954 results about "Demand analysis" patented technology

The invention relates to the technical field of intelligent operation and maintenance, and discloses an intelligent operation and maintenance method fusing multi-modal data and active learning, and the method comprises the steps: deploying a hierarchical Internet of Things equipment network in a target operation and maintenance region, collecting a heterogeneous operation and maintenance data set, and generating an operation and maintenance feature data set through employing a unified building operation and maintenance service framework cooperating with a plurality of MCPs; inputting the operation and maintenance feature data set and the user feedback information into a deep learning intention recognition model for intention recognition and demand analysis to obtain structured user demand data and an operation and maintenance task priority sequence; according to the operation and maintenance feature data set and the structured user demand data, performing real-time evaluation on the equipment operation state to obtain fault risk early warning data; autonomous decision analysis is carried out through an agent type artificial intelligence engine, an equipment maintenance scheme and a resource scheduling scheme are generated, and then the problems that in traditional operation and maintenance, fault prediction is single, the model adaptability is poor, and prediction results are difficult to convert into effective decisions are solved.

The invention provides an industrial park multi-target collaborative optimization scheduling method and system based on artificial intelligence, relates to the technical field of intelligent scheduling of industrial parks, and aims to establish a digital twinborn model and a dynamic topological graph, perform resource flow relationship modeling by using a graph neural network, and perform resource demand analysis in combination with a multi-model prediction framework. A multi-agent reinforcement learning algorithm is adopted to generate a scheduling strategy, migration from a global strategy to a local decision module is realized through a strategy distillation technology, robustness of the scheduling strategy is simulated and verified in a digital twin model, the strategy is adjusted according to a simulation result, and finally efficient scheduling of park resources is realized. The method effectively improves the intelligentization and collaboration level of the operation of the industrial park, reduces the energy consumption and carbon emission, enhances the ability of the park to cope with complex scenes, and is suitable for the efficient management of the modern industrial park.

The invention discloses a multi-round knowledge-guided question-answering method and system fusing a large language model and a knowledge graph. The method comprises the following steps: S1, constructing the knowledge graph; s2, performing retrieval and clarification; s3, extracting labels and classifying questions; s4, performing scene guidance and dynamic retrieval; and S5, answer generation and formatting output. The method aims at solving the fuzzy problem in complex policy and regulation questions and answers, and the accuracy of user demand analysis and the comprehensiveness of answers are improved. According to the method, a knowledge graph is taken as a core, and multi-path semantic analysis and problem guidance capabilities are provided by constructing a hierarchical structure covering first-level items, second-level items, scene categories and keyword nodes. In combination with strong semantic comprehension and generation capabilities of the pre-trained large language model, the system can dynamically track context information in user input, iteratively access related nodes along different paths of the knowledge graph, and gradually clarify user intentions.

The invention discloses an ST code generation method based on graph alignment coding large model and multi-agent collaboration, and the method comprises the steps: receiving an ST code programming demand inputted by a user through a demand analysis module, refining and analyzing the demand based on multiple rounds of interactive conversations of the user and insight agents, and generating a standardized ST code programming demand; the retrieval module receives a standardized ST code programming requirement, and retrieves and obtains related knowledge through a retrieval agent in combination with an ST code knowledge base; and the double-agent collaborative self-correction code generation module receives standardized ST code programming requirements and retrieved related knowledge, a graph alignment coding large model constructed based on a graph neural network and a cross-modal alignment technology serves as a coding agent to cooperatively work with a review agent, ST code structure information is injected into the large model, and a final ST code is generated. According to the method, high-accuracy and high-reliability ST code automatic generation can be realized, and the development efficiency of a PLC program in the industrial control field is improved.

The invention relates to the technical field of artificial intelligence, in particular to an agent dynamic decision network generation method based on reinforcement learning. The method comprises the following steps: receiving information demand data input by a user; performing feature analysis on the information demand data, and extracting a business target, a constraint condition and a key parameter to obtain an information demand analysis result; identifying a task flow corresponding to the information demand analysis result, and matching an API call chain according to the task flow to obtain a task demand technology blueprint; generating an agent workflow according to the task demand technology blueprint by using a preset dynamic workflow engine; performing context analysis on the language demand analysis result to obtain context information; the agent workflow is divided into ultra-long thinking chains based on context information. In conclusion, through the reinforcement learning technology, the intelligent agent can be automatically generated and continuously optimized according to user requirements, and efficient decision making and dynamic adaptation of complex service scenes are supported.

The invention discloses a customizable AI agent management platform for enterprise demands and a management method thereof, and relates to the technical field of artificial intelligence, and the platform comprises a demand acquisition module, a demand analysis module, an application scene decomposition module, an AI agent deployment module and a monitoring feedback module. Collecting enterprise demand description data for preprocessing, extracting demand elements, converting the demand elements into execution tasks, and constructing an execution task sequence; decomposing the execution task sequence based on the application scene, and constructing a sub-task execution chain adaptive to the demand application scene; constructing an agent recommendation model, selecting a model from the pre-training model library according to the subtask execution chain, and configuring an AI Agent for deploying an application scene; and evaluating the operation effect according to the execution result of the AI Agent, and carrying out optimization and improvement. The AI Agent is flexibly configured according to enterprise requirements, multi-scene application is supported, the threshold of using the AI technology by an enterprise is lowered, and the application effect of the AI technology in actual business is improved.

The invention relates to the technical field of system engineering modeling, and particularly discloses an MBSE optimization method based on a large language model, which realizes MBSE whole process automation and intelligentization by constructing a'demand-knowledge-model 'dynamic closed-loop framework and fusing RAG and LLM. The method specifically comprises the following steps: constructing a domain knowledge enhancement library, and integrating LLM to construct a demand analysis engine and a dynamic modeling optimization system; a domain expert inputs a demand through a natural language interaction interface, and the demand is analyzed into structured data through LLM; generating a parameterized model conforming to the MBSE specification by combining the RAG technology with the knowledge in the library; after the model runs through a simulation tool chain, the LLM adjusts parameters according to a simulation result to generate an iteration scheme; and after the modeler passes verification, storing the model and data into a database to form a knowledge source. According to the method, domain knowledge dual-drive modeling and cross-role collaboration are achieved, the knowledge base self-evolution capacity is achieved, the problems that traditional MBSE is high in manual dependence and insufficient in semantic fault and knowledge fusion are effectively solved, and the modeling efficiency and reliability are remarkably improved.

The invention discloses a charging pile intelligent control management platform, relates to the technical field of charging pile intelligent control, and can identify the peak and valley periods of the power grid load in advance by collecting and analyzing the power grid load data and the operation state of a charging pile in real time and combining a regression model of a load prediction and demand analysis module. The power output of each charging pile is dynamically adjusted through the charging pile scheduling and power adjusting module, the power grid resource distribution is optimized, the overload phenomenon is avoided, and the stability of power grid operation is improved. By monitoring and feeding back the charging behavior data of the user, the platform can reasonably plan the working state of the charging pile. Therefore, too fast loss of some charging piles due to continuous overload is avoided, resource waste of idle charging piles is reduced, the utilization efficiency of equipment is improved on the whole, and the service life of the charging piles is prolonged. By using the load prediction and demand analysis module, the platform can pre-judge the charging demand peak of the user and optimize the scheduling strategy in advance, thereby shortening the waiting time of the user.

The invention discloses a knowledge question and answer rapid processing system based on artificial intelligence. The system comprises a knowledge graph construction module, a semantic entity analysis module, a user demand analysis module, a question and answer generation and interaction module, a back-end service interface module, a user feedback and optimization module and a security and privacy protection module. The knowledge graph construction module is used for constructing a knowledge graph; the semantic entity analysis module can identify semantic entities in questions of a user; the user demand analysis module can analyze the questioning demand of the user; the question and answer generation and interaction module is used for generating answers according to the recognized semantic entities and user requirements; the back-end service interface module provides an interface to realize load balancing and automatic capacity expansion and contraction; the user feedback and optimization module collects user evaluation and optimizes the model according to feedback; and the security and privacy protection module is used for protecting the privacy of user data and the security of the system. The user intention and entity information can be accurately recognized, and the response speed of the question answering system is remarkably improved.

The invention provides a supercomputing resource intelligent allocation method and system based on AI load monitoring, and the method comprises the steps: firstly obtaining a real-time load data set of a plurality of computing nodes in a supercomputing system, including a resource occupancy rate index and a task queue state parameter, then carrying out the load feature extraction processing of the real-time load data set, generating resource occupancy fluctuation characteristics and task queue evolution characteristics, performing resource demand analysis on the load characteristic set by using a pre-constructed resource demand prediction model, predicting resource demands of each computing node in a subsequent time window, determining resource allocation priorities and migration strategy parameters based on prediction results, and performing resource allocation according to the resource allocation priorities and the migration strategy parameters. The resource dynamic allocation instruction is generated and sent to the target computing node, the resource reallocation operation is triggered, intelligent and efficient allocation of super computing system resources is achieved, the resource utilization rate and the task execution efficiency are improved, and the overall energy efficiency ratio and stability of the system are optimized.

The invention discloses a CAD automatic generation system and method based on intelligent model selection and application, and aims at achieving automatic modeling under the multi-modal design requirement. The system comprises a user interaction module for receiving multi-modal input such as natural language, sketch and voice; the intelligent demand analysis module is used for combining an industrial large language model and a product knowledge graph, combining semantic analysis and generating a structured demand; the intelligent model selection calculation module is used for matching the optimal parameter combination and the component list based on a multi-objective optimization algorithm; the CAD automatic generation module calls a parametric modeling engine to generate an editable three-dimensional model; the constraint solving module is used for processing hard constraints and soft constraints in real time and dynamically adjusting model parameters; and the model output and interaction module feeds back a design state and supports user iteration. The system realizes full-process automation from the design intention to the CAD model, improves the design efficiency and accuracy, and is suitable for the fields of mechanical design, intelligent manufacturing and the like.

The invention provides a cold-chain transportation demand analysis method and system based on big data prediction, and the method comprises the steps: firstly obtaining a historical cold-chain order data set which comprises temperature-sensitive cargo transportation records of multiple time periods and corresponding environment parameters in a target region, and then extracting a transportation demand feature set from the historical cold-chain order data set; the characteristics of correlation between cargo types and temperature sensitivity, balance between transportation aging and equipment energy consumption, matching between regional climate and storage conditions and the like are covered. And then inputting the feature set into a pre-trained space-time prediction model, and generating multi-dimensional cold chain demand parameters, such as a temperature fluctuation tolerance threshold value and the like, of the target area in a specified period in the future. And finally, based on the multi-dimensional cold chain demand parameters, generating a cold chain transportation dynamic scheduling scheme with a timestamp. According to the method, the cold chain transportation requirements can be accurately analyzed by integrating multi-dimensional factors, scientific dynamic scheduling is realized, and the cold chain transportation efficiency and quality are improved.

The invention provides an enterprise product intelligent quotation collaborative decision-making platform. A platform architecture comprises a user interaction layer, a data processing layer and a decision-making layer, and platform functions are realized by relying on an intelligent agent. When a user releases a product quotation task, an authority management agent verifies the identity and access authority of the user, and a user interaction and demand analysis agent in a user interaction layer converts the natural language demand of the user into a structured instruction; the data processing layer receives an instruction, basic data and a special cost agent in the layer pull data from an external system through a cross-system calling agent, and a price calculation agent integrates the data through multi-objective optimization to generate candidate quoted prices; and finally, a man-machine cooperation and intelligent decision-making auxiliary agent in a decision-making layer displays all candidate schemes, and an expert performs fine adjustment to select an optimal quotation. According to the invention, rapid and accurate quotation is realized for enterprise products, the quotation model is continuously optimized through continuous feedback and self-learning, and finally a highly autonomous intelligent quotation system is formed.

The invention discloses an AIGC enabling auxiliary system for college second foreign language teaching and an implementation method. The system is cooperatively operated by five core modules; the intelligent voice recognition and user demand analysis module analyzes the learning demand of a learner by using a voice-to-text technology, and assists the customization of teaching content; the AIGC content generation module gathers multi-source data, generates personalized teaching resources by using a frontier model and continuously updates and evolves the personalized teaching resources; the learning progress management and evaluation module collects learning data, dynamically adjusts a personalized learning plan and provides evaluation and feedback; the real-time interaction and feedback module builds a multi-dimensional interaction channel, collects feedback of all parties, analyzes and processes the feedback and presents an application result; the multi-platform support module guarantees the stable operation of the system on a plurality of operating systems and devices, and efficiently completes data synchronization sharing and performance optimization adaptation. According to the invention, an all-around learning environment is established for second foreign language learners in universities, and the learning efficiency and experience are significantly improved.

The invention discloses a management decision-making method and system based on a knowledge base construction technology. The method comprises the following steps: acquiring business demand information input by a target object; determining a target business type corresponding to the business demand information, and determining a target knowledge graph corresponding to the target business type from a preset knowledge base; performing semantic analysis and intention recognition on the business demand information by using a pre-trained demand analysis model to obtain a target demand feature corresponding to the business demand information; analyzing the target knowledge graph and the target demand features by using a graph neural network to obtain multi-dimensional decision suggestions for the service demand information; and feeding back the multi-dimensional decision suggestions to the target object. The technical problem that user experience is affected due to the fact that decision suggestions generated by a system in related business decision scenes are not highly matched with user requirements is solved.

The invention relates to the technical field of traffic planning, in particular to a transfer connection optimization method in multi-mode traffic integrated planning, which comprises the steps of data acquisition and demand analysis, multi-mode transfer network topology design, multi-mode timetable collaborative optimization, transfer service system development and facility transport capacity dynamic matching. According to the transfer connection optimization method in the multi-mode traffic integrated planning, a dynamic and static combined multi-source data acquisition framework is constructed based on a multi-dimensional data fusion system, the data precision can be improved, the network can be perceived in real time, association modeling of weather events and demand fluctuation is realized based on construction of a multi-agent simulation system, and the optimization of the transfer connection in the multi-mode traffic integrated planning is realized. A dynamic weight mechanism is introduced based on a hypergraph model, the robustness of the algorithm is improved, a layered optimization framework processes global optimization through upper-layer integer programming, dynamic adjustment is achieved through lower-layer deep reinforcement learning, a buffer time intelligent insertion strategy can improve the late absorption capacity, and a facility utility function model is established to improve the transfer efficiency.

The invention relates to the technical field of flavor customization recommendation, in particular to a flavor and flavor customization formula recommendation system and method. The method comprises the following steps: obtaining user sensory preference data and environment scene parameters, and carrying out multi-dimensional demand feature extraction to obtain a user scent demand feature set; performing raw material compatibility analysis based on the user flavor type demand feature set, and generating a basic raw material screening data set; according to the basic raw material screening data set, volatile component detection and odor molecular map construction are carried out on essence and perfume raw materials, and raw material odor molecular characteristic values are extracted; performing note synergistic effect simulation based on the odor molecule characteristic values to generate note synergistic optimization parameters; and constructing a dynamic formula model in combination with the user aroma type demand feature set and the aroma collaborative optimization parameters. According to the invention, through personalized demand analysis, scientific raw material screening, smell synergistic effect simulation and intelligent simulation feedback, the precision and personalized degree of essence and perfume customized formula recommendation are improved.

The invention provides a method for generating a front-end prototype based on an artificial intelligence technology. The method comprises the steps of data acquisition, knowledge base construction, model training and optimization, demand analysis and prototype generation. The method comprises the following steps: constructing a knowledge base supporting multi-dimensional query by collecting and verifying historical data; in combination with the content of the knowledge base, a large language model is used for performing field specialized fine tuning, and low-rank matrix optimization and comparative learning are adopted to improve the task similarity detection precision; semantic analysis and structured conversion are carried out on the natural language requirements of the user; a front-end prototype is automatically generated based on structured data, and user interaction feedback and dynamic optimization are supported. According to the method, efficient data processing, intelligent model optimization and flexible prototype design are realized, the intelligent level and development efficiency of prototype generation are improved, and powerful support is provided for front-end design under rapid iteration and complex requirements.

The invention relates to the technical field of AGV control, in particular to a multi-AGV scheduling method. The method comprises the following steps: obtaining a real-time logistics instruction of a working area, carrying out logistics scheduling demand analysis and global task planning, and constructing a global execution task atlas; aGV real-time monitoring parameters are obtained, multi-state comprehensive evaluation is carried out, dynamic task demand allocation processing is carried out according to the global execution task atlas, and an AGV dynamic task set is constructed; carrying out AGV operation behavior intention identification one by one according to the AGV dynamic task set, carrying out multi-task cooperative carrying evolution, and constructing a multi-level space-time path network; performing potential AGV collision early warning according to the multi-level space-time path network, and marking local AGV space-time collision nodes; and performing dynamic task priority decision according to the global execution task atlas, performing intelligent conflict planning on local AGV space-time conflict nodes, and constructing a local conflict scheduling strategy. According to the invention, through cooperative control of multiple AGVs, the operation efficiency, the scheduling precision and the safety level of the operation area are improved.

The invention relates to the technical field of model quantification and compression, and discloses a low-computing-power environment-oriented model quantification and compression method, which comprises the following steps of: analyzing the demand of a large model in a specific task, and evaluating the hardware resource limitation of a low-computing-power environment by using a demand analysis tool; dynamically optimizing the quantization process of the neural network model by using a reinforcement learning method, and constructing a quantization perception training framework; performing structure optimization on the neural network model by applying a pruning technology and a rarefaction technology, and removing redundant parameters and connections; performing adversarial ability testing on the compressed neural network model by using an adversarial sample generation method, and evaluating the robustness of the model; converting the optimized neural network model into a low-computing-power environment format, and converting the model into a low-precision format by using a quantification tool; by monitoring operation feedback of the neural network model, a performance analysis tool is used to collect reasoning results and resource consumption data. The method has the advantage of improving the adaptability of the model in a low-computing-power environment.

The invention discloses a digital intelligent service system for enterprise services, which belongs to the field of enterprise digital services and comprises a user demand analysis module, an intelligent data adaptation module, a service resource matching module, a service efficiency evaluation module and a user-friendly interface. The user demand analysis module is used for semantic analysis and classification decision making; the intelligent data adaptation module identifies and analyzes the protocol type, and establishes an intelligent data transmission channel; the service resource matching module carries out resource pre-screening and accurate matching; the service efficiency evaluation module tracks an identifier and captures a four-dimensional index system in real time, and the dynamic evaluation unit realizes service state grading early warning through a sliding window algorithm; according to the invention, accurate demand identification is realized, the response speed and the data transmission efficiency are improved, the user satisfaction is improved, the service matching accuracy is enhanced, the resource utilization efficiency is optimized, the service capability and the customer experience are improved, and maximization of economic benefits and social values is realized.

The invention provides an intelligent service recommendation method based on multi-dimensional scene perception and dynamic portrait modeling. The intelligent service recommendation method comprises the steps that 1, distributed edge computing nodes are deployed, space-time tetrad data operated by a user are collected in real time, user behaviors and labels are stored, and a high-dimensional recommendation database is built; 2, constructing a dynamic portrait engine, and constructing a user long-term behavior pattern library; 3, deploying a real-time streaming and offline double-engine architecture, fusing real-time scene matching and offline portrait prediction, and performing personalized function and user deep demand analysis; step 4, for new users, synchronously calling geofences to obtain regional hot services, and realizing cold start optimization based on meta reinforcement learning in parallel; and 5, establishing an intelligent recommendation closed loop associated with weather characteristics. According to the method, multi-dimensional features such as space-time tetrad data, user tag information and dynamic interest weights are fully utilized, and the real-time performance, interpretability and generalization ability of a recommendation system can be effectively improved.

The invention relates to the field of data processing, and particularly provides a big data mining method and system based on digital enterprise management, and the method comprises the steps: obtaining multi-modal session data generated in the process of carrying out digital service interaction between a target enterprise and a user, extracting a user demand feature set from the multi-modal session data, and based on a preset multi-task learning framework, carrying out joint training on the user demand feature set, generating a demand analysis model, calling the demand analysis model to analyze the user demand feature set, generating an optimization strategy set associated with the target enterprise service process, and sending the optimization strategy set to the server. And updating the digital service execution logic of the target enterprise according to the optimization strategy set, and performing incremental training on the demand analysis model based on user feedback data. According to the invention, the response efficiency and the function adaptability of the digital service process can be improved.

The invention relates to an intelligent interview scoring system capable of explaining decisions based on a large language model, and the system comprises a multi-mode resume analysis and feature coding unit, a resume feature adaptive matching unit, an interactive scoring and knowledge enhancement unit, and an answer quality evaluation unit. Text, image and audio features are extracted through a cross-modal attention mechanism of a multi-modal large language model, resume features are encoded into dynamic word vectors, and entity-level feature vectors are extracted; the post description text is encoded into a demand feature vector by a resume feature adaptive matching unit; calculating semantic similarity between the resume entity feature vector and the demand feature vector; the interactive scoring and knowledge enhancement unit dynamically retrieves knowledge fragments to generate a preliminary evaluation report containing a scoring basis; and the answer quality evaluation unit fuses the information density, the fluency and the integrating degree to generate a final score. And the whole-process intelligence from demand analysis to final decision making is realized.

The invention provides a code generation method and system based on a waterfall model and multi-agent collaboration, and the method comprises the steps: constructing a multi-agent collaboration framework which comprises a problem analysis agent, a solution agent, a pseudo-code agent, a coding agent and a restoration agent based on a software development waterfall model thought; and the interaction process of each agent is coordinated through a dynamic cooperation algorithm. Wherein the problem analysis intelligent body checks similar problems and solutions; the solution intelligent agent generates and evaluates candidate schemes; performing scheme conversion by the pseudo-code intelligent agent; the coding agent generates an executable code; and the repair agent performs fine-grained repair on the code in grammar, runtime and semantic levels through a two-dimensional repair mechanism. According to the method, the limitation of a single agent in a complex programming task is broken through, automatic code generation covering the whole process of demand analysis, scheme design, code implementation and test repair is realized, and the quality of generated codes and the reliability of operation are remarkably improved.

The invention discloses an energy storage battery health management system based on digital twinning and an energy storage system, and the system comprises a demand analysis module which is used for determining operation and maintenance demand parameters of equipment in the energy storage system, and determining a demand element frame according to the operation and maintenance demand parameters of the energy storage system; the demand element framework represents a result of carrying out importance classification on the operation and maintenance demand parameters; the digital twinborn model construction module is used for determining an operation scene of the energy storage system according to the demand element framework, matching an equipment simulation model meeting the operation scene and a hyper-parameter combination of the equipment simulation model from a model library, and fusing to obtain a digital twinborn model of the energy storage system; and the data analysis module is used for receiving the real-time operation feedback data of the energy storage system and outputting the virtual simulation data and a health assessment result corresponding to the real-time operation feedback data based on the digital twin model. In the embodiment of the invention, the digital twin model matched with the energy storage system can be provided.

The invention relates to the technical field of electric power energy storage, in particular to an energy storage demand analysis method of a high-proportion new energy power grid, which comprises the following steps of: obtaining a power predicted value and a measured value through a prediction platform to calculate deviation, performing sliding window segmentation, dynamically adjusting length based on volatility to extract variance characteristics, and calculating the energy storage demand of the high-proportion new energy power grid; and the center number is optimized through K-means clustering, a classification result is output, a capacity adjustment value is calculated according to a matching grade adjustment coefficient, time points are extracted and sorted according to a load and output correlation coefficient, and an energy storage capacity space-time distribution table is generated. According to the invention, through prediction of deviation sequence sliding window segmentation and variance feature extraction, dynamic identification of new energy output fluctuation intensity, construction of a deviation fluctuation and energy storage capacity mapping model, quantification of load and output coupling intensity, and construction of a multi-dimensional energy storage correction system, energy storage and source load dynamic matching is realized, and response sensitivity is improved. The risk of resource mismatching is reduced, the robustness of the system is enhanced, and the cooperative efficiency of charging and discharging strategies is optimized.

The invention provides a multi-dimensional driver capability evaluation and intelligent matching scheduling system, and the system comprises a data collection module which is used for obtaining driver driving behavior data, vehicle state data and environment data in real time; the preprocessing module is used for carrying out noise filtering, missing value filling and standardization on the acquired data; the multi-dimensional capability evaluation module is used for calculating a driving safety score, an efficiency score and an emergency response score of the driver through a dynamic weight distribution algorithm based on the preprocessed data; the demand analysis module is used for analyzing the route complexity, the time sensitivity and the special service demand of the passenger order; the matching scheduling module is used for generating a matching result according to the driver ability score and the passenger demand and outputting a scheduling instruction; the dynamic optimization module monitors the driver state and the road condition change in real time and adjusts the matching weight; and the interaction module is used for pushing real-time scheduling information and abnormal event early warning to the driver and the passenger. The scheduling efficiency and safety can be improved, and the passenger travel experience and the operation management level are improved.

The invention discloses a human resource information management system and method based on big data, and relates to the technical field of human resources, and the system comprises a project demand analysis module which is used for extracting a skill list required by new project post personnel, determining task complexity, and generating skill parameter requirements; the employee screening module is used for obtaining the basic information of all the employees in service and dividing the employees in service into two types of crowds, one type is the crowd meeting the new project employment, and the other type is the crowd not meeting the new project employment, through big data analysis and multi-dimensional evaluation, accurate matching of the project demand and the employee ability is achieved, and the work efficiency is improved. Project execution quality and efficiency are improved, enterprise internal human resource potential is fully excavated, personnel can be reasonably allocated, and waste and idleness of human resources can be avoided.

The invention relates to the technical field of market demand analysis, in particular to a big data-based cross-border e-commerce market demand prediction system, which comprises a culture trend identification module, a sudden demand evaluation module and a demand prediction output module, wherein the culture trend identification module is used for capturing text data of sudden popular culture in a short period through a multi-dimensional data source; the sudden demand evaluation module is combined with sudden popular culture to analyze and quantify demand fluctuation for hot commodities in a short period; and the demand prediction output module provides promotion and marketing activity suggestions of hot commodities and inventory adjustment strategies according to the demand change prediction result. According to the method, the dependence of a traditional demand prediction method on a long-term trend is broken through, the accuracy of short-term demand prediction is remarkably improved, and a cross-border e-commerce platform is helped to respond to market changes in time.