576results about How to "Improve management level" patented technology

The invention discloses a construction progress control method in project supervision, and the method comprises the steps: calculating the current punctual completion rate and process handover delay time of each subpackage provider according to a real-time progress data set and a pre-established project network diagram, recognizing the progress fluctuation modes of different project stages through a time sequence analysis method, and carrying out the construction progress control according to the progress fluctuation modes. Establishing a correlation model of the sub-packaging quotient and the engineering stage; according to the priority scores, taking minimization of overall construction period delay and resource idleness as objective functions, applying an optimization algorithm, dynamically adjusting a construction plan and a resource allocation scheme of each subpackage provider, and generating an optimized project progress plan which comprises specific work arrangement and resource allocation suggestions of each subpackage provider; a successful coordination strategy in a historical project is analyzed, coordination measures are recommended for project management personnel and a set of communication matrix is generated according to characteristics and progress conditions of a current project, and the coordination measures comprise resource allocation suggestions, process optimization schemes and communication strategy templates.

The invention discloses a power grid data management system and method based on artificial intelligence data analysis in the technical field of power grid anomaly detection. The method comprises the steps of obtaining operation data of nodes in a power grid system; calculating an exception comprehensive score of the node based on the operation data by combining an exception detection model deployed by edge computing equipment; determining an abnormal response level of the node based on the abnormal comprehensive score; and triggering a corresponding response strategy based on the exception response level of the node, and performing exception processing on the node. According to the method, the lightweight anomaly detection model is deployed on the edge computing equipment, the power grid operation data is monitored in real time, the abnormal condition is quickly identified, the reliability and safety of power grid operation are improved, and the problems of response delay, network bandwidth limitation, single-point fault and the like of a traditional centralized anomaly detection system are solved.

The invention relates to the technical field of smart communities, and discloses a smart community resident health management method based on machine learning, and the method comprises the steps: continuously collecting the health feedback data of residents, such as the physical condition change after outdoor activities, carrying out the correlation analysis of the health feedback data with the previous health data and environment data, dynamically updating and optimizing a correlation model, and obtaining the health feedback data of the residents; the prediction precision of the model and the effectiveness of suggestions are improved, the relationship between the use condition of the community public space and the resident health indexes is analyzed, key public facilities and areas influencing resident health are identified, data support and decision basis are provided for community planning and facility reconstruction, and the method is suitable for popularization and application. Community resident health and environment condition analysis reports are generated regularly, and resident health index change trends are visually displayed for reference of community managers and residents. According to the invention, intelligent analysis and application of environment and health data are realized, and technical support is provided for building a healthy community.

The invention provides a modular power supply system configuration method and system for a micro-module machine room, and relates to the technical field of electric power, and the method comprises the steps: collecting power utilization state data and load distribution data of functional equipment in the micro-module machine room, building a power supply network topology model, dividing a power supply system into a first-stage load, a second-stage load and a third-stage load, an independent power supply loop and a data acquisition unit are respectively configured in the first-stage load, the second-stage load and the third-stage load; a power supply network topology model is updated according to the power utilization data acquired by the data acquisition unit, a reinforcement learning algorithm is adopted to generate an adaptive power distribution strategy, and a modular energy storage unit is arranged to provide a standby power supply for a primary load and a secondary load; and constructing a machine learning fault prediction model based on the power consumption data, and dynamically adjusting a power supply path and a power distribution scheme of a power supply loop.

The invention relates to the technical field of medical data processing, in particular to a risk assessment system for chronic diseases. The system is used for performing physiological index analysis according to a medical physiological monitoring signal to generate physiological index data; performing individual physiological index weight empowerment on the physiological index data based on genomics data to obtain individual physiological index data; performing quantitative analysis on pathological influence factors of chronic disease types according to the electronic health record data and the individual physiological index data, and generating individual chronic disease influence factor quantitative data; performing composite interaction risk factor analysis on the individual chronic disease influence factors based on the quantitative data of the individual chronic disease influence factors to generate individual chronic disease composite interaction risk factor data; and performing individual chronic disease risk assessment based on the individual chronic disease composite interaction risk factor data to generate individual chronic disease risk assessment data. According to the invention, chronic disease health management and accurate evaluation are realized through collaborative evaluation work of chronic disease risks.

The invention discloses a water supply network water quality prediction method and software system based on a space-time diagram neural network, and relates to the technical field of water supply networks, and the method comprises the following steps: obtaining water quality sensor monitoring data and external environment factor data in a water supply network in a database, and carrying out the preprocessing; based on a preset graph convolutional neural network model, adding a time convolution module and a space convolution module, and building a water supply network water quality prediction model; inputting the real-time monitoring data of the water quality sensor into the water supply network water quality prediction model to obtain a water quality index prediction value of the water quality sensor; according to the water supply network water quality prediction method based on the space-time diagram neural network and the software system, data of a plurality of water quality sensors can be processed at the same time, and the water quality state of the water supply network can be predicted through graph convolution and time sequence analysis. And the accuracy of water quality prediction is improved.

The invention relates to the technical field of medicine information management, in particular to a traceability method and system based on a medicine consumable traceability code. The method comprises the following steps: acquiring medicine purchasing information data; generating a unique code for the number of medicine purchasing information so as to generate a medicine warehousing traceability code; performing drug information association on the drug purchase information data according to the drug warehousing traceability code to generate a drug warehousing associated traceability code; storing the medicine warehousing associated traceability code in an information base to generate a medicine warehousing traceability code information base; performing scanning access mode division on the medicine warehousing traceability code, and generating a medicine dispensing scanning request mode and a medicine returning scanning request mode; and performing drug splitting tracing on the drug warehousing tracing code in a drug dispensing scanning request mode to generate drug splitting tracing data. According to the invention, through full-process closed-loop management, data association enhancement, intelligent scheduling optimization and drug return tracing improvement, the efficiency and precision of the drug consumable traceability code tracing method are improved.

The invention relates to an electric power virtual simulation optimization method based on a digital twin technology, and the method comprises the following steps: S1, deploying sensors at key nodes of an electric power system, and integrating an intelligent electric meter, weather forecast, social media information and market data to form a comprehensive data pool; s2, constructing a digital twinborn model of the power system; s3, optimizing the digital twin physical model based on a Bayesian optimization method and an adversarial neural network; s4, applying a bee colony algorithm to formulate a cooperation strategy, and optimizing transmission and load adjustment of power in different areas; s5, introducing an adaptive neural fuzzy inference system, and further optimizing a cooperation strategy; and S6, based on the optimized digital twinborn model and the operation strategy, performing large-scale distributed simulation by using cloud resources. According to the invention, accurate construction of the electric power simulation model and accurate implementation of electric power virtual simulation are realized.

The invention provides a sewage treatment progress monitoring method and system, and the method comprises the steps: collecting and processing a first water quality parameter value of a key node of a sewage treatment facility in a historical time period, constructing a dynamic model of a sewage treatment process through a deep neural network, and introducing a genetic algorithm to optimize the structure and parameters of the dynamic model. An optimized dynamic model is obtained, the optimized dynamic model is used for predicting the sewage treatment effect, and a prediction result and operation data of the sewage treatment facility are obtained; using a fuzzy logic control algorithm to adjust the operation parameter setting value of the sewage treatment system, and using a support vector machine algorithm to classify and identify the fault mode of the sewage treatment system to obtain a fault classification result; based on the prediction result, the operation data, the optimized operation parameter setting value and the fault classification result, a comprehensive monitoring management system based on the cloud platform is constructed; according to the invention, comprehensive monitoring and fault identification of the sewage treatment process are realized, and efficient, stable and reliable operation of sewage treatment is ensured.

The invention relates to the technical field of illumination night scene control, in particular to an urban travel illumination night scene interconnection control method and system based on a photovoltaic technology. The method comprises the following steps: acquiring scenic spot people flow monitoring parameters and photovoltaic illumination night scene equipment state data; carrying out spatial position positioning calculation on all the photovoltaic illumination night scene equipment, and carrying out spatial distribution analysis to obtain a plurality of equipment area distribution diagrams; carrying out regional electricity load calculation on the plurality of equipment region distribution diagrams based on the state data of the photovoltaic illumination night scene equipment, and carrying out electricity load demand prediction so as to generate electricity load demand prediction data of each region; and carrying out people flow distribution calculation on the scenic area people flow monitoring parameters based on the plurality of equipment area distribution diagrams, and carrying out short-term people flow situation prediction to generate people flow situation prediction data of different areas. According to the invention, illumination night scene control with efficient response and low energy consumption is realized.

The invention belongs to the technical field of intelligent operation and maintenance management, provides a high-speed rail station building electromechanical equipment monitoring method and system, and aims to solve the problem of low management level of electromechanical equipment in a high-speed rail station in the prior art. The method comprises the following steps: receiving real-time operation state data of each electromechanical device in a high-speed rail station building; comparing with preset normal operation state data, automatically identifying the electromechanical equipment in an abnormal state, and generating early warning information; in combination with the three-dimensional digital model of the high-speed rail station building, the early warning information is mapped to a corresponding three-dimensional model position, and a visual monitoring interface is formed; when the electromechanical device in the abnormal state is detected, a preset linkage rule is triggered, and the operation mode of the associated device is automatically adjusted to reduce the fault influence; the occurrence time, the processing process and the result of each abnormal event are recorded, and a historical case database is formed and used for subsequent fault analysis and prevention strategy making. According to the technical scheme provided by the invention, the management level of the electromechanical equipment in the high-speed rail station is improved.

The invention relates to the technical field of substation operation and maintenance, in particular to a substation communication power supply storage battery internal resistance detection method and system, electronic equipment and a storage medium, which can ensure the stable operation of a substation communication power supply system and improve the operation safety of a power network. The method comprises the following steps: collecting storage battery multi-element characteristic parameter information, storage battery multi-element external cause parameter information and a storage battery real-time internal resistance value of the substation communication power supply; performing data processing on the multivariate characteristic parameter information of the storage battery, and inputting the processed multivariate characteristic parameter information into a pre-constructed storage battery characteristic parameter analysis model to obtain a storage battery theoretical internal resistance value corresponding to the multivariate characteristic parameter information of the storage battery; performing external cause influence error compensation on the real-time internal resistance value of the storage battery based on the multi-element external cause parameter information of the storage battery to obtain a corrected internal resistance value of the storage battery; and calculating the internal resistance deviation amplitude between the storage battery correction internal resistance value and the storage battery theoretical internal resistance value.

The invention provides an intelligent forestry monitoring system and method based on edge computing, and the method comprises the steps: obtaining the related data of the topography and landform of a forest region, and obtaining a digital elevation model of the topography and landform of the forest region through the processing of the obtained related data; storing the obtained digital elevation model into a cloud platform; and analyzing the digital elevation model of the forest landform stored in the cloud platform through an artificial intelligence algorithm, determining a sensor deployment position according to an analysis result, and deploying a corresponding sensor according to the sensor deployment position. The method comprises the following steps: acquiring original data through a sensor, connecting edge equipment with the sensor, and acquiring the original data acquired by the sensor in real time; through real-time data acquisition and processing of the edge device and the sensor, monitoring data can be timely transmitted to the cloud platform for further analysis and processing.

The invention discloses a method, a system and equipment for optimizing batching parameters in an industrial silicon smelting process. The method comprises the following steps: firstly, acquiring process parameters in an industrial silicon smelting process, standardizing the process parameters, then converting the process parameters into weight factors influencing a smelting result, and mapping a relation between the smelting result and the weight factors into a parameter equation based on a preset multiple linear regression model, obtaining a parameter set containing a weight factor and a linear regression coefficient; then, based on the parameter set, the initial raw material parameters and the reaction process parameters, an equivalent circuit model is constructed and simulated to generate smelting result data under different parameter combinations, and therefore parameter adjustment variables are obtained; according to the numerical range of the parameter adjustment variables and the initial raw material parameters, an optimal batching parameter combination is obtained; and finally, performing optimization treatment on batching parameters in the industrial silicon smelting process. According to the method, accurate control and rapid optimization adjustment of batching parameters can be stably achieved in the industrial silicon smelting process.

The invention provides a control method of a forest fire prevention early warning system, which comprises the following steps: aiming at different types of litters, carrying out a combustion characteristic test, and measuring ignition points, heat values and combustion rate parameters of the litters to obtain combustion characteristic data of the different types of litters; on the basis of grading of the litter layer fire danger, analyzing spatial distribution characteristics of the litter layer fire danger, identifying a fire danger high-incidence area, and representing spatial aggregation and relevance of the litter layer fire danger by adopting a spatial autocorrelation analysis method; analyzing dynamic change characteristics of the litter layer fire danger through a litter layer fire danger assessment result, summarizing a seasonal change rule and an annual change trend of the litter layer fire danger by adopting a time sequence analysis method, and identifying a fire danger high-incidence time period; a geographical detector method is adopted to quantitatively analyze the influence degree of terrain, weather and vegetation on the litter layer fire danger, the dominant key factors are identified, and the key factors influencing the litter layer fire danger are explored.

The invention belongs to the technical field of wind power generation, and discloses a wind power plant digital twin modeling system and a use method, the system comprises an equipment module, the equipment module comprises a biological bionic adaptive fan blade system, a quantum sensing network, a laser radar system, a high-resolution multi-modal imaging system and an intelligent autonomous flying robot group system; the network module comprises a 5G private network, a quantum communication network and an edge computing network; the data module comprises a distributed database system, a big data processing platform and an AI training platform; the AI module comprises a wind regime prediction model, a power prediction model, an equipment health state monitoring model and a self-adaptive control algorithm model; the application module comprises a digital twinborn visualization platform, a holographic mixed reality operation and maintenance system and an intelligent scheduling system. Modeling and intelligent management of the wind power plant can be realized, the power generation efficiency can be improved, the operation and maintenance cost can be reduced, and the service life of equipment can be prolonged.

The invention relates to the technical field of photovoltaic construction, and discloses an intelligent monitoring device and method for photovoltaic construction, and the device comprises a data collection module which collects the equipment data of a photovoltaic power station in real time; the real-time monitoring module is used for displaying the operation and state of the equipment in the forms of graphs and tables; the remote monitoring module is used for remotely monitoring the overall operation condition; the data storage module is used for storing sunlight amplitude, temperature, wind speed meteorological information and operation parameters of the module; and an early warning module. Through real-time monitoring and early warning, problems can be found and solved in time, system fault time is reduced, power generation efficiency is improved, maintenance cost caused by delayed problem finding is reduced, power station operation cost is reduced, through an intelligent scheduling module, operation and maintenance processes are optimized, operation and maintenance efficiency is improved, manpower resource waste is reduced, and through data analysis and report service, the system can be used for monitoring and early warning in real time. Decision basis is provided for work development of the photovoltaic power station, and the management level is improved.

The invention relates to the technical field of power supply monitoring, in particular to a power supply monitoring system with a hierarchical authority encryption function, which comprises an edge calculation processing module for reading voltage, current and load data based on a power supply monitoring node, calculating voltage fluctuation in sections, monitoring current change in real time, comparing amplitude and screening a sudden change value; load balance and abnormity are judged, and power node fluctuation characteristics are generated. According to the method, the current and voltage signal characteristics are analyzed step by step by adopting the mode matching algorithm, the power abnormal event is accurately captured, the identification efficiency of the fault event is effectively improved, the false alarm and missing report conditions are reduced, and the accuracy and timeliness of power monitoring are ensured; the fault source node is positioned by using the Bayesian network in combination with the power grid topological relation, so that the fault point is quickly and accurately judged, the positioning time is shortened, the monitoring and management level of the power system is improved, and the stability and safety of the system are enhanced.

The invention provides a micro-nano impact press-in tester operation monitoring system based on digital twinning, and is suitable for the field of precise instrument maintenance. The system realizes multi-physical field coupling modeling in a micro-nano press-in test through a digital twinning technology, and can monitor the state of an instrument in real time with high precision, predict faults and perform dynamic optimization. The system is integrated with a high-precision sensor, data such as micro force, extremely high temperature and nanoscale displacement are collected in real time, a digital twin model is constructed, and the operation state of an instrument is mapped. Historical and real-time data are analyzed by means of a machine learning algorithm, potential faults can be accurately predicted, maintenance suggestions can be provided, and the precision retentivity and the test stability of the instrument under the material service working condition are ensured. The interaction center provides a visual interface, supports real-time monitoring, dynamic adjustment and early warning functions, and remarkably improves the intelligent maintenance level of the instrument. The invention is suitable for the fields of material science, micro-nano mechanical test and the like, and provides an innovative precise instrument intelligent management scheme.

The invention discloses a pre-accepted work order closed-loop management system, which comprises a sensitive first-aid repair work order automatic research and judgment module, a non-sensitive work order handling module, a pre-accepted work order accurate closed-loop grading management and control module, a closed-loop process management module, a comprehensive auditing module and a visualization module, an intelligent grading management and control rule, a responsibility investigation mechanism, a timestamp technology and the like are embedded into the corresponding module to enhance the disposal effect of the pre-accepted work order; according to the invention, by establishing an online pre-acceptance work order closed-loop management system, sensitive emergency pre-acceptance work orders can be found in time, and the core problems of untimely work order reporting, information blocking, large workload, difficult supervision, difficult management and control, difficult statistical analysis, difficult employee assessment and the like in the existing pre-acceptance work order processing process are solved; and workers are assisted to realize whole-process closed-loop management of processing time node management and control of each link of a pre-accepted work order, accurate order making of sensitive problems, time-limited grading, automatic circulation and the like, and the quality and efficiency of power supply service are improved.

The invention relates to the technical field of medical behavior data monitoring, in particular to a medical insurance common outpatient service supervision system and method based on a fraud abnormal portrait technology, and the system comprises a medical transaction data collection module, a medical behavior analysis module, a risk assessment feedback module and a medical transaction database. According to the medical insurance ordinary outpatient service supervision system and method based on the fraud anomaly portrait technology, a more comprehensive risk assessment system is provided, and by means of an intelligent means, the medical object behavior anomaly risk, the medical institution behavior anomaly risk and the number of abnormal behavior overlapping cycles are comprehensively analyzed, so that the risk assessment efficiency is improved. According to the invention, the method achieves the precise recognition, early warning and prevention of fraud abnormal behaviors in a common medical insurance outpatient service, facilitates the improvement of the use efficiency of medical insurance funds, guarantees the fairness and sustainability of a medical insurance system, protects the benefits of a patient, maintains the fairness and efficiency of a medical system, and also facilitates the improvement of the management level and service quality of a medical institution.

The invention relates to the technical field of reservoir dispatching, in particular to a method for handling events influencing cascade reservoir dispatching operation, which comprises the following steps: acquiring real-time monitoring data of a cascade reservoir to be detected; performing data preprocessing on the real-time monitoring data of the to-be-detected cascade reservoir to obtain preprocessed data; inputting the preprocessed data into a trained key factor determination model, and obtaining a key data type and corresponding data; determining whether an abnormal event occurs or not and the type of the abnormal event according to the key data type and the corresponding data; when an abnormal event occurs, inputting the type of the abnormal event into a preset intelligent diagnosis system, and obtaining a plurality of exception handling schemes; and SWOT analysis is carried out on the various exception handling schemes to obtain a final handling result. According to the method, the safety and reliability of cascade reservoir dispatching operation are improved, and the cascade reservoir operation management level is improved.

The invention discloses a real-time laser coal checking system and method, and relates to the technical field of material management and inventory checking, and the system comprises the following components: a laser detection module, a data processing module, a communication module, a monitoring module and a server module. The laser detection module is adopted to perform high-frequency scanning on the coal pile to obtain the three-dimensional point cloud data of the coal pile, the crown block displacement data measured by the laser range finder and the material and density information of the material pile are combined, the volume and weight of the coal pile are calculated through an algorithm, the outer contour of the coal pile can be determined more accurately, and the accuracy of the coal pile is improved. According to the shape characteristics and the density distribution condition of the coal pile, the coal pile is divided into a plurality of small geometries for volume calculation, and compared with traditional manual measurement or simple geometric shape approximation, the method has the advantages that the measurement precision of the volume and the weight of the coal pile can be remarkably improved; and more accurate data support is provided for material management and inventory checking of enterprises.

The invention provides a Texaco gasification device corrosion problem analysis method, which comprises the following steps: selecting key monitoring points to monitor the corrosion condition of a target device, installing intelligent sensors at the points, collecting operation data in real time and transmitting the operation data to a central processing unit; analyzing the data, identifying an area possibly having a corrosion risk and an abnormal change trend, and determining a suspected corrosion area; carrying out deep detection on the areas to obtain detection data; detection data, equipment operation data and maintenance records are encrypted and stored, so that the authenticity, non-tampering and traceability of the data are ensured, and a reliable basis is provided for subsequent analysis and decision making; and in combination with an intelligent manufacturing technology, operation parameters and maintenance plans of the device are optimized according to a corrosion analysis result, and accurate analysis and effective prevention and control of corrosion problems are realized. By means of the scheme, accurate analysis and effective prevention and control of the corrosion problem of the Texaco gasification device are achieved, and the overall equipment management level and safety are improved.

The invention relates to the technical field of intelligent chemical engineering, and discloses an intelligent chemical engineering construction safety and quality detection method based on big data, and the method comprises the following steps: 1, installing a monitor and a sensor; step 2, intelligent analysis; step 3, predictive maintenance; step 4, risk assessment; step 5, quality control; step 6, safety management; 7, accident prevention; 8, remote real-time management is carried out; step 9, decision support; step 10, intelligent reporting; and step 11, displaying an interface. According to the intelligent chemical engineering site construction safety and quality detection method and system based on big data, through real-time monitoring and risk assessment, the intelligent chemical engineering site system can improve the safety management level of a construction site and reduce accidents, and the system can predict potential safety risks and take measures to avoid the potential safety risks by utilizing predictive maintenance and accident prevention technologies; and by using big data and an intelligent optimization algorithm, the resource allocation and the construction process are optimized, and the construction efficiency is improved.

The invention discloses an intelligent automobile path tracking system for urgent danger avoidance, and particularly relates to the technical field of intelligent automobiles, the system comprises an environment sensing module, the output end of the environment sensing module is connected with a danger assessment module, and the output end of the danger assessment module is connected with a path planning module. The output end of the path planning module is connected with a decision planning module, the output end of the decision planning module is connected with a vehicle control module, and the output end of the vehicle control module is connected with a communication and interaction module. Through cooperative work of all the modules, perception of the surrounding environment of the vehicle, danger assessment, path planning, decision making, vehicle control and communication interaction with the outside are achieved, under the dangerous condition, the system can make a response rapidly, effective danger avoiding measures are taken, the driving safety of the vehicle and the accuracy of path tracking are ensured, and the driving safety of the vehicle is improved. And a comprehensive and reliable guarantee is provided for safe driving of the intelligent automobile.

The invention relates to the technical field of farm disinfection, in particular to an intelligent disinfection system for a farm. The system comprises an initial determination module, a periodic disinfection module, an environment monitoring module, a first analysis control module, a second analysis control module and a feedback correction module. According to the method, the initial disinfection parameter cluster of each breeding area in the target breeding farm is determined according to the actual growth stages of the breeding targets and the actual number corresponding to each actual growth stage, each breeding area is periodically disinfected according to the initial disinfection parameter cluster, and each breeding area is monitored in real time so as to be singly judged; and performing first-class adjustment and second-class adjustment on the initial disinfection parameter cluster based on a first verification result and a second verification result in combination with the first verification result and the second verification result to determine whether the initial disinfection parameter cluster is subjected to first-class adjustment and second-class adjustment. And according to the first analysis control result and the second analysis control result, determining whether to perform feedback correction on a preset initial disinfection period of each breeding area.

The invention discloses a pig health assessment method based on a rail-mounted inspection robot, and the method comprises the following steps: S1, collecting an RGB image, an infrared image and a depth image of a pig through the rail-mounted inspection robot, and recording the health condition of the pig; s2, extracting physiological and behavior characteristics of the pig through a deep learning model according to the RGB image, the infrared image and the depth image; s3, obtaining health indexes of the pigs according to the physiological and behavior characteristics of the pigs; s4, standardizing the health indexes of the pigs through z-score to obtain scores of the health indexes of the pigs; and S5, constructing a random forest model according to the health condition of the pig, taking the score of the health index of the pig as an independent variable, training through the random forest model to obtain the weight of the health index, establishing a pig health scoring model according to the weight of the health index, and calculating the health score of the pig, thereby realizing automatic pig health assessment.

The invention discloses a big data system based on building industry multi-data source project information unification, and relates to the technical field of big data systems. The system comprises a data acquisition layer which is directly connected with an external data source and is responsible for acquiring data and providing original materials for subsequent processing; the data preprocessing and cleaning layer is used for receiving data input of the data acquisition layer and carrying out primary processing on original data; the data storage layer is used for receiving the data of the data preprocessing and cleaning layer, storing the cleaned data, providing a basis for data analysis and supporting the storage requirement of large-scale data; the data processing layer is used for directly reading the data of the data storage layer and deeply analyzing the stored data; the big data system provided by the invention solves the problem of project information management in the building industry through improvements in the aspects of data unified management, data integrated analysis, decision-making efficiency improvement, data security guarantee and the like, and brings better effects and positive influences.

The invention provides a thermal power generating unit energy consumption real-time monitoring and playback system based on SVG vector pictures, which comprises a data acquisition module, an SVG picture engine, a WEB server, a historical database and a playback service module which are connected through a data bus, technologies such as self-adaptive sampling, intelligent layout and multi-layer scaling are adopted, the energy consumption state of the thermal power generating unit can be comprehensively and clearly displayed in a multi-angle mode, and the monitoring capacity of the system is improved; by adopting technologies of WebSocket communication, intelligent load balancing and the like, real-time transmission of energy consumption data and high-availability deployment of the system are realized, and timeliness and stability of a monitoring picture are ensured; the technologies of hierarchical storage, automatic life cycle management and the like are adopted, efficient storage and management of mass historical energy consumption data are achieved, and the requirements of data playback and analysis mining are met; through adoption of technologies such as adaptive frame rate control and intelligent scene retrieval, smooth playback and rapid positioning of a historical energy consumption scene are realized, and data support is provided for anomaly diagnosis and trend analysis.