48 results about "Resource based" patented technology

The invention discloses a multi-dimensional scientific and technological achievement and innovation resource matching method based on big data, and relates to the technical field of science and technology finance, and the method comprises the steps: obtaining enterprise working condition data and insurance and financial terms, inputting a dynamic classification model, outputting an enterprise type, and associating to an achievement transformation stage; the insurance scheme priority is rendered on the insurance interface, the corresponding insurance scheme is set according to the enterprise type, format contract terms are embedded into the insurance description, and insurance decision data are generated; and insuring decision data is fed back to the entropy risk assessment mechanism, and the weight coefficient and the trial period parameter of the difference factor are updated. According to the method, historical performance data, real-time trial period parameters and matching decisions of credit institutions are fused through an entropy risk assessment mechanism, migration risk values are dynamically generated, risk quantification of multi-source heterogeneous data is realized, and enterprise risk migration trajectories are accurately captured.

The invention relates to the technical field of EPC engineering information management, in particular to an EPC engineering information management system supporting bidirectional matching of a business strategy and a technical path. The data acquisition and integration unit is used for acquiring multi-source heterogeneous data from the whole project implementation process; constructing a resource optimization allocation model based on the preprocessed business side data, and generating an actually executable resource allocation scheme; based on the resource configuration scheme and the model constraint condition, combining the technical side data and the external environment data to generate a technical path candidate set according with the business constraint condition; and based on the technical path candidate set and the business side data, constructing a bidirectional coupling optimization model, and performing multi-objective optimization solution to obtain an implementation scheme with an optimal score. A resource allocation scheme is mapped to each construction node, and a technical path candidate set conforming to business constraints is generated in combination with a BIM model and external environment data, so that accurate scheduling of materials, equipment and manpower and construction period prediction are realized.

The invention discloses a hotel customer demand intelligent analysis and response method, device and equipment and a medium, and relates to the technical field of hotel management intellectualization, and the method comprises the steps: mapping a customer behavior time sequence data set into a hypergraph structure, calculating an attention weight coefficient based on a multi-head attention mechanism, and generating a hypergraph feature vector; constructing a resource state graph based on the resource state time sequence data set, inputting the hypergraph feature vector and the resource state graph into a multi-objective optimization model based on the game theory, defining a customer satisfaction function, a hotel revenue function and a resource consumption function, and taking the functions as game participants; calculating a non-dominated solution set of a game participant in an equilibrium state through a Pareto frontier solver, and screening out an optimal response strategy; according to the method, the multi-modal data are dynamically fused through the hypergraph feature vectors and the multi-head attention weights, and the problem of multi-modal data relevance missing is solved.

The invention relates to the technical field of enterprise resource configuration, in particular to an intelligent enterprise resource configuration method and system based on dynamic priority. Generating a kurtosis-valley model of potential transition by constructing a dynamic execution state baseline of a target service and injecting a service mutation disturbance event; then constructing a search tree architecture, and planning state transition and priority regular vector guidance of a target service; and dynamically allocating and rendering a kurtosis-valley model of the potential transition according to the guidance to obtain a dynamic priority result. If the target service is a class of scheduling service, low-load and high-availability resources are preferentially allocated to the target service; and if the scheduling service is the second-class scheduling service, conflict optimization configuration is carried out according to the function efficiency coverage condition of the resources, and corresponding resource configuration schemes are generated respectively. According to the invention, the intelligent and dynamic configuration of enterprise resources according to the dynamic priority of the business mutation is realized, and the rationality and the strain capacity of resource allocation are obviously improved.

The present invention discloses a method and system for co-construction and sharing of power infrastructure in 5G to support power transmission and distribution, including: using a unified resource modeling module to model and associate resources on the operator side and the power side; the power resource management module provides a full-view list of resources for co-construction and sharing of the entire power grid to power grid management personnel based on the model and resource library provided by the unified resource modeling module; based on the full-view list of resources, the external enterprise demand management module provides information and demand query and registration to external enterprises through an open interface. The present invention optimizes the addressing process, accurately matches demand, increases the speed of co-construction and sharing, improves efficiency, and effectively promotes the matching of 5G and power demand; provides a 5G-based real-time backhaul method for the transmission mode of drones; based on the matching of operator signal quality and power resources, flexibly carries out site planning according to the needs of specific power consumption services, and provides a specific business optimization method based on 5G.

The invention relates to an electromechanical engineering resource allocation method based on a spatial coupling degree, and the method comprises the steps: determining a process method of each BIM component based on a pre-constructed mapping relation between a WBS task and the BIM component and an industry quota, generating a process bounding box of each BIM component according to the process method and a three-dimensional model of the component, determining a parameter value of each process parameter based on a process bounding box and a process sequence, performing construction priority ranking of the components according to the parameter values and weights corresponding to the parameters, constructing a total resource pool and a task set in a construction period, and generating a resource competition model based on the total resource pool, the task set and a construction priority ranking result, and obtaining a construction strategy and a resource configuration strategy corresponding to the construction strategy according to the resource competition model and the resource condition of the resource total amount pool. Through the method and the device, the problem of low utilization rate of electromechanical engineering resources is solved, accurate investment of the resources is realized through the resource competition model, and idle and accumulation of the resources in a construction site are avoided.

The invention provides a multi-time scale resource aggregation optimization method and system based on a state space model, and is applied to the technical field of smart power grids. The method comprises the following steps: constructing a dynamic state space model for representing internal health state evolution of distributed physical resources; aggregation optimization is carried out in a multi-time scale optimization framework, and a day-ahead optimization layer determines a day-ahead scheduling baseline considering long-term service life of resources based on a non-cooperative game mechanism introducing physical attenuation cost; the intra-day optimization layer carries out rolling correction on a day-ahead scheduling baseline through a local game taking correction income maximization as a target so as to cope with volatility of renewable energy sources and market price changes; the second-level optimization layer responds to a power grid real-time power regulation instruction; and finally generating a target power control instruction to control resource operation. According to the method, the resource long-term health cost and the multi-scale marketization game are deeply fused, and the economic benefit, the response capability and the asset preservation rate of the system are remarkably improved.

The invention discloses a building flexible resource collaborative optimization configuration method, which comprises the steps of obtaining equipment resource data, material resource data and human resource data of a to-be-regulated building, obtaining time-shifting resources and non-time-shifting resources on the basis of the human resource data, dividing start-stop resources in the non-time-shifting resources into flexible resources, and storing the flexible resources into a server; the method comprises the steps of obtaining a resource configuration time sequence, calculating an association target and task resource loss among different resources according to the resource configuration time sequence, performing differential pre-configuration by using the association target to obtain differential pre-loss, and obtaining a pre-regulation curve based on the task resource loss and the differential pre-loss. And carrying out cooperative configuration on the flexible resources and the rigid resources through the pre-regulation curve according to the construction task. According to the method, a pre-regulation curve is generated through hierarchical classification and incremental case-based reasoning, and a rigid-flexible resource cooperation strategy is dynamically adjusted in combination with historical construction data and current project difference, so that a configuration scheme meets actual construction requirements, and scheduling deviation and rework cost are reduced.

The present application relates to the technical field of power systems, and particularly to a method and device for optimizing the probabilistic configuration of a virtual power plant in multiple scenarios. The method includes: determining the revenue function of each resource based on a high-dimensional game model, and using the Nash equilibrium principle to describe the influence conditions of the target resource allocation decision on the resource revenue, so as to calculate the opportunity cost of each resource according to the influence conditions and the revenue function; probabilistically modeling the future scenarios of the virtual power plant by using a Markov decision process and Monte Carlo simulation to calculate the expected value of resource revenue, and using a non-linear function to characterize the matching degree between the resource regulation characteristics and the demand external characteristics of the virtual power plant; calculating the comprehensive configuration priority of the virtual power plant based on the resource opportunity cost and the matching degree, so that the target virtual power plant performs resource allocation according to the comprehensive configuration priority. Thus, the problems of poor adaptability and robustness of the resource allocation results of the existing optimization configuration methods and high computational complexity are solved.

The invention provides a self-adaptive alliance forming method for resource competitive tasks in multiple industrial chains, and aims to solve the problems of low task completion rate and low benefit caused by poor alliance forming sequence, resource redundancy and high transportation cost. The method comprises the following steps: firstly, establishing an alliance formation model considering resource transportation cost and redundancy cost; secondly, resource efficiency is calculated according to resource requirements, scarcity and expected benefits of the tasks, and task priorities are determined in a self-adaptive mode according to the resource efficiency; when an alliance is established, the method comprises the following steps: firstly, based on situation resource adaptation degrees, selecting an alliance leader with the highest adaptation degree as the alliance leader; and then, selecting the alliance members according to the coordination resource adaptation degree until the task requirements are met. According to the method, by dynamically optimizing the task sequence and accurately constructing the alliance, the resource consumption cost can be effectively reduced, and the task completion rate and the total income of the multi-industrial-chain network are remarkably improved under the condition that the resources are limited.

The invention belongs to the technical field of complex heavy equipment, and particularly relates to a customer demand preference-based complex heavy equipment manufacturer recommendation method, which comprises the following steps of S1, constructing a customer demand index system and a manufacturer resource evaluation index system; s2, acquiring demand preference data of a customer; s3, calculating a subjective weight vector omega 1 of a resource evaluation index by using the customer demand index system, the resource evaluation index system and the demand preference data; s4, calculating an objective weight vector omega2 of the resource evaluation indexes based on the comparison strength and conflict between the resource evaluation indexes; s5, solving an optimal combination coefficient # imgabs0 #, and calculating a comprehensive weight vector omega of the resource evaluation index according to # imgabs1 #; and S6, calculating the comprehensive score of each manufacturer by using the comprehensive weight vector omega and combining the data of each resource evaluation index of each manufacturer. According to the method, personalized requirements of customers on complex heavy equipment manufacturers can be accurately identified, and the manufacturer with the highest adaptation degree can be recommended.

The invention relates to the technical field of building information management, in particular to a building engineering cost analysis method and system based on big data, and the method comprises the following steps: obtaining component plan and resource measurement data, screening resource labels in a time window, matching construction behaviors, building a binding path table, extracting measurement data, and analyzing an input difference; according to the method, the accurate corresponding relation between the resources and the components is established through grouping association of the component operation time and the resource labels, the traceability of the resource data is improved, the resource use link is positioned in combination with the construction path overlapping relation, and the construction cost estimation efficiency is improved. The space-time response capability of resource configuration is enhanced, key nodes are identified based on resource input fluctuation, the scheduling rhythm adaptability is improved, through mapping of resources and component structures, the structured expression of resource consumption is enhanced, the average resource consumption of unit components in a region is extracted, and the estimated granularity and structural logic are enhanced.

The invention provides a resource quota configuration method and device and a training method of a credit evaluation model, relates to the technical field of data processing, and can optimize the quota of resource application of a stock user, improve the passing rate of resource application of the stock user and reduce the mobility of the stock user. According to the specific technical scheme, credit data such as business conditions of stock users, historical resource application performance data, credit evaluation data and resource management scale AUM data are collected; and evaluating the credit rating of the stock user according to the credit data by using a pre-trained credit evaluation model. And determining a resource limit coefficient of the stock user in combination with the credit rating of the stock user, the service demand information of the resource application service and the multi-dimensional risk assessment parameter. And determining an initial quota, a first configuration quota and a second configuration quota based on the resource quota coefficient, the historical resource application performance data and the application time, and determining a resource use quota which can be applied by the stock user according to the data.

The invention discloses an engineering constructor financial management system optimization method, which relates to the technical field of financial management, and comprises the following steps: after financial abnormity is identified, constructing a fund use stability index based on an expenditure change curve, and meanwhile, constructing a resource optimization index based on a resource utilization condition; and jointly inputting the fund use stability index and the resource optimization index into an intelligent optimization model trained by historical data, automatically calculating a financial coordination index, and if the financial coordination index is lower than a preset standard, automatically judging that financial management and resource allocation are not in an optimal state, based on the financial coordination index calculated in real time and the fluctuation of the financial coordination index in different time periods, a series of optimization strategies are selected and executed autonomously, and the optimization strategies comprise intelligent budget adjustment and expenditure control, man-hour and resource dynamic optimization and real-time financial health report generation and intelligent reminding. According to the optimization method, timely identification and resource optimization of financial abnormities are realized through the intelligent optimization model, and the financial management efficiency is effectively improved.

The present disclosure provides a resource recommendation method, apparatus, device, and storage medium, which relate to technical fields such as big data, information flow, search technology, and recommendation technology in the field of data processing. The resource recommendation method includes: in response to a resource recommendation request, according to a first resource quantity threshold, filtering from a candidate resource set to obtain a first recommended resource and a recommendation score corresponding to the first recommended resource; obtaining a second recommended resource and a recommendation score corresponding to the second recommended resource, the second recommended resource and the recommendation score being obtained by filtering from the candidate resource set according to a second resource quantity threshold, the second resource quantity threshold being greater than the first resource quantity threshold; determining a target recommended resource from the first recommended resource and the second recommended resource based on the recommendation scores corresponding to the first recommended resource and the second recommended resource respectively; and recommending the target recommended resource. Thus, the first recommended resource is enriched with the second recommended resource filtered out on a larger scale, thereby reducing the resource duplication rate.

The invention discloses a hybrid critical-level task scheduling method and device based on dependency perception, and relates to the technical field of task scheduling, and the method comprises the steps: receiving a task data set, a task dependency graph and a resource dependency graph, and when the actual execution time of a high-critical-level task exceeds the worst execution time in a low-critical-level mode, executing the task data set; if yes, switching the overtime high-critical-level tasks and the high-critical-level tasks belonging to the same function with the overtime high-critical-level tasks to a high-critical-level mode; based on the task dependency graph, low-critical-level tasks which belong to the same function as the high-critical-level tasks switched into the high-critical-level mode are screened, and the dynamic importance of the low-critical-level tasks is improved; based on the resource dependency graph, screening low-critical-level tasks having a resource competition relationship with the high-critical-level tasks switched into the high-critical-level mode, and reducing the dynamic importance of the low-critical-level tasks; and sorting based on the adjusted dynamic importance, and sequentially discarding the low-key-level tasks one by one according to the dynamic importance from low to high until the schedulability of the system is met.

The present invention discloses a resource cost calculation method based on the RSOP model for the production manufacturing field. The present invention proposes the RSOP (Resource - Site - Operation - Product) production calculation method, which is an innovative resource cost - operation revenue calculation method used to optimize organizational resource management and promote collaborative work in the industrial chain. The RSOP model provides a new perspective for analyzing and managing organizational resources by refining resource types, establishing the smallest calculation unit, and applying the concept of continuous units (cRSOP). Different from traditional resource management methods, the RSOP model emphasizes being centered around the site, configuring resources based on the site, and supporting organizational decision - making through precise calculations. This model is not only applicable to the production manufacturing process but also can be applied to all aspects of organizational management. The present invention provides a new type of calculation method for organizations in the digital transformation process, helping organizations to re - understand data, discover data, and calculate data.

This invention discloses an optimization method for a financial management system for construction workers, relating to the field of financial management technology. After identifying financial anomalies, a fund utilization stability index is constructed based on expenditure change curves. Simultaneously, a resource optimization index is constructed based on resource utilization. The fund utilization stability index and the resource optimization index are jointly input into an intelligent optimization model trained on historical data. This model automatically calculates a financial coordination index. If the financial coordination index is lower than a preset standard, it automatically determines that financial management and resource allocation have not reached an optimal state. Based on the real-time calculated financial coordination index and its fluctuations over different time periods, the model autonomously selects and executes a series of optimization strategies, including intelligent budget adjustment and expenditure control, dynamic optimization of working hours and resources, and real-time generation of financial health reports and intelligent reminders. This optimization method, through an intelligent optimization model, achieves timely identification of financial anomalies and resource optimization, effectively improving financial management efficiency.

An example embodiment of the present disclosure provides a measurement method, including receiving resource data related to an input, determining a first parameter based on an initial resource value and a final resource value included in the resource data; determining a second parameter based on a historical resource sequence between an initial time and a final time included in the resource data; determining a ratio based on the first parameter and the second parameter, wherein the ratio reflects the performance of the input; comparing or ranking the performance of the input based on the ratio; and selecting one input for operation based on the comparison or ranking. Or generating a warning that the performance of the investment is lower than a predetermined threshold based on the ratio, and reducing the risk of resource loss caused by the investment of the investor based on the warning. In this manner, the performance measurement scheme of the present disclosure may be more robust, fair, consistent, and / or objective.

This application relates to a resource allocation method for electromechanical engineering based on spatial coupling degree. The method includes: determining the construction process and method for each BIM component based on a pre-built mapping relationship between WBS tasks, BIM components, and industry quotas; generating process bounding boxes for each BIM component based on the process bounding boxes and the component's 3D model; determining the parameter values ​​for each process based on the process bounding boxes and process sequence; prioritizing the construction of components according to the parameter values ​​and their corresponding weights; constructing a resource pool and task set for the construction cycle; generating a resource competition model based on the resource pool, task set, and construction priority ranking results; and obtaining construction strategies and corresponding resource allocation strategies based on the resource competition model and the resource pool's resource status. This application solves the problem of low resource utilization in electromechanical engineering by achieving precise resource allocation through the resource competition model, avoiding resource idleness and accumulation on the construction site.

The invention discloses a project resource calling method and device, and equipment, and a storage medium, and relates to the technical field of data processing, and the method comprises the steps: carrying out the priority division of a real-time business data set corresponding to a target project based on a preset review mechanism, and obtaining a business data set corresponding to a target priority; calling a preset efficiency model to carry out value evaluation on the business data set to obtain an evaluation result; compared with a traditional resource calling method that resources cannot be reasonably arranged, so that the resources are idle and the output efficiency is low, the resource allocation method has the advantages that priority division and value evaluation are carried out on the business data through a pre-customized review mechanism and an efficiency model, and the resource allocation efficiency is improved. And resource allocation is completed according to the divided priorities and the evaluation result, so that resource flow is effectively promoted, resources are reasonably arranged, and the output efficiency is improved.

This invention relates to the field of computer system resource management technology, and in particular to a method for dynamic allocation and adjustment of computer system resources based on big data analysis. This method involves collecting and standardizing hardware resource status data, business load characteristic data, and historical allocation data. A two-dimensional evaluation model is established to determine resource allocation priorities. An optimal resource allocation scheme is obtained by constructing an objective function that maximizes resource utilization and minimizes business performance deviations. This reduces resource allocation problems caused by human error. Furthermore, a dynamic adjustment mechanism prioritizes high-urgency business needs in mild conflict scenarios. The allocation effect is monitored in real time through a three-dimensional scoring system of resource utilization, business performance, and system stability, with feedback processing of abnormal indicators to obtain a list of abnormal indicators. This effectively avoids the risk of the optimal allocation scheme failing, ensuring that the implemented scheme matches theoretical expectations and preventing resource waste.

The invention discloses an enterprise resource management method and management platform based on cloud computing, and the method comprises the steps: obtaining real-time operation data of various resources of an enterprise, and uploading the real-time operation data to a cloud platform; preprocessing the resource data, wherein the preprocessing comprises data denoising, format unification and structure modeling; based on the change characteristics of the resource data in the monitoring time period, calculating the change entropy of the resource state to evaluate the fluctuation degree of the resource state; determining a coherence index of the resource behavior based on the direction vector aggregation degree of the resource state change; dynamically calculating the size of a scheduling window of the resource according to the change entropy of the resource and a coherence index; generating a state threshold value according to the resource state mean value and the standard deviation in the scheduling window; identifying and grading the resources according to the state threshold to form resource classification and scheduling suggestions; and a scheduling result is provided for enterprise users through a visual interface for decision support.

The invention discloses an automobile door hinge production line resource state scheduling system based on the industrial Internet of Things, and the system comprises a production line data module which collects the global real-time data of a production line, and obtains a fusion resource state matrix through the multi-modal feature synchronization and alignment preprocessing operation; the resource analysis module is used for performing dynamic resource calculation on the fusion resource state matrix to obtain hinge production stability characteristics and hinge production fluctuation trend characteristics, and obtaining a dynamic efficiency vector of each resource entity based on the hinge production stability characteristics and the hinge production fluctuation trend characteristics; the decision analysis module is used for constructing a discrete event simulation engine based on the dynamic efficiency vector, performing multi-round Monte Carlo deduction in combination with the current work order queue to generate a resource future conflict probability graph, and generating an optimal scheduling instruction sequence based on the resource future conflict probability graph and in combination with a directed graph network; and the scheduling execution module is used for realizing resource state scheduling of the automobile door hinge production line based on the optimal scheduling instruction sequence.

The application relates to the technical field of artificial intelligence, can be applied to business system platforms such as medical health and financial technology, and discloses a resource pool management method based on clustering analysis, which comprises the following steps: acquiring multidimensional data, pre-processing the multidimensional data, and outputting numerical characteristics; acquiring industry characteristic data, updating parameters of a clustering model based on a contour coefficient, an elbow rule and the industry characteristic data, inputting customer resources into the updated clustering model, classifying the customer resources based on the numerical characteristics by the clustering model, and outputting clustering grouping results; scoring the customer resources according to the clustering grouping results and customer dynamic indexes, generating customer dimension indexes, scoring business personnel according to business personnel dynamic indexes, generating business dimension indexes, calculating priority evaluation results through the customer dimension indexes and the business dimension indexes; and matching optimal selected business personnel for to-be-matched customers according to the priority evaluation results.

The invention discloses a park demand side flexible resource modeling method, system and device, a storage medium and a product, and the method comprises the steps: classifying demand side resources according to a regulation and control mode and time coupling constraint, and determining to-be-modeled flexible resources; a general linear model of flexible resources in a time domain is constructed, vectors are used for simultaneously representing power at multiple moments, a polyhedral model of each flexible resource is constructed based on the general linear model, and the polyhedral model is used for constraining any flexible resource by using a combination of power constraint, climbing constraint and energy constraint. And establishing a unified polyhedral model for the polyhedral models of all the flexible resources. According to the method, simple, efficient and unified modeling can be carried out on large-scale heterogeneous resources in the park, an aggregation framework based on the model can standardize a data transmission form, the communication and calculation burden of a load aggregator of the park is reduced, and powerful support is provided for aggregation utilization of large-scale heterogeneous demand side flexible resources in the area.

The invention discloses an enterprise operation state real-time analysis method and system based on multi-source data fusion, and relates to the technical field of enterprise operation management, and the method comprises the steps: collecting enterprise production, market and supply chain data, carrying out the multi-source data fusion, constructing a business resource association graph, recognizing a resource competition hotspot, and carrying out the multi-source data fusion; and calculating a resource gap amount and an idle amount, establishing a risk degree function to predict a resource mismatching probability, and finally carrying out optimal scheduling on the resources based on a time sequence multiplexing principle. According to the invention, the real-time monitoring of the operation state of the enterprise and the accurate configuration of resources are realized, the resource utilization efficiency is improved, the operation risk is reduced, and the practical value is good.

This invention discloses a method for evaluating the energy sharing efficiency of demand-side flexible resources based on multi-agent game theory, belonging to the field of resource assessment technology. The steps include: obtaining basic parameters of multiple stakeholders in the power market from a database; obtaining the structured equipment state matrix and historical measured regulation potential values ​​under different power market types; using a deep neural network model to determine the theoretical maximum adjustable power of demand-side flexible resources; determining the market dispatch command quantity for virtual power plants based on the theoretical maximum adjustable power of demand-side flexible resources; determining the demand-side regulation power target and the total actual regulation power under the market dispatch command quantity; and determining the energy sharing efficiency evaluation coefficient based on the demand-side regulation power target, the total actual regulation power, and the theoretical maximum adjustable power of demand-side flexible resources. This invention provides a quantifiable decision-making basis for optimizing demand-side resource dispatch strategies in the power market.

This application provides a medium- to long-term power system planning method that considers the flexible production mode of nuclear power. The method includes constructing a system operation model with multiple power source categories based on resource data and power parameters, and constructing a capacity planning model with the objective of minimizing the annualized comprehensive cost of the system, combined with first-cost data. Constraints include nuclear power flexibility operation constraints, power supply and demand balance constraints, system reserve constraints, installed capacity constraints, cross-sectional constraints, carbon emission constraints, and energy storage operation constraints. Based on the nuclear power flexibility operation constraints and output data, the system operation model is used to perform production simulation to obtain load forecast data and second-cost data. Based on the load forecast data and second-cost data, the capacity planning model is solved to obtain the power system planning scheme. This method simultaneously considers economic efficiency, low carbon emissions, and flexible regulation capabilities, achieving multi-objective collaborative planning and providing scientific support for the optimization of the power source structure of new power systems.

The present application relates to the technical field of industrial platform data analysis, in particular to a renewable resource recycling data management system based on the Internet of Things, which comprises synchronously acquiring the weight, spectral and microwave characteristic data of the measured resource through a data acquisition module, performing moisture weight decoupling operation by using a resource matching module, outputting standard dry basis asset weight with cross-environment consistency, then, the inventory evolution module uses an evolution model integrated with long and short-term memory network to track the performance loss of the resource in real time, generates resource attenuation weight, and combines real-time inventory saturation to construct a two-dimensional decision matrix containing scheduling priority and pretreatment intensity level instructions, finally, the clean-up and dispatching module executes dynamic pruning and bidirectional optimization through a resource scheduling model, accurately allocates the loading share and access order of each node, and generates a dynamic instruction set containing delivery order. The present application realizes homogeneous feeding through multi-dimensional collaborative analysis of industrial data.