82 results about "Cost evaluation" patented technology

The invention provides a logistics transportation optimization method and system based on a traffic logistics large model, and the method comprises the steps: carrying out the element extraction through receiving a basic scheduling instruction of a logistics transportation task, generating core element information through semantic scene analysis, and carrying out the transportation scene adaption based on the core element information. Generating a scene influence factor set in combination with a depth feature mining result of the historical transportation scene database; then, calling a pre-trained traffic logistics large model to carry out collaborative path decision on the scene influence factor set, and outputting a candidate path scheme population containing a path topological structure and cost feature distribution through interactive iteration of a path generation unit and a cost evaluation unit in the model; and performing multi-round evolution screening on the candidate path scheme population according to a preset optimization target, determining an optimal path scheme meeting transportation requirements, and generating a transportation scheduling instruction set to be pushed to a transportation execution terminal after conversion. According to the invention, the adaptability, decision-making precision and reliability in the logistics transportation optimization process can be effectively improved.

The invention discloses an automatic shipping operation scheduling method for a container wharf, and the method is realized through the following steps: determining a current workable candidate container list based on a preset on-board position dependence relation and a storage yard real-time state through feasibility screening; performing execution cost evaluation and opportunity utility evaluation on the candidate jobs respectively, quantifying the costs of equipment movement, overturning and ramming operation and the like by the execution cost evaluation, and actively and quantitatively unlocking the key jobs by the opportunity utility evaluation, thereby forming earnings brought by double-box collaboration or matching of wharf resources; and calculating a final decision score through a weighting algorithm fusing utility and cost, and selecting an optimal job generation instruction according to the final decision score. According to the method, a complex scheduling problem is decomposed into a plurality of evaluation levels, refined and intelligent management of ship loading operation decisions is realized, and the overall efficiency, balance and economical efficiency of automatic wharf ship loading operation can be effectively improved.

The invention belongs to the technical field of computing resource allocation, and discloses a task-driven computing resource dynamic allocation system, which is characterized in that task differentiation features are extracted through a task feature intelligent analysis module, and global optimal allocation, 'task fingerprint 'matching and opportunity cost evaluation mechanisms are realized in combination with a reinforcement learning scheduling decision module. Mismatching conditions such as allocation of lightweight tasks to a high-video-memory GPU are avoided, meanwhile, the elastic resource pool dynamically adjusts the capacity, reduces resource locking during task pause, remarkably reduces resource waste, improves the overall utilization rate of computing resources such as the GPU, can quickly expand the capacity through the elastic resource pool to cope with burst tasks, and a cross-node resource cooperation module constructs a global view and improves the overall utilization rate of the computing resources. Efficient calling of idle resources is realized; a'logic super node 'and zero-aware migration technology breaks node barriers, avoids global redundancy caused by local optimum, enables resources to flexibly flow along with task loads, and improves the adaptive capacity of a system to dynamic tasks.

The invention discloses an unmanned aerial vehicle trajectory optimization and power distribution method, system and device based on cost evaluation reinforcement learning, and a medium, and the method comprises the steps: constructing an unmanned aerial vehicle data service scene under safety constraints, modeling a multi-objective optimization problem of unmanned aerial vehicle trajectory optimization and power distribution, and designing an unmanned aerial vehicle data service evaluation index; converting a multi-objective optimization problem into a constrained Markov decision problem; respectively designing a state space, an action space, an instant reward function and a corresponding cost function; constructing a cost evaluation critic network parallel to the reward evaluation critic network, training and updating network parameters of the unmanned aerial vehicle by using a cost evaluation SAC algorithm according to a constrained Markov decision problem, and calculating trajectory optimization and power distribution parameters of the unmanned aerial vehicle; the system, the equipment and the medium are used for implementing the method. According to the invention, ground user data service requirements of a data service scene under random and dynamic security constraints are met.

The invention discloses a cross-regional municipal road maintenance resource scheduling method and system. The scheduling method comprises the following steps: collecting road disease data, maintenance historical records and real-time traffic flow data of each region; constructing a judgment matrix by applying an analytic hierarchy process, calculating a road factor weight through a root method, calculating a maintenance emergency degree score of each region according to the quantized basic data and the weight, and dividing region maintenance grades; according to the maintenance level of each area, maintenance groups with different priorities are divided; counting the existing maintenance resources of each region, and predicting the resource demand quantity of each region by combining a maintenance group division result and taking historical maintenance resource consumption data as a benchmark; for a resource shortage region, selecting a scheduling path by adopting a greedy algorithm, and formulating a preliminary cross-region resource scheduling scheme; and constructing a multi-target cost evaluation algorithm, and iteratively optimizing the scheme in combination with a particle swarm optimization algorithm. According to the invention, efficient, accurate and low-cost scheduling of cross-regional municipal road maintenance resources is realized.

The invention provides an index optimization method and device, electronic equipment and a storage medium, and relates to the technical field of database query. The method comprises the steps that a query log of a database is analyzed, query detailed information is obtained, the query log comprises at least one query statement, and the query detailed information comprises query conditions indicated by the query statement, sorting fields, table connection relations and grouping information; the detailed query information is analyzed and counted to obtain query statistical data, and the query statistical data comprises the field access frequency, the data distribution condition and the index use condition; generating at least one candidate query path based on the query statistical data, wherein each candidate query path comprises a query index and a use sequence of the query index; and performing query cost evaluation on the at least one candidate query path, and determining a recommended index scheme. The method can automatically recommend a proper index scheme and improve the index optimization efficiency.

The invention provides an execution optimization method for database operation statements, a product and equipment. The method comprises the steps of determining system resource data of a target database in a current state in response to a database operation statement; obtaining a query tree corresponding to the database operation statement and decomposing the query tree to obtain a plurality of execution path nodes, wherein each execution path node corresponds to an operator type; performing cost evaluation on an operator type and an operator execution mode of each execution path node according to the system resource data, and determining a target operator corresponding to the execution path node according to a cost evaluation result; and combining the target operators corresponding to each execution path node to obtain an optimal execution plan of the database operation statement in the target database. By means of the method, the system resource data of the database can be dynamically obtained, the optimal execution plan better conforming to the current system resource use condition is generated, and therefore the execution efficiency of the database operation statement is improved.

The embodiment of the invention provides an intelligent decision-making method and device for cross-border logistics, electronic equipment and a storage medium, and is applied to the technical field of logistics decision-making. The intelligent decision-making method for cross-border logistics comprises the steps of collecting heterogeneous logistics data according to user demand information, performing language entity alignment processing on the heterogeneous logistics data to obtain standardized shipping data, performing transportation cost evaluation according to the standardized shipping data to generate comprehensive cost data, performing path search according to the comprehensive cost data through a heuristic algorithm to obtain an initial feasible path set, and performing multi-target optimization screening processing on the initial feasible path set to generate an initial logistics planning scheme set; and carrying out risk simulation and TOPSIS preference sorting on the initial logistics planning scheme set to obtain an optimal logistics planning scheme and a corresponding reported value. According to the invention, through cost evaluation, constraint path search, multi-target optimization and risk quantification, a more accurate transportation scheme is provided for the user, and the cross-border logistics operation efficiency is improved.

The invention discloses a GPU cluster resource allocation method and device, equipment and a storage medium, and relates to the technical field of GPU cluster resource allocation. According to the method, a hardware performance description vector is generated by fusing static hardware parameters and dynamic micro-benchmark test data, the limitation that the GPU performance is represented only by depending on the static parameters is broken through, and the actual operation capacity of different architecture GPUs in a heterogeneous cluster is accurately matched; task feature vectors are generated by extracting task calculation features and resource demands, and quantitative description of reasoning task resource consumption features is achieved; information of hardware, tasks and load dimensions is integrated through a machine learning model, performance degradation characteristics during multi-task parallel are effectively captured, and the accuracy of execution time prediction is improved; scheduling decision operation including candidate node screening, comprehensive cost evaluation and resource reservation backfilling is executed in combination with the predicted execution time, and the task execution efficiency and cluster load balancing are both considered.

The invention relates to the technical field of trajectory planning, and provides a rapid evaluation and search method and system for an optimal trajectory and terminal equipment, and the method comprises the steps: sampling a plurality of state points in a pre-planning space, and carrying out the pre-collision detection of the state points to remove points conflicting with an obstacle, and obtaining effective sampling points; performing cost evaluation on the effective sampling points, wherein the cost evaluation comprises heuristic cost based on historical planning trajectory information and static cost based on motion features; and iteratively searching from the initial state point based on the result of the cost evaluation: calculating the trajectory cost of the neighborhood state point and updating the search path along the gradient direction until the optimal feasible trajectory is output. According to the method, invalid sampling points are screened out through pre-collision detection, a gradient iteration search mechanism is adopted, and the track search efficiency is improved.

When a trigger event causing the priority change of the production task is recognized, the influence degree of the trigger event on an overall production plan is evaluated, whether the influence degree of the trigger event on the overall production plan is low or not is judged, and when the judgment result is no, the priority of the production task is changed according to the priority of the production task. Generating a plurality of candidate priority change schemes, performing priority change cost evaluation on the candidate priority change schemes, and determining a target priority change scheme in the candidate priority change schemes according to a priority change cost evaluation result, the priority of the production task is adjusted according to the target priority change scheme, the optimal production priority change scheme can be produced in combination with the actual situation, and the adverse effect caused by production priority change is effectively reduced.

The invention discloses an informatization software project cost evaluation system and method, and the system comprises a data collection module, a data processing module, a cost evaluation module and a result output module, and relates to the technical field of software project management. According to the informatization software project cost evaluation system and method, through cooperative work of the cost evaluation unit and the value evaluation unit of the cost evaluation module, a quantitative evaluation model is constructed based on historical data and market dynamics, accurate measurement and calculation of the whole life cycle cost of a software project are realized, and when the deviation analysis unit finds that the cost is abnormal, the evaluation result is accurate. And the data can be immediately fed back to the data processing module and the cost evaluation module for dynamic adjustment, so that a closed-loop management mechanism of estimation-evaluation-deviation correction is formed, the problem of disordered cost management is fundamentally solved, the cost is reduced, and meanwhile, reasonable allocation of resources and stability of software quality are ensured through matching analysis of the cost and the value.

The invention discloses a data-intensive workflow dynamic task allocation method and system oriented to cloud-edge cooperative computing, and relates to the technical field of cloud computing and edge computing collaboration.The method comprises the steps that workflow and cloud-edge resources are modeled in a unified mode, and a comprehensive cost function containing time, economy and energy consumption is defined; analyzing the workflow to obtain a conflict task set and a maximum data path set; in combination with resources and network states monitored in real time, candidate nodes are filtered according to data privacy constraints, edges or cloud nodes with the minimum comprehensive cost are selected for tasks for allocation, and sorting is performed according to priorities during multiple tasks; the system comprises a resource monitoring module, a workflow analysis and preprocessing module, a cost evaluation module and a dynamic scheduler module, the modules cooperate to realize dynamic allocation, the problems of resource heterogeneity, network dynamic and the like under cloud edge cooperation are solved, multi-objective optimization and privacy protection are balanced, and the workflow execution efficiency is improved.

The invention provides a research and development cost evaluation method, device and equipment. The method comprises the following steps: determining an initial function point scale of a to-be-evaluated software project; obtaining an updated function point scale according to the demand change coefficient corresponding to the software project and the initial function point scale of the software project; the demand change coefficient is used for calibrating the influence of software demand change on the software scale; determining a function point unit price of the software project and an adjustment factor of software project research and development cost; and determining the research and development cost of the software project according to the updated function point scale, the function point unit price of the software project and the adjustment factor of the research and development cost of the software project. The method provided by the embodiment of the invention effectively improves the accuracy of the project research and development cost estimation result.

The application provides a cost rapid evaluation method and system in engineering change management, relates to the technical field of engineering cost management, and comprises the following steps: acquiring an engineering type, establishing an engineering knowledge base; defining a change influence network, performing logical compilation and training on the change feature-variable-change influence network, and constructing an engineering evaluator; uploading an engineering change order to an engineering management system by an engineering party, triggering the engineering evaluator, performing change feature self-attention import and coding, performing attribution based on implicit variables and explicit variables, converting into a change influence network group, performing cost evaluation and comprehensive calculation, and determining a change evaluation order; and performing zero-knowledge proof on the cost rationality of the change evaluation order by an auditing party, and displaying on a terminal interface. The application solves the technical problem that, in the prior art, the change influence chain is long, the change influence analysis is not comprehensive, and the accuracy of cost evaluation is affected, and improves the accuracy of engineering change cost evaluation.

The invention discloses an intelligent construction management system and method based on data analysis, and belongs to the technical field of construction management, and the method comprises the steps: obtaining original plan feature data and change estimation feature data of a target change task in a construction project, and generating a basic change cost evaluation index of the target change task; establishing a task relationship network among all tasks in the construction project; obtaining the number of affected tasks actually affected by the target change task in the completed tasks of the construction project, and generating a target change task change feasibility evaluation index in combination with the target change task basic change cost evaluation index; obtaining a change reason of the target change task in the construction project, and generating a final change feasibility evaluation index of the target change task in combination with the change feasibility evaluation index of the target change task, thereby managing the target change task in the construction project; according to the invention, construction resource distribution and progress control precision can be optimized.

The embodiment of the application provides a neural network model configuration determination method and device, equipment and a storage medium, according to the dimension information of the target network layer and the hardware instruction of the target hardware, the prior information and the universal information in the preset model cost evaluation table are inquired, the preset configuration that makes the number of clock cycles of each target network layer shortest can be obtained, that is, the configuration of each target network layer when the number of clock cycles of the target neural network model is shortest, so that the optimal configuration of the target neural network model in the target hardware is obtained, and the optimal configuration can be used to realize the rapid landing of the neural network model in the target hardware subsequently.

The application relates to the technical field of trajectory planning, and proposes a fast evaluation search method, a system and a terminal device of an optimal trajectory. The fast evaluation search method comprises the following steps: sampling a plurality of state points in a pre-planning space, performing pre-collision detection on the state points to remove points in conflict with obstacles, and obtaining effective sampling points; performing cost evaluation on the effective sampling points, wherein the cost evaluation comprises heuristic cost based on historical planning trajectory information and static cost based on motion characteristics; based on the result of the cost evaluation, iteratively searching from an initial state point: calculating the trajectory cost of a neighborhood state point and updating a search path in a gradient direction until an optimal feasible trajectory is output. The application improves the search efficiency of the trajectory by pre-collision detection to screen out invalid sampling points and a gradient iterative search mechanism.

This invention discloses a traffic signal control method and system based on safety reinforcement learning. The method includes: acquiring multiple historical state information and processing them using an attention network, an actor network, a reward critic network, and a cost critic network to obtain a first probability value, a first reward value, a second reward value, a first cost value, and a second cost value; then obtaining an advantage evaluation value and a cost evaluation value through a reward advantage evaluation function and a cost advantage evaluation function; optimizing the actor network, reward critic network, and cost critic network based on the advantage evaluation value, cost evaluation value, first probability value, reward value, first reward value, cost value, and first cost value; repeating the above process until a preset number of times is exceeded to obtain a trained actor network; acquiring the observation information at the current moment and inputting it into the trained actor network to obtain the current action to control the traffic signal. This invention ensures the safety of intersections while maintaining efficiency.

The invention provides a recommendation method for an optimal maintenance scheme of the Internet of Things. The recommendation method comprises the following steps: step 1, acquiring system input; step 2, performing WSN simulation and cost evaluation based on the system input; and step 3, performing optimal maintenance scheme search under budget constraint by adopting an intelligent search algorithm. According to the method, the automatic recommendation of the maintenance module and the parameter combination configuration thereof can be realized by establishing an optimization model between the overall maintenance cost and the system reliability under the budget constraint aiming at factors such as network scales, node distribution, fault types and occurrence probabilities, energy consumption models and the like in different application scenes. According to the method, typical maintenance function modules such as remote restart, heartbeat detection and firmware updating serve as candidate components capable of being started as required, key parameters such as an energy early warning threshold value and heartbeat frequency are combined, the actual effect of a maintenance strategy is evaluated based on system modeling and simulation analysis, and therefore the optimal maintenance scheme is screened out.

The invention provides a modular design and production method for assembly type ice and snow building components based on cost and manufacturability analysis. According to the method, the cost-manufacturability evaluation curve is automatically generated in the dividing and checking process, the optimal cost solution and the machinable boundary are output in a visual mode, and a basis is provided for design decision making of the ice shell structure. A data file output by the method comprises discrete unit geometric parameters, mold numbers, machining sizes, cost evaluation results and assembly sequences, and can be directly used for follow-up mechanical arm path planning and mold production.

This invention provides an ADRC parameter optimization method for variable-sweep aircraft considering deformation safety constraints. Based on the desired velocity, desired altitude, and current flight state of the variable-sweep aircraft, the current deviation information and current control variables of the variable-sweep aircraft are determined through an ADRC simulation model and a longitudinal motion model. A current state vector is constructed based on the current deviation information, safety constraint information, and deformation configuration perception information of the variable-sweep aircraft. Based on the current state vector and current control variables, the ADRC parameters of the ADRC simulation model are optimized through a preset reward function, a preset cost function, and pre-trained policy network, reward evaluation network, and cost evaluation network. This invention can alleviate the performance degradation problem of fixed-parameter ADRC controllers caused by drastic changes in aerodynamic parameters during the deformation process of variable-sweep aircraft.

This invention discloses a scalar-vector hybrid query optimization system applied to a columnar-stored vector database. The system includes an optimizer, a cost estimator, an executor, and a analyzer. The optimizer rewrites and encodes the hybrid query, generating one or more query plans. It then calls the cost estimator to evaluate the cost of each query plan and selects the optimal plan after cost evaluation. The cost estimator evaluates the execution cost of each query plan and feeds back the evaluation result to the optimizer. The executor executes the selected optimal query plan and collects and records runtime information during execution. The analyzer analyzes the optimal query plan and the runtime information recorded by the executor to further optimize the cost estimator. This invention also discloses a scalar-vector hybrid query optimization method with wide application scenarios.

The invention discloses a finite-state agent behavior decision-making method, device and equipment, a medium and a product. The method comprises the following steps: acquiring system related information; according to the system related information, a system action graph Action-Graph is constructed, and the Action-Graph comprises nodes and edges; according to the Action-Graph, a logic mapping set is constructed, and the logic mapping set comprises an initial function set, an operation set and a dependency relationship between functions and operations; according to the logic mapping set, the Action-Graph and the task description information, an initial decision is determined, the initial decision comprises at least one of a task identifier, an operation sequence, a data dependence constraint and a cost evaluation index, through the technical scheme of the invention, an end-to-end automatic process suitable for the intelligent agent can be constructed, and the efficiency of the end-to-end automatic process is improved. And full-process automatic processing from task description of a natural language to task execution is realized.

The invention belongs to the technical field of industrial data analysis, and discloses an intelligent engineering cost evaluation method and system based on data analysis, and the method comprises the steps: collecting engineering related data, carrying out the structural analysis of the engineering related data, and disassembling the engineering related data into element blocks containing element attribute vectors; identifying different cost trigger factors based on the element blocks, and constructing a sensitivity vector and corresponding change intensity for each cost trigger factor according to a preset engineering specification to form a sensitivity matrix of the influence of the cost trigger factors on engineering nodes; according to a preset project subitem structure, a construction sequence and a cost rule, constructing a dependency relationship matrix between project nodes, and describing an influence link generated by structural association between the project nodes; based on the element blocks, the sensitivity matrix and the dependency relationship matrix, performing sectional deduction on the total cost of each project node; and the accuracy, the stability and the automation level of engineering cost analysis are improved.

The invention discloses a relational multi-table connection optimization method and system. The relational multi-table connection optimization system comprises a multi-table connection query module, an adjacent query operator generation module and a multi-table connection optimization module, the multi-table connection query module detects whether an edge table exists in query based on a pre-constructed inter-table graph structure, and if the edge table exists, whether graph query can be constructed or not is judged according to an entity table ID associated with the edge table; the adjacency query operator generation module dynamically generates an adjacency query operator for a table conforming to a graph query structure, an Expand operator directly locates cross-table associated data through a preset adjacency index, linearization acceleration of a multi-table connection path is achieved, and meanwhile an optimizer generates the execution cost of the Expand operator; the multi-table connection optimization module executes a dynamic pruning strategy, selectively evaluates whether to continue to explore an execution plan of a Join operator according to the known cost of the Expand operator, an optimizer synchronously injects the cost of the Expand operator and the Join operator into a cost model, high-cost branches are quickly eliminated through a hybrid cost evaluation framework, and the optimal cost is obtained. And finally selecting the execution plan with the minimum global cost as a detection result. According to the method, the optimization delay problem in a large-scale table connection scene is remarkably relieved, the system resource consumption is reduced, and meanwhile, the redundant data scanning amount is reduced through an adjacent index jump mechanism.

The invention, which relates to the technical field of construction engineering cost assessment, discloses a big-data-based construction engineering cost assessment system comprising a historical engineering data acquisition module, a cost deviation allowable interval acquisition module, a cost assessment deviation area map acquisition module and an assessment cost abnormal signal module. An actual evaluation deviation transformation curve corresponding to a real-time building stage is arranged in an evaluation deviation area map corresponding to the real-time building stage; the real-time evaluation deviation and the stage deviation interval corresponding to the real-time building stage are compared, analyzed and judged to obtain a construction cost abnormal signal, and visualization and advanced recognition of the construction cost risk are achieved through deviation area map and curve superposition. The whole-process cost management and control capability is remarkably improved, dynamic monitoring and instant early warning of cost deviation are realized through cost abnormal signals, overestimation or underestimation risks can be found in real time in the construction process, and passive adjustment in the settlement stage is reduced.

The invention discloses a thermal power generating unit deep peak regulation cost evaluation and allocation method, relates to the technical field of electric power system auxiliary service cost accounting, and can solve the problems that existing thermal power generating unit deep peak regulation cost evaluation lacks staged modeling, scene design is single, and an allocation mechanism is not combined with the benefit degree. The method comprises the following steps: constructing a staged deep peak regulation cost model of the thermal power generating unit, and respectively establishing accounting formulas of coal consumption cost, rotor life loss cost and oil investment cost; load working condition scenes are divided according to system load peak-valley characteristics, and working condition occurrence probabilities are calculated; time sequence production simulation in a conventional peak regulation mode and a deep peak regulation mode is carried out; calculating a deep peak regulation benefit value expected value of each load main body; and calculating the total cost of deep peak regulation and completing cost allocation according to the benefit proportion. According to the method, staged quantification and multi-working-condition scene modeling of cost evaluation and cost allocation based on the benefit degree are realized.

The invention discloses a civil engineering cost evaluation optimization system, and relates to the technical field of civil engineering cost management. The system comprises a PLM full-life-cycle cost data closed-loop management module, a PDM cross-organization collaborative management and control module, a PLM supply chain real-time data coupling module and a PDM cost process dynamic optimization module. The PLM full life cycle module realizes standardized acquisition, storage, analysis and continuous optimization of multi-source heterogeneous data, the PDM cross-organization module ensures efficient and safe cooperation of multiple participants through a unified data standard and a three-dimensional dynamic permission model, and the PLM supply chain module completes deep fusion and dynamic correction of supply chain real-time data and cost data. And the PDM cost optimization module realizes automatic identification and accurate quantification of changes and intelligent generation and rapid examination and approval of multiple sets of optimization schemes. According to the method, cost evaluation is promoted to be transformed from static lag to dynamic real-time and from dispersed independence to collaborative closed loop, the management and control precision and efficiency are improved, and technical support is provided for efficient landing of large EPC projects.