129 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 high-efficiency multi-core processor task allocation method, and relates to the technical field of computer multi-core processors, and the method comprises the following specific steps: S100, task analysis and classification: analyzing a to-be-allocated task, and according to task characteristics and system states, performing task allocation on the to-be-allocated task; classifying the tasks into task groups which can be executed in parallel, need to be executed in sequence and have a specific dependency relationship; according to the method, the migration cost evaluation model is accurately constructed, a plurality of key factors such as the data moving cost, the context switching overhead and the performance loss are comprehensively considered, accurate evaluation of the task migration cost is achieved, a scheme with the minimum migration cost can be preferentially selected in the task distribution and migration process, and the task migration cost evaluation efficiency is improved. Therefore, unnecessary resource consumption and performance loss are effectively reduced, in addition, through real-time monitoring and dynamic adjustment of task allocation and migration strategies, it can be ensured that the multi-core processor always keeps high overall performance during operation, and complex and variable task requirements are met.

The invention relates to the technical field of GPU scheduling, and provides a cross-domain GPU affinity scheduling method and system, and the method comprises the following steps: obtaining GPU information in each resource pool and index data of a to-be-processed application; classifying according to the index data of the to-be-processed application to obtain the category of the to-be-processed application; according to historical matching information in a historical matching library, filtering the existing GPU resources based on the category of the to-be-processed application, and screening out a GPU resource list similar to the category of the to-be-processed application; and for the obtained GPU resource list, evaluating the affinity between the application and the GPU resources by using multi-dimensional indexes, calculating affinity scores and sorting, and selecting the GPU resource with the highest score for scheduling. Through resource list screening, affinity calculation and cost evaluation, the scheduling range is gradually reduced, and the accuracy and efficiency of GPU resource configuration are improved.

The invention discloses a task unloading and resource allocation optimization method for a vehicle-assisted edge computing network. The method comprises the following steps: establishing a basic model of a vehicle auxiliary edge computing network; establishing a vehicle auxiliary edge calculation task cost evaluation and optimization model, quantifying the cost and solving an optimization problem; according to the system state information, obtaining an unloading decision of the sub-task by using a multi-agent deep reinforcement learning method based on MADDPG; according to the unloading decision, obtaining a transmitting power and computing resource allocation decision by using a PSO-based particle swarm optimization method; and after the decision is completed, system state information and buffer area information are updated, and a multi-agent neural network is sampled and trained. According to the method, the problems of limited vehicle equipment resources, heavy burden of the MEC server, complex task unloading decision and the like can be solved, the purposes of reducing task execution delay, optimizing the resource utilization rate and reducing the system cost are achieved, and the performance of the vehicle-assisted edge computing network is improved.

The invention discloses a line path planning scheme generation method and system based on deep reinforcement learning, and the method comprises the steps: firstly constructing a grid map and an evaluation index system of a planning region, determining the index weight based on a Critic method to construct a grid comprehensive cost evaluation model, and then constructing a path planning model with the minimum total cost as a target, the method comprises the following steps of: firstly, extracting an optimal power transmission line planning path, describing the optimal power transmission line planning path as a Markov decision process, then introducing mixed noise and an epsilon-greedy strategy into a depth deterministic strategy gradient algorithm, improving a priority experience playback mechanism to obtain an improved depth deterministic strategy gradient algorithm, and finally training an intelligent agent based on the algorithm and outputting an optimal power transmission line planning path. According to the method, mixed noise is added, the exploring ability of the intelligent agent is improved, a greedy strategy is introduced to accelerate the learning efficiency of the initial stage of the algorithm and reduce the exploring frequency so as to reduce unnecessary trial and error, and finally, an instant reward value is fused into a priority experience playback mechanism to help the intelligent agent to accurately distinguish the importance of different experiences and accelerate the learning efficiency of the intelligent agent.

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 a design cost optimization method, system and device for a real estate project and a medium. The method comprises the steps that a plurality of product modular units are generated in a target engineering project; associating the product modular unit with a project unit identifier; in the target engineering project, obtaining a corresponding project product list library and a contract planning list; taking each product modular unit as a basic unit, determining a corresponding design cost influence factor according to the corresponding project product list library and the contract planning list, and performing engineering project cost budgeting to obtain a project cost budgeting result; in the planning and construction process, obtaining project construction data and actual construction cost of each product modular unit; obtaining a corresponding budget cost early warning interval based on the project unit identifier, judging whether the actual construction cost is within the corresponding budget cost early warning interval, and obtaining a budget cost evaluation result; according to the invention, the cost budgeting accuracy of the real estate project is improved.

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 power field digital project cost review method based on impact factors, and belongs to the technical field of digital project cost review. The invention provides a solution for the problem that the score of the influence factor with floating score interval cannot be accurately evaluated in the practical application of intelligent review by using the model. Project data are automatically analyzed through big data analysis, a machine learning algorithm and other technical means, the cost is accurately predicted, potential risks are found in time, human errors are effectively avoided, resource allocation is optimized, a more scientific and objective cost basis is provided for decision making of power enterprises, deep development of digital transformation in the power field is promoted, and the economic benefits of the power enterprises are improved. And double improvement of economic benefits and social benefits is realized. According to the method, objectivity, scientificity and accuracy of a digital project cost review result in the electric power field are improved, and the working efficiency of digital project cost review can be improved.

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 embodiment of the invention discloses a data query method, and the method comprises the steps: dividing a data query task into a plurality of sub-tasks, determining a feasible processing mode of each sub-task, and obtaining a plurality of task combinations according to the feasible processing mode of each sub-task. Wherein the processing modes of the sub-tasks in the task combination can influence the query cost when the data query task is executed based on the task combination, namely, the comprehensive cost of the execution efficiency and the execution cost of the data query task is often different under different combination modes of the multiple sub-tasks and the corresponding processing modes. Based on this, in the method, the target task combination with the lowest query cost can be determined from the plurality of task combinations through the cost evaluation model, so that the allocation of the batch tasks can be adaptively realized, and the resources of the batch-stream integrated data processing framework can be fully utilized to perform data query in a comprehensive optimal manner of query efficiency, cost and the like; and obtaining a query result.

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 invention provides a development project cost evaluation method and device based on improved grey correlation analysis, and belongs to the technical field of data processing. The method comprises the following steps: determining pricing indexes and pricing index weights corresponding to science and technology projects by using an expert evaluation method and an analytic hierarchy process; an important pricing index corresponding to the development project cost is screened by using a grey correlation analysis method and each pricing index weight; and performing project cost calculation according to the important pricing indexes. In the process, factors with relatively low contribution degree are eliminated, so that the benefit of model calculation is increased, and the accuracy of cost calculation is improved. And in combination with expert evaluation and an analytic hierarchy process, the importance of pricing indexes is reasonably embodied. According to the method, a grey correlation analysis method and pricing index weights are utilized to screen out important indexes having significant influence on the project cost, and accurate cost calculation is carried out according to the important indexes, so that the evaluation efficiency and accuracy are improved.

The invention discloses a rail transit cost evaluation method and system based on big data and a storage medium, and the method comprises the steps: obtaining a design file of rail transit from a preset database, determining a corresponding influence parameter factor group for each engineering project of the design file, searching a plurality of historical influence parameter value groups corresponding to the influence parameter factor groups, generating influence factor weights corresponding to the influence parameter factor groups based on the historical influence parameter value groups, correcting cost values in a project list corresponding to the engineering project, and generating project cost corresponding to the engineering project; and based on the project cost generated by each engineering project, obtaining a traffic cost corresponding to the rail transit, and carrying out cost evaluation on the rail transit. According to the method, factors causing cost change in the similar historical engineering projects of each split engineering project are comprehensively analyzed, and the influence factor weights are generated to correct the project cost of the engineering projects, so that accurate evaluation of the rail transit cost is realized.

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 present application discloses a method, device, equipment and storage medium for evaluating construction cost, and relates to the technical field of construction cost. The method for evaluating construction cost is disclosed, including: after receiving an evaluation instruction for construction cost, obtaining the engineering drawings of the construction project to be evaluated; inputting the engineering drawings into a preset cost evaluation model to obtain the construction cost of the construction project to be evaluated, wherein the preset cost evaluation model is obtained by training the to-be-trained model based on annotated historical engineering drawings, and the annotated historical engineering drawings are historical engineering drawings annotated with additional costs that will be incurred during the construction process according to actual working conditions. That is, when evaluating the construction cost through the preset cost evaluation model, the additional costs that will be required during the construction of the construction project to be evaluated will be combined for evaluation, and the construction cost of the construction project to be evaluated is obtained so that the cost of the construction project to be evaluated is more in line with the actual situation, thereby improving the accuracy of the construction cost evaluation.

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.