1174 results about "Ambient data" patented technology

The invention discloses a power distribution room anomaly detection system based on cloud side-end cooperation, and belongs to the technical field of intelligent power grids. In order to solve the problems of high network bandwidth pressure, insufficient edge computing capability, low anomaly detection accuracy, difficulty in multi-source data fusion and the like caused by the adoption of an end-cloud direct connection architecture in an existing power distribution room monitoring system, the system comprises: a data acquisition layer configured with various heterogeneous sensors to acquire operating parameters and environmental data in real time; the edge storage and calculation layer carries out local real-time processing, anomaly detection, model training and visual display, an anomaly detection module of the edge storage and calculation layer carries out research and judgment on real-time data to generate early warning information, and a prediction and detection linkage module monitors an anomaly probability trend and adjusts a sampling frequency; the edge gateway realizes protocol conversion and data forwarding; and the cloud decision-making layer aggregates multi-edge node data, optimizes a global model through federal learning, and issues and updates a local model. The system is used for improving the accuracy, real-time performance and reliability of anomaly detection of the power distribution room, reducing the operation and maintenance cost and realizing intelligent operation and maintenance.

The invention discloses a distribution line first-aid repair operation risk assessment method, system and equipment based on multi-source perception data and a medium, and belongs to the technical field of first-aid repair operation risk assessment. The method comprises the steps that multi-source data of perception data, operation data and environment data are collected and subjected to time-space alignment, quality verification and missing repair are carried out, and a first-aid repair result is obtained; generating fusion features with credibility weights; constructing a dynamic graph structure taking the space nodes and the participation roles as vertexes in the operation scene; a physical consistency index constraint used for reflecting an operation safety boundary is introduced into the graph structure, and an interpretable risk characterization mechanism is formed; carrying out distribution robustness and interval calibration on the risk characterization result; carrying out anti-fact simulation on the plurality of candidate coping schemes based on digital twin modeling, and triggering the sensing equipment to execute active supplementary mining operation according to an information value maximization strategy; sensing and simulation results are fed back to the model for updating and iteration. According to the invention, intelligent risk identification and adaptive control of the whole operation process of the power transmission line are realized.

The invention discloses a multi-modal perception and optimization method and system for a low-power-consumption AR device, and the method comprises the steps: collecting multi-modal data, task demands, resource state data and environment data for the AR device; lightweight processing is carried out on the multi-modal neural network model through model pruning, parameter quantification and distillation technologies; inputting the collected data into a lightweight multi-modal neural network model for dynamic reasoning to obtain a multi-modal recognition result; comprising the steps of executing modal adaptive weight acquisition based on task requirements and environment data; executing energy consumption constraint scheduling according to the equipment resource state data, dynamically selecting a reasoning path strategy, and obtaining corresponding modal feature output; multi-modal feature fusion is carried out, task reasoning is completed, and a multi-modal recognition result is obtained; early-leaving control is executed based on a middle-layer confidence coefficient threshold value in the reasoning process; and interactively outputting a real-time multi-mode identification result. According to the invention, energy efficiency and precision balance and multi-mode fusion low-power-consumption optimization can be realized.

A system for data collection related to a fluid conveyance environment includes a data acquisition circuit comprising inputs and outputs; input sensors to provide sensor data values, coupled to a component in the fluid conveyance environment; and a processor comprising the data acquisition circuit. The processor is configured to determine a data storage profile; responsive to the data storage profile, configure the data acquisition circuit to selectively couple at least one of the inputs to at least one of the outputs; interpret the at least one of the sensor data values; store at least a portion of the at least one of the sensor data values in response to the data storage profile; analyze a set of the sensor data values and determine a data quality parameter; and adjust at least one of the data storage profile and a data collection routine in response to the data quality parameter.

The invention discloses a power equipment dynamic health degree assessment and predictive maintenance method, and belongs to the technical field of railway power system operation and maintenance. The method comprises the following steps: constructing a parameterized digital twinborn body of power equipment, and collecting real-time operation data, resume data and environment data; based on the parameterized digital twins and the collected data, equipment health degree components are calculated through a multi-model cooperation method, and a comprehensive health index is generated through fusion; performing equipment life prediction and maintenance decision generation according to the comprehensive health index, and outputting an optimal maintenance strategy; and performing visual virtual rehearsal and augmented reality auxiliary execution on the optimal maintenance strategy to form a closed-loop maintenance system. According to the method, the problems of data and model separation, model static stiffness and health assessment deficiency in the prior art are solved, dynamic perception, accurate assessment and predictive maintenance of the equipment state are realized, and the operation and maintenance efficiency and the system reliability are improved.

The invention relates to an intelligent driving safety system and method based on AI multi-source data fusion. The system comprises an environment modeling sensing module which obtains vehicle end multi-mode sensing data and vehicle size parameters to construct a surrounding environment model, and multi-vehicle environment data of collaborative sensing is formed through vehicle-vehicle communication; the behavior scene generation module analyzes the traffic flow characteristics of the multi-vehicle environment data, calculates the attention weight of the vehicle characteristics, associates the attention weight with the target size information, and generates a unified behavior scene representation. The collision risk judgment module fuses the unified behavior scene representation and the dynamic environment modeling data to generate multi-target fusion trajectory data, and if the uncertainty exceeds a threshold value, a historical database is checked to judge the risk level; and the path planning generation module performs classification according to risk levels and risk levels, and performs matching and speed adjustment instruction integration on the collaborative path optimization instruction to obtain a path planning rule. By adopting the system, the sensing precision, risk response capability and cooperative efficiency of the automatic driving vehicle can be improved, and the driving safety is guaranteed.

The invention relates to the technical field of intelligent decision rule base construction, and discloses an intelligent decision framework construction method and device based on a dynamic ontology. The method comprises the following steps: acquiring original data streams from a plurality of heterogeneous data sources in real time, and constructing an initial dynamic ontology structure through a dynamic ontology modeling unit; performing semantic annotation and relation extraction on the original data flow by using the structure to generate semantic enhanced data; generating a candidate decision rule set based on the semantic enhancement data, and obtaining a verified rule set through consistency verification and conflict detection; calculating the adaptability score of the verified rule set in combination with the real-time environment data, and screening out an optimal decision rule subset according to the score and a preset threshold value; the method is integrated into an intelligent decision rule base, and an optimization loop is triggered based on an update state of the base. According to the method, the data utilization efficiency and the rule quality are improved, the rule base can dynamically adapt to the environment, and decision effectiveness and reliability are enhanced.

The invention provides a dynamic traceability identifier generation and verification method based on multi-node environment data fusion, and relates to the technical field of information security and Internet of Things data credibility. The method comprises the following steps: firstly generating a seed identifier, then collecting environment data through each node to construct a multi-dimensional vector and carrying out weighted fusion, after serialization, generating a dynamic hash identifier by combining with an upper-level identifier, and meanwhile, introducing an offline trusted evolution, time drift tolerance and self-adaptive risk control mechanism; and finally, backtracking the traceability chain through recursion verification, and guaranteeing privacy in combination with zero-knowledge proof. The method solves the problems that a static traceability code is easy to copy and the data credibility is low, achieves the dynamic anti-counterfeiting and tamper-proofing of the traceability identifier, enables the digital identifier to be strongly associated with physical circulation, improves the credibility of the traceability process, can automatically find abnormal circulation and give an alarm, and is suitable for a commodity traceability scene.

An approach is disclosed for enabling contextually relevant conversational interaction. Environment data is received by an AI System which detects a plurality of physical objects in a physical environment and forms a contextual understanding of the plurality of physical objects and the physical environment and identifies a user relevant to the contextual understanding. A most relevant contextual information to the user is predicted by the AI system and transformed into a textual form. A set of intents and objectives is predicted by the AI system for user-centered interaction. The AI system and the user interact iteratively through the user-centered interaction to determine an understanding of a most relevant intent and a most relevant objective which is validated by the AI system with the user until the user agrees. The validated most relevant intent and the most relevant objective is utilized to facilitate the user-centered and contextually relevant conversational interaction.

The invention provides a digital twin smart park security and protection management system based on artificial intelligence, and relates to the field of security and protection management, and the system comprises a multi-mode sensing network which is used for collecting park environment data through a multi-mode sensor network, and carrying out the preprocessing of the data, and obtaining an environment data set; the digital twin modeling module is used for constructing a park three-dimensional virtual model based on the environment data set; the semantic alignment module is used for defining a dynamic security semantic rule based on the three-dimensional virtual model and carrying out semantic alignment of multi-modal features on the environment data set; the threat assessment module is used for performing threat assessment on the aligned semantics based on a knowledge graph and a Bayesian network; and the resource scheduling execution end is used for generating a disposal plan according to the evaluation result and realizing security response through an execution terminal. The method is used for solving the problems of information isolated island, response lag, high false alarm rate, fuzzy situation awareness and the like of a park security system in the prior art.

The invention relates to the technical field of equipment energy consumption monitoring and fault diagnosis, and discloses a large-scale equipment energy consumption anomaly detection method and a storage medium. The method comprises the following steps: synchronously acquiring equipment energy consumption, state and environment data, fusing to generate multi-dimensional initial features, and performing multi-scale transformation. And performing preliminary detection on each scale feature subset to generate abnormal confidence and mode description. And screening the feature subsets according to the confidence coefficient, and dynamically selecting a corresponding detection strategy according to the mode description. And performing deep feature extraction on the feature subset based on the selected strategy, inputting the obtained high-dimensional feature vector into a corresponding anomaly evaluation network, calculating a standard state matching degree, comparing with a dynamic threshold to generate a fine-grained judgment result, and finally finishing positioning and attribution analysis in combination with anomaly description to form a detection report. According to the method, efficient and accurate large-scale equipment energy consumption anomaly detection is realized.

The embodiment of the invention provides an equipment control method and device, electronic equipment and a readable storage medium. The method comprises the steps that equipment state information, user behavior data and environment data are acquired in real time; constructing constraint conditions based on the equipment state information, the user behavior data and the environment data under the condition that a scene generation request is received; the scene generation request is an instruction used for indicating a scene type; the constraint condition is a rule entry used for indicating a local model output range; inputting the constraint condition and the scene generation request into the local model, and controlling the local model to output a scene control strategy which is matched with the scene type and meets the constraint condition; and according to the scene control strategy, each device is controlled to operate, so that the efficiency and safety of generating the scene control strategy are improved.

The invention discloses an environment perception and command control intelligent deduction platform based on bionic unmanned equipment, and relates to the field of intelligent deduction, and the platform comprises the steps: collecting environment data through a multi-mode sensor, encrypting the environment data through an anti-interference communication relay module, and transmitting the encrypted environment data to a central control platform; the data fusion preprocessing module is used for processing multi-source data and outputting a structured data stream, and the environment intelligent modeling module is combined with a geographic information system to construct a three-dimensional dynamic environment model; an intelligent deduction module carries out multi-scene deduction based on the model, and a self-adaptive control strategy generation module converts a decision tree into an anti-disturbance control sequence; and the man-machine collaborative decision-making module fuses operator intervention to generate a final instruction, and the instruction distribution and equipment collaborative control module issues the instruction to the bionic unmanned equipment to realize intelligent collaborative control. The method has the advantages that bionic perception, anti-interference communication and intelligent deduction are integrated, efficient cooperation and adaptive control of unmanned equipment in a complex environment are achieved, and the intelligence and robustness of task execution are remarkably improved.

The invention discloses an eSIM equipment intelligent network selection method based on environment perception and an AI strategy, and the method comprises the steps: collecting and preprocessing multi-source environment data, and generating a multi-mode original feature vector; constructing a three-dimensional situation tensor, generating a causal structure diagram, clustering to obtain a situation identifier, and embedding a situation; constructing a situation feature table, improving TabNet to perform feature selection, and outputting a high-dimensional situation representation vector; based on the situation characterization and the capability characteristics, performing chain type prediction on the performance and generating an anti-factual income score; constructing short-time domain and long-time domain preferences, generating a comprehensive preference value after fusion, and determining a target connection object; and an eSIM strategy configuration instruction is generated, intelligent network selection switching is carried out, and model parameters are incrementally updated. According to the method, active high-stability intelligent network selection of the eSIM equipment in a complex scene is realized through fusion of multi-source environment perception, causal inference and an AI intelligent strategy.

The invention provides a fabricated building structure stability monitoring method and system, and relates to the technical field of building safety monitoring, and the method comprises the steps: obtaining node monitoring data at a plurality of moments of a monitoring period through a sensor combination disposed at a node; acquiring real-time environment data at a plurality of moments of the monitoring period; obtaining building design information; determining a node health coefficient according to the node monitoring data; determining a dynamic node weight according to the node health coefficient, the building design information and the node monitoring data; determining an overall building stability coefficient according to the real-time environment data, the dynamic node weight and the node health coefficient; and according to the node health coefficient and the overall building stability coefficient, carrying out structural stability monitoring on the to-be-detected building. According to the invention, the health conditions of the nodes and the whole building can be monitored respectively, and the accuracy of building structure stability monitoring is improved.

The invention belongs to the field of unmanned aerial vehicle (UAS) traffic management (UTM) and computer science, and particularly relates to a method and system for airspace modeling and path planning of a logistics unmanned aerial vehicle, and the method comprises the steps: S1, receiving multi-source data, and constructing a hierarchical mixed data model; s2, coding the control constraint data and the dynamic environment data into semantic attributes of nodes in a two-dimensional quadtree layer and / or voxels in a three-dimensional octree layer so as to generate a semantic airspace model containing computable rules; s3, based on the semantic airspace model, executing a hierarchical path planning algorithm to determine a flight path from the starting point to the ending point; and S4, combining the refined paths, and outputting a four-dimensional flight path containing a timestamp. The airspace rule is internalized into the model attribute, the real-time performance, safety and compliance of path planning are remarkably improved, the airspace resource utilization efficiency is improved, and key technical support is provided for large-scale urban unmanned aerial vehicle logistics operation.

An intelligent MES data analysis system and method for workshop production, relating to the technical field of MESs. The system comprises a data collection module, a data processing module and an optimization scheduling module. The data collection module is used for acquiring key data from a workshop device and ambient data of a workshop in real time. The data processing module comprises a prediction unit and a quality management unit, wherein the prediction unit is used for predicting a possible device fault on the basis of data of the data collection module, which is beneficial to scheduling maintenance in advance. By evaluating and calculating the remaining useful life of a production device in the workshop, the time when a next fault occurs in the production device in the workshop can be predicted in advance, thereby facilitating subsequent maintenance, and reducing the loss caused by the fault; and according to production requirements and resource conditions, resource allocation on a production line, such as manpower, materials and machinery, can be automatically adjusted, thereby improving the resource utilization rate, reducing waste, and quickly responding to production changes.

The invention discloses a multi-mode dynamic globe interaction system based on three-dimensional lamination and capacitive touch control, and relates to the field of interaction data processing, the system comprises a cloud platform module, an interaction module and a rendering module, and the interaction module comprises an interaction pre-training unit, an interaction identification unit and an interaction layer control unit. According to the method, a machine learning model is adopted to intelligently recognize interaction behaviors, the control activation state of each interaction layer is dynamically adjusted, conflicts are avoided through multi-layer interaction coordination, and the interaction experience is optimized. Through combination of a light source path algorithm, environment data such as temperature and humidity and a hardware resource state of the interaction module are monitored in real time, and interaction sensitivity is intelligently adjusted. Through a self-adaptive precision compensation mechanism, the system dynamically adjusts the precision of the touch layer according to a physical motion state, and high-precision interaction is ensured. The interaction precision and the response speed of the equipment are improved, and stable and efficient user experience is provided in a changing environment and hardware state.

The invention provides a metal printing control method, electronic equipment, a system and a storage medium, and is applied to the technical field of additive manufacturing. The method comprises the steps that multi-dimensional monitoring data of the metal printing process are obtained, wherein the multi-dimensional monitoring data at least comprise a molten pool visual image, molten pool temperature field distribution, printing layer three-dimensional shape data and forming cabin environment data; performing corresponding dimension feature extraction on each dimension of monitoring data in the multi-dimensional monitoring data to obtain a multi-dimensional feature parameter; based on the multi-dimensional characteristic parameters, performing evaluation to obtain a state evaluation result; according to a state evaluation result, determining a to-be-adjusted process parameter and a target adjustment amount corresponding to the process parameter; and according to the target adjustment amount, an execution mechanism corresponding to the technological parameters in the metal printing control system is controlled to act so as to correct the printing state. The method is used for achieving the effects that the printing yield is improved, the printing quality is guaranteed, and then the process quality and the process stability of the metal parts are effectively guaranteed.

The invention belongs to the technical field of data processing, and particularly discloses an intelligent path planning and control system for an automatic driving carrying robot, and the system is operated through the following method: collecting environment data through a multi-mode sensor, and processing a laser radar point cloud and a camera image; fusing multi-source information to generate a real-time map, and obtaining a three-dimensional environment model containing dynamic obstacle positions; according to the three-dimensional environment model, obstacle features are extracted, an improved neural network model is adopted to identify a plurality of current obstacles, obstacle types and movement tracks are judged, and a classified obstacle data set is obtained; through the classified obstacle data set, the distance and the relative speed between the obstacle and the current position of the robot are calculated, and if the distance is smaller than a preset threshold value and the speed is larger than zero, high-priority interference is marked; the objective of the invention is to solve the problem that accurate sensing, real-time decision making and efficient obstacle avoidance are difficult to realize in a complex dynamic environment in the prior art.

The invention discloses a project quality detection method and system based on green building design, and relates to the technical field of project quality detection. The engineering quality detection method and system based on the green building design comprises the steps that S1, BIM structured data, real-time monitoring data, structure acceptance data and construction event and window data are preprocessed; s2, on the basis of BIM structured data and in combination with a BIM model, the qualification state of the component and the quality grade of the partition are judged; s3, identifying a working condition switching anchor point and an event, and performing segmented division of a working condition process; s4, feature clustering and performance state discrimination are carried out, different working condition types are identified, and tagging management is carried out; s5, identifying dominant factors and performing attribution assignment through multi-dimensional label attribution and traceability judgment; and S6, performing grade judgment and grade response through multi-source time sequence item-by-item difference analysis. The problems of state interval disjunction and difficulty in accurate attribution and stable diagnosis caused by space-time mismatching of construction progress, BIM nodes and energy consumption environment data and data asynchronization are solved.

The invention relates to the technical field of Internet of Vehicles and big data processing, and discloses an Internet of Vehicles user perception method and system based on multi-modal data fusion and dynamic portray.The method comprises the following steps that a vehicle terminal collects operation, interaction and environment data and sends the data to a cloud; the cloud access module cleans and vectorizes data; the real-time calculation module generates a fused context feature by using sliding window aggregation and spatial association; the offline module outputs long-term preference and attribute correction characteristics; the dynamic portrait module combines the above characteristics, and constructs a user portrait comprising basic attributes, real-time states and intention labels by using a rule discrimination and deep learning double-track architecture and an adaptive weighting algorithm; and generating and issuing a control instruction according to the matching rule. According to the invention, through spatio-temporal data alignment, static attribute dynamic correction and intention real-time prediction, the problems of portrait lag and prediction delay are solved, and the accuracy and active perception ability of the Internet of Vehicles service are improved.

The invention belongs to the technical field of urban traffic flow prediction, and more specifically relates to a traffic flow prediction method based on self-supervision spatio-temporal representation and scene adaptation. The method comprises the following steps: acquiring traffic flow data, environment data and static region semantic embedding data, respectively performing preprocessing operation, and fusing the three data to obtain standardized tensor input; meanwhile, constructing structure prior data to constrain and correct subsequent model prediction output; constructing a traffic flow prediction model, inputting the preprocessed data into the prediction model, and outputting a future traffic flow prediction result; a road mask and a capacity upper bound are introduced in training and reasoning for constraint, and the stability of prediction result output is improved. The invention aims to enhance the modeling capability for nonlinear space-time dependence, sudden disturbance and exogenous environment coupling influence.

The invention relates to the technical field of environmental monitoring of artificial intelligence, and particularly discloses a dangerous waste storage environment real-time monitoring and risk early warning system based on digital twinning, which collects multi-modal environmental data in real time through an intelligent sensor network deployed in a dangerous waste storage facility, and constructs a digital twinning model synchronized with a physical entity; a multi-modal data fusion and physical embedding technology is adopted, and virtual risk parameters of an area which is not actually measured are calculated based on an environment evolution rule; an abnormal association mode between the measured data and the virtual risk parameters is analyzed and identified through the dynamic association network, and risk level judgment and early warning signal triggering are achieved; a sensor monitoring strategy is dynamically adjusted according to an early warning result to form closed-loop management and control; according to the invention, the sensor limitation of the traditional monitoring system is broken through, the advanced accurate early warning of the hidden risk is realized, and the monitoring efficiency is obviously improved through adaptive resource configuration.

The invention relates to the technical field of data processing, and particularly provides an intelligent decision-making method and system for multi-source data fusion, and the method comprises the steps: carrying out the parallel collection of environment data and equipment state data under the same timestamp, obtaining multi-source data, transmitting the multi-source data to a PLC at an equipment end, and carrying out the local preprocessing, performing time sequence feature extraction and anomaly detection on the preprocessed multi-source data to obtain labeled multi-source data and an abnormal event list, and inputting the labeled multi-source data and abnormal event list into a preset weight adjustment model to obtain a dynamically adjusted weight vector; and carrying out weighted fusion on the labeled multi-source data by using the dynamically adjusted weight vector to obtain fused data, and carrying out intelligent decision making based on the fused data. By carrying out dynamic weighted fusion on the environment data and the equipment state data, the overall intelligent regulation and control capability of the system is improved, and the problems that the reliability of multi-source data fusion is insufficient and the weight cannot be adaptively adjusted are solved.

The invention relates to the technical field of coal mine automation control, and discloses a coal flow windscreen wiper multi-device cooperative control method and system. The method comprises the following steps: obtaining an original environment data set of each sensor node in an underground environment, processing the original environment data set to obtain a communication quality index, calculating bandwidth demand change of each node in combination with historical transmission records of the nodes, and predicting a resource allocation proportion; when the current available network resources cannot meet the requirements, sorting the key data packets to obtain a priority transmission sequence; extracting high-priority data from the sequence, temporarily storing the high-priority data to a local cache unit, and determining the state of a buffer area; dynamically adjusting bandwidth allocation by adopting an intelligent optimization model, and updating communication resource configuration; and judging whether the resource configuration meets a load balance requirement, if so, sending a data packet through a wireless channel, determining a transmission performance index, and generating a resource management scheme of a next period. The cooperative work efficiency of multiple devices of the coal flow windscreen wiper in the high-dust and high-humidity environment is improved.

The invention provides a Beidou-based hoisting positioning error dynamic compensation method, system and platform, and relates to the technical field of intelligent construction. In order to solve the problem that in the prior art, a hoisting positioning error compensation method is low in compensation precision, positioning data, attitude data and environment data of a bridge component are synchronously collected through Beidou positioning equipment; decomposing the positioning data into an effective signal component, a multi-path interference component and a noise component, and extracting the effective signal component; carrying out dynamic fusion on the effective signal component, the attitude data and the environment data to obtain fusion data; according to hoisting historical data in a preset time period, training to obtain an error prediction model; inputting the positioning data, the attitude data and the environment data obtained in real time into the prediction model to obtain a prediction error; and performing dynamic compensation on the fused data according to the prediction error, and outputting final positioning data. The dynamic compensation method is high in compensation precision.

The invention discloses a pre-configuration protection method based on risk identification, and the method comprises the steps: collecting multi-dimensional data of a target electric power optical communication network, including logic topology information, a service demand set and dynamic environment data; logic links in the target power optical communication network are identified and divided into different shared risk groups, and a comprehensive risk index of each shared risk group is calculated; adding the shared risk groups of which the comprehensive risk indexes are greater than a preset risk index threshold value into a to-be-protected queue, and generating an optimal standby network loop for each shared risk group in the queue to obtain a network protection ring of the to-be-protected queue; when a to-be-processed service request is received, selecting a target protection path for the to-be-processed service request from the network protection ring, so that the to-be-processed service request is switched to the target protection path when a fault occurs; according to the invention, the network can be rapidly switched when a fault occurs, and the reliability and recovery speed of the power optical communication network are improved.

The invention provides a robot task planning method and device, computer equipment and a storage medium, and the method comprises the steps: receiving a natural language instruction of a user; determining a task target based on the natural language instruction and scene context information; constructing a semantic map based on the environment data and the visual identification and relation reasoning result; based on the task target and the semantic map, generating an action sequence through a formalized planning engine; and controlling the robot to execute the action sequence. By adopting the method, the usability, accuracy and reliability of robot task planning can be improved.

The invention discloses an intelligent energy-saving scheduling system and method based on an industrial computing power data center, and the method comprises the steps: collecting all kinds of operation data in the data center and external environment data in real time, and carrying out the preprocessing and cleaning of the data; using a machine learning algorithm to predict future load demand and energy price fluctuation; in combination with load prediction and energy price prediction, a multi-objective optimization algorithm is adopted to generate a resource scheduling plan; dynamically adjusting the working state of the cooling system according to the load change and the environment temperature and humidity data; executing a task according to the scheduling plan, feeding back according to the real-time monitoring data and an execution result, and adjusting a scheduling strategy; according to the intelligent energy-saving scheduling system and method based on the industrial computing power data center, by introducing an intelligent algorithm and multi-source data analysis, the energy utilization efficiency of the data center is improved, the operation cost is reduced, and optimal scheduling is achieved under the condition of dynamic load and energy price fluctuation.