95 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 relates to the technical field of project cost review auxiliary methods, in particular to a power grid project cost review auxiliary method and system based on multi-modal feature fusion, and the method comprises the steps: obtaining project quantity list information, drawing information and historical cost data of a power grid project construction project; constructing an engineering quantity list knowledge graph based on the engineering quantity list information and the drawing information; constructing a historical project cost data graph based on the historical cost data; according to the project quantity list knowledge graph and the historical project cost data graph, generating a fusion feature vector through a multi-modal feature fusion network; predicting the future project cost based on the fused feature vector; and outputting the future project cost prediction result and the cost review suggestion, and through multi-modal feature fusion, the project quantity list, the drawing information and the historical cost data are comprehensively utilized, so that the data utilization rate and the comprehensiveness of information extraction are greatly improved.

The invention discloses a database optimization method and device based on a large model, equipment and a medium, and relates to the technical field of database performance optimizing.The method comprises the steps that a target SQL query is analyzed to obtain a target abstract syntax tree corresponding to the target SQL query, and candidate execution plans corresponding to the target SQL query are determined, determining a target feature vector of each candidate execution plan based on the target abstract syntax tree; training a preset deep learning model based on historical SQL query to obtain a target cost evaluation model, and determining a plan evaluation result corresponding to each candidate execution plan based on the target feature vector by using the target cost evaluation model; and determining a target execution plan from the candidate execution plans according to the plan evaluation result and a preset optimization plan selection mechanism, and executing the target SQL query based on the target execution plan by using the target database. In this way, the prediction precision of the SQL query execution cost can be remarkably improved.

The invention relates to the technical field of data processing, in particular to a data mining system based on artificial intelligence. According to the method, a finer computing resource regulation and control mechanism is constructed through links of inter-task correlation analysis, load task priority adjustment, computing cost evaluation, abnormal task identification, resource use trend analysis and the like. A topological structure is constructed for the relevance between the tasks through a graph neural network, and fuzzy matching is performed in combination with the computing resource sharing degree, so that automatic reasoning of the task dependency relationship is realized, and the intelligence of scheduling optimization is improved. The task priority adjustment adopts a mode of combining a CPU utilization rate and a calculation time proportion, load tasks are dynamically marked, a scheduling sequence is optimized in combination with a task time overlapping degree, and calculation resource conflicts are effectively reduced. According to calculation resource consumption analysis, task calculation load influence coefficients are calculated from multiple dimensions of task execution time, data reading amount and I / O operation times, and the reasonability of calculation resource allocation is 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 provides a load balancing algorithm system of a server cluster, which relates to the field of computer server clusters and comprises a priority management module, a resource monitoring module, a load distribution module, a cooperative communication module, a cost evaluation module and a large flow algorithm splitting and sequential execution module. The large-scale process algorithm splitting and sequential execution module comprises a process splitting unit, a sequential arrangement unit and a load balancing execution unit. The method has the beneficial effects that a large-scale process algorithm is split and sequenced, so that the method runs according to a specified sequence under the balance of a plurality of loads, and the complex service processing capability of the system is improved; and meanwhile, algorithms such as service priority, resource adaptation, distributed cooperation and cost benefit optimization are comprehensively applied, the resource utilization rate, the system performance and the cost benefit are improved, the load balancing effect of the server cluster is enhanced, a distributed cooperation mechanism avoids a single-point fault, local load changes can be quickly responded, and the stability and availability of the cluster are improved.

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 provides a BIM model-based engineering cost evaluation method and system, and the method achieves the engineering cost evaluation through dividing BIM model data into a plurality of construction regions, and comprises the steps: firstly, dividing a BIM model into a plurality of construction region data; determining material, labor and equipment demand data of each area; obtaining current market price data; respectively calculating material cost, labor cost and equipment cost of each area; combining the three types of costs to generate a regional estimated comprehensive cost; and finally, integrating the costs of all regions to form project initial cost data. According to the method, regional cost calculation is realized, the accuracy of engineering cost estimation is improved, the cost is more transparent, and cost control and optimization of each region are facilitated. Meanwhile, the method supports the real-time supervision of the construction cost of each region in the construction process, and provides a more scientific and efficient decision basis for project management.

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 relates to a building pipeline arrangement optimization technology, and provides a collaborative arrangement method for multi-terminal branch pipelines in order to solve the problem of collaborative planning of the multi-terminal branch pipelines. Firstly, an improved A * algorithm distinguishes a main pipeline and a branch pipeline through dynamic grid modeling, and local path generation is optimized by combining a path length, a turning number, a multi-cost evaluation function of grid energy and a dynamic weight Manhattan distance; secondly, global optimization is carried out by adopting an NSGA-II algorithm, a progeny is generated through intra-group crossing and inter-group crossing by taking a multi-pipeline overlap ratio and a turning number as targets, diversity is maintained by introducing a genetic variation and population de-duplication mechanism, and a Pareto solution set is screened by utilizing non-dominated sorting; according to the method, the limitation of traditional single-target optimization and sequential arrangement is overcome, multi-pipeline collaborative layout is achieved, the space utilization rate and the solution set diversity are remarkably improved, and the method is suitable for automatic and efficient arrangement of complex building pipelines.

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.