157 results about "Cost optimization" patented technology

The invention provides a truck-unmanned aerial vehicle cooperative dynamic scheduling method and system in emergency logistics, and relates to the technical field of unmanned aerial vehicle intelligent scheduling. Constructing a truck-unmanned aerial vehicle cooperative distribution network, wherein each truck is equipped with a cooperative unit comprising a large unmanned aerial vehicle and a small unmanned aerial vehicle; the network comprises warehouses, hub points and demand points; establishing a mixed integer programming model by taking minimization of the total operation cost as a target and taking electric quantity management, service distribution, a time window and collaborative feasibility as constraints; and solving the model by adopting a randomized greedy algorithm, and synchronously deciding a truck path and an unmanned aerial vehicle task in single iteration to obtain a truck path scheme, an unmanned aerial vehicle service distribution scheme and a dynamic electric quantity scheduling scheme. Therefore, the cost optimization and dynamic coordination of emergency logistics scheduling are realized, and the efficiency and feasibility of emergency distribution are effectively improved.

This invention discloses a method, system, and storage medium for electric vehicle charging scheduling based on dynamic data fusion, relating to the field of data processing technology. The method includes the following steps: acquiring the status monitoring parameters of all standby charging interfaces and filtering out available charging piles that meet the requirements; classifying vehicles into charging-priority vehicles and cost-priority vehicles based on the real-time status data and environmental parameters of the electric vehicles requesting charging; for charging-priority vehicles, adjusting the charging pile search range, introducing output power to determine the target charging pile and performing a locking operation; for cost-priority vehicles, calculating the cost optimization coefficient based on arrival time prediction and electricity price fluctuations within the time period, and generating a preferred charging pile sequence. The user finally selects and locks the charging pile, while dynamically adjusting the locking time according to road congestion. This method achieves efficient allocation of charging pile resources, optimizing user experience and economic costs.

The invention provides a cross-day two-stage random scheduling method for an industrial park integrated energy system, and the method comprises the steps: fitting the output fluctuation characteristics of new energy power generation equipment in a cross-day time scale based on historical data, and constructing an uncertainty scene set containing a new energy prediction error; establishing a cross-day two-stage stochastic programming model; according to the two-stage model, an optimal scheduling scheme is solved so as to minimize the overall operation cost, and the operation cost comprises the demand electric charge, the electricity purchase electric charge, the new energy power abandoning cost, the unit start-stop cost and the standby penalty cost; and on the basis of tie line power constraint and dynamic response characteristics of the multi-energy coupling equipment, feasibility verification and rolling optimization adjustment are performed on the scheduling scheme. According to the method, the new energy consumption capability of the industrial park integrated energy system under the cross-day time scale can be effectively improved, the total operation cost is reduced, and collaborative scheduling of demand cost optimization and spot market participation is realized.

The invention relates to the technical field of e-commerce logistics and supply chain management, and discloses a multi-channel e-commerce platform order processing optimization system and method, and the method comprises the steps: correcting physical inventory data through a Lagrange dual variable fed back in a previous period, calculating a virtual inventory, and receiving a discrete order flow; constructing a space-time hypergraph model of the to-be-processed order according to the commodity homogeneous attribute and the spatial neighborhood attribute; performing constraint optimization solution on the hypergraph model based on a Lagrangian relaxation algorithm, and generating an optimal order allocation matrix meeting productivity constraints and a Lagrangian dual variable of a current period; and analyzing the distribution matrix to generate an operation instruction, issuing the operation instruction to the physical warehouse, and feeding back the dual variables to the inventory calculation module. According to the method, collaborative operation benefits are mined through the space-time hypergraph, and a negative feedback closed loop with physical capacity pointing to front-end sales is constructed by using dual variables, so that performance cost optimization and flow adaptive control are realized.

The invention belongs to the technical field of wind power operation and maintenance, and discloses a wind turbine generator intelligent maintenance system combined with component residual life evaluation, which comprises a sensing edge layer, a station cooperation layer, a cloud intelligent center, an execution and safety interlocking layer and a credible evidence storage layer. The system collects unit operation and environment data through the edge layer, gathers the data through the station cooperation layer, inputs the data into the cloud intelligent center for multi-domain degradation modeling and life prediction, and determines a maintenance window and a resource scheduling scheme based on a comprehensive cost optimization model. The cloud center realizes cross-station model sharing and self-learning through a federated learning mechanism, the execution layer completes maintenance tasks and safety locking control, and the trusted evidence storage layer performs block chain abstract evidence storage on key data. According to the system, health state evaluation, intelligent maintenance decision making and whole-process credible tracing of the wind turbine generator are realized, and the operation and maintenance safety and economy are improved.

The invention provides an operator-level data consanguinity automatic generation method based on a large model, and relates to the field of electric digital data processing, and the method comprises the steps of S1, heterogeneous script preprocessing and semantic normalization, S2, symbol enhanced LLM consanguinity reasoning, S3, causal logic verification and consanguinity correction, S4, dynamic adaptation, and S5, consanguinity intelligent analysis and visualization. The method is suitable for multi-source, multi-format and multi-language data processing scenes, the automation degree, the refinement capability and the intelligent level of data blood relationship construction can be improved, and more accurate and efficient technical support is provided for data governance, data security, system migration / reconstruction, data quality management and control, cost optimization and the like.

The invention discloses a cloud service cost optimization and prediction analysis system based on artificial intelligence, and relates to the technical field of cloud service cost optimization, and the system comprises a cost feature deep analysis module which accesses a cloud service full life cycle standardized cost data set, and generates a cost efficiency evaluation matrix and an abnormal attribution report; the multi-modal prediction engine module is used for constructing a multi-scene cost prediction model; the dynamic optimization decision module is used for generating a resource dynamic scheduling strategy, a service type selection optimization scheme and a cost budget dynamic allocation plan, and calculating an expected cost saving rate and a risk coefficient of each scheme; and the closed-loop iteration upgrading module tracks the implementation effect of the optimization scheme in real time and updates the three-dimensional characteristic spectrum and prediction model parameters. According to the invention, through a three-dimensional characteristic spectrum, multi-algorithm fusion prediction, SLA constraint verification and a closed loop iteration mechanism, and by matching with an intelligent interaction visualization and early warning module, intelligent transformation of the cloud service cost from passive accounting to active prediction and from experience optimization to scientific decision is realized.

The invention discloses a multi-target cost optimization scheduling system for a wind-light-gas storage micro-grid, and relates to the technical field of micro-grid scheduling, and the system comprises an equipment health parameter collection module which collects the operation parameters of wind-light-gas storage equipment through a sensor, and transmits the operation parameters to a health state evaluation module after preprocessing; the module constructs a multi-dimensional model to calculate a health index and predict an attenuation trend; an aging loss cost quantification module constructs a dynamic model to calculate the cost; the multi-target optimization scheduling module sets multiple targets and searches an optimal strategy; the strategy execution and feedback module executes the instruction and returns data; according to the invention, by constructing a linkage mechanism of the equipment health parameter acquisition module and the health state evaluation module, real-time perception and quantitative analysis of the state of the wind-light-gas storage micro-grid equipment are realized, and a multi-dimensional stress-fatigue accumulation model is adopted to calculate the health index of the equipment and divide the health level. The problems that in a traditional method, health assessment is high in subjectivity, and stress accumulation influences cannot be quantified are solved.

The invention relates to the technical field of network security asset risk pricing and management, in particular to a business influence-oriented network security asset risk pricing and management method and system. Historical business data, infrastructure resource data and external environment data are integrated to construct a business-resource portrait library; a potential interruption event prediction model is trained based on a random forest or a long short-term memory (LSTM) network, a multi-target cost optimization function including resource preset cost, service interruption loss cost and resource idle penalty cost is constructed, and an optimal resource preset strategy is solved by adopting a non-dominated sorting genetic algorithm NSGA-II or a particle swarm optimization algorithm. And after execution, feedback data update models and functions form closed-loop optimization. According to the method, accurate risk prediction, dynamic cost balance and continuous strategy adaptation are realized, the service continuity is effectively guaranteed, the overall operation cost of an enterprise is reduced, and the pertinence and effectiveness of network security asset risk pricing and management are improved.

The invention discloses an online temporary structure intelligent aided design method and system based on a big language model, and the method comprises the steps: receiving a user natural language temporary structure design demand, a big language model recognition intention, extracting parameters, complementing missing parameters in combination with a specification and a knowledge base, verifying the reasonability, and supporting the interaction adjustment. And forming a standardized design parameter set. And synchronously generating a BIM model and an FEM model based on the parameter set, submitting the FEM model to a calculation engine for analysis, and associating the BIM model through a homologous ID to realize result visualization. If the result reaches the standard, an optimization suggestion is given in combination with multi-source data, and a user selects whether to optimize or not; and if not reaching the standard or receiving a cost optimization instruction, directly optimizing. A large model is utilized to generate a scheme analysis text, a standardized calculation book is automatically assembled, and multi-level auditing is started and traces are reserved. And storing the approved scheme into a knowledge base in a structured manner to form a knowledge closed loop. And the efficiency, accuracy and standardization level of temporary structure design are improved.

The invention discloses a clothing multi-mode collaborative design method driven by small-number ornaments. The clothing multi-mode collaborative design method specifically comprises the steps of receiving an ornament plane image uploaded by a user, extracting an ornament main body contour through an image segmentation network, executing three-level attribute classification in parallel based on contour features, and generating a structured feature matrix M; inputting the feature matrix M into a copyright filtering module, and outputting a costume design constraint set; encoding the constraint set C into embedded vectors with the same dimension as the feature matrix M, performing LayerNorm standardization and splicing, inputting the embedded vectors into a generative adversarial network, and outputting a matching degree score S; performing process decision based on the matching degree score S, including: generating a cost optimization model in combination with the version complexity and the fabric unit price; the metal ornament is associated with anti-wrinkle fabric, and a laser cutting process is started for a special-shaped outline; and outputting a cutting scheme and a bill of materials which can be directly put into production.

The invention discloses a power enterprise agent platform based on a large language model, and relates to the technical field of artificial intelligence and power systems, and the platform comprises a data governance module, a model deployment module, a platform building module and an application development module. Through the technical path of large model enabling, agent landing and electric power scene deep adaptation, the core pain points of a traditional electric power enterprise in the aspects of efficiency, safety, data utilization, service quality and the like are systematically solved, four dimensions of intelligent operation and maintenance, data-driven decision, user experience upgrading and cost optimization are covered, and the method has the advantages of being high in practicability and easy to popularize. The digital competitiveness of a power enterprise is remarkably improved, and the requirements of a novel power system for high automation, high robustness and agile response are met; the intelligent agent and the workflow are effectively combined together, a single automation mode is replaced, complex task processing time is shortened, conventional task efficiency is improved, and flexibility and execution stability are balanced.

The application discloses a cost-optimized out-of-season clothing renovation design method and an intelligent processing system, relates to the field of clothing intelligent design systems, and has the technical scheme as follows: a data acquisition module, which collects original data of out-of-season clothing and popular trend matching accurate marketing landing; a data analysis and prediction module, which evaluates potential risks and feasibility in the renovation process based on the collected data; a design scheme generation module, which automatically generates multiple feasible renovation design schemes according to the data analysis results and personalized requirements input by a user, and determines the best design scheme; and the application integrates a complete closed-loop process from data acquisition to evaluation, combines an artificial intelligence deep learning algorithm to complete demand prediction, design scheme generation and optimization, and significantly improves production efficiency and reduces artificial dependence.

This invention discloses a method for optimizing the cost management of flue gas pollution control, comprising: Step 1: generating a set of operating units for control equipment; Step 2: generating a sequence of combined energy consumption paths; Step 3: inputting the combined energy consumption path sequence into an improved Koopa model; Step 4: generating initial path features through a path embedding module and generating steady-state evolution features through a steady-state evolution coding module; Step 5: generating rearranged path features through a disturbance track rearrangement module using a smoke load tidal migration mechanism and generating a path cost evolution sequence through a cost trend output module; Step 6: performing weighted splitting of path affiliation to generate single-path control cost units; Step 7: performing emission contribution mapping to generate a path contribution sequence and using the MCTS algorithm for tree search to generate a control path adjustment set; Step 8: outputting the cost optimization results. This invention combines the improved Koopa model and the MCTS algorithm to achieve dynamic optimization of flue gas control paths.

The invention belongs to the technical field of enterprise management, and particularly relates to a green transformation cost optimization method and system based on green premium, and the method comprises the steps: collecting the specified parameters of a target enterprise, so as to determine the green premium cost, the green premium cost comprises self-generation and self-use cost, special line direct supply cost, green electricity direct purchase cost, green electricity transaction participation cost, green certificate cost and green electricity investment cost; and formulating or optimizing an existing green transformation method according to the green premium cost so as to minimize the green transformation cost. Collecting specified parameters of the target enterprise to determine green premium cost; and according to the green premium cost, an existing green transformation method is formulated or optimized, so that the green transformation cost is minimized, reasonable suggestions can be provided for transformation of enterprises, and the transformation efficiency is improved.

The invention provides a method and device for carrying out cost optimization prediction through multi-dimensional business travel behavior analysis, and belongs to the technical field of enterprise business travel management.The method comprises the steps that business travel data are collected, cleaning, duplicate removal and entity alignment operation are carried out, and a business travel behavior data set is constructed; executing a preset query script on the business travel behavior data set, extracting characteristic indexes from preset dimensions, executing descriptive analysis, diagnostic analysis and clustering analysis, and generating a business travel behavior analysis report; and inputting the business travel behavior data set and the business travel behavior analysis report into a cost optimization prediction model to carry out business travel demand prediction, business travel price prediction and business travel travel optimization, generating an optimal travel travel scheme set, pushing the optimal travel travel scheme set to the employee user, collecting a feedback result of the employee user on the optimal travel travel scheme set, updating the business travel behavior data set, and obtaining a business travel result. And incremental training and iterative optimization are carried out on the cost optimization prediction model. Through multi-dimensional analysis and prediction, the travel cost is optimized, and the management efficiency is improved.

The invention discloses a storage equipment predictive maintenance method and device, a storage medium and a computer program product, and relates to the technical field of industrial Internet of Things, and the method comprises the steps: collecting multi-source modal data of storage equipment, and generating a structured feature vector; using a lightweight decision tree model to carry out fault positioning on error code associated features in the structured feature vector to obtain a preliminary fault positioning result; generating a cloud fault diagnosis report according to the preliminary fault positioning result, cloud fault diagnosis and pre-integrated supply chain information; generating a plurality of candidate maintenance schemes in combination with a priority function based on the preliminary fault positioning result and / or the cloud fault diagnosis report; and selecting an optimal maintenance scheme from the plurality of candidate maintenance schemes through a cost optimization module. On the basis of fusing the supply chain operation data, the diagnosis accuracy and the operation and maintenance economy of the predictive maintenance of the storage equipment are improved.

The invention discloses a warehouse management system for accurately managing and controlling inventory, and the system constructs a three-dimensional physical model through collecting a warehouse physical structure, a dynamic constraint factor and cargo information. Meanwhile, an optimal transportation path is planned based on the storage coordinates and the shipping port coordinates, and the transportation cost and the current storage cost are calculated; calculating a residual value through attributes such as the goods immediacy degree and the goods delivery frequency; using an ARIMA-LSTM fusion model to predict the future storage cost; and finally, the warehouse-out priority is calculated through the multi-dimensional parameters, and the to-be-scrapped goods are automatically marked in combination with an early warning threshold value. According to the method, the problems of single warehouse-out decision, extensive path cost calculation and poor suitability of medium and large-scale warehouses of a traditional system are solved, the inventory management and control precision, the warehouse-out process high efficiency and the operation cost optimization are realized, and the inventory turnover rate and the warehouse management comprehensive benefits are improved.

The invention discloses a cigarette order delivery warehouse intelligent matching method, system and device and a medium, belongs to the technical field of intelligent manufacturing and intelligent logistics, and aims to solve the technical problem of how to realize intelligent distribution and logistics cost optimization of multi-warehouse and multi-specification cigarette orders. According to the technical scheme, the method comprises the following steps: collecting relevant information of cigarette orders: obtaining order information, delivery warehouse information, customer address information, specification demand quantity and transportation cost information through an enterprise resource planning system, a warehouse management system or a geographic information system; data preprocessing: cleaning, normalizing and formatting the collected cigarette order related information, and constructing a data set; building an intelligent matching model of the cigarette order delivery warehouse: building the intelligent matching model of the cigarette order delivery warehouse through the processes of defining decision variables, minimizing the total logistics cost and setting constraint conditions according to the specification items of the order and the delivery warehouse information; and optimizing the cigarette order delivery warehouse intelligent matching model based on an improved adaptive algorithm.

The application provides a workflow scheduling method considering security risks and cost optimization in a cloud environment, which reduces the overall security risks and execution costs of the workflow under the given deadline constraint of a user. Firstly, the cloud computing resources are defined, the workflow structure is abstracted, and a security risk calculation method is proposed. Then, a reinforcement learning model oriented to security risks and cost optimization is constructed. Next, an action selection algorithm in the learning process is determined. Finally, iterative learning is carried out based on the Q learning algorithm idea, and finally the task scheduling scheme is determined, and the overall security risks and total costs of the workflow are obtained.

The invention belongs to the technical field of training cost optimization, and discloses an unmanned aerial vehicle cluster-oriented federated learning training cost optimization method, which comprises the following steps of: jointly optimizing a local convergence threshold value, a local iteration frequency, computing resource allocation, bandwidth allocation and transmitting power allocation; the training cost (defined as a weighted sum of training energy consumption and training time) is minimized. The framework comprises a dichotomy-based joint optimization algorithm, each sub-optimization problem is solved alternately, and balance between energy and time is ensured. The core elements of the framework are as follows: in a system modeling stage, the training cost is defined as the weighted sum in the whole federal learning process, and a problem is expressed as a non-convex mixed integer programming problem.

The embodiment of the invention relates to the technical field of artificial intelligence, and provides an apartment financial management intelligent auxiliary method and system based on artificial intelligence, and the method comprises the steps: obtaining apartment management data, hotel management data and transaction data disclosed by a market, and obtaining multi-source data; storing the multi-source data to a cloud financial management center and encrypting the multi-source data; outputting a prediction result of the multi-source data through a financial analysis prediction network; the financial analysis and prediction network comprises an income prediction model and a component analysis optimization model which run in parallel; constructing a visual report form and an analysis report based on the prediction result, and dynamically displaying the report form and the analysis report to the user; and inputting the multi-source data into the financial management model, performing analysis through an auditing rule in the financial management model to generate a financial auditing report, calling the financial risk model to monitor the financial auditing report, and synchronously pushing the financial auditing report to a manual auditing terminal for verification and approval processing. According to the method, the data utilization rate is increased, and the cost optimization accuracy, the financial management efficiency and the safety are improved.

The invention discloses a project group owner risk cost optimization method under an uncertain delay perspective. The method comprises the following steps: generating a plurality of groups of project group delay scenes by using a Monte Carlo method; putting forward a prediction model based on a GNN-GRNN algorithm, taking the predicted completion time of each project and the completion degree of each project as the input of the GNN algorithm to obtain an association relationship among the projects, inputting the three kinds of data into the GRNN algorithm on the basis of a generated delay scene, and predicting the project group material demand quantity at the next moment; constructing a project group owner risk cost optimization model under an uncertain delay perspective; and the optimization model is solved by using a DDPG algorithm to obtain the material supply amount at the next moment, the project hurry-up strategy and the risk cost after optimization, so that the decision-making difficulty of a manager is reduced.

The invention discloses an enterprise internal acquisition cost optimization decision support system based on big data analysis. The system comprises a data aggregation and integration module, a purchase behavior feature extraction module, a cost driving factor deconstruction module, a supplier multi-dimensional evaluation module, a purchase strategy intelligent customization module, an enterprise purchase data warehouse and an enterprise purchase supervision terminal. The purchase strategy suitable for the enterprise is intelligently generated by integrating the purchase behavior characteristics, the cost driving factors and the supplier evaluation result, the purchase cost composition and the dynamic change can be accurately informed, scientific and reasonable purchase decision suggestions are provided for the enterprise, the purchase cost is effectively reduced, and the purchase efficiency is improved. And the execution effect of the purchasing strategy is monitored, evaluated and fed back in real time, so that each link of the purchasing process can be monitored in real time, potential problems can be found and solved in time, the purchasing efficiency and quality are improved, the optimization of the purchasing cost and the maximization of the overall benefit are realized, the purchasing supervision difficulty of an enterprise is remarkably reduced, and the intelligent level is high.

The invention discloses a high-temperature heat storage material cost optimization method and system, and relates to the field of high-temperature heat storage material cost optimization. According to the high-temperature heat storage material cost optimization method and system disclosed by the embodiment of the invention, a dual-drive mode of a solid waste resource coupling process and a policy lever is constructed, high-quality carbon fibers are efficiently produced by utilizing waste resources of an inferior coal power plant, and remaining residues are treated by a special process to form a novel high-temperature heat storage material; the directional conversion process of coal-series solid waste is formed, the problem of preparation of solid waste in the whole life cycle through SiC is completely solved, the requirements of a national clean production promotion method and an energy-saving method are met, waste generated by a project is effectively utilized, harmlessness and recycling of garbage are truly achieved, and the double targets of energy circulation and clean production are achieved.

The invention discloses a market mechanism-based power distribution network distributed clean energy bearing optimization method. The method comprises the following steps of 1, constructing a planning decision model of a power distribution network distributed energy system and a small gas turbine; step 2, constructing a power purchase decision optimization model of the power purchase side power distribution network so as to obtain a decision scheme with the minimum total power purchase cost; and step 3, on the basis of the bidding strategy game model of the distributed power generation unit, constructing a market clearing model of the power transaction center, and obtaining an optimal decision scheme under the model. According to the method, the defect that existing power distribution network planning is difficult to consider clean energy bearing capacity, investment uncertainty and cost optimization is overcome, low-cost power purchase and efficient clean energy consumption are realized, the reliability and flexibility of the system are improved, and the bearing capacity and operation economy of the power distribution network are effectively enhanced.

This invention discloses a full-process monitoring and management system for chlor-alkali chemical production based on the Industrial Internet of Things (IIoT), specifically relating to the field of industrial data processing technology. It includes a data sensing and acquisition module, a raw salt intelligent proportioning and cost optimization module, a brine quality early warning and reagent dosing module, an electrolyzer intelligent optimization module, and a full-process collaborative management feedback module. The data sensing and acquisition module uses sensor technology to collect chlor-alkali production operating data in real time and performs preprocessing to obtain a standard dataset for full-process monitoring and management. Through the brine quality early warning and reagent dosing module and the electrolyzer intelligent optimization module, this invention achieves full-process collaborative management, enabling early transmission of upstream water quality fluctuations and reverse tracing of downstream voltage anomalies. This ensures the safe operation of the electrolyzer, reduces power consumption per ton of alkali, extends the service life of core equipment, reduces maintenance costs, and achieves the comprehensive goals of improving quality, reducing consumption, increasing production, and extending the lifespan of chlor-alkali production.

The application provides a large model-based operator-level data blood relationship automatic generation method, relates to the field of electric digital data processing, and comprises the following steps: step S1, heterogeneous script preprocessing and semantic normalization; step S2, symbol-enhanced LLM blood inference; step S3, causal logic verification and blood correction; step S4, dynamic adaptation; and step S5, blood intelligent analysis and visualization. The method is suitable for multi-source, multi-format and multi-language data processing scenes, can improve the automation degree, fine capacity and intelligent level of data blood construction, and provides more accurate and efficient technical support for data governance, data security, system migration / reconstruction, data quality control, cost optimization and the like.

The invention relates to the technical field of resource planning, and discloses a smart city resource flow simulation and planning system based on a city metabolism model, and the system comprises a parameter collection module which determines a time boundary parameter and a scale parameter; the scale parameterization module is used for calculating metabolic integral scale elasticity; the effective budget generation module is used for calculating to obtain an elastic weighted effective budget; the resource allocation solving module is used for solving the allocation amount of each type of source; the scheduling curve mapping module is used for calculating to obtain a scheduling curve; and the planning result output module outputs the allocation list. According to the method, the robust budget is constructed through the scale parameterization model and the metabolic integral scale elasticity, and the planning deviation under different statistical granularities is reduced. The objective function is fused with the energy cost, the source cost and the scale robust item, the balance of cost control and scale adaptability is realized, and the execution deviation caused by purely pursuing the optimal cost is avoided.

This invention discloses a cost data optimization method and system based on a flexible task work order mechanism, belonging to the field of work order allocation technology. The system includes a dynamic sensing module, an efficiency analysis module, a cost optimization module, and a data storage module. The dynamic sensing module receives task work orders from clients and collects task records from all service objects within the park. The efficiency analysis module analyzes all work order records completed by each service object in each cycle based on the task records, calculates the cost index, and fits the efficiency index relationship by combining the interval duration of the work order records. The cost optimization module calculates the cost index of each task work order, sorts them, and performs the first round of allocation to service objects. Based on the efficiency index relationship, it predicts the efficiency index changes of each service object, determines the interval duration, and iteratively allocates task work orders. The data storage module generates a task list for each service object, records the process information of executing task work orders, and saves it to the task record.