CN117671979A - Smart city data management system and method based on knowledge graph - Google Patents
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Abstract
The invention discloses a knowledge graph-based smart city data management system and a knowledge graph-based smart city data management method, which belong to the field of data processing systems specially suitable for management purposes.
Description
Technical Field
The invention belongs to the technical field of data processing systems specially suitable for management purposes, and particularly relates to a system and a method for managing smart city data based on a knowledge graph.
Background
With the development of new generation information technologies such as the Internet of things and smart cities, the information age has entered a new stage, and the interactive fusion of information space and physical space is formed. In the construction of smart cities, massive data needs to be processed, massive data such as text data, image data, audio data and video data are generated at the same time, and how to effectively organize, manage, process and retrieve the massive data is a very difficult problem. In addition, the storage and the retrieval can not meet the demands of urban users, and the value of the information can be better improved by combining the information with specific application scenes and mining and utilizing beneficial semantic information.
For example, chinese patent with grant publication number CN116721001B proposes a digital twinning-based smart city resource management method, which includes: acquiring resource management related data, acquiring path similarity according to the similarity degree of management data on a link path, acquiring a path similarity matrix and a merging index according to the path similarity, acquiring a summary node according to the merging index, acquiring an integrated data set according to the summary node, acquiring a backward association degree according to an association rule corresponding to the integrated data set, acquiring a screening threshold according to the backward association degree and a threshold of the confidence coefficient of the association rule, finishing updating nodes in the FP-Tree according to the screening threshold, and acquiring a strong association rule in the management data set according to the updated FP-Tree. The method and the system self-adaptively acquire the merging strategy of the nodes, reduce useless branches of the FP-Tree, solve the problem of low recursion efficiency caused by excessive frequent item set number in the traditional FP growth algorithm, accelerate the data mining efficiency and improve the resource management efficiency;
meanwhile, for example, in the Chinese patent with the authority of CN116823580B, a smart city energy-saving and emission-reducing management method and system based on cloud computing are disclosed, which particularly relates to the field of cloud computing energy-saving and emission-reducing management, and comprise a data acquisition and supervision module, a data storage management module, a data analysis decision module, an energy-saving strategy regulation module, a visual participation module and a safety privacy protection module.
The problems proposed in the background art exist in the above patents: a large number of knowledge maps are established for different application scenes and different data sources. The knowledge graph generated by the prior art describes concepts, entities and relations among the concepts, entities in the objective world in a structured manner, expresses data in a form which is easier to understand for human cognition, and provides a capability for better organizing, managing and understanding massive data. However, since each knowledge graph has its own entity representation, relationship description method and concept description logic, a large knowledge gap exists between different knowledge graphs as well: different knowledge patterns are mutually independent and cannot be shared. Likewise, tasks for a specific range or topic cannot be efficiently executed on multiple knowledge maps at the same time, and it is also difficult to search results on multiple knowledge maps at the same time. The prior art cannot exert the effect of larger knowledge graph, for example, the traffic light communication time cannot be accurately controlled according to the specific conditions of a road and a vehicle when road management is carried out, the problems exist in the prior art, and in order to solve the problems, the application designs a smart city data management system and method based on the knowledge graph.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides a knowledge graph-based smart city data management system and method, which are used for collecting a piece of road traffic related data and historical traffic related data information of the road from an acquisition data source, wherein the related data comprises weather data, date data, road maintenance data and road traffic data, constructing a road knowledge graph model needing to be managed and controlled, acquiring related data of a historical period from the constructed knowledge graph model, importing the acquired related data of the historical period and the current period into a screening strategy for screening the related data of the historical period to obtain screening data, substituting the acquired screening data into a traffic prediction model construction strategy to construct a traffic prediction model, importing the acquired lower period related data into the traffic prediction model to predict lower period traffic, importing the estimated lower period traffic into the traffic control strategy to output traffic light traffic time, effectively improving the accuracy of traffic light traffic time prediction, greatly facilitating the rapid construction of the smart city.
In order to achieve the above purpose, the present invention provides the following technical solutions:
a smart city data management method based on a knowledge graph comprises the following specific steps:
s1, collecting a piece of road traffic related data and historical traffic related data information of the road from a collected data source, wherein the related data comprise weather data, date data, road maintenance data and road flow data, and constructing a road knowledge graph model to be controlled;
s2, acquiring relevant data of a historical period from the constructed knowledge graph model, and importing the acquired relevant data of the historical period and the historical period into a screening strategy to screen the relevant data of the historical period to obtain screening data;
s3, substituting the obtained screening data into a traffic flow prediction model construction strategy to construct a traffic flow prediction model;
s4, importing the acquired relevant data of the next period into a traffic flow prediction model to predict the traffic flow of the next period;
s5, guiding the estimated next-period traffic flow into a traffic flow management and control strategy to output traffic light passing time.
Specifically, the step S1 includes the following specific steps:
s11, acquiring road traffic related data of a road to be managed and controlled through a data acquisition terminal, transmitting the road traffic related data to a management terminal in real time, acquiring weather data, date data, road maintenance data and road flow data in the road traffic related data, and simultaneously acquiring road historical traffic related data to be managed and controlled;
and S12, obtaining a corresponding weather data change curve, a date data change curve, a road maintenance data change curve and a road flow data change curve based on the acquired real-time weather data, date data, road maintenance data and road flow data, historical weather data, date data, road maintenance data and road flow data, and forming a road knowledge graph model needing management and control.
Specifically, the specific content of the screening policy in S2 is as follows:
s21, acquiring weather data in the historical road traffic related data, wherein the weather data comprises temperature data, humidity data and rainfall and snowfall data, acquiring date data in the historical road traffic related data, wherein the date data is the same date as the working day, and the date data is replaced by a week, such as Monday and Tuesday, to acquire road maintenance data of the historical road traffic related data, wherein the road maintenance data is width data of a road occupied by maintenance of the road section, and road flow data of the historical road traffic related data is acquired;
s22, substituting weather data, date data and road maintenance data of the same length time period corresponding to the management and control threshold time into a difference value calculation formula to calculate a difference value between the historical time period and the period, wherein the difference value calculation formula is as follows:wherein c i For item i, < in the weather data of the present period, <>For the ith item in the weather data of the history period,a i the duty ratio coefficient of the ith item in the weather data, n is the number of parameters in the weather data, z 1 For date data of the present period, z 2 For date data of history period, m 1 Maintenance data for the road of the present period, m 2 Maintenance data for historic period road lambda 1 Lambda is the weather duty factor 2 Lambda is the duty cycle of the date 3 Maintaining a data duty cycle for the road, wherein +.>λ 1 +λ 2 +λ 3 ＝1；
S23, comparing the calculated difference value of each historical time period with a set difference threshold, setting a corresponding historical time period with the difference value smaller than or equal to the set difference threshold as a screening time period, and setting relevant data of the screening time period as screening data.
Specifically, the specific content of the traffic flow prediction model construction strategy in S3 is as follows:
s31, acquiring the acquired screening data, acquiring a time-dependent change curve of weather data in the screening data, a time-dependent change curve of road maintenance data in the screening data, a date change curve and a time-dependent change curve of road flow data, constructing a deep learning neural network model which is input into the time-dependent change curve of the weather data in the screening data, the time-dependent change curve of the road maintenance data in the screening data and the date change curve of the road maintenance data in the screening data, and outputting the deep learning neural network model into the time-dependent change curve of the road flow data as a traffic flow prediction model;
s32, dividing the acquired time-dependent change curve of the weather data in the screening data, the time-dependent change curve of the road maintenance data in the screening data, the date change curve and the time-dependent change curve of the road flow data into a 70% parameter training set and a 30% parameter test set; inputting 70% of parameter training sets into a deep learning neural network model for training to obtain an initial deep learning neural network model; testing the initial deep learning neural network model by using 30% of parameter test sets, and outputting an optimal initial deep learning neural network model meeting the preset road flow data prediction accuracy as the deep learning neural network model, wherein the formula in the deep learning neural network model is as follows:
wherein->For the output of m+1 layer s term neurons,/->For the connection weight of the mth layer jth neuron and the m+1 layer s neuron,/for the mth layer jth neuron>Input representing the jth neuron of the mth layer,/->Representing the bias of the linear relationship of the mth and m+1th layer s neurons, σ () represents the Sigmoid activation function, and w is the number of neurons in the nth layer deep learning neural network model.
Specifically, the step S4 includes the following specific steps:
s41, acquiring weather data, date data and road maintenance data of the period, and simultaneously acquiring a corresponding traffic flow prediction model;
s42, importing the acquired weather data, date data and road maintenance data of the period into a traffic flow prediction model to derive a corresponding predicted value of the traffic flow of the next period.
Specifically, the traffic control strategy in S5 includes the following specific steps:
s51, obtaining the value of the traffic flow corresponding to each intersection in the next period, substituting the value of the traffic flow into a green light duty ratio calculation formula to calculate the green light time of the corresponding intersection, wherein the green light time calculation formula is as follows:wherein T is a For the green time of the a intersection, r a Is an intersection aPredictive value of the lower cycle traffic flow, r e Is the predicted value of the traffic flow of the next period of the e intersection, S is the total number of intersections, t zt Is the overall cycle time;
s52, the traffic light control module adjusts traffic lights according to the output green light time output values of the intersections.
Specifically, a smart city data management system based on a knowledge graph is realized based on the smart city data management method based on the knowledge graph, which specifically comprises the following steps: the system comprises a knowledge graph model construction module, a data screening module, a traffic flow prediction model construction module, a next period traffic flow prediction module, a traffic light time output module and a control module, wherein the knowledge graph model construction module is used for collecting a piece of road traffic related data and historical traffic related data information of the road from an acquisition data source, the related data comprise weather data, date data, road maintenance data and road flow data, a road knowledge graph model needing to be controlled is constructed, the data screening module is used for acquiring related data of a historical period from the constructed knowledge graph model, and the acquired related data of the historical period and the current period are imported into a screening strategy to screen the related data of the historical period, so that screening data is obtained.
Specifically, the traffic flow prediction model construction module is used for constructing a traffic flow prediction model by substituting the obtained screening data into a traffic flow prediction model construction strategy, the lower period traffic flow prediction module is used for guiding the obtained lower period related data into the traffic flow prediction model to predict the lower period traffic flow, and the traffic light time output module is used for guiding the predicted lower period traffic flow into the traffic flow management and control strategy to output traffic light passing time.
Specifically, the control module is used for controlling the operation of the knowledge graph model construction module, the data screening module, the traffic flow prediction model construction module, the next period traffic flow prediction module and the traffic light time output module.
Specifically, an electronic device includes: a processor and a memory, wherein the memory stores a computer program for the processor to call;
the processor executes a knowledge graph-based smart city data management method as described above by calling a computer program stored in the memory.
Specifically, a computer readable storage medium stores instructions that, when executed on a computer, cause the computer to perform a method for managing smart city data based on a knowledge graph as described above.
Compared with the prior art, the invention has the beneficial effects that:
the invention collects a piece of road traffic related data and historical traffic related data information of the road from an acquisition data source, wherein the related data comprises weather data, date data, road maintenance data and road traffic data, a road knowledge graph model needing to be managed and controlled is constructed, the related data of a historical period is obtained from the constructed knowledge graph model, the obtained related data of the historical period and the current period are imported into a screening strategy to screen the related data of the historical period, the obtained screening data is obtained, the obtained screening data is substituted into a traffic flow prediction model construction strategy to construct a traffic flow prediction model, the obtained next-period related data is imported into the traffic flow prediction model to predict the next-period traffic flow, the estimated next-period traffic flow is imported into a traffic flow management and control strategy to output traffic light traffic time, the accuracy of traffic light traffic time prediction is effectively improved, and the rapid construction of intelligent cities is facilitated.
Drawings
FIG. 1 is a schematic flow chart of a knowledge graph-based smart city data management method according to the present invention;
FIG. 2 is a schematic diagram of a specific flow of step S1 of a knowledge-based smart city data management method according to the present invention;
FIG. 3 is a schematic diagram of a specific flow of step S2 of the smart city data management method based on knowledge graph according to the present invention;
fig. 4 is a schematic diagram of a smart city data management system architecture based on a knowledge graph according to the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments.
Example 1
Referring to fig. 1-3, an embodiment of the present invention is provided: a smart city data management method based on a knowledge graph comprises the following specific steps:
s1, collecting a piece of road traffic related data and historical traffic related data information of the road from a collected data source, wherein the related data comprise weather data, date data, road maintenance data and road flow data, and constructing a road knowledge graph model to be controlled;
the traffic flow of a road is affected by a number of factors, which can be divided into the following categories:
road length and width: longer or wider roads can typically accommodate more vehicles, affecting traffic road layout: road junctions, entrances, bottleneck road segments, etc. can affect the speed and flow of traffic.
Terrain and climate: the fluctuation of the topography and the climate conditions (such as rain, snow, haze and the like) can influence the running speed and the safety coefficient of the vehicle, thereby influencing the traffic flow;
signal lights and traffic signs: the traffic light control and reasonable traffic sign arrangement can guide vehicles to orderly run, so that the road traffic capacity is improved;
public transportation: public transportation facilities (such as public transportation lanes, subway stations and the like) can influence the use of private vehicles, so that the traffic flow of roads is changed;
parking lots and parking facilities: the position, number and capacity of the parking lot can influence the time and route selection of the vehicle driving on the road, and further influence the traffic flow;
travel time: the time of going to and from work and going to and from school of groups such as office workers, students and the like can cause fluctuation of road traffic flow;
travel mode: the traffic flow of roads can be affected by the choice of travel modes such as walking, riding, public transportation, private cars and the like;
by comprehensively considering the factors, the traffic departments and the management departments can take corresponding measures to optimize traffic planning and management, improve the road traffic capacity, reduce the congestion degree and ensure the traffic safety;
it should be noted that, S1 includes the following specific steps:
s11, acquiring road traffic related data of a road to be managed and controlled through a data acquisition terminal, transmitting the road traffic related data to a management terminal in real time, acquiring weather data, date data, road maintenance data and road flow data in the road traffic related data, and simultaneously acquiring road historical traffic related data to be managed and controlled;
s12, obtaining a corresponding weather data change curve, a date data change curve, a road maintenance data change curve and a road flow data change curve based on the acquired real-time weather data, date data, road maintenance data and road flow data, historical weather data, date data, road maintenance data and road flow data, and forming a road knowledge graph model to be controlled;
the following is a simple example code showing how to generate a C language code of weather data profile, date data profile, road maintenance data profile, and road traffic data profile based on the acquired data:
this is simply an example code, the actual implementation may need to be appropriately adjusted and extended according to specific needs, and further, the function implementation part in the code needs to be further developed to implement the actual calculation logic;
s2, acquiring relevant data of a historical period from the constructed knowledge graph model, and importing the acquired relevant data of the historical period and the historical period into a screening strategy to screen the relevant data of the historical period to obtain screening data;
it should be noted that the specific content of the screening strategy in S2 is as follows:
s21, acquiring weather data in the historical road traffic related data, wherein the weather data comprises temperature data, humidity data and rainfall and snowfall data, and acquiring date data in the historical road traffic related data;
s22, substituting weather data, date data and road maintenance data of the same length time period corresponding to the management and control threshold time into a difference value calculation formula to calculate a difference value between the historical time period and the period, wherein the difference value calculation formula is as follows:wherein c i For item i, < in the weather data of the present period, <>Is the ith item, a in the weather data of the history period i The duty ratio coefficient of the ith item in the weather data, n is the number of parameters in the weather data, z 1 For date data of the present period, z 2 For date data of history period, m 1 Maintenance data for the road of the present period, m 2 Maintenance data for historic period road lambda 1 Lambda is the weather duty factor 2 Lambda is the duty cycle of the date 3 Maintaining a data duty cycle for the road, wherein +.>λ 1 +λ 2 +λ 3 ＝1；
S23, comparing the calculated difference value of each historical time period with a set difference threshold, setting a corresponding historical time period with the difference value smaller than or equal to the set difference threshold as a screening time period, and setting relevant data of the screening time period as screening data;
s3, substituting the obtained screening data into a traffic flow prediction model construction strategy to construct a traffic flow prediction model;
the specific content of the traffic flow prediction model construction strategy in S3 is as follows:
s31, acquiring the acquired screening data, acquiring a time-dependent change curve of weather data in the screening data, a time-dependent change curve of road maintenance data in the screening data, a date change curve and a time-dependent change curve of road flow data, constructing a deep learning neural network model which is input into the time-dependent change curve of the weather data in the screening data, the time-dependent change curve of the road maintenance data in the screening data and the date change curve of the road maintenance data in the screening data, and outputting the deep learning neural network model into the time-dependent change curve of the road flow data as a traffic flow prediction model;
the following is a simple code describing how to use Python and Tensorflow to build a simple neural network model to predict the change curve of road traffic data over time:
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a great deal of debugging and optimization are required for actual operation; meanwhile, the quality and the quantity of data are also very important for the accuracy and the reliability of the model, and the data are required to be fully analyzed and preprocessed;
s32, dividing the acquired time-dependent change curve of the weather data in the screening data, the time-dependent change curve of the road maintenance data in the screening data, the date change curve and the time-dependent change curve of the road flow data into a 70% parameter training set and a 30% parameter test set; inputting 70% of parameter training sets into a deep learning neural network model for training to obtain an initial deep learning neural network model; testing the initial deep learning neural network model by using 30% of parameter test sets, and outputting an optimal initial deep learning neural network model meeting the preset road flow data prediction accuracy as the deep learning neural network model, wherein the formula in the deep learning neural network model is as follows:
wherein->For the output of m+1 layer s term neurons,/->For the connection weight of the mth layer jth neuron and the m+1 layer s neuron,/for the mth layer jth neuron>Representing the input of the jth neuron of the mth layer,a bias representing the linear relationship of the jth neuron of the m-th layer and the s neuron of the m+1 layer, sigma () represents a Sigmoid activation function, and w is the number of neurons in the n-th layer deep learning neural network model;
s4, importing the acquired relevant data of the next period into a traffic flow prediction model to predict the traffic flow of the next period; the method comprises the following steps: s41, acquiring weather data, date data and road maintenance data of the period, and simultaneously acquiring a corresponding traffic flow prediction model;
s42, importing the acquired weather data, date data and road maintenance data of the period into a traffic flow prediction model to derive a corresponding predicted value of the traffic flow of the next period;
s5, guiding the estimated next-period traffic flow into a traffic flow management and control strategy to output traffic light passing time;
it should be noted that, in S5, the traffic control strategy includes the following specific steps:
s51, obtaining the value of the traffic flow corresponding to each intersection in the next period, substituting the value of the traffic flow into a green light duty ratio calculation formula to calculate the green light time of the corresponding intersection, wherein the green light time calculation formula is as follows:wherein T is a For the green time of the a intersection, r a Is the predicted value of the lower period vehicle flow of the a intersection, r e Is the predicted value of the traffic flow of the next period of the e intersection, S is the total number of intersections, t zt Is the overall cycle time;
s52, the traffic light control module adjusts traffic lights according to the output green light time output values of the intersections.
The implementation of the embodiment can be realized: and collecting road traffic related data and historical traffic related data information of the road from an acquisition data source, wherein the related data comprises weather data, date data, road maintenance data and road traffic data, constructing a road knowledge graph model which needs to be controlled, acquiring related data of a historical period from the constructed knowledge graph model, importing the acquired related data of the historical period and the current period into a screening strategy to screen the related data of the historical period, obtaining the screening data, substituting the obtained screening data into a traffic flow prediction model construction strategy to construct a traffic flow prediction model, importing the acquired next-period related data into the traffic flow prediction model to predict the next-period traffic flow, importing the predicted next-period traffic flow into the traffic flow control strategy to output traffic time, thereby effectively improving the accuracy of traffic time prediction, greatly facilitating the traffic of vehicles, and being beneficial to the rapid construction of smart cities.
Example 2
As shown in fig. 4, a knowledge-graph-based smart city data management system is implemented based on the above-mentioned knowledge-graph-based smart city data management method, which specifically includes: the system comprises a knowledge graph model construction module, a data screening module, a traffic flow prediction model construction module, a next period traffic flow prediction module, a traffic light time output module and a control module, wherein the knowledge graph model construction module is used for collecting a piece of road traffic related data and historical traffic related data information of the road from an acquisition data source, the related data comprise weather data, date data, road maintenance data and road flow data, constructing a road knowledge graph model to be controlled, the data screening module is used for acquiring related data of a historical period from the constructed knowledge graph model, and importing the acquired historical period and the related data of the current period into a screening strategy to screen the related data of the historical period to obtain screening data; the traffic flow prediction model construction module is used for constructing a traffic flow prediction model by substituting the obtained screening data into a traffic flow prediction model construction strategy, the lower period traffic flow prediction module is used for guiding the obtained lower period related data into the traffic flow prediction model to predict the lower period traffic flow, and the traffic light time output module is used for guiding the lower period traffic flow obtained by the prediction into the traffic flow management and control strategy to output traffic light passing time; the control module is used for controlling the operation of the knowledge graph model construction module, the data screening module, the traffic flow prediction model construction module, the next period traffic flow prediction module and the traffic light time output module.
Example 3
The present embodiment provides an electronic device including: a processor and a memory, wherein the memory stores a computer program for the processor to call;
the processor executes a knowledge-graph-based smart city data management method as described above by invoking a computer program stored in the memory.
The electronic device may have a relatively large difference due to different configurations or performances, and may include one or more processors (Central Processing Units, CPU) and one or more memories, where at least one computer program is stored in the memories, and the computer program is loaded and executed by the processors to implement a smart city data management method based on a knowledge graph provided by the above method embodiment. The electronic device can also include other components for implementing the functions of the device, for example, the electronic device can also have wired or wireless network interfaces, input-output interfaces, and the like, for inputting and outputting data. The present embodiment is not described herein.
Example 4
The present embodiment proposes a computer-readable storage medium having stored thereon an erasable computer program;
the computer program, when run on the computer device, causes the computer device to perform a knowledge-graph-based smart city data management method as described above.
For example, the computer readable storage medium can be Read-Only Memory (ROM), random access Memory (Random Access Memory, RAM), compact disk Read-Only Memory (Compact Disc Read-Only Memory, CD-ROM), magnetic tape, floppy disk, optical data storage device, etc.
It should be understood that, in various embodiments of the present application, the sequence numbers of the foregoing processes do not mean the order of execution, and the order of execution of the processes should be determined by the functions and internal logic thereof, and should not constitute any limitation on the implementation process of the embodiments of the present application.
It should be understood that determining B from a does not mean determining B from a alone, but can also determine B from a and/or other information.
The above embodiments may be implemented in whole or in part by software, hardware, firmware, or any other combination. When implemented in software, the above-described embodiments may be implemented in whole or in part in the form of a computer program product. The computer program product comprises one or more computer instructions or computer programs. When the computer instructions or computer program are loaded or executed on a computer, the processes or functions in accordance with embodiments of the present invention are produced in whole or in part. The computer may be a general purpose computer, a special purpose computer, a computer network, or other programmable apparatus. The computer instructions may be stored in or transmitted from one computer-readable storage medium to another, for example, by way of wired or/and wireless networks from one website site, computer, server, or data center to another. Computer readable storage media can be any available media that can be accessed by a computer or data storage devices, such as servers, data centers, etc. that contain one or more collections of available media. The usable medium may be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. The semiconductor medium may be a solid state disk.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
It will be clear to those skilled in the art that, for convenience and brevity of description, specific working procedures of the above-described systems, apparatuses and units may refer to corresponding procedures in the foregoing method embodiments, and are not repeated herein.
In the several embodiments provided by the present invention, it should be understood that the disclosed systems, devices, and methods may be implemented in other manners. For example, the apparatus embodiments described above are merely illustrative, e.g., the partitioning of units is merely one, and there may be additional partitioning in actual implementation, e.g., multiple units or components may be combined or integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or units, which may be in electrical, mechanical or other form.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in the embodiments of the present invention may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit.
In the description of the present specification, the descriptions of the terms "one embodiment," "example," "specific example," and the like, mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present invention. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiments or examples. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
The preferred embodiments of the invention disclosed above are intended only to assist in the explanation of the invention. The preferred embodiments are not exhaustive of all details. Obviously, many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described in order to best explain the principles of the invention and the practical application, to thereby enable others skilled in the art to best understand and utilize the invention. The invention is limited only by the claims and the full scope and equivalents thereof.
Claims (10)
1. The intelligent city data management method based on the knowledge graph is characterized by comprising the following specific steps of:
s1, collecting a piece of road traffic related data and historical traffic related data information of the road from a collected data source, wherein the related data comprise weather data, date data, road maintenance data and road flow data, and constructing a road knowledge graph model to be controlled;
s2, acquiring relevant data of a historical period from the constructed knowledge graph model, and importing the acquired relevant data of the historical period and the historical period into a screening strategy to screen the relevant data of the historical period to obtain screening data;
s3, substituting the obtained screening data into a traffic flow prediction model construction strategy to construct a traffic flow prediction model;
s4, importing the acquired relevant data of the next period into a traffic flow prediction model to predict the traffic flow of the next period;
s5, guiding the estimated next-period traffic flow into a traffic flow management and control strategy to output traffic light passing time.
2. The method for managing smart city data based on a knowledge graph as claimed in claim 1, wherein said S1 comprises the following steps:
s11, acquiring road traffic related data of a road to be managed and controlled through a data acquisition terminal, transmitting the road traffic related data to a management terminal in real time, acquiring weather data, date data, road maintenance data and road flow data in the road traffic related data, and simultaneously acquiring road historical traffic related data to be managed and controlled;
and S12, obtaining a corresponding weather data change curve, a date data change curve, a road maintenance data change curve and a road flow data change curve based on the acquired real-time weather data, date data, road maintenance data and road flow data, historical weather data, date data, road maintenance data and road flow data, and forming a road knowledge graph model needing management and control.
3. The knowledge-graph-based smart city data management method of claim 2, wherein: the specific content of the screening strategy in the step S2 is as follows:
s21, acquiring weather data in the historical road traffic related data, wherein the weather data comprises temperature data, humidity data and rainfall and snowfall data, and acquiring date data in the historical road traffic related data;
s22, substituting weather data, date data and road maintenance data of the same length time period corresponding to the management and control threshold time into a difference value calculation formula to calculate a difference value between the historical time period and the period, wherein the difference value calculation formula is as follows:wherein c i For item i, < in the weather data of the present period, <>Is the ith item, a in the weather data of the history period i The duty ratio coefficient of the ith item in the weather data, n is the number of parameters in the weather data, z 1 For date data of the present period, z 2 For date data of history period, m 1 Maintenance data for the road of the present period, m 2 Maintenance data for historic period road lambda 1 Lambda is the weather duty factor 2 Lambda is the duty cycle of the date 3 Maintaining a data duty cycle for the road, wherein +.>λ 1 +λ 2 +λ 3 ＝1；
S23, comparing the calculated difference value of each historical time period with a set difference threshold, setting a corresponding historical time period with the difference value smaller than or equal to the set difference threshold as a screening time period, and setting relevant data of the screening time period as screening data.
4. The method for managing smart city data based on a knowledge graph as claimed in claim 3, wherein the specific content of the traffic flow prediction model construction strategy in S3 is:
s31, acquiring the acquired screening data, acquiring a time-dependent change curve of weather data in the screening data, a time-dependent change curve of road maintenance data in the screening data, a date change curve and a time-dependent change curve of road flow data, constructing a deep learning neural network model which is input into the time-dependent change curve of the weather data in the screening data, the time-dependent change curve of the road maintenance data in the screening data and the date change curve of the road maintenance data in the screening data, and outputting the deep learning neural network model into the time-dependent change curve of the road flow data as a traffic flow prediction model;
s32, dividing the acquired change curve of the weather data in the historical screening data along with time, the change curve of the road maintenance data in the screening data along with time, the date change curve and the change curve of the road flow data along with time into a 70% parameter training set and a 30% parameter test set; inputting 70% of parameter training sets into a deep learning neural network model for training to obtain an initial deep learning neural network model; testing the initial deep learning neural network model by using 30% of parameter test sets, and outputting an optimal initial deep learning neural network model meeting the preset road flow data prediction accuracy as the deep learning neural network model, wherein the formula in the deep learning neural network model is as follows:
wherein->For the output of m+1 layer s term neurons,/->For the connection weight of the mth layer jth neuron and the m+1 layer s neuron,/for the mth layer jth neuron>Input representing the jth neuron of the mth layer,/->Representing the bias of the linear relationship of the mth and m+1th layer s neurons, σ () represents the Sigmoid activation function, and w is the number of neurons in the nth layer deep learning neural network model.
5. The method for managing smart city data based on a knowledge graph of claim 4, wherein said step S4 comprises the steps of:
s41, acquiring weather data, date data and road maintenance data of the period, and simultaneously acquiring a corresponding traffic flow prediction model;
s42, importing the acquired weather data, date data and road maintenance data of the period into a traffic flow prediction model to derive a corresponding predicted value of the traffic flow of the next period.
6. The knowledge graph-based smart city data management method of claim 5, wherein said traffic control strategy in S5 comprises the following steps:
s51, obtaining the value of the traffic flow corresponding to each intersection in the next period, substituting the value of the traffic flow into a green light duty ratio calculation formula to calculate the green light time of the corresponding intersection, wherein the green light time calculation formula is as follows:wherein T is a For the green time of the a intersection, r a Is the predicted value of the lower period vehicle flow of the a intersection, r e Is the predicted value of the traffic flow of the next period of the e intersection, S is the total number of intersections, t zt Is the overall cycle time;
s52, the traffic light control module adjusts traffic lights according to the output green light time output values of the intersections.
7. A knowledge-graph-based smart city data management system implemented based on a knowledge-graph-based smart city data management method as claimed in any one of claims 1-6, comprising: the system comprises a knowledge graph model construction module, a data screening module, a traffic flow prediction model construction module, a next period traffic flow prediction module, a traffic light time output module and a control module, wherein the knowledge graph model construction module is used for collecting a piece of road traffic related data and historical traffic related data information of the road from an acquisition data source, the related data comprise weather data, date data, road maintenance data and road flow data, a road knowledge graph model needing to be controlled is constructed, the data screening module is used for acquiring related data of a historical period from the constructed knowledge graph model, and the acquired related data of the historical period and the current period are imported into a screening strategy to screen the related data of the historical period, so that screening data is obtained.
8. The knowledge graph-based smart city data management system of claim 7, wherein the traffic flow prediction model construction module is configured to construct a traffic flow prediction model by substituting the obtained screening data into a traffic flow prediction model construction strategy, the lower period traffic flow prediction module is configured to introduce the obtained lower period related data into the traffic flow prediction model to predict the lower period traffic flow, the traffic light time output module is configured to introduce the estimated lower period traffic flow into the traffic flow control strategy to output traffic light traffic time, and the control module is configured to control operations of the knowledge graph model construction module, the data screening module, the traffic flow prediction model construction module, the lower period traffic flow prediction module and the traffic light time output module.
9. An electronic device, comprising: a processor and a memory, wherein the memory stores a computer program for the processor to call;
the processor performs a knowledge-graph-based smart city data management method as claimed in any one of claims 1-6 by invoking a computer program stored in the memory.
10. A computer-readable storage medium, characterized by: instructions stored thereon which, when executed on a computer, cause the computer to perform a knowledge-graph-based smart city data management method as claimed in any one of claims 1 to 6.
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