CN117273414A - System and method for analyzing and identifying big data of smart city - Google Patents
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Abstract
The invention relates to the technical field of big data analysis, in particular to a big data analysis and identification system and method for a smart city. According to the invention, the data acquisition module rapidly processes the original data by utilizing an edge computing technology, the data transmission and storage requirements are reduced, the processing speed is improved, the data preprocessing module intelligently processes and improves the data quality, an accurate basis is provided for subsequent analysis, the resource demand prediction module accurately predicts the future resource demand, the multi-objective optimization and intelligent algorithm of the resource optimization configuration module is crucial to the reasonable allocation and saving, the resource utilization efficiency is improved, the real-time monitoring and intelligent regulation and control functions of the environment monitoring and traffic regulation and control module improve the dynamics and the fineness of urban management, and the satisfaction degree and the life quality of residents are improved.
Description
Technical Field
The invention relates to the technical field of big data analysis, in particular to a system and a method for analyzing and identifying big data of a smart city.
Background
Big data analysis is a technical field related to processing and analyzing large-scale, diversified, high-dimensional data. It includes data collection, storage, cleaning, processing, analysis, and mining, aimed at extracting useful information and insight from a large volume of data. This field uses a variety of techniques and methods of computer science, data mining, machine learning, statistics, etc. to address the challenges of big data to support decision making, problem solving, and prediction.
The system is used for analyzing and identifying the big data of the smart city, and is a big data analysis system which is focused on the field of the smart city. It is an integrated software system that is designed to collect, manage, analyze and identify large amounts of data related to urban operation and management. Such data includes various city data of traffic, environment, energy, society, etc. The main purpose is to provide support and insight for the fields of city planning, resource allocation, security management, environmental improvement and the like by deeply analyzing the data. These insights may be used to optimize city operation, improve efficiency, improve resident quality of life, reduce resource waste, and address challenges such as traffic congestion, pollution, emergency management, and the like. Through the system, the real-time monitoring and analysis of the urban data can be realized, trends, modes and abnormal conditions are found, and important information of decision makers on urban operation is provided so as to make intelligent decisions. The effects include improving the sustainability of cities, reducing the operation cost, improving the life of residents, improving the safety of cities and the like. To achieve this goal, the system typically uses large data processing techniques, including methods of data acquisition, data cleansing, data storage, data analysis, machine learning, and data visualization. The system comprises a sensor network, cloud computing and the internet of things technology, so that real-time monitoring and data collection are facilitated. Meanwhile, data mining and machine learning algorithms are adopted to identify patterns, predict trends and identify anomalies, thereby providing a decision maker with beneficial information about urban operation. Through data visualization, the results can be presented in an easily understood and decision-making manner.
The existing system has data transmission and storage bottlenecks when processing big data, and lacks effective edge computing application, so that the original data processing is not efficient in time. In terms of data preprocessing, conventional systems often lack sufficient utilization of data resources due to lack of advanced data cleansing algorithms. In addition, existing systems often lack accuracy in resource demand prediction, failing to fully utilize time series and seasonal decomposition techniques, resulting in inefficient resource allocation and scheduling. In terms of resource optimization configuration, the conventional system often lacks an efficient optimization algorithm, and cannot realize optimal configuration of resources. The environment monitoring and traffic regulation and control functions are also original, a real-time monitoring and intelligent regulation and control mechanism is lacked, and complex requirements of modern city management are difficult to meet. These deficiencies limit the level of intelligent management of cities, affecting the quality of life of residents and the sustainable development of cities.
Disclosure of Invention
The invention aims to solve the defects in the prior art, and provides a system and a method for analyzing and identifying big data of a smart city.
In order to achieve the above purpose, the present invention adopts the following technical scheme: the system for analyzing and identifying the big data of the smart city comprises a data acquisition module, a data preprocessing module, a resource demand prediction module, a resource optimal configuration module, an environment monitoring module and a traffic regulation module;
The data acquisition module is based on a sensor network deployed in urban multi-region, integrates multi-source data comprising traffic and energy sources by adopting a real-time data acquisition technology, performs preliminary data screening and compression through edge calculation, and generates an original data set;
the data preprocessing module is used for filling missing values and removing abnormal values of data based on an original data set by adopting a data cleaning algorithm, screening data subjects and regulating formats, and generating a cleaning data set by adopting a data normalization technology;
the resource demand prediction module is used for constructing an autoregressive integral moving average model based on a cleaning data set by utilizing a time sequence analysis method, carrying out trend and periodic deep analysis by combining a seasonal decomposition technology, and generating a resource demand prediction report;
the resource optimization configuration module is used for carrying out resource optimization configuration by adopting a multi-objective optimization algorithm and combining a genetic algorithm and particle swarm optimization based on a resource demand prediction report to generate a resource optimization scheme;
the environment monitoring module is used for carrying out real-time monitoring on environment data by adopting an anomaly detection algorithm based on the environment sensor data, introducing a natural language processing technology, carrying out emotion analysis on social media data, obtaining people feedback information and generating an environment monitoring report;
The traffic regulation and control module adopts a graph algorithm to perform structural analysis of an urban traffic network based on a traffic flow prediction report, and dynamically performs intelligent regulation and control of traffic signals and traffic paths by matching with a fuzzy logic controller to generate a traffic regulation and control strategy;
the original data set is real-time data comprising traffic, energy, environment and public service, the resource demand prediction report is a demand prediction result comprising urban traffic, energy and municipal resources, the resource optimization scheme is a resource allocation scheme aiming at multiple periods and multiple areas, and the environment monitoring report is a real-time state, an abnormal state and predicted influence based on urban environment change.
As a further scheme of the invention, the data acquisition module comprises a traffic sensing sub-module, an energy monitoring sub-module and a public service sub-module;
the data preprocessing module comprises a data cleaning sub-module, a data normalization sub-module and a data compression sub-module;
the resource demand prediction module comprises a traffic flow prediction sub-module, an energy demand prediction sub-module and a public service prediction sub-module;
the resource optimization configuration module comprises a traffic optimization sub-module, an energy scheduling sub-module and a public resource allocation sub-module;
The environment monitoring module comprises an air quality monitoring sub-module, a water quality monitoring sub-module and a sound pollution monitoring sub-module;
the traffic regulation and control module comprises a signal lamp regulation and control sub-module, a traffic flow optimization sub-module and a congestion prevention sub-module.
As a further scheme of the invention, the traffic sensing submodule is based on a sensor network deployed by urban traffic nodes, adopts a real-time traffic acquisition technology to sense traffic and integrate data to generate a traffic real-time data set;
the energy monitoring submodule is based on an energy consumption monitoring system, performs trend analysis by utilizing an energy consumption monitoring technology, integrates data of energy use conditions and generates an energy consumption data set;
and the public service submodule performs data summarization of social media and an online service platform by adopting a public feedback collection mechanism based on public service facilities to generate a public service data set.
As a further scheme of the invention, the data cleaning submodule adopts a data cleaning algorithm to fill missing values and reject abnormal values based on an original comprehensive data set, screens and regulates data formats, and generates a preprocessed data set;
The data normalization submodule performs data normalization on the preprocessed data set by using a normalization processing method, unifies the scale and the range of the data, and generates a normalized data set;
the data compression sub-module adopts a data compression technology to perform data compression on the normalized data set, optimizes the storage space and the processing efficiency, and generates a compressed data set.
As a further scheme of the invention, the traffic flow prediction submodule adopts an autoregressive integral moving average model to carry out traffic flow sequence modeling based on the cleaned data set, and combines seasonal decomposition technology to carry out trend and periodicity analysis to generate a traffic flow prediction report;
the energy demand prediction submodule is used for quantifying energy demand and generating an energy demand prediction report by referring to the relevance of traffic flow and energy consumption by adopting a multiple linear regression method based on the traffic flow prediction report;
and the public service prediction submodule optimizes a prediction model by adopting an exponential smoothing method based on the energy demand prediction report, captures short-term changes of the public service demand and generates a public service demand prediction report.
As a further scheme of the invention, the traffic optimization submodule optimizes traffic flow and road use efficiency by adopting a multi-objective optimization combined genetic algorithm based on a public service demand prediction report to generate a traffic resource optimization scheme;
The energy scheduling submodule is based on a traffic resource optimization scheme, and utilizes a particle swarm optimization algorithm to dynamically adjust energy supply and demand to generate an energy scheduling scheme;
the public resource allocation submodule adopts a simulated annealing algorithm to search a public resource allocation strategy based on an energy scheduling scheme, meets the requirements of multiple areas and time, and generates a public resource allocation scheme.
As a further scheme of the invention, the air quality monitoring submodule monitors the content of harmful substances in the air in real time by adopting an anomaly detection algorithm based on machine learning based on fine granularity data of an environmental sensor, and generates real-time air quality index data through data cleaning and normalization processing;
the water quality monitoring submodule continuously monitors the concentration of the water body pollutants based on the real-time air quality index data by using a time sequence analysis technology, and judges the water quality class by combining a clustering algorithm to generate the real-time water quality index data;
the sound pollution monitoring submodule monitors environmental noise in real time by applying a frequency domain analysis method based on the real-time water quality index data, and distinguishes natural noise and artificial noise by utilizing a sound identification technology to generate real-time sound pollution level data.
As a further scheme of the invention, the traffic signal lamp regulation and control submodule optimizes the traffic signal lamp time sequence based on road sensor data by applying a deep reinforcement learning strategy and combines simulation to generate a signal lamp scheduling scheme;
the traffic flow optimizing submodule is based on a signal lamp scheduling scheme, adopts a graph theory algorithm to conduct deep analysis on a traffic network, optimizes vehicle allocation through a shortest path and a maximum flow algorithm, and generates an optimized traffic flow control scheme;
the congestion prevention sub-module dynamically adjusts traffic strategies based on the optimized traffic flow control scheme by using a fuzzy logic control technology, predicts and prevents traffic congestion, and generates a congestion prevention strategy.
A method for smart city big data analysis and recognition, which is executed based on the above system for smart city big data analysis and recognition, comprising the steps of:
s1: based on a sensor network deployed by urban traffic nodes, adopting a real-time flow acquisition technology to perform flow sensing and data integration to generate a traffic real-time data set;
s2: based on the traffic real-time data set, adopting a data cleaning algorithm to perform missing value filling, abnormal value removing and data format regularity to generate a preprocessed data set;
S3: based on the preprocessed data set, adopting an autoregressive integral moving average model to carry out traffic flow sequence modeling, and carrying out trend and periodicity analysis to generate a traffic flow prediction report;
s4: based on the traffic flow prediction report, adopting multiple linear regression analysis to quantify energy demands and generating an energy demand prediction report;
s5: based on the energy demand prediction report, optimizing traffic flow and road use efficiency by adopting a multi-objective genetic algorithm, and generating a traffic resource optimization scheme;
s6: based on the traffic resource optimization scheme, adopting a graph theory algorithm to perform traffic network analysis, and generating an optimized traffic flow control scheme through a shortest path and a maximum flow algorithm;
s7: and dynamically adjusting the traffic strategy based on the optimized traffic flow control scheme by adopting a fuzzy logic control technology, preventing congestion and generating a congestion prevention strategy.
As a further scheme of the invention, the traffic real-time data set is specifically real-time information summary comprising traffic flow and pedestrian flow in multiple traffic nodes, the traffic flow prediction report is specifically prediction analysis on trend and periodic fluctuation of traffic flow in a future time period, the energy demand prediction report is specifically quantification prediction on energy demand in the future time period, the optimized traffic flow control scheme is specifically optimization regulation and control on vehicle flow in a traffic network, and the congestion prevention strategy is specifically a comprehensive scheme comprising dynamic traffic strategy adjustment, real-time congestion early warning and prevention measures.
Compared with the prior art, the invention has the advantages and positive effects that:
according to the invention, the advanced data processing and analysis technology is integrated, so that the data management capability and response efficiency of the smart city are remarkably improved. The data acquisition module utilizes the edge computing technology to rapidly process the original data, thereby remarkably reducing the data transmission and storage requirements and improving the data processing speed. The intelligent processing of the data preprocessing module greatly improves the data quality, and provides a more accurate basis for subsequent analysis. The resource demand prediction module can accurately predict future resource demands, which is important for reasonable allocation and saving of resources. The multi-objective optimization and intelligent algorithm of the resource optimization configuration module effectively improves the resource utilization efficiency. The environment monitoring module and the traffic control module have the functions of real-time monitoring and intelligent control, so that the dynamics and the fineness of urban management are improved, and the satisfaction degree and the life quality of residents are improved.
Drawings
FIG. 1 is a system flow diagram of the present invention;
FIG. 2 is a schematic diagram of a system framework of the present invention;
FIG. 3 is a flow chart of a data acquisition module according to the present invention;
FIG. 4 is a flow chart of a data preprocessing module according to the present invention;
FIG. 5 is a flow chart of a resource demand prediction module of the present invention;
FIG. 6 is a flow chart of a resource optimization configuration module of the present invention;
FIG. 7 is a flow chart of an environmental monitoring module of the present invention;
FIG. 8 is a flow chart of a traffic control module of the present invention;
FIG. 9 is a schematic diagram of the method steps of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present invention more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
In the description of the present invention, it should be understood that the terms "length," "width," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," and the like indicate orientations or positional relationships based on the orientation or positional relationships shown in the drawings, merely to facilitate describing the present invention and simplify the description, and do not indicate or imply that the devices or elements referred to must have a specific orientation, be configured and operated in a specific orientation, and therefore should not be construed as limiting the present invention. Furthermore, in the description of the present invention, the meaning of "a plurality" is two or more, unless explicitly defined otherwise.
Embodiment one: referring to fig. 1, the present invention provides a technical solution: the system for analyzing and identifying the big data of the smart city comprises a data acquisition module, a data preprocessing module, a resource demand prediction module, a resource optimal configuration module, an environment monitoring module and a traffic regulation module;
the data acquisition module is based on a sensor network deployed in urban multi-region, integrates multi-source data comprising traffic and energy sources by adopting a real-time data acquisition technology, performs preliminary data screening and compression through edge calculation, and generates an original data set;
the data preprocessing module is used for filling missing values and removing abnormal values of data based on an original data set by adopting a data cleaning algorithm, screening data subjects and regulating formats, and generating a cleaning data set by adopting a data normalization technology;
the resource demand prediction module is used for constructing an autoregressive integral moving average model based on a cleaning data set by utilizing a time sequence analysis method, carrying out trend and periodic deep analysis by combining a seasonal decomposition technology, and generating a resource demand prediction report;
the resource optimization configuration module is used for carrying out resource optimization configuration by adopting a multi-objective optimization algorithm and combining a genetic algorithm and particle swarm optimization based on a resource demand prediction report to generate a resource optimization scheme;
The environment monitoring module monitors environment data in real time by adopting an anomaly detection algorithm based on the environment sensor data, introduces a natural language processing technology, performs emotion analysis on social media data, acquires people feedback information and generates an environment monitoring report;
the traffic regulation and control module adopts a graph algorithm to carry out structural analysis of the urban traffic network based on the traffic flow prediction report, and dynamically carries out intelligent regulation and control of traffic signals and traffic paths by matching with the fuzzy logic controller to generate a traffic regulation and control strategy;
the original data set is real-time data comprising traffic, energy, environment and public service, the resource demand prediction report is a demand prediction result comprising urban traffic, energy and municipal resources, the resource optimization scheme is a resource configuration scheme aiming at multiple periods and multiple areas, and the environment monitoring report is a real-time state, an abnormal state and the predicted influence based on urban environment change.
Through the data acquisition module and the edge computing technology, multi-source data such as urban traffic, energy sources, environments and public services can be accurately acquired in real time, and a solid foundation is provided for subsequent data analysis and decision making. And secondly, the data preprocessing module adopts a data cleaning algorithm and a normalization technology to carry out missing value filling, outlier rejection and format regularity on the original data set, so that the quality and usability of the data are improved. The resource demand prediction module can deeply mine the trend and the periodic variation of the data by utilizing a time sequence analysis method and a seasonal decomposition technology, generates an accurate resource demand prediction report, and provides a scientific basis for reasonable allocation of resources. The resource optimization configuration module adopts a multi-objective optimization algorithm and a genetic algorithm, and combines particle swarm optimization, so that an optimal resource optimization scheme can be found, and efficient utilization and saving of resources are realized. The environment monitoring module can monitor the change of urban environment in real time through an anomaly detection algorithm and a natural language processing technology, and performs emotion analysis on social media data to acquire people feedback information, thereby providing an important reference for environment protection and management. The traffic control module is based on a traffic flow prediction report and a graph algorithm, and is combined with the fuzzy logic controller, so that the traffic signal lamp and the traffic path can be intelligently controlled, the traffic efficiency is improved, and the congestion is reduced.
Referring to fig. 2, the data acquisition module includes a traffic sensing sub-module, an energy monitoring sub-module, and a public service sub-module;
the data preprocessing module comprises a data cleaning sub-module, a data normalization sub-module and a data compression sub-module;
the resource demand prediction module comprises a traffic flow prediction sub-module, an energy demand prediction sub-module and a public service prediction sub-module;
the resource optimization configuration module comprises a traffic optimization sub-module, an energy scheduling sub-module and a public resource allocation sub-module;
the environment monitoring module comprises an air quality monitoring sub-module, a water quality monitoring sub-module and a sound pollution monitoring sub-module;
the traffic control module comprises a signal lamp control sub-module, a traffic flow optimization sub-module and a congestion prevention sub-module.
In the data acquisition module, the traffic sensing sub-module is responsible for collecting traffic data of each area of the city, including information such as vehicle flow, speed and the like; the energy monitoring submodule is used for collecting energy use conditions of cities, such as consumption of electric power, fuel gas and the like; the public service sub-module is responsible for collecting public service data of cities, such as water supply, power supply, garbage disposal and the like.
In the data preprocessing module, the data cleaning sub-module is responsible for carrying out missing value filling and abnormal value removing on an original data set, so that the integrity and the accuracy of data are ensured; the data normalization sub-module is responsible for converting data of different units into a unified standard so as to facilitate subsequent analysis and comparison; the data compression sub-module is responsible for compressing the data set, and reduces the storage space and the transmission time.
In the resource demand prediction module, a traffic flow prediction submodule predicts urban traffic flow by using a time sequence analysis method and a seasonal decomposition technology so as to make traffic scheduling and planning in advance; the energy demand prediction submodule predicts the energy demand of the city according to the historical data and trend analysis, and prepares for energy supply; the public service prediction sub-module predicts the demand of public service by analyzing factors such as population growth, economic development and the like, and provides reference for government decision.
In the resource optimization configuration module, a traffic optimization submodule adopts a multi-objective optimization algorithm and a genetic algorithm, and performs optimization configuration on traffic resources by combining particle swarm optimization so as to improve traffic efficiency and reduce congestion; the energy scheduling sub-module reasonably schedules energy supply according to the energy demand prediction result, so that the high-efficiency utilization of resources is realized; and the public resource allocation sub-module reasonably allocates public resources according to the public service demand prediction result to meet the demands of citizens.
In the environment monitoring module, an air quality monitoring sub-module monitors air quality indexes of cities, such as PM2.5 concentration and the like, in real time through an environment sensor; the water quality monitoring submodule monitors water sources of cities, and comprises the pollution degree of the water bodies and the change trend of the water quality; the sound pollution monitoring sub-module monitors the sound level of the city through the sensor to evaluate the influence of noise on residents.
In the traffic control module, a signal lamp control sub-module dynamically adjusts a timing scheme of the traffic signal lamp according to a traffic flow prediction result and a graph algorithm so as to optimize traffic flow; the traffic flow optimizing sub-module adjusts the timing strategy of the traffic path and the signal lamp in real time through the fuzzy logic controller so as to reduce congestion and improve traffic efficiency; the congestion prevention sub-module predicts the possible congestion area and time period by analyzing the historical data and the real-time traffic condition, and takes corresponding measures for prevention.
Referring to fig. 3, the traffic sensing sub-module adopts a real-time traffic acquisition technology to sense traffic and integrate data based on a sensor network deployed by urban traffic nodes to generate a traffic real-time data set;
the energy monitoring submodule is based on an energy consumption monitoring system, performs trend analysis by utilizing an energy consumption monitoring technology, integrates data of energy use conditions and generates an energy consumption data set;
the public service submodule is based on public service facilities, adopts a public feedback collection mechanism to collect data of social media and an online service platform, and generates a public service data set.
The traffic sensing sub-module is provided with sensor devices at urban traffic nodes (such as intersections, toll stations and the like) which can monitor information of traffic flow, speed and the like of vehicles in real time. And transmitting the acquired data to a data acquisition module for processing and integration through a wireless communication technology.
The energy monitoring submodule acquires consumption data of energy sources such as electric power, fuel gas and the like through cooperation with an energy supplier. These data are counted and analyzed to understand the energy usage and trends. And integrating the analysis result into a resource demand prediction report, and providing scientific basis for reasonable allocation of resources.
The public service sub-module sets a feedback box or an online platform in public service facilities (such as parks, hospitals, schools and the like) to encourage citizens to provide evaluation and suggestion of public services. And carrying out emotion analysis on the data on the social media by using a natural language processing technology to acquire feedback information of people. The data is summarized and consolidated to generate a common service data set. Incorporating the data set into the environmental monitoring report provides an important reference for environmental protection and management.
Referring to fig. 4, the data cleaning submodule performs missing value filling and outlier rejection by adopting a data cleaning algorithm based on the original comprehensive data set, screens and scales the data format, and generates a preprocessed data set;
the data normalization submodule performs data normalization on the preprocessed data set by using a normalization processing method, unifies the scale and the range of the data, and generates a normalized data set;
The data compression sub-module adopts a data compression technology to perform data compression on the normalized data set, optimize the storage space and the processing efficiency, and generate a compressed data set.
The data cleaning sub-module performs integrity checking on the original integrated data set to identify the fields or records where missing values exist. And selecting a proper filling method for filling according to the type and distribution condition of the missing values, such as using a mean value, a median value or a regression model. Abnormal values are identified and removed through a statistical method or domain knowledge, and accuracy and reliability of data are ensured. And screening and regulating the data format, unifying the representation mode and structure of the data, and generating a preprocessed data set.
The data normalization submodule performs data normalization on the preprocessed data set by using a normalization processing method. The fields or features to be normalized are determined, and appropriate normalization methods, such as maximum-minimum normalization, Z-score normalization, etc., are selected. According to the selected normalization method, mapping the values in the preprocessed data set to a uniform scale and range, and eliminating the dimensional differences among different features. And generating a normalized data set, and providing a consistent data basis for subsequent resource demand prediction and optimization configuration.
The data compression submodule selects a suitable data compression algorithm, such as a lossless compression algorithm (e.g. LZ77, huffman coding) or a lossy compression algorithm (e.g. JPEG, MP 3). And according to the selected compression algorithm, compressing the normalized data set, and reducing the storage space and the transmission time of the data. And generating a compressed data set, and improving the data processing efficiency and the system response speed.
Referring to fig. 5, the traffic flow prediction sub-module uses an autoregressive integral moving average model to model a traffic flow sequence based on the cleaned data set, and combines seasonal decomposition technology to analyze trend and periodicity, so as to generate a traffic flow prediction report;
the energy demand prediction submodule is used for quantifying energy demand by referring to the relevance of traffic flow and energy consumption by adopting a multiple linear regression method based on the traffic flow prediction report and generating an energy demand prediction report;
the public service prediction submodule optimizes a prediction model by adopting an exponential smoothing method based on the energy demand prediction report, captures short-term changes of public service demands and generates a public service demand prediction report.
And the traffic flow prediction sub-module performs stability test on the cleaned data set, and performs differential processing if the data does not meet the stability condition. And selecting proper model parameters according to the autocorrelation and seasonal characteristics of the time sequence, and establishing an autoregressive integral moving average model. Model training and parameter estimation are performed by using historical traffic flow data. And predicting the traffic flow in a future period by using the model, and carrying out trend and periodicity analysis by combining a seasonal decomposition technology to generate a traffic flow prediction report.
The energy demand prediction sub-module collects relevant energy consumption data and traffic flow data. And determining the relevance between the traffic flow and the energy consumption through statistical analysis, and establishing a multiple linear regression model. Model training and parameter estimation are performed by using historical data. And inputting the traffic flow prediction result into a multiple linear regression model to obtain an energy demand prediction result and generating an energy demand prediction report.
The public service prediction submodule collects related public service demand data and energy demand data. And establishing an exponential smoothing model according to the relation between the public service demand and the energy demand. Model training and parameter estimation are performed by using historical data. And inputting the energy demand prediction result into the exponential smoothing model to obtain a public service demand prediction result, and generating a public service demand prediction report.
Referring to fig. 6, the traffic optimization submodule optimizes traffic flow and road use efficiency based on a public service demand prediction report by adopting a multi-objective optimization combined genetic algorithm to generate a traffic resource optimization scheme;
the energy scheduling submodule is based on a traffic resource optimization scheme, and utilizes a particle swarm optimization algorithm to dynamically adjust energy supply and demand so as to generate an energy scheduling scheme;
The public resource allocation submodule adopts a simulated annealing algorithm to search the allocation strategy of the public resources based on the energy scheduling scheme, meets the requirements of multiple areas and time, and generates a public resource allocation scheme.
And the traffic optimization submodule determines the demand and flow conditions of each node in the traffic network according to the public service demand prediction report. And (3) establishing a multi-objective optimization model, and optimizing by considering a plurality of indexes such as traffic flow, road use efficiency and the like. And solving the multi-objective optimization model by utilizing a genetic algorithm, and generating a plurality of candidate solutions through operations such as crossing, mutation and the like. And selecting an optimal traffic resource optimization scheme according to a certain evaluation index.
And the energy scheduling submodule determines the energy demand of each area according to the traffic flow and the road use condition in the traffic resource optimization scheme. And (3) establishing an energy scheduling model, and considering the balance relation between energy supply and demand. And solving the energy scheduling model by using a particle swarm optimization algorithm, and searching an optimal energy scheduling scheme by continuously updating the positions and the speeds of particles. And dynamically adjusting the energy supply and the demand according to the optimal energy scheduling scheme.
And the public resource allocation submodule determines the public resource demand of each region according to the energy supply and demand conditions in the energy scheduling scheme. And (3) establishing a public resource allocation model, and considering the requirement constraint of multiple areas and multiple times. And solving the public resource allocation model by using a simulated annealing algorithm, and searching an optimal public resource allocation scheme by continuously searching and updating the state of the solution. And carrying out public resource allocation strategy implementation according to the optimal public resource allocation scheme.
Referring to fig. 7, the air quality monitoring submodule monitors the content of harmful substances in the air in real time based on fine granularity data of an environmental sensor by adopting an anomaly detection algorithm based on machine learning, and generates real-time air quality index data through data cleaning and normalization processing;
the water quality monitoring submodule continuously monitors the concentration of the water body pollutants based on the real-time air quality index data by using a time sequence analysis technology, and judges the water quality class by combining a clustering algorithm to generate the real-time water quality index data;
the sound pollution monitoring submodule monitors environmental noise in real time by applying a frequency domain analysis method based on the real-time water quality index data, and distinguishes natural noise and artificial noise by utilizing a sound identification technology to generate real-time sound pollution level data.
The air quality monitoring submodule collects pollutant concentration data in the air through the environment sensor. And carrying out anomaly detection on the data by using a machine learning algorithm, and identifying possible problems or anomalies. And cleaning and normalizing the original data to remove noise and abnormal values and ensure the accuracy and reliability of the data. And generating real-time air quality index data according to the processed data, and evaluating and monitoring air quality conditions.
The water quality monitoring submodule collects time series data of the concentration of the water body pollutants. The data is processed and modeled using time series analysis methods to reveal features such as trends, periodicity, and seasonal variations. And the water quality class is judged by combining a clustering algorithm, and the water body is divided into different pollution grades or types. And generating real-time water quality index data according to the analysis result, and evaluating and monitoring the water quality condition.
The sound pollution monitoring sub-module collects sound signals in the environment through the sound sensor. And analyzing and processing the sound signal by using a frequency domain analysis method, and extracting information of different frequency components. Natural and artificial noise is distinguished by voice recognition technology, and the source and the nature of the noise are judged according to the characteristics of the voice. And generating real-time sound pollution level data according to the analysis result, and evaluating and monitoring the sound pollution level.
Referring to fig. 8, the traffic light control sub-module optimizes traffic light timing based on road sensor data by applying a deep reinforcement learning strategy, and combines simulation to generate a light scheduling scheme;
the traffic flow optimizing submodule is based on a signal lamp scheduling scheme, adopts a graph theory algorithm to conduct deep analysis on a traffic network, optimizes vehicle distribution through a shortest path and a maximum flow algorithm, and generates an optimized traffic flow control scheme;
The congestion prevention sub-module dynamically adjusts traffic strategies based on the optimized traffic flow control scheme by using a fuzzy logic control technology, predicts and prevents traffic congestion, and generates a congestion prevention strategy.
The signal lamp regulation and control submodule collects data of traffic flow and vehicle running state through the road sensor. These data are input into a deep reinforcement learning model that is trained to optimize the timing schedule of traffic lights. And evaluating and verifying the optimized traffic signal lamp scheduling scheme by using an analog simulation technology. And generating a signal lamp scheduling scheme according to the evaluation result for actual traffic signal lamp control.
The traffic flow optimization sub-module models the traffic network in the form of a graph, wherein nodes represent intersections or road segments and edges represent road connection relationships. And calculating the shortest path of the vehicle in the traffic network by using a shortest path algorithm so as to reduce the running time and the congestion condition. And carrying out flow distribution on the traffic network by using a maximum flow algorithm to ensure that the road capacity is fully utilized. And generating an optimized traffic flow control scheme according to the analysis result, and guiding traffic management and vehicle running.
The congestion prevention sub-module collects real-time traffic data including traffic flow, speed, road conditions, and the like. Then, the data are processed and analyzed by a fuzzy logic control method, and the possible congestion situation is predicted. And dynamically adjusting traffic strategies according to the prediction results, such as changing signal lamp time sequence, implementing traffic restriction measures and the like. And generating a congestion prevention strategy according to the adjusted traffic strategy, wherein the congestion prevention strategy is used for preventing and relieving traffic congestion in advance.
Referring to fig. 9, a method for analyzing and identifying big data of a smart city, which is performed based on the system for analyzing and identifying big data of a smart city, includes the following steps:
s1: based on a sensor network deployed by urban traffic nodes, adopting a real-time flow acquisition technology to perform flow sensing and data integration to generate a traffic real-time data set;
s2: based on the traffic real-time data set, adopting a data cleaning algorithm to perform missing value filling, abnormal value removing and data format regularity to generate a preprocessed data set;
s3: based on the preprocessed data set, adopting an autoregressive integral moving average model to carry out traffic flow sequence modeling, and carrying out trend and periodicity analysis to generate a traffic flow prediction report;
s4: based on the traffic flow prediction report, adopting multiple linear regression analysis to quantify the energy demand and generating an energy demand prediction report;
s5: based on the energy demand prediction report, optimizing traffic flow and road use efficiency by adopting a multi-objective genetic algorithm, and generating a traffic resource optimization scheme;
s6: based on a traffic resource optimization scheme, adopting a graph theory algorithm to perform traffic network analysis, and generating an optimized traffic flow control scheme through a shortest path and a maximum flow algorithm;
S7: based on the optimized traffic flow control scheme, a fuzzy logic control technology is adopted to dynamically adjust traffic strategies, prevent congestion and generate congestion prevention strategies.
The traffic real-time data set is specifically a real-time information summary comprising traffic flow and pedestrian flow in multiple traffic nodes, the traffic flow prediction report is specifically a prediction analysis on trend and periodic fluctuation of traffic flow in a future time period, the energy demand prediction report is specifically a quantitative prediction on energy demand in the future time period, the optimized traffic flow control scheme is specifically an optimized regulation and control on vehicle flow in a traffic network, and the congestion prevention strategy is specifically a comprehensive scheme comprising dynamic traffic strategy adjustment, real-time congestion early warning and prevention measures.
Real-time information such as traffic flow, pedestrian flow and the like in the multiple traffic nodes is acquired through a real-time flow acquisition technology, and basic data support is provided for subsequent data analysis and decision. The original data is preprocessed by adopting a data cleaning algorithm, missing values are filled, abnormal values and regular data formats are removed, the quality and accuracy of the data are improved, and a reliable data base is provided for subsequent modeling and predictive analysis. Based on the preprocessed data set, adopting an autoregressive integral moving average model to carry out traffic flow sequence modeling, and carrying out trend and periodicity analysis to generate a traffic flow prediction report. This helps the urban traffic management to learn the trend and periodic fluctuations in traffic flow over future time periods, thereby developing reasonable traffic management strategies and resource allocation schemes. And quantitatively correlating the traffic flow with the energy demand through multiple linear regression analysis to generate an energy demand prediction report. The method is beneficial to urban traffic management departments to accurately predict the energy demand in a future time period, provides scientific basis for energy supply and scheduling, and realizes reasonable utilization and saving of energy. And optimizing traffic flow and road use efficiency by adopting a multi-objective genetic algorithm to generate a traffic resource optimization scheme. And analyzing the traffic network through a graph theory algorithm, and generating an optimized traffic flow control scheme through a shortest path and a maximum flow algorithm. The measures can improve the efficiency and the mobility of the traffic network, reduce the occurrence of congestion and improve the traveling experience of urban residents. Based on the optimized traffic flow control scheme, the traffic strategy is dynamically adjusted by adopting a fuzzy logic control technology, so that congestion is prevented. This includes a comprehensive solution of dynamic traffic policy adjustment, real-time congestion pre-warning and precautions. By means of timely adjusting the time sequence of the traffic signal lamp, implementing traffic limiting measures and the like, the congestion condition can be effectively relieved, the traffic capacity of roads is improved, and traffic accidents are reduced.
The present invention is not limited to the above embodiments, and any equivalent embodiments which can be changed or modified by the technical disclosure described above can be applied to other fields, but any simple modification, equivalent changes and modification made to the above embodiments according to the technical matter of the present invention will still fall within the scope of the technical disclosure.
Claims (10)
1. A big data analysis recognition system for a smart city, which is characterized in that: the system for analyzing and identifying the big data of the smart city comprises a data acquisition module, a data preprocessing module, a resource demand prediction module, a resource optimal configuration module, an environment monitoring module and a traffic regulation module;
the data acquisition module is based on a sensor network deployed in urban multi-region, integrates multi-source data comprising traffic and energy sources by adopting a real-time data acquisition technology, performs preliminary data screening and compression through edge calculation, and generates an original data set;
the data preprocessing module is used for filling missing values and removing abnormal values of data based on an original data set by adopting a data cleaning algorithm, screening data subjects and regulating formats, and generating a cleaning data set by adopting a data normalization technology;
The resource demand prediction module is used for constructing an autoregressive integral moving average model based on a cleaning data set by utilizing a time sequence analysis method, carrying out trend and periodic deep analysis by combining a seasonal decomposition technology, and generating a resource demand prediction report;
the resource optimization configuration module is used for carrying out resource optimization configuration by adopting a multi-objective optimization algorithm and combining a genetic algorithm and particle swarm optimization based on a resource demand prediction report to generate a resource optimization scheme;
the environment monitoring module is used for carrying out real-time monitoring on environment data by adopting an anomaly detection algorithm based on the environment sensor data, introducing a natural language processing technology, carrying out emotion analysis on social media data, obtaining people feedback information and generating an environment monitoring report;
the traffic regulation and control module adopts a graph algorithm to perform structural analysis of an urban traffic network based on a traffic flow prediction report, and dynamically performs intelligent regulation and control of traffic signals and traffic paths by matching with a fuzzy logic controller to generate a traffic regulation and control strategy;
the original data set is real-time data comprising traffic, energy, environment and public service, the resource demand prediction report is a demand prediction result comprising urban traffic, energy and municipal resources, the resource optimization scheme is a resource allocation scheme aiming at multiple periods and multiple areas, and the environment monitoring report is a real-time state, an abnormal state and predicted influence based on urban environment change.
2. The system for intelligent city big data analysis and identification of claim 1, wherein: the data acquisition module comprises a traffic sensing sub-module, an energy monitoring sub-module and a public service sub-module;
the data preprocessing module comprises a data cleaning sub-module, a data normalization sub-module and a data compression sub-module;
the resource demand prediction module comprises a traffic flow prediction sub-module, an energy demand prediction sub-module and a public service prediction sub-module;
the resource optimization configuration module comprises a traffic optimization sub-module, an energy scheduling sub-module and a public resource allocation sub-module;
the environment monitoring module comprises an air quality monitoring sub-module, a water quality monitoring sub-module and a sound pollution monitoring sub-module;
the traffic regulation and control module comprises a signal lamp regulation and control sub-module, a traffic flow optimization sub-module and a congestion prevention sub-module.
3. The system for intelligent city big data analysis and identification of claim 2, wherein: the traffic sensing submodule is based on a sensor network deployed by urban traffic nodes, adopts a real-time flow acquisition technology to sense flow, integrates data and generates a traffic real-time data set;
The energy monitoring submodule is based on an energy consumption monitoring system, performs trend analysis by utilizing an energy consumption monitoring technology, integrates data of energy use conditions and generates an energy consumption data set;
and the public service submodule performs data summarization of social media and an online service platform by adopting a public feedback collection mechanism based on public service facilities to generate a public service data set.
4. The system for intelligent city big data analysis and identification of claim 2, wherein: the data cleaning submodule adopts a data cleaning algorithm to fill missing values and reject abnormal values based on the original comprehensive data set, screens and regulates the data format, and generates a preprocessed data set;
the data normalization submodule performs data normalization on the preprocessed data set by using a normalization processing method, unifies the scale and the range of the data, and generates a normalized data set;
the data compression sub-module adopts a data compression technology to perform data compression on the normalized data set, optimizes the storage space and the processing efficiency, and generates a compressed data set.
5. The system for intelligent city big data analysis and identification of claim 2, wherein: the traffic flow prediction submodule carries out traffic flow sequence modeling by adopting an autoregressive integral moving average model based on the cleaned data set, and carries out trend and periodicity analysis by combining a seasonal decomposition technology to generate a traffic flow prediction report;
The energy demand prediction submodule is used for quantifying energy demand and generating an energy demand prediction report by referring to the relevance of traffic flow and energy consumption by adopting a multiple linear regression method based on the traffic flow prediction report;
and the public service prediction submodule optimizes a prediction model by adopting an exponential smoothing method based on the energy demand prediction report, captures short-term changes of the public service demand and generates a public service demand prediction report.
6. The system for intelligent city big data analysis and identification of claim 2, wherein: the traffic optimization submodule optimizes traffic flow and road use efficiency based on a public service demand prediction report by adopting a multi-objective optimization combined genetic algorithm to generate a traffic resource optimization scheme;
the energy scheduling submodule is based on a traffic resource optimization scheme, and utilizes a particle swarm optimization algorithm to dynamically adjust energy supply and demand to generate an energy scheduling scheme;
the public resource allocation submodule adopts a simulated annealing algorithm to search a public resource allocation strategy based on an energy scheduling scheme, meets the requirements of multiple areas and time, and generates a public resource allocation scheme.
7. The system for intelligent city big data analysis and identification of claim 2, wherein: the air quality monitoring submodule monitors the content of harmful substances in the air in real time by adopting an anomaly detection algorithm based on machine learning based on fine granularity data of an environmental sensor, and generates real-time air quality index data through data cleaning and normalization processing;
the water quality monitoring submodule continuously monitors the concentration of the water body pollutants based on the real-time air quality index data by using a time sequence analysis technology, and judges the water quality class by combining a clustering algorithm to generate the real-time water quality index data;
the sound pollution monitoring submodule monitors environmental noise in real time by applying a frequency domain analysis method based on the real-time water quality index data, and distinguishes natural noise and artificial noise by utilizing a sound identification technology to generate real-time sound pollution level data.
8. The system for intelligent city big data analysis and identification of claim 2, wherein: the traffic signal lamp regulation and control submodule optimizes the traffic signal lamp time sequence by applying a deep reinforcement learning strategy based on the road sensor data and combines simulation to generate a signal lamp scheduling scheme;
The traffic flow optimizing submodule is based on a signal lamp scheduling scheme, adopts a graph theory algorithm to conduct deep analysis on a traffic network, optimizes vehicle allocation through a shortest path and a maximum flow algorithm, and generates an optimized traffic flow control scheme;
the congestion prevention sub-module dynamically adjusts traffic strategies based on the optimized traffic flow control scheme by using a fuzzy logic control technology, predicts and prevents traffic congestion, and generates a congestion prevention strategy.
9. A method for smart city big data analysis and recognition, characterized in that it is performed by the system for smart city big data analysis and recognition according to any one of claims 1-8, comprising the steps of:
based on a sensor network deployed by urban traffic nodes, adopting a real-time flow acquisition technology to perform flow sensing and data integration to generate a traffic real-time data set;
based on the traffic real-time data set, adopting a data cleaning algorithm to perform missing value filling, abnormal value removing and data format regularity to generate a preprocessed data set;
based on the preprocessed data set, adopting an autoregressive integral moving average model to carry out traffic flow sequence modeling, and carrying out trend and periodicity analysis to generate a traffic flow prediction report;
Based on the traffic flow prediction report, adopting multiple linear regression analysis to quantify energy demands and generating an energy demand prediction report;
based on the energy demand prediction report, optimizing traffic flow and road use efficiency by adopting a multi-objective genetic algorithm, and generating a traffic resource optimization scheme;
based on the traffic resource optimization scheme, adopting a graph theory algorithm to perform traffic network analysis, and generating an optimized traffic flow control scheme through a shortest path and a maximum flow algorithm;
and dynamically adjusting the traffic strategy based on the optimized traffic flow control scheme by adopting a fuzzy logic control technology, preventing congestion and generating a congestion prevention strategy.
10. The method for intelligent city big data analysis and identification of claim 9, wherein: the traffic real-time data set is specifically real-time information summary comprising traffic flow and pedestrian flow in multiple traffic nodes, the traffic flow prediction report is specifically prediction analysis on trend and periodic fluctuation of traffic flow in a future time period, the energy demand prediction report is specifically quantitative prediction on energy demand in the future time period, the optimized traffic flow control scheme is specifically optimized regulation and control on vehicle flow in a traffic network, and the congestion prevention strategy is specifically a comprehensive scheme comprising dynamic traffic strategy adjustment, real-time congestion early warning and prevention measures.
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