, a feature mapping module, a state acquisition module, a potential field construction module, a scheduling calculation module, and a feedback execution module. The system receives task drawing data and bill of materials data, extracts geometric feature bounding dimension data and machining tool vector sequence data, and tensors and encodes them. It collects the current tool library configuration status parameters of the target machining equipment and the physical attribute parameters of work-in-process, constructs a continuous potential energy field distribution matrix, and dynamically refreshes it. It calculates the potential energy gradient descent direction of the multidimensional machining feature tensor flow, generates a task queue of associated paths and a theoretically estimated execution cycle time sequence, issues CNC machining task plans and automated guided vehicle (AGV) allocation instructions, and feeds back the actual cycle deviation correction value for closed-loop updates. This invention makes the scheduling results closer to the actual executable state of the workshop.

The invention relates to a logistics order allocation method and device, computer equipment and a storage medium. The method comprises the following steps: in response to an order type of a to-be-created logistics order being a new order type and receiving user voice input in a voice input box in a logistics order creation interface, performing voice recognition processing on the user voice to obtain mailing information of the to-be-created logistics order; in response to a received mailing product photo uploaded in a photo uploading box in the logistics order creation interface, carrying out photo analysis processing on the mailing product photo to obtain mailing cargo product information of the to-be-created logistics order; and generating a logistics order creation request according to the mailing information and the mailing product information, and sending the logistics order creation request to an order scheduling server, so that the order scheduling server creates and distributes a logistics order according to the logistics order creation request. By adopting the method, the user order reporting efficiency can be improved.

The present application belongs to the technical field of order scheduling, and discloses a multi-platform order collaborative fulfillment automated scheduling method and system, the method comprising: obtaining a reception timestamp when receiving inventory data of each warehouse and transportation capacity information of each logistics company, and based on the reception timestamp, calculating the timeliness score of each inventory data and each transportation capacity information in real time, which are recorded as a first timeliness score and a second timeliness score respectively; according to the first timeliness score and the second timeliness score, adjusting the allocated orders and allocating new orders to each platform to obtain order scheduling results; adjusting the allocated orders and allocating new orders to each platform according to the timeliness score of the inventory data and transportation capacity information determined by the reception timestamp when the information is received, thereby improving the scheduling efficiency of orders.

The invention discloses an intelligent management system for a packaging production line, and relates to the technical field of production line packaging, and the system comprises a product identification module which is configured to read a unique identifier of a product through a two-dimensional code scanner and / or an RFID reader; the parameter storage and mapping module is arranged on the edge equipment, and is configured to store a mapping relation between the product and the corresponding packaging parameter, and query and generate an equipment control instruction set according to the ProductID; the edge controller module comprises an industrial IPC and PLC interface board card and is used for receiving the equipment control instruction set and issuing the equipment control instruction set to packaging execution equipment through an industrial communication protocol; the packaging execution equipment can automatically adjust the mechanical position and the technological parameters according to the received instruction; the rapid type changing buffer module comprises double buffer queues and is configured to pre-load parameters of a next order during the operation period of the current order and realize non-stop parameter changing in an order changing empty window period; and the order scheduling optimization module is configured to optimize an order execution sequence based on the remodeling cost matrix.

The invention discloses a work order production scheduling method and device, and a storage medium. The method comprises the steps of obtaining a genetic algorithm, parameters and order information for optimizing a k-means clustering algorithm; initializing a k-means clustering algorithm according to the order information, and optimizing the genetic algorithm according to a preset production scheduling scene; based on the parameters and a genetic algorithm, optimizing the initialized k-means clustering algorithm to obtain a k-means clustering algorithm optimized based on the genetic algorithm; and calculating according to the k-means clustering algorithm to obtain an optimal production scheduling scheme, and performing work order production scheduling according to the optimal production scheduling scheme. According to the method, the k-means clustering algorithm is improved through the genetic algorithm, and the work order scheduling flow is optimized in combination with the improved algorithm, so that the accuracy of the optimization result is improved.

Embodiments of the present application provide an order processing method, apparatus, electronic device, and readable storage medium. Among them, the order processing method includes: obtaining a plurality of orders to be processed; inputting the order feature information of each of the plurality of orders to be processed into a combined parameter value prediction model to obtain parameter values for performing combined operations on the plurality of orders to be processed respectively; combining the plurality of orders to be processed according to the combined parameter values corresponding to each of the plurality of orders to be processed to obtain a plurality of order packages to be processed; adding the plurality of order packages to a scheduling pool for an order scheduling system to schedule the plurality of order packages to be processed. There is no need to keep a fixed order pressing time for each order, and orders are flexibly and dynamically combined at the order granularity. By using the combined parameter value prediction model to comprehensively consider the feature information of multiple orders, the obtained combination strategy is relatively reasonable, and order combination and scheduling are performed according to the combination strategy.

The invention discloses an intelligent work order scheduling method and system based on multi-factor cost prediction, and belongs to the technical field of work order management. According to the method, a multi-factor dynamic cost model comprehensively considering in-transit time, skill matching degree, work order priority and service time limit SLA risk is constructed by acquiring work order and personnel states in real time, and a cost index is calculated for each potential scheduling scheme. According to the system, a variable neighborhood search VNS optimization algorithm is adopted, and global solution is carried out on the basis of a cost matrix so as to find an optimal task allocation scheme with the lowest total cost. According to the method, the weight can be dynamically adjusted and optimized according to operation states such as real-time traffic and personnel load, self-adaptive intelligent decision making is realized, and finally, an optimization scheme is automatically distributed to a personnel terminal, so that the work order scheduling efficiency, the resource utilization rate and the SLA fulfillment rate are comprehensively improved.

The present invention relates to the field of artificial intelligence and government service technology, and discloses a government work order scheduling system based on deep reinforcement learning, including: an intelligent agent scheduling module, which configures deep reinforcement learning scheduling intelligent agents for multiple government service areas, outputs execution work order allocation and priority ranking; a fairness monitoring module, which defines and monitors fairness indicators; a bias analysis module analyzes attention patterns in scheduling decisions, identifies potential bias features associated with changes in fairness indicators, and quantifies the impact of potential bias features on scheduling fairness; an intelligent agent optimization module, which optimizes the scheduling intelligent agent; a collaborative scheduling and auditing module, which builds a collaborative scheduling system and records the scheduling process to form an auditable log; the present invention makes the originally opaque deep reinforcement learning decision-making process understandable by integrating an explainable artificial intelligence module, thereby improving the transparency of scheduling decisions.

The invention relates to an order scheduling method and device, computer equipment, a computer readable storage medium and a computer program product. The method comprises the following steps: acquiring a precast pile demand order to be scheduled at present; according to the precast pile demand order, a plurality of candidate pile matching schemes and production information of a plurality of factories used for producing precast piles are obtained; constructing an optimization target by minimizing the total transportation cost, minimizing the total yield of precast piles, minimizing the total remodeling cost of the factory floor mold pool and maximizing the minimum residual capacity of the factory floor mold pool; scheduling processing is carried out through the optimization target based on the multiple candidate pile allocation schemes and the production information of the multiple factories, the scheduling result about the precast pile demand order is obtained, the scheduling result comprises the candidate pile allocation schemes, the inventory distribution plan and the sub-pile production order when the optimization target is met, and the accuracy of order scheduling is improved.

The present invention discloses an instruction scheduling method, apparatus, system, product and medium, relating to the technical field of processor design. Aiming at the problem that the current out-of-order scheduling strategy cannot accurately schedule the dynamic changes in the instruction issue time, an instruction scheduling method is provided. The historical delay information of each instruction is stored in a delay cache. When the instruction execution delay corresponding to an instruction is a dynamic delay, the dynamic delay is predicted through the historical delay information stored in the delay cache, so as to perform instruction scheduling. Based on the above dynamic delay prediction mechanism, this method incorporates dynamic delays with undetermined delay times into the consideration of instruction scheduling. In scenarios of dynamic changes in instruction issue time such as memory access delay and cache miss based on this method, the optimal issue time of instructions can be accurately predicted, thereby further improving the effect of instruction scheduling on processor performance improvement.