147 results about "Technology fusion" patented technology

The invention discloses a numerical control machine tool wear monitoring and intelligent compensation control method based on deep learning, and relates to the field of numerical control machine tool wear monitoring. Multi-source data are collected through a sensor, and are preprocessed and fused through edge calculation; deep features are extracted and enhanced through an improved network, the abrasion state is evaluated through a mixed expert model, and life is predicted in combination with survival analysis; based on enhanced transfer learning, generating an intelligent compensation strategy according to a processing target, and executing the intelligent compensation strategy after verification in a virtual environment; a closed-loop system is constructed, all modules are acquired, fed back and optimized, and full-process intelligent management of functions such as knowledge graph early warning and multi-machine-tool cooperation is integrated. According to the method, the wear monitoring accuracy is improved, and early wear is accurately recognized; machining parameters are intelligently compensated and optimized, precision is improved, and the service life of a tool is prolonged; closed-loop control and multiple technologies are fused, the response time is shortened, and shutdown is reduced; the operation efficiency and reliability of the numerical control machine tool are improved, and the cost is reduced.

The embodiment of the invention provides a code exception analysis method and device, a storage medium and a program product. In the embodiment of the invention, in the preset plug-in, the global knowledge graph is constructed through the logic syntax tree of the plurality of original business codes, so that intelligent anomaly analysis is carried out on the modified to-be-detected codes, multi-language and heterogeneous programming styles and complex dependency limitation are broken through, and cross-language and cross-file analysis is supported; change code snippets and associated influence code snippets thereof are accurately identified, and a data basis is tamped and analyzed; the analysis depth is deepened and the accuracy is improved through a multi-dimensional anomaly analysis large model and four-dimensional collaborative analysis in combination with an adaptive review rule and a knowledge graph; and finally, performing correction based on the generated repair scheme. Through the technical fusion of an MCP protocol, a multi-AI model, a syntax tree and a knowledge graph, the high efficiency, accuracy and real-time performance of code examination and repair are realized, and the service development quality and the system operation stability are remarkably improved.

The invention discloses an urban planning design method based on a live-action three-dimensional simulation system, relates to the technical field of urban planning, and is used for solving the problem of insufficient technology fusion depth in an existing method. Firstly, an initial data set is formed by collecting multi-source city data; performing cross-modal feature extraction and alignment based on a deep learning model to generate a fusion feature vector; constructing a dynamic three-dimensional city model by using the fusion feature vector, and embedding a GIS spatial analysis module to perform preliminary layout simulation; vR interaction logic is injected into the model, user behavior feedback is simulated, and environment influence indexes are calculated; according to simulation results and index feedback optimization planning parameters, multi-technology collaborative decision making is achieved; outputting an optimization scheme and generating a visual report, and supporting real-time iterative updating; according to the method, the technology fusion depth is remarkably improved, the response delay is shortened to millisecond level, the planning precision is improved, the environmental influence evaluation deviation is reduced, and efficient and sustainable urban planning practice is realized.

The invention discloses a monitoring management method and system for multi-dimensional construction, and relates to the field of construction monitoring management, and the method comprises the following operation steps: S1, top layer design and system planning; s2, data acquisition and preprocessing; s3, performing data fusion and AI depth analysis; s4, grading an intelligent early warning and response mechanism; s5, construction dynamic optimization and resource scheduling; and S6, quality full-period traceability and management and control are carried out to ensure that the quality of each process is controlled, and problems can be traced and prevented. According to the monitoring management method and system for multi-dimensional construction, an intelligent closed-loop method and system covering construction total factors and whole processes are constructed through innovative technology fusion, and normal form transformation from passive risk coping to active prevention and dynamic optimization can be achieved; the construction safety, quality, efficiency, sustainability and team cooperation level can be remarkably improved, the government supervision compliance rate can be met, and the risk of penalty due to shutdown is avoided.

The invention discloses an intelligent pipe network leakage active prediction and early warning system based on multi-technology fusion, and the system comprises a multi-source heterogeneous data fusion collection module, a spatial-temporal feature depth extraction module, a degradation trend prediction and residual life evaluation module, and a multi-stage early warning and decision generation module. The multi-source heterogeneous data fusion acquisition module acquires and fuses ultrasonic guided wave signals, pressure flow time sequence data and environmental factor data; the spatial-temporal feature depth extraction module extracts spatial-temporal fusion features through continuous wavelet transform and a CNN-LSTM hybrid network; the degradation trend prediction and residual life evaluation module determines a degradation level, predicts residual life and quantifies a pipe explosion risk probability; the multi-stage early warning and decision generation module generates graded early warning signals and maintenance strategy suggestions, the technology crossing from post-event detection to pre-event prediction is realized, and the scientificity and refinement level of operation and maintenance management of a pipe network are effectively improved.

The invention relates to a respiratory dust concentration online monitoring device and method for a coal mine, and belongs to the technical field of respiratory dust concentration monitoring. The device comprises a virtual impact separation module, a light scattering detection module, a flow control module and a data processing module. The virtual impact separation module is used for separating respirable dust and non-respirable dust; the respirable dust enters the light scattering detection module, the respirable dust is irradiated by a laser beam which is incident in the direction perpendicular to the flowing direction of the respirable dust, a scattered light signal obtained after dust scattering is transmitted to the data processing module, and the concentration of the respirable dust is calculated in real time; the flow control module is used for controlling the rotating speed of the sucking pump in real time. Through multi-technology fusion and intelligent algorithm compensation, the accuracy and environmental adaptability of respiratory dust concentration measurement are improved, the problems that a traditional separation device is prone to blockage and needs frequent maintenance are solved, and long-term continuous separation monitoring of respiratory dust can be achieved.

The invention discloses an engineering construction whole process supervision method and system based on multi-technology fusion, and relates to the technical field of engineering supervision, and the supervision steps are as follows: S1, collecting construction site data through a mobile terminal multi-source channel, carrying out the space-time alignment and association fusion of data preprocessing, and carrying out the panoramic visual monitoring of a construction site; abnormal events and potential risks are identified through a preset rule and a machine learning algorithm; and S2, analyzing the engineering data by using a natural language processing technology. According to the invention, on the aspect of data and risk management and control, panoramic visual monitoring is realized through multi-source data fusion, and risks are accurately identified through an intelligent algorithm; the NLP technology analyzes the engineering data, automatically generates the check list, ensures compliance, realizes conversion from passive supervision to active prevention and from artificial experience to data driving, has remarkable effects in the aspects of improving management efficiency, reducing risks, saving cost and enhancing transparency, and is suitable for various large complex engineering scenes.

The invention discloses an intelligent medical inquiry and medical record generation method and system based on multi-technology fusion, and the system comprises a specialized dynamic pre-inquiry module which is used for sequentially executing the analysis of a patient chief complaint, the matching of a dynamic template, the intelligent inquiry and the completion and verification of a knowledge graph according to a chief complaint text inputted by a patient, and obtaining the comprehensive information of the patient; the dual-drive medical risk control module is used for performing dominant and recessive risk identification based on a rule engine and a large model in combination with an RAG knowledge base according to the comprehensive information of the patient, and realizing a collaborative early warning closed loop; the voice structuring and medical record generation module is used for executing double-track voice separation and transcription, specialized structuring processing and multi-source data fusion and EMR template matching in the dialogue process of the patient and the doctor to generate a structured electronic medical record; and the system integration and performance optimization module is used for adapting to a hospital HIS / EMR system through a standardized interface and executing data preloading and cache optimization to guarantee the response performance of the system.

The invention discloses an Internet of Things and AI energized smart energy multi-energy complementary heating control system, which relates to the technical field of smart energy heating control, and comprises an atomic layer deposition sensing module for realizing ultra-sensitive measurement of temperature and humidity; edge nodes are calculated through photons, and the data processing energy efficiency is improved; the shape memory alloy-piezoelectric composite execution module is used for accurate and rapid adjustment; the multi-scale heat coordination module is used for strengthening heat transmission; the magnetofluid sealing and regulating module is used for dynamically controlling the flow; the terahertz metasurface communication module guarantees high-speed communication, and the system optimizes energy complementation and intelligent regulation and control through multi-technology fusion. According to the invention, the sensing precision, the data processing speed and the execution precision are improved, the heat transmission efficiency is enhanced, the flow dynamic regulation and control and high-speed reliable communication are realized, the room temperature standard reaching rate is improved and the energy consumption is reduced through digital twin optimization control, and an efficient low-carbon scheme is provided for smart energy heating.

The invention discloses an unmanned aerial vehicle multi-technology fusion intelligent control method. The method comprises the following steps: establishing serial port communication between an airborne terminal and an unmanned aerial vehicle; the airborne terminal acquires model information by receiving a heartbeat packet actively sent by the unmanned aerial vehicle, and switches to a corresponding control protocol according to the model information for controlling the unmanned aerial vehicle; and the airborne terminal is used for dynamically monitoring the signal quality of the communication link and adaptively switching the optimal communication link. According to the intelligent control system and method provided by the invention, intelligent switching can be performed according to indexes such as signal strength, delay and stability, and an early warning value is set to deal with a sudden communication loss problem, so that the communication continuity and the data transmission reliability of the unmanned aerial vehicle in a task execution process are ensured.

The invention relates to the technical field of intelligent education systems, in particular to a subjective question automatic correction method based on multi-technology fusion, which comprises the following steps of: firstly, identifying whether a question is a strong proposition or not through a regular expression, and then performing knowledge point extraction, off-question judgment and double similarity evaluation on the strong proposition question; plagiarism detection, cosine similarity calculation and user-defined rounding are carried out on all the questions, and finally personalized comments are generated and correction results are output. According to the method, through double similarity evaluation, semantic relevance between answers and standard answers as well as questions is comprehensively considered, the misjudgment rate is remarkably reduced, meanwhile, through strong proposition recognition, knowledge point extraction and double similarity evaluation, the pertinence and accuracy of correction are remarkably improved, whether the answers of students meet the requirements of the questions or not can be deeply analyzed, and the correctness of the questions is improved. Misjudgment caused by insufficient semantic understanding in the prior art is avoided, and the method is particularly suitable for accurate scoring of strong proposition questions in subjective questions.

The invention, which belongs to the technical field of data processing, relates to an intelligent convenient gravel aggregate transaction system comprising a user interaction layer used for receiving a user instruction and displaying information; the cloud intelligent central layer is in communication connection with the user interaction layer and is used for performing intelligent calculation, data collaboration and safety control; and the full-link system docking layer is in communication connection with the cloud intelligent center layer and is used for realizing data intercommunication with an external service system. Through technology fusion of AI, the block chain and the Internet of Things and whole-process collaborative design, information islands are eliminated, automatic closed loop of the whole transaction process and whole quality traceability are guaranteed, and the transaction risk is reduced.

The invention provides a hotspot technology mining method and system based on scientific and technological literature data, and relates to the technical field of scientific and technological information mining, and the method comprises the steps: firstly obtaining a scientific and technological literature data set containing papers and patent literatures, and constructing a current technical element network through technical phrase extraction and semantic normalization processing; dividing the technical element network segment into a time-serialized technical element network segment set according to time slices, calculating nodes and edge time sequence importance indexes to judge an emerging technical cluster, carrying out dynamic graph representation learning and future associated edge prediction on the set, generating a potential technical combination prediction set, and evaluating the potential technical combination prediction set; and finally, generating an emerging technology theme list and a technology fusion direction list in combination with an emerging technology cluster node growth trend and a prediction association edge evaluation result, thereby improving the accuracy, comprehensiveness and foresight of hotspot technology mining.

The invention discloses a cross-border logistics service quotation system and a real-time dynamic quotation adjustment method thereof, and the method comprises the steps: dynamically obtaining multi-source heterogeneous data, and constructing a door-to-door service chain graph through a knowledge graph technology; business rules are compiled into a calculation unit capable of being subjected to hot updating through a dynamic rule engine, and automatic compliance auditing is carried out; generating a candidate logistics path combination based on a service chain graph and the dynamic rule, and filtering by using a path pruning algorithm; and carrying out parallel real-time calculation on the effective path by utilizing a stream-oriented calculation framework to obtain a plurality of quotation schemes, and carrying out multi-dimensional visual output on the quotation schemes. Through technology fusion, the beneficial effects of remarkably improving the quotation precision, greatly increasing the calculation efficiency, enhancing the system flexibility, reducing the user decision cost and the like are realized.

The invention relates to the technical field of management systems, and particularly discloses a three-dimensional industrial design material AI intelligent recommendation management system. Comprising a dynamic knowledge graph module, a multi-source perception input module, a neural symbol fusion engine, a situation self-adaptive output module, a self-evolution optimization module, a recommendation rule updating module, a cold start processing module and a multi-target decision module. The neural symbol fusion engine realizes symbol logical reasoning through Transform, the cold start processing module generates a user portrait through gradient embedding and generates virtual user features through cross-map feature propagation, the situation adaptive output module dynamically switches rendering modes according to a design stage, and the self-evolution optimization module performs meta-learning and concept drift detection; through multi-technology fusion and module cooperation, intelligence, precision and high efficiency of three-dimensional industrial design material recommendation are realized, the design efficiency is remarkably improved, and the project landing risk is reduced.

The invention provides a smart financial product recommendation method and device, equipment and a storage medium, and the method comprises the steps: firstly generating a key pair and a digital ID according to a user certificate, and constructing a safety identity identification system; and obtaining user financial data through the digital ID, obtaining a credit value by combining a credit calculation intelligent contract, and screening selectable financial products from a product library. And then the data is stored in a distributed manner according to business departments to form a multi-node computing architecture. And each node adopts a K-means + + algorithm to perform initial clustering and calculate model parameters, and optimal model parameters are determined through target function evaluation and neighbor node parameter migration. And calculating the posterior probability of the user data point based on the optimal parameter, determining the category of the user data point, and outputting a clustering result. And finally, generating a grouping label according to a clustering result, and screening and recommending financial products from the selectable products. According to the method, through multi-technology fusion, accurate grouping and recommendation are realized while the data security is guaranteed, and the recommendation efficiency and reliability are improved.

The invention discloses an intelligent man-machine interaction method for an interactive robot. According to the method, the interaction accuracy and adaptability are remarkably improved through multi-dimensional technology fusion. On the intention understanding level, deep analysis of user requirements is achieved through dynamic feature weight distribution, and a strategy is generated by combining cross-domain knowledge graph embedding and emotional expression, so that response content can accurately match user core appeals and can also be naturally fused into professional knowledge and emotional temperature, and information transmission deviation is effectively reduced. According to the real-time feedback mechanism, a dynamic preference model is constructed by capturing facial expressions, voice intonation and behavior data, so that the system can adjust a content generation strategy according to instant emotional fluctuation and attention change of a user, personalized experience in an interaction process is enhanced, mechanical feeling brought by a fixed response mode is avoided, and user experience is improved. Cooperation of content generation and rhythm control is realized, so that the speed, pause and emotion expression of voice response form a natural rhythm, and the comfort level of information receiving is improved.

The invention relates to the technical field of photovoltaic power station management, in particular to a photovoltaic operation and maintenance management method and system based on edge computing, and the method comprises the steps: obtaining multi-source data through a sensor, and carrying out the preprocessing of the multi-source data through an edge computing node; fusing the preprocessed multi-source data by using an auto-encoder, mining depth correlation features, and obtaining a comprehensive feature data set; on the basis of the comprehensive feature data set and the real-time data, a future generating capacity predicted value is obtained through a dynamic self-adaptive model; comparing the future power generation prediction value with the actual power generation result, judging whether a fault point exists or not according to the comparison result, and positioning a potential fault point in combination with a knowledge graph and a historical record to obtain a fault diagnosis result; and generating an equipment maintenance plan and a power generation scheduling scheme by comprehensively considering the actual operation condition and resource configuration of the power station according to a future power generation prediction value and a fault diagnosis result. According to the scheme, the operation and maintenance efficiency of the power station is improved through edge calculation and multi-technology fusion.

The application relates to a control communication interface optimization method in semi-physical simulation, in particular to the field of ship gas turbine semi-physical simulation, which effectively improves the timing accuracy and anti-interference of semi-physical simulation control through multi-technology fusion, realizes high-precision error collection by using an atomic clock marker, dynamically optimizes control instructions in combination with an intelligent algorithm, compensates for current transmission delay, prospectively suppresses future disturbance through a neural network, and finally outputs accurate frequency compensation through an adaptive fusion mechanism, thereby realizing signal alignment and closed-loop feedback, and significantly enhancing the stability and response speed of the system under complex working conditions.

The application discloses a building concrete component compressive strength detection method based on multi-technology fusion and belongs to the technical field of concrete components. The building concrete component compressive strength detection method based on multi-technology fusion comprises the following steps: collecting multi-dimensional detection data; obtaining a characteristic vector; obtaining a fusion feature; obtaining a preliminary compressive strength result; optimizing deep learning feature fusion model parameters; constructing a concrete component digital twin model, realizing strength dynamic prediction and tracing; obtaining a final detection result; and displaying through a visual terminal. The application solves the problem that the prior art is prone to causing difficulty in guaranteeing the detection precision of high-strength and special-shaped concrete components, effectively eliminates the influence of interference factors such as environmental temperature, component moisture content and detection angle on the detection result, greatly improves the reliability of the detection result, can fully show the evolution process of the component strength, greatly reduces human operation errors and improves the detection efficiency.

The invention relates to the technical field of bridge detection, in particular to a steel-concrete composite bridge multistage damage identification method and system.The steel-concrete composite bridge multistage damage identification system comprises a data acquisition module, a data transmission module, a data analysis module and a decision module, and the data acquisition module comprises an acquisition unit, a preprocessing unit and a compression unit; the acquisition unit acquires bridge structure response and environment information in real time to obtain acquired data, the preprocessing unit preprocesses the acquired data to obtain processed data, the compression unit unifies and compresses the processed data to obtain compressed data, and the data analysis module decompresses and restores the processed data, analyzes the data and identifies the type and degree of bridge damage. And the decision module displays the damage position, degree and evolution trend in real time and generates a reinforcement decision, and the system realizes full-process automatic monitoring of the steel-concrete composite bridge from early warning to accurate quantitative evaluation through a hierarchical and multi-technology fused architecture, and can significantly improve the efficiency and reliability of bridge safety monitoring.

The application discloses a user charging and changing behavior multi-dimensional feature space-time quantification method considering cross-domain factors, relies on user travel law, charging and changing service level, power grid price level, traffic network data, and meteorological factors of the place, and objectively quantifies the charging behavior of the user. In the method, not only the influence of the user charging behavior on the surrounding road traffic is proposed, the blank in the field is made up, but also the influence of the charging behavior on the area is fully considered, the traditional single evaluation dimension is transcended, the basic ability construction of vehicle-road-network coordination optimization can be better supported, the scientificity and precision of charging and changing infrastructure planning are improved, the user behavior modeling theory is enriched, and cross-domain technology integration is promoted.

The invention discloses a multi-technology fusion prediction method based on hypergraph modeling, and relates to the technical field of computers. According to the method, hyperedge weights are corrected by introducing a probability-based zero model so as to eliminate deviations caused by patent number differences among different technical fields, and then a multi-technology fusion mode with statistical significance is identified. Further converting a multi-technology fusion prediction problem into a hyperedge prediction problem in a hypergraph, calculating an average similarity among technologies in a technology combination by adopting a random walk algorithm with restart, and establishing a mapping relation between a feature and a hyperedge formation probability through a logistic regression model by taking the average similarity as the feature; therefore, prediction of the technology fusion probability is realized. According to the method, the fusion trend of three or more technical fields can be effectively identified and predicted, the method is more suitable for the analysis requirement of current multi-technology cross-field cross fusion, and data support and decision basis are provided for technical innovation layout and industrial policy planning.

This invention discloses an automatic identification and evaluation system for cable-stayed bridge cable forces based on dynamic modeling, specifically relating to the field of dynamic modeling. The system includes: a sensor preprocessing module, a baseline modeling module, a hybrid prediction module, an evaluation and early warning module, and a self-optimizing iterative module. It proposes a pure data-driven AI model that integrates multi-source data fusion. This model fuses vibration signal data features with prior knowledge of string vibration through a dual-channel architecture and is trained using a composite loss function constrained by physics and rate of change, achieving intelligent identification of cable forces under complex working conditions. Furthermore, it constructs an intelligent evaluation framework with online cross-validation, trend prediction, and multi-source correlation, achieving model self-optimization and long-term adaptation through incremental learning. Addressing the problems of weak generalization, physical inconsistency, and poor adaptability of data-driven AI in engineering, this invention proposes a general architecture integrating multiple technologies to achieve accurate identification of tension states in multiple scenarios.

The invention provides a power grid data management system and method based on multi-source technology fusion, and relates to the technical field of power data processing, and the system comprises local management modules which are deployed in power grid nodes and are used for collecting power grid data of the power grid nodes, and the power grid data comprise various types of data; and the dispatching management module is in communication connection with the local management module, is used for classifying the power grid data of each power grid node and generating a corresponding log file, and is also used for screening fault data from the power grid data of each power grid node and generating a fault management and control form according to the fault data. The power grid data of each power grid node can be automatically collected, the log file is generated according to the power grid data, and the problem of long time consumption of daily filling of repetitive log files by a user is solved. Meanwhile, the fault data is screened out from the power grid data to generate the fault control form, so that the fault line can be repaired conveniently according to the fault control form, and the timeliness of power supply recovery of the line is ensured.

The invention discloses a unified label index library construction method based on a multi-dimensional application field, and relates to the technical field of financial data management. The method comprises the following steps: extracting a general rule, establishing a reusable label index template, and intensively constructing three types of basic libraries; integrating real-time data of the message center of the head office and local data sources of the branches, and synchronizing customer transaction data by everyday increment ETL; a unified real-time query interface is provided, the real-time query interface is packaged through an Flink + ES engine, and all target data are returned through a single request; setting an independent data item space, a control data authority, sensitive information desensitization processing and a parameterized flexible configuration interface for each branch; and establishing a causal chain between metadata and a future trend thermodynamic diagram based on the business application timeliness iteration library content. According to the unified label index library construction method based on the multi-dimensional application field, the problems of fragmentation, low timeliness and high security risk of branch data assets are solved, and'sharing reuse and business technology fusion 'of data are realized.

The invention relates to the technical field of educational informatization and block chain technology fusion, and particularly discloses a digital archive management system based on a multi-modal knowledge graph and a block chain, which comprises four core layers: a self-adaptive block chain evidence storage layer ensures credible sharing of cross-mechanism data; the multi-modal knowledge graph intelligent construction layer realizes unified understanding and correlation analysis of multi-modal data; an evaluation engine driven by deep learning completes intelligent evaluation and personalized recommendation; and the intelligent education evaluation support layer provides decision support and risk early warning. According to the method, Transform, a graph neural network, a time sequence graph neural network and the like are fused, the problems that in the prior art, achievement verification is difficult, multi-modal processing is weak, graph construction is immature, evaluation is rigid and the like are solved, data credibility, system adaptability, decision making scientificity and user experience are remarkably improved, and advanced technical support is provided for educational informatization.

The application discloses a storage redundancy closed-loop regulation method based on recent data loss probability, and the core lies in introducing a closed-loop feedback control framework into a storage system, regulating redundancy gradually, realizing reliability and performance stability, and avoiding "reconstruction storm" and operation and maintenance burden caused by centralized data reconstruction due to traditional static configuration. The method is based on four technical integrations: 1. state observable: based on hard disk failure prediction, the device health state is observed in real time. 2. target controllable: through a recent data loss probability model, long-term reliability is quantified as a recent risk margin. 3. regulation feasible: the structured design of the data strip makes the redundancy rate adjustable and the risk margin controllable. 4. execution without disturbance: through background gradual regulation, the disturbance to the front-end business performance is strictly bounded. The application realizes the reliability and I / O performance stability under failure, optimizes the storage efficiency, and is suitable for various scenes such as large-scale data centers and AI training.

The invention discloses a flow-state solidified soil detection method for backfilling, and belongs to the technical field of flow-state solidified soil detection.The flow-state solidified soil detection method comprises the following steps that S1, a modular sensing array is constructed, and the modular sensing array comprises a construction parameter module, a mechanical property module and an environment-friendly module; sensor calibration is completed through two processes of factory calibration and on-site secondary calibration; an AI model of CNN quality prediction is constructed; s2, detecting environmental protection indexes of the raw materials through a green environmental protection module; calculating a carbon footprint and setting an emission reduction reference value; inputting an AI model to pre-judge the rationality of the ratio; s3, layered monitoring and dynamic verification in construction; s4, AI prediction and long-acting tracking after construction; and S5, carrying out complete-cycle data fusion and traceability. According to the invention, the problems of one-sided detection dimension, fragmented process, insufficient precision, low intelligent degree, poor scene adaptability and the like in the prior art are solved through the modular sensor array, the full-period closed-loop process and multi-technology fusion.

The utility model belongs to the technical field of material management, and discloses a material quantity automatic monitoring system, which comprises a sensing device and a processor, the sensing device comprises RFID tags, an RFID reader, a weighing unit and a position monitoring unit, the RFID reader, the weighing unit and the position monitoring unit are connected with the processor, each material is provided with the RFID tag, the RFID tag stores the name and the specification of the corresponding material, and the weighing unit is connected with the position monitoring unit. The RFID reader is used for reading the RFID tags and preliminarily detecting the quantity of the materials, the position monitoring unit is used for monitoring the positions of the materials, judging whether the materials move or not and assisting in monitoring the quantity of the materials, then the weighing unit is used for monitoring the weight of the multiple goods shelves, further assisting in monitoring the quantity of the materials through weight changes, and an all-around material quantity monitoring system is formed. The accuracy and efficiency of quantity monitoring are improved through multi-technology fusion, the processor receives the material name, the material specification, the weight information and the position information and generates a report, and then accurate prediction of the material demand is achieved.