CN115775085A - Smart city management method and system based on digital twin - Google Patents
Smart city management method and system based on digital twin Download PDFInfo
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Abstract
The invention discloses a digital twin-based smart city management method and a digital twin-based smart city management system, which comprise the following steps of: acquiring city geographic information of a target city by using information acquisition equipment, and performing three-dimensional reconstruction based on the city geographic information of the target city to obtain a three-dimensional model of the target city; and uniformly packaging the three-dimensional model of the target city into a target city twin model through a digital twin comprehensive service platform. The method utilizes the urban business information of the target city to carry out model rectification on the urban business model of the neighborhood city to obtain the urban business model of the target city, so that the urban business model of the target city overcomes the limitation of the data volume of the urban business information of the target city, better business state judgment accuracy is obtained, the sample volume required by training can be greatly reduced, the training time is reduced by reducing the training parameters, and the real-time display of the business state in the target city is realized in the twin model of the target city to assist city management.
Description
Technical Field
The invention relates to the technical field of smart cities, in particular to a smart city management method and system based on digital twins.
Background
The digital twin is a simulation process integrating multiple disciplines, multiple physical quantities, multiple scales and multiple probabilities by fully utilizing data such as a physical model, sensor updating and operation history, and mapping is completed in a virtual space, so that the full life cycle process of corresponding entity equipment is reflected. Digital twinning is an beyond-realistic concept that can be viewed as a digital mapping system of one or more important, interdependent equipment systems.
The prior art CN113609656A discloses a digital twin-based intelligent city decision system and method, which includes a twin subsystem, a twin subsystem for sensing and collecting city resource information in real time; collecting urban resource information; fusing urban resource information, an urban geographic information model, an urban building information model and an urban traffic network model to generate an urban information model, and forming an urban digital twin space according to the urban information model; constructing a digital twin city operating system corresponding to at least one decision dimension according to the city information model; the deduction subsystem is used for carrying out virtual-real cooperative deduction aiming at least one decision dimension according to the city resource information and the city information model and synchronization with a real city; and the decision subsystem is used for analyzing the deduction data of the deduction subsystem aiming at least one decision dimension in the urban digital twin space based on the twin subsystem to obtain at least one decision scheme aiming at the at least one decision dimension.
Although the prior art can realize the dimension decision of city management by combining the digital twin technology in the smart city, the single city dimension decision data volume is limited, so that the accuracy of the digital twin-based smart city decision is limited, and the auxiliary function of city management is poor.
Disclosure of Invention
The invention aims to provide a smart city management method and system based on digital twins, and aims to solve the technical problem that in the prior art, the decision accuracy of a smart city based on digital twins is limited due to the fact that the city dimension decision data volume is limited.
In order to solve the technical problems, the invention specifically provides the following technical scheme:
a smart city management method based on digital twin comprises the following steps:
acquiring city geographic information of a target city by using information acquisition equipment, and performing three-dimensional reconstruction based on the city geographic information of the target city to obtain a three-dimensional model of the target city;
uniformly packaging the three-dimensional model of the target city into a target city twin model through a digital twin comprehensive service platform;
acquiring city service information of each neighborhood city of a target city, and performing deep learning on the city service information of each neighborhood city to obtain a city service model of the neighborhood city;
performing model rectification on the city service model of the neighborhood city by using the city service information of the target city to obtain the city service model of the target city;
and placing the city business model of the target city in each business node of the target city twin model to obtain the business twin model of the target city, and realizing real-time display of the business state in the target city twin model to assist city management.
As a preferred scheme of the present invention, the urban geographic information includes spatial geographic position data, spatial image data, spatial video data, remote sensing image data, POI list/standing book data.
As a preferred scheme of the present invention, the city service information includes personnel service data, environment service data, and intelligent control service data, and the city service model includes a personnel service model, an environment service model, and an intelligent control service model.
As a preferred scheme of the present invention, the constructing of the urban business model includes:
marking the state of personnel business based on personnel business data of each neighborhood city, and performing deep learning on the personnel business data serving as an input item and the personnel business state serving as an output item by utilizing a neural network to obtain a personnel business model of the neighborhood city;
marking the state of the environmental service based on the environmental service data of each neighborhood city, and performing deep learning on the personnel service data serving as an input item and the personnel service state serving as an output item by utilizing a neural network to obtain an environmental service model of the neighborhood city;
and marking the state of the intelligent control service based on the personnel service data of each neighborhood city, and performing deep learning on the intelligent control service data serving as an input item and the intelligent control service state serving as an output item by utilizing a neural network to obtain an intelligent control service model of the neighborhood city.
As a preferred solution of the present invention, the obtaining of the city business model of the target city includes:
calculating model parameters of a city service model of the target city by utilizing a maximum posterior estimation algorithm based on the city service information of the target city;
the calculation of the model parameters of the urban business model of the target city comprises:
calculating the Gaussian distribution similarity of each service data in the urban service information of the target city and the urban service model of the neighborhood city, wherein the Gaussian distribution similarity calculation formula is as follows:
in the formula, pr: (i|x i ) In the city service information of the target cityiA service data x i City business model with neighborhood cityiSimilarity of Gaussian distribution, w i In the city service information of the target cityiA service data x i Gaussian weight of, w j In city business information of target cityiA service data x j Gaussian weight of p (x) i ) And p (x) j ) Distribution is x i And x j M is the total amount of traffic data,i，jis a counting variable;
calculating model parameters of an urban business model of a target city based on Gaussian distribution similarity, wherein the calculation formula of the model parameters is as follows:
in the formula, n i In the city service information of the target cityiA service data x i Is updated with the gaussian weight of E i (x) In city business information of target cityiA service data x i Mean update value of, E i (x 2 ) In the city service information of the target cityiA service data x i The variance of (2) is updated;
fusing model parameters of an urban business model of a target city with model parameters of an urban business model of a field city to obtain an urban business model of the target city;
the fusion formula of the model parameters is as follows:
in the formula, rw i 、ru i And rd i Are respectively the meshGaussian weights, means and variances in the city traffic model for a target city,a i v 、a i y 、a i z are each rw i 、ru i And rd i And n i 、E i (x) And E i (x 2 ) Adaptive fusion parameters of (1).
As a preferred embodiment of the present invention, the spatial geographic position data, the spatial image data, the spatial video data, the remote sensing image data, and the POI list/ledger data are normalized during operation.
As a preferred scheme of the invention, a high-speed AI camera, an edge intelligent analysis device, a control device and a three-party sensing device are used for collecting spatial geographical position data, spatial image data, spatial video data, remote sensing image data and POI list/standing book data.
As a preferable scheme of the invention, the target city twin model is arranged on a digital twin comprehensive service platform, and the digital twin comprehensive service platform is in communication connection with the information acquisition equipment.
As a preferable aspect of the present invention, the present invention provides a smart city management system according to the smart city management method, including:
the information acquisition equipment is used for acquiring city geographic information of the target city;
the twin model building module is used for building a target city twin model;
and the digital twin comprehensive service platform is used for operating the target city twin model.
As a preferred aspect of the present invention, the information acquisition device includes a high-speed AI camera, an edge intelligent analysis device, a control device, and a three-party sensing device.
Compared with the prior art, the invention has the following beneficial effects:
the method utilizes the city business information of the target city to carry out model rectification on the city business model of the neighborhood city to obtain the city business model of the target city, so that the city business model of the target city overcomes the limitation of the data volume of the city business information of the target city, better business state judgment accuracy is obtained, the sample volume required by training can be greatly reduced, the training time is reduced by reducing training parameters, the city business model of the target city is placed in each business node of the twin model of the target city to obtain the twin model of the target city, and the twin model of the target city realizes the real-time display of the business state in the target city to assist city management.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below. It should be apparent that the drawings in the following description are merely exemplary, and that other embodiments can be derived from the drawings provided by those of ordinary skill in the art without inventive effort.
Fig. 1 is a flowchart of a smart city management method according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
As shown in fig. 1, the present invention provides a digital twin-based smart city management method, which includes the following steps:
acquiring city geographic information of a target city by using information acquisition equipment, and performing three-dimensional reconstruction based on the city geographic information of the target city to obtain a three-dimensional model of the target city;
uniformly packaging the three-dimensional model of the target city into a target city twin model through a digital twin comprehensive service platform;
acquiring city business information of each neighborhood city of a target city, and performing deep learning on the city business information of each neighborhood city to obtain a city business model of the neighborhood city;
performing model rectification on the city service model of the neighborhood city by using the city service information of the target city to obtain the city service model of the target city;
and placing the city business model of the target city in each business node of the target city twin model to obtain the business twin model of the target city, and realizing real-time display of the business state in the target city twin model to assist city management.
The urban geographic information comprises spatial geographic position data, spatial image data, spatial video data, remote sensing image data and POI (point of interest) list/standing book data.
The urban business information comprises personnel business data, environment business data and intelligent control business data, and the urban business model comprises a personnel business model, an environment business model and an intelligent control business model. The smart city application platform is applied to the upper layer based on user service industry-oriented artificial intelligence data, wherein the intelligent control service comprises automatic detection of states and parameters of public facilities such as a lighting system, an exhaust system, a hydrothermal system and an elevator, automatic intelligent control of equipment, energy consumption situation analysis, energy consumption planning and allocation, analysis of crowd behaviors such as crowd gathering and people fighting, analysis and positioning of people tracks and illegal invasion of people, and intelligence monitoring and intelligent defense of natural environments such as fire and smoke, emergency intelligent response, emergency scheme management, machine room fault detection, unattended machine room, intelligent parking, property management and other application scenes.
The construction of the urban business model comprises the following steps:
marking the state of personnel business based on personnel business data of each neighborhood city, and performing deep learning on the personnel business data serving as an input item and the personnel business state serving as an output item by utilizing a neural network to obtain a personnel business model of the neighborhood city;
marking the state of the environmental service based on the environmental service data of each neighborhood city, and performing deep learning on the personnel service data serving as an input item and the personnel service state serving as an output item by utilizing a neural network to obtain an environmental service model of the neighborhood city;
and marking the state of the intelligent control service based on the personnel service data of each neighborhood city, and performing deep learning on the intelligent control service data serving as an input item and the intelligent control service state serving as an output item by utilizing a neural network to obtain an intelligent control service model of the neighborhood city.
The obtaining of the city business model of the target city comprises:
calculating model parameters of a city service model of the target city by utilizing a maximum posterior estimation algorithm based on the city service information of the target city;
the calculation of the model parameters of the city traffic model of the target city comprises:
and calculating the Gaussian distribution similarity of each service data in the urban service information of the target city and the urban service model of the neighborhood city, wherein the Gaussian distribution similarity calculation formula is as follows:
wherein Pr (m)i|x i ) In city business information of target cityiA service data x i City business model with neighborhood cityiSimilarity of Gaussian distribution, w i In the city service information of the target cityiA service data x i Gaussian weight of (w) j In the city service information of the target cityiA service data x j Gaussian weight of p (x) i ) And p (x) j ) Distribution is x i And x j M is the total amount of traffic data,i，jis a counting variable;
calculating model parameters of the urban business model of the target city based on the Gaussian distribution similarity, wherein the calculation formula of the model parameters is as follows:
in the formula, n i In the city service information of the target cityiA service data x i Is updated with the gaussian weight of E i (x) In city business information of target cityiA service data x i Mean update value of, E i (x 2 ) In city business information of target cityiA service data x i The variance of (2) is updated;
fusing model parameters of the urban business model of the target city with model parameters of the urban business model of the field city to obtain an urban business model of the target city;
the fusion formula of the model parameters is as follows:
in the formula, rw i 、ru i And rd i Gaussian weights, means and variances in the city traffic model of the target city,a i v 、a i y 、a i z are respectively asrw i 、ru i And rd i And n i 、E i (x) And E i (x 2 ) Adaptive fusion parameters of (1).
The service data volume in the city service information of the target city is limited, and the deeply learned city service model is easy to have under-fitting, so that the service data in the city service information of the adjacent city of the target city is utilized to pre-train to obtain a pre-trained model, namely the city service model of the adjacent city, and the pre-trained city service model of the adjacent city is finely adjusted to the city service model of the target city in the city service model of the adjacent city through the self-adaption of the maximum posterior estimation algorithm, so that the problem of limited accuracy of the city service model caused by limited city service data of the target city can be effectively solved. And this way the amount of samples and training time required for training can be greatly reduced (by reducing the training parameters).
The self-adaptation of the maximum posterior estimation algorithm can enable the city business model of the target city to realize self-circulation, self-adaptation and self-learning fitted city business change to provide auxiliary support for city management and efficient development of city science, and assist in solving complex city business problems.
And carrying out normalization processing on the spatial geographic position data, the spatial image data, the spatial video data, the remote sensing image data and the POI list/standing book data during operation.
And acquiring spatial geographic position data, spatial image data, spatial video data, remote sensing image data and POI list/standing book data by utilizing a high-speed AI camera, edge intelligent analysis equipment, control equipment and three-party sensing equipment.
The target city twin model is arranged on the digital twin comprehensive service platform, and the digital twin comprehensive service platform is in communication connection with the information acquisition equipment.
The invention provides a smart city management system based on a smart city management method, which comprises the following steps:
the information acquisition equipment is used for acquiring city geographic information of the target city;
the twin model building module is used for building a target city twin model;
and the digital twin comprehensive service platform is used for operating the target city twin model.
The information acquisition equipment comprises a high-speed AI camera, edge intelligent analysis equipment, control equipment and three-party sensing equipment.
The city business model of the target city is placed in each business node of the target city twin model to obtain the business twin model of the target city, so that the real-time display of the business state in the target city is realized in the target city twin model to assist city management.
The above embodiments are only exemplary embodiments of the present application, and are not intended to limit the present application, and the protection scope of the present application is defined by the claims. Various modifications and equivalents may be made to the disclosure by those skilled in the art within the spirit and scope of the disclosure, and such modifications and equivalents should also be considered as falling within the scope of the disclosure.
Claims (10)
1. A smart city management method based on digital twin is characterized by comprising the following steps:
acquiring city geographic information of a target city by using information acquisition equipment, and performing three-dimensional reconstruction based on the city geographic information of the target city to obtain a three-dimensional model of the target city;
uniformly packaging the three-dimensional model of the target city into a target city twin model through a digital twin comprehensive service platform;
acquiring city business information of each neighborhood city of a target city, and performing deep learning on the city business information of each neighborhood city to obtain a city business model of the neighborhood city;
performing model rectification on the city service model of the neighborhood city by using the city service information of the target city to obtain the city service model of the target city;
and placing the city business model of the target city in each business node of the target city twin model to obtain the business twin model of the target city, and realizing real-time display of the business state in the target city twin model to assist city management.
2. The intelligent city management method based on digital twin as claimed in claim 1, wherein: the urban geographic information comprises spatial geographic position data, spatial image data, spatial video data, remote sensing image data and POI (point of interest) list/standing book data.
3. The intelligent city management method based on digital twin as claimed in claim 2, wherein: the city business information comprises personnel business data, environment business data and intelligent control business data, and the city business model comprises a personnel business model, an environment business model and an intelligent control business model.
4. The intelligent city management method based on digital twin as claimed in claim 3, wherein: the construction of the urban business model comprises the following steps:
marking the state of personnel business based on personnel business data of each neighborhood city, and performing deep learning on the personnel business data serving as an input item and the personnel business state serving as an output item by utilizing a neural network to obtain a personnel business model of the neighborhood city;
marking the state of the environmental service based on the environmental service data of each neighborhood city, and performing deep learning on the personnel service data serving as an input item and the personnel service state serving as an output item by utilizing a neural network to obtain an environmental service model of the neighborhood city;
and marking the state of the intelligent control service based on the personnel service data of each neighborhood city, and performing deep learning on the intelligent control service data serving as an input item and the intelligent control service state serving as an output item by utilizing a neural network to obtain an intelligent control service model of the neighborhood city.
5. The intelligent city management method based on digital twins as claimed in claim 4, wherein: the obtaining of the city business model of the target city comprises:
calculating model parameters of a city service model of the target city by utilizing a maximum posterior estimation algorithm based on the city service information of the target city;
the calculation of the model parameters of the city business model of the target city comprises:
and calculating the Gaussian distribution similarity of each service data in the urban service information of the target city and the urban service model of the neighborhood city, wherein the Gaussian distribution similarity calculation formula is as follows:
in the formula, pr: (i|x i ) In city business information of target cityiA service data x i City business model with neighborhood cityiSimilarity of Gaussian distribution, w i In the city service information of the target cityiA service data x i Gaussian weight of, w j In the city service information of the target cityiA service data x j Gaussian weight of p (x) i ) And p (x) j ) Distribution is x i And x j M is the total amount of traffic data,i，jis a counting variable;
calculating model parameters of an urban business model of a target city based on Gaussian distribution similarity, wherein the calculation formula of the model parameters is as follows:
in the formula, n i In the city service information of the target cityiA service data x i Is updated with the gaussian weight of E i (x) In city business information of target cityiA service data x i Mean update value of E i (x 2 ) In the city service information of the target cityiA service data x i The variance update value of (a);
fusing model parameters of an urban business model of a target city with model parameters of an urban business model of a field city to obtain an urban business model of the target city;
the fusion formula of the model parameters is as follows:
in the formula, rw i 、ru i And rd i Gaussian weights, mean and variance in the city traffic model for the target city,a i v 、a i y 、a i z are each rw i 、ru i And rd i And n i 、E i (x) And E i (x 2 ) Adaptive fusion parameters of (1).
6. The intelligent city management method based on digital twins as claimed in claim 5, wherein: and the spatial geographic position data, the spatial image data, the spatial video data, the remote sensing image data and the POI list/standing book data are subjected to normalization processing during application.
7. The digital twin-based smart city management method as claimed in claim 6, wherein high speed AI camera, edge intelligent analysis device, control device, three-party sensing device are used to collect spatial geographical position data, spatial image data, spatial video data, remote sensing image data, POI list/standing book data.
8. The intelligent city management method based on digital twin as claimed in claim 7, wherein the target city twin model is placed on a digital twin integrated service platform, and the digital twin integrated service platform is connected with an information acquisition device in a communication way.
9. A smart city management system based on the digital twin smart city management method according to any one of claims 1 to 8, comprising:
the information acquisition equipment is used for acquiring the urban geographic information of the target city;
the twin model building module is used for building a target city twin model;
and the digital twin comprehensive service platform is used for operating the target city twin model.
10. The intelligent city management system according to claim 9, wherein the information collecting device comprises a high-speed AI camera, an edge intelligent analysis device, a control device, and a three-party sensor device.
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