423 results about "Task list" patented technology

The invention provides a project management method, system and device and a medium, and belongs to the field of project management. The method comprises the following steps: collecting project related data, and sorting and cleaning the project related data to obtain a structured data set; adopting an entity identification and relation extraction algorithm to carry out entity labeling and relation extraction processing on the structured data set so as to construct a knowledge graph related to the project; the method comprises the following steps: performing task decomposition by adopting a graph traversal algorithm on the basis of a knowledge graph, generating a task list and a priority sequence thereof, and monitoring task progress and resource utilization rate in real time on the basis of a dynamic optimization algorithm so as to dynamically adjust task priority and a resource configuration strategy. The task module is automatically decomposed based on the knowledge graph, the task decomposition efficiency and accuracy are improved, intelligent resource allocation and task priority adjustment are achieved in combination with dynamic planning and an optimization algorithm, the team cooperation efficiency is improved, and the flexibility and adaptability of a project are improved.

The invention discloses an intelligent auxiliary bid evaluation method and system for an electric power bid invitation specialty, and the method comprises the steps: obtaining uploaded purchase information, and creating purchase batches based on configured basic parameters and purchase review rules, so as to generate a review task list with a unique batch number; and based on the review task list and the configured basic parameters, extracting context information of each review task to construct context feature vectors of the review tasks. And matching the context feature vector with a preset negative rule to obtain a negative suggestion list corresponding to each review task in the review task list. And binding the negative suggestion list with the corresponding bidding task, and pushing the bidding task bound with the negative suggestion list to the user terminal. According to the technical scheme, the user terminal receives the operation of the user on each negative suggestion so as to generate the final negative suggestion, and the final negative suggestion is stored and archived, so that the negative suggestion generation efficiency is improved, the negative expression is standardized, and the work intensity of experts is reduced.

The invention provides an intelligent production scheduling system and method for a coal industry scene, and the method comprises the steps: fusing multi-source production information (including equipment state information, coal quality index information and safety detection information) through a server, constructing a fault prediction model, and carrying out the fault prediction of the real-time production information; and generating a task list based on the fault prediction result, the real-time production information and the historical production information, matching a workflow and an optimal executor for each task in the task list, then sending the task to the PC terminal for execution, monitoring the execution progress of each task in real time, and urging the overdue task. And generating a corresponding report for each executed task based on the fused information, the fault prediction result, the real-time production information, the historical production information and the execution process information of each task, and sending the report to a display device for display. By adopting the method, the efficiency and the accuracy of production scheduling in a coal industry scene can be improved, the real-time performance of production scheduling can be ensured, and the statistics and the checking of the whole process link can be facilitated.

The invention belongs to the technical field of AGVs, and provides an AGV transportation method and a robot, and the method comprises the steps: obtaining a task list and the initial position of each AGV; the task execution sequence is optimized through tabu search so as to minimize the time consumption of final task completion, the average completion time and the weighted cost of the number of pauses; according to the optimization sequence, constructing a space-time diagram of node representation time and space positions for each AGV; planning the path of each AGV on the space-time diagram by adopting a time enhancement algorithm, regarding the paths of other AGVs as time obstacles, and adopting a path cost function; in the path planning process, the AGVs are allowed to wait in situ to avoid collision, and the paths of the AGVs are updated in real time; performing Bezier curve smoothing on the planned discrete path for three times to generate a continuous and controllable driving track; and finally, each AGV is controlled to execute the task along the track. According to the invention, the problems of low task scheduling efficiency and frequent path conflicts of a plurality of AGVs in the prior art can be solved.

The invention provides an artificial intelligence routing method. The method comprises the following steps: S100) an AI routing agent receives and analyzes at least one piece of prompt information; s200) the AI routing agent analyzes at least one request intention of the user according to the prompt information, decomposes and splits the request intention into a plurality of sub-tasks, and plans an interdependence relationship among the sub-tasks to form a task list; s300) the AI routing agent selects an AI model agent interface registered on an AI gateway according to the characteristics of an artificial intelligence model so as to match the requirements of the task list, and the selected AI model matching the requirements of the task list is the selected AI model; s400) calling and executing each selected AI model, and returning a result to the AI routing agent; and S500) integrating the prediction results of all the selected AI models by the AI routing agent, and generating a response.

The invention discloses a path planning method and system based on an automatic three-dimensional warehouse four-way shuttle vehicle, and the method comprises the steps: building a storage environment model through a grid method, making traffic rules according to a grid map, and building a map table; based on the map table, generating a task list by using a data memorization processing technology, and sorting according to task priorities; the distributor performs task distribution on the shuttle vehicle according to the task list, performs path planning by using an improved A * algorithm, divides an obtained path into a plurality of road sections, generates time window list information of the road sections, checks whether a time window conflicts with a time window of a planned path node or not, shields the road section corresponding to the time window if the time window conflicts with the time window of the planned path node, and does not shield the path corresponding to the time window if the time window conflicts with the time window of the planned path node. Re-planning the path; otherwise, time window dynamic adjustment is carried out, and the path is executed. According to the invention, conflict judgment is carried out through the time window table of the shuttle vehicle, the possibility of conflict occurrence is further reduced, and the operation efficiency is improved.

The present disclosure is directed toward systems, methods, and non-transitory computer readable media for utilizing a catalyst application to analyze digital content of another computer application to generate or extract tasks. The catalyst application can accompany and operate in conjunction with another computer application to extract data from the application and generate tasks. For example, the disclosed systems can scan displayed (or otherwise presented) digital content in an application and can generate a prompt for causing a large language model to identify or extract tasks from the digital content. Through the catalyst application, the disclosed systems can thus implement a large language model to generate a task list from the digital content of another computer application. The disclosed systems can further utilize the catalyst application to automatically execute or perform extracted tasks.

The invention provides a computing power center resource centralized self-adaptive control scheduling system and method, and the method comprises the steps: S1, a user submits a computing power training task through an API, and sends the task to a DAG task composer, and forms a task list to be scheduled; s2, the intelligent monitoring analysis module is used for continuously collecting node data and calculating real-time CS and HS scores of each node; s3, analyzing the dependency relationship type data by a task composer, and calling a dynamic programming algorithm in an adaptive scheduler to make a decision; s4, the adaptive scheduler performs dynamic task distribution according to the task list of the DAG task orchestrator and the intelligent monitoring analysis module, and S5, the distributed runtime and environment management module is used for uploading the prepared Docker mirror image to the distributed storage; and S6, through the unified communication adaptation layer, the mirror image is pulled from the distributed storage on the target node and the container is started, the user does not need to concern the difference of the operating system in the whole process, and efficient and intelligent adaptive scheduling is realized.

The invention provides an intelligent customer service system, which comprises an information acquisition module used for identifying a customer identity according to a user login account and obtaining historical data under the account; the grading module is used for establishing a customer portrait model according to the historical data and classifying customer types; the matching module is used for automatically matching a service strategy model version corresponding to the account according to the customer type obtained by the grading module; the knowledge graph response module is used for carrying out deep analysis on client input contents based on the natural language understanding capability of a large language model in combination with a context, a client portrait and historical data after the version of the service strategy model is determined, automatically generating a task list and a semantic tag tree for a dialogue, and continuously optimizing the task list and the semantic tag tree; starting from a problem node of the task list, analyzing a corresponding semantic tag along an optimized and trained path in the knowledge graph, and matching a service processing action; and triggering a back-end system interface associated with the node according to needs, transferring real data, and solving client problems.

The invention relates to a cache data processing method and device, computer equipment, a computer readable storage medium and a computer program product. The method is applied to a distributed storage system adopting a three-layer architecture design, and the three-layer architecture comprises a service application layer, a data processing layer and a persistence layer. Comprising the following steps: acquiring access request monitoring information through a service application layer to obtain an access feature data set, and performing pattern classification identification on the access feature data set to obtain a predicted access pattern; generating a prefetching task list and prefetching data according to the predicted access mode, and storing the prefetching data to a data processing layer for caching processing; and executing each prefetching task according to the sequence of the prefetching task list, and reading the prefetching data from the mechanical hard disk and writing the prefetching data into a cache space in a solid state disk resource pool under the condition that the prefetching data does not exist in the solid state disk resource pool of the persistence layer. By adopting the method, the cache performance can be improved.

The invention discloses a surgical instrument automatic scheduling method, system and terminal based on pre-operation scheduling, and the method comprises the steps: obtaining pre-operation scheduling information which comprises an operation style, an operator, a time period and inter-operation scheduling; a standard instrument set template is obtained according to the operation mode, historical instrument use behavior data of the operator is obtained, and the historical use behavior data is used for dynamically correcting the standard instrument set template to obtain a personalized template; acquiring instrument inventory information in the time period, and intelligently scoring the standard instrument package template and the personalized template according to the instrument inventory information and the recommendation model to obtain a target template; obtaining a scheduling task list according to the target template, the operation style, the time period and the inter-operation arrangement; and inputting the scheduling task list into an automatic transfer system, generating a route task package, and packaging, delivering and delivering the instruments according to the route task package and the scheduling instruction. According to the method, the instrument set is automatically generated and optimized, and the surgical instrument configuration efficiency is remarkably improved.

A data processing system implements receiving a first request to collaboratively author a mixed reality experience with a vision-language model planner, the mixed reality experience comprising an interactive guide for performing a task involving a complex multipart object; obtaining 3D object geometry information for the complex multipart object; obtaining a description of the task to be performed including a plurality of subtasks each associated with a user action to be performed on a respective part of the complex multipart object; constructing a prompt to the model using a prompt construction unit, the prompt instructing the model to generate a task list based on the geometry information and the description of the task to be performed; providing the prompt as an input to the model to obtain the task list; and generating content for the mixed reality experience using the task list in response to a second request to execute the mixed reality experience.

The invention relates to the technical field of computers, in particular to an AGV task allocation method and system based on multi-objective optimization, and the method comprises the steps: obtaining a task demand set and a state information set of AGV equipment in a current operation scene, and the task demand set comprises a plurality of task units; performing feature extraction processing on the task demand set and the state information set to obtain task feature description of each task unit and capability feature description of each AGV device; performing joint optimization matching processing according to the task feature description and the capability feature description to generate a matching association relationship set of the task and the AGV equipment, wherein the matching association relationship set comprises a corresponding relationship between the task unit and the AGV equipment and a task execution sequence relationship; and generating a task allocation scheme based on the matching association relationship set, wherein the task allocation scheme comprises a to-be-executed task list of each AGV device and execution time sequence arrangement information of each task. Therefore, task requirements and equipment states can be accurately grasped, and efficient and matched innovative task distribution processing is realized.

The invention discloses a business data query reconstruction method and device, equipment and a storage medium. Comprising the steps of obtaining a query task list constructed by a user, and performing permission verification on the query task list; after the verification is passed, reconstructing the query task list to generate a standard query command; executing the standard query command to obtain original query data, desensitizing the original query data to generate safe query data, and displaying the safe query data to a user. The user permission is verified through the permission verification process, the unauthorized query is intercepted from the source, and illegal access to the production database is avoided. By generating the standard query command, the row-level permission condition can be embedded into the query logic, so that the generated query command is ensured to be compliant, permission bypassing is avoided, errors and performance risks of manual writing are avoided, and meanwhile, the development efficiency is improved. By dynamically desensitizing the original query data, on the premise of guaranteeing the service continuity, the data can be available and invisible, the compliance requirement is accurately met, and the data leakage risk is reduced.

The invention discloses a robot and an intelligent interaction method thereof, and relates to the technical field of robots, and the robot comprises an instruction obtaining module which is used for converting external instruction information into text information; the artificial intelligence module is used for performing vectorization processing and similarity comparison on the text information and keywords in a preset task list through a large model to obtain a conventional target task and an unconventional target task; the task decomposition module is used for decomposing the conventional target task or the unconventional target task into a plurality of sub-tasks and determining solutions matched with the sub-tasks according to the types of the sub-tasks; the task execution module is used for supervising and controlling execution of each subtask; the memory module is used for storing key data and a knowledge graph database in the execution process of each subtask; and the planning decision module is used for controlling the operation of the modules. According to the invention, man-machine interaction and autonomous planning and intelligent control of the robot body can be realized.

The invention relates to the technical field of loading of computer programs, in particular to a task unloading method and system based on task similarity and a storage medium, and the method comprises the following steps: initializing an unmatched edge server set and an unmatched equipment set, and generating a similarity score between equipment based on a task list of the equipment; the edge server and the equipment respectively establish respective preference lists according to respective preference functions; matching the equipment with an edge server; calculating the shortest distance between every two clusters based on the equipment task type, and outputting a result cluster; selecting a cluster head from the result cluster, and sending a task main body code and an input parameter to an edge server by the cluster head; other devices in the result cluster send task input parameters to the edge server; channel allocation is carried out on the devices in the result cluster based on the size proportion of the device unloading tasks in the cluster; and carrying out power optimization on the equipment based on an equipment matching result and a sub-channel allocation result. According to the method, the unloading task can be completed with low energy consumption.

The invention discloses an automatic code debugging and optimizing system and method based on an AI agent, and relates to the technical field of artificial intelligence and automatic programming, the system comprises a coordinator module, a plan generation module, a code generation module, a test verification module, a deployment operation module, a project summary module, a language model module and a context management module; the coordinator module supports to select and call an Agent for specific task execution according to the current task process, and the plan generation module is configured to automatically generate a development task plan according to the requirements of a user and transmit a task list to the code generation module. Through the exception retry mechanism and the task backtracking mechanism, the system can automatically detect errors in the code generation process and repair the errors, and the tedious process of traditional manual debugging is avoided. The system optimizes the code generation process by backtracking the historical task and adjusting the generation strategy, and the quality of the final code is ensured.

The invention provides a computing power priority scheduling method and device for an intelligent computing center, and relates to the technical field of intelligent computing centers, intelligent computing centers and computing power infrastructure, and the method comprises the following steps: S1, based on the task type of each to-be-scheduled computing task in a plurality of to-be-scheduled computing tasks on the intelligent computing center, determining the computing power priority of each to-be-scheduled computing task; determining a scheduling strategy of each to-be-scheduled calculation task; s2, determining a task scheduling queue of the intelligent computing center according to the scheduling strategy of each to-be-scheduled computing task and a preset target task list; the scheduling priority of each calculation task in the target task list is higher than the scheduling priorities of the plurality of calculation tasks to be scheduled; and S3, scheduling a plurality of to-be-scheduled computing tasks on the intelligent computing center according to the task scheduling queue. According to the method, scheduling of tasks of different task types in the intelligent computing center can be optimized, and reasonable resource utilization and efficient task scheduling are achieved.

The invention discloses an automatic design method and system based on BIM software. The method comprises the following steps: initializing an adaptation layer dynamic link library, and establishing a parameter transmission channel with BIM software; preparing BIM design data, registering a basic task in BIM software based on the BIM design data, and establishing a standardized project directory; establishing a parameterized set of tasks which can be automatically modeled; serially arranging the task set into a task list according to design logic, and recording the task list to a project project; and calling the adaptation layer interface through the parameter transmission channel to drive the BIM software or a professional application module in the BIM software to complete automatic modeling. According to the method, the engineering design rule is solidified into the accurate task sequence by calling the BIM software or the interface of the professional application module in the BIM software, automatic execution of the whole process is achieved, the technical problems that manual operation efficiency is low and an existing RPA technology cannot process complex BIM design logic are solved, and design efficiency and quality are remarkably improved.

The invention discloses an online product ordering verification method, device and equipment and a storage medium, and relates to the technical field of computers, and the online product ordering verification method comprises the steps that case matching information is acquired, and the case matching information comprises a case material number of a matched target case; determining a node material number corresponding to the case material number based on a pre-constructed material number online table, wherein the node material number is a material number of a target node forming an online product with the target case; generating node matching information of the target node according to the node material number; generating a product configuration table according to the node matching information and the case matching information; and verifying the product configuration table based on a pre-constructed verification rule table, and generating a production task list of the online product under the condition of successful verification. According to the method, the selected configuration is verified in real time, it is ensured that the selected configuration meets related requirements, the technical problem that configuration verification is difficult is solved, and the technical effect of efficient and accurate material configuration is achieved.

The invention discloses an execution method and device of a cloud management task, a storage medium, equipment and a program, and relates to the field of cloud computing, and the execution method comprises the following steps: matching a text corresponding to a received user instruction with a first dictionary to determine a processing object allowed to process the user instruction, the first dictionary is associated with the business field of the cloud management task; under the condition that it is determined that the processing object comprises the large language model and the risk decision of the first intention of the user instruction is a first value, according to the text and a preset vector database, a cue word of the large language model is constructed, so that an ordered task list corresponding to the user instruction output by the large language model is obtained; and executing the cloud management tasks in the ordered task list. Through application of the method and the device, the problems that a cloud management mode in related technologies mainly depends on a graphical user interface and a command line interface to realize management, the operation is complicated and the efficiency is low are solved, and the cloud management efficiency is improved.

The invention relates to the technical field of remote measurement, in particular to a very simple protocol efficient and high-density meter reading method which comprises the following steps: acquiring data frame content, eliminating identity fields, generating a simplified format identifier, constructing a peak shifting scheduling list, detecting sequence consistency, generating a frame supplementing instruction, and dividing synchronous triggering groups to sort meter reading records. According to the method, identity field compression is replaced, frame body space proportion conversion is introduced, data structure content is simplified, a rhythm dislocation sequence is constructed in combination with time migration to achieve high-density node scheduling, frame loss behaviors are screened based on serial number jumping, a frame compensation response task list is automatically constructed, and a synchronous trigger sequence is constructed in combination with time difference judgment. The volume of transmission data is effectively compressed, the utilization rate of channel resources is improved, peak-shifting uploading between nodes is realized to reduce the probability of data collision, overhigh communication load caused by frequent handshake response and multi-level response is avoided, and the continuity, fault tolerance and rhythm collaboration of meter reading scheduling are enhanced.

The invention relates to the technical field of software development, and discloses a code testing method and device, computer equipment and a storage medium. The code testing method comprises the following steps: determining test input information and a target test type through an obtained target test request; determining a to-be-tested code and a test scheme file according to the test input information, and preprocessing the to-be-tested code and the test scheme file to obtain a target knowledge graph corresponding to the target test request; searching a target test design task matched with the target test type from a preset test task list; determining a task cue word, a task analysis model and a task output template according to the target test design task, and obtaining a task output result according to the task cue word, the target knowledge graph, the task analysis model and the task output template; when it is confirmed that the task output result is the target test case, test processing on the to-be-tested code is executed according to the target test case, and a target test result is obtained. According to the invention, the interaction complexity is reduced, and the test efficiency and the test accuracy are improved.

The invention discloses a load-algorithm bidirectional elastic scheduling method and device for a hybrid energy storage system, and belongs to the technical field of water, wind and light stations, and the method comprises the steps: obtaining the operation data of a water, wind and light hybrid energy storage system through a collection CPU; performing wind and light ultra-short-term prediction and load evaluation through the prediction CPU, and evaluating the battery state and the water and electricity available capacity to obtain a prediction result; computing resources, priorities, resource allocation proportions and levels of energy storage algorithms of control tasks of multiple time scales are determined by scheduling a CPU according to the operation data and the prediction result, calculation-communication pre-preemption is triggered after an early warning event is generated, and a task list and a resource allocation matrix at the current moment are generated; executing a control operation by controlling the CPU according to the task list and the resource allocation matrix; adjusting a next scheduling cycle strategy or executing a thermal migration mechanism according to a result after execution control, and performing backfilling execution on selectable sub-graphs of the directed acyclic task graph in an empty window period; and bidirectional elastic adaptation of the load and the algorithm is ensured.

The invention provides a multi-device cooperative control method and device, computer equipment and a storage medium. The method comprises the following steps: monitoring device events corresponding to all devices in a registered device list; judging whether the equipment event meets a triggering condition corresponding to any scene to be triggered in a scene list or not; if yes, determining the scene to be triggered as a target scene; generating a to-be-executed task according to the configuration information of the target scene, and adding the to-be-executed task into a task list; in the task list, determining target tasks in sequence according to task priorities; determining the target equipment from the registered equipment list according to the target task; and issuing the target task to the target device for execution. According to the whole scheme, the flexibility of collaborative logic and the feasibility of user-defined scenes are effectively improved, and the automation and intelligence level of the system is greatly enhanced.

The invention relates to the technical field of building construction, in particular to an intelligent scheduling building construction progress optimization method, which comprises the following steps of: acquiring construction environment data in real time by utilizing a sensor network, and dynamically evaluating a resource state in combination with a historical record and a task list; generating an initial construction plan by using a multi-objective optimization algorithm, and performing real-time correction through a fuzzy logic dynamic adjustment mechanism; the resource use condition is monitored by means of Internet of Things equipment, and a data optimization strategy is fed back; and finally determining the arrangement by comparing the optimization plan with the actual progress. The method can significantly improve the accuracy and timeliness of the construction plan, cope with a complex environment, reduce human intervention, ensure that key tasks are completed preferentially, and improve the construction efficiency and the resource utilization rate.

The invention provides a task execution method and device, electronic equipment and a storage medium, and relates to the technical field of large models, in particular to the technical field of artificial intelligence such as natural language processing, image vision and deep learning. Obtaining a target sub-task list after the target task is disassembled, the target sub-task list comprising a task execution sequence of each target sub-task; an execution agent of each target subtask in the target subtask list is determined from a preset agent cluster, and a support mode of each agent in the agent cluster is at least one of multi-mode data; and based on the task execution sequence, sending a corresponding task execution instruction to each execution agent to obtain a target task execution result of the target task.

A method for task management and a system therefor are provided. The method according to some embodiments may include generating a task list by inputting user information of a user into a first model, the task list including a plurality of tasks expected to be processed by the user, generating a plurality of items related to the plurality of tasks by inputting the user information into the first model, receiving, from the user, a selection of a task to be processed by the user from among the plurality of tasks, receiving, from the user, a selection of an item related to the selected task from among the plurality of items, composing a first prompt by inputting the selected item into a second model, the first prompt requesting the selected task to be processed based on the selected item and outputting a processing result for the selected task by inputting the first prompt into a third model.

The present invention is directed to a software-based technology system for managing and executing high volumes of work on complex projects across multiple organizations involving multiple users that results in superior outcomes at significantly reduced time and cost. Litigation is the initial application of this invention; each case is handled as a project. As applied to litigation, the system of the invention empowers a novel method of end-to-end case assessment, planning, execution and management by integrating tools, processes and procedures allowing for more informed decision-making and deliberate execution of all case-related actions as well as enhanced coordination and communication among all constituents, resulting in efficient achievement of targeted outcomes. The system provides intuitive, menu-driven functionality for inputting, processing and utilizing vast amounts of information, including company and case-specific information, attorney discretionary evaluation and input, document templates and monitoring and reporting requirements. A threat algorithm cross-correlates via multivariate analysis critical data points with customized company-specific information and business logic to generate a numeric threat score as an output. The system also predicts the expected settlement range with a high degree of precision for a specific case as the output of one or a combination of multiple artificial intelligence (AI) / machine learning algorithms. The system then generates a unique case strategy (which includes an expected outcome, including projected outcome, Case Plan and estimated attorneys' fees and costs). System logic learns from thousands of examples of similar cases and directs highly effective and efficient execution of a specific Case Plan providing each attorney and staff member assigned a role in a case with a prioritized task list that is updated in real time as other users complete tasks and litigation priorities change. For each user, the system integrates assigned tasks across multiple cases and multiple client portfolios into a single, prioritized task list. The execution features become increasingly valuable given the volume and complexity of projects across multiple organizations and users, allowing users to focus on their area of expertise and avoid the wasted time and distraction of trying to determine what they should be working on or recording notes to communicate what they have been doing since the system automatically performs these functions and directs the various users' next steps while monitoring their progress against the plan. Integration of administrative functions, including automated time tracking, billing and reporting without the need to rely on users to update this information, further increases quality and efficiency of legal services. Key objectives of the system of the present invention include eliminating gaps between business objectives and litigation strategy and optimizing communication and coordination of case-related tasks and priorities in a fully automated fashion, which are all achieved by leveraging data and technology to enable a fundamentally different approach that becomes smarter and more precise via machine learning and predictive modeling.

The invention discloses a task node execution control method and device based on a data task network and a storage medium, and belongs to the field of data processing, and the method comprises the steps: generating a virtual execution network of tasks according to a plurality of to-be-executed tasks; according to the virtual execution network, determining the semaphore of each task, and according to the virtual execution network, generating a task list for recording the semaphore and the task content of the task; and determining a target task from the task list according to the semaphore of each task, and executing the target task. By generating the virtual execution network, determining the semaphore and dynamically adjusting the task execution sequence, complex data tasks can be efficiently managed and executed, the defect that a traditional predefined scheduling table cannot cope with task parameter changes is effectively overcome through a dynamic adjustment mechanism, task execution is more flexible and adaptive, and the task execution efficiency is improved. The problem that the applicability to complex tasks is low under the condition that parameters are dynamic is solved.