CN114187580A - Method for updating digital twin model state and related equipment - Google Patents
Method for updating digital twin model state and related equipment Download PDFInfo
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Abstract
The embodiment of the disclosure provides a method for updating a digital twin model state and related equipment, and belongs to the technical field of computers and communication. The method comprises the following steps: acquiring a current traffic signal lamp image at the current moment; processing the current traffic signal lamp image by using a machine vision model to obtain the current traffic signal lamp state information; and updating the corresponding traffic signal lamp state in the digital twin model according to the current traffic signal lamp state information.
Description
Technical Field
The present disclosure relates to the field of computer and communications technologies, and in particular, to a method, system, computer device, and computer-readable storage medium for updating a state of a digital twin model.
Background
In the process of building a digital road, a road simulation service is involved, a twin model (called as a digital twin model) of the digital road needs to be built, static data of the road can be collected and combined through technologies such as oblique photography and laser point cloud, and dynamic data needs to be obtained through a service system and then fused with model data (or local states of the model are updated). The traffic light state of a road intersection is an important component of a digital twin, and is limited by the closure of a traffic system (private network communication is required and the digital twin cannot be opened to the outside), a contractor of a digital road cannot acquire the state information of the traffic light signal, and the digital twin lacks necessary signal light state information when supporting scenes such as road simulation, traffic and the like, so that the effect is limited.
Disclosure of Invention
The embodiments of the present disclosure provide a method, an apparatus, a computer device, and a computer-readable storage medium for updating a digital twin model state, which may at least partially solve the technical problems in the prior art described above.
The embodiment of the present disclosure provides a method for updating a digital twin model state, where the method includes: acquiring a current traffic signal lamp image at the current moment; processing the current traffic signal lamp image by using a machine vision model to obtain the current traffic signal lamp state information; and updating the corresponding traffic signal lamp state in the digital twin model according to the current traffic signal lamp state information.
The disclosed embodiment provides a system for updating a digital twin model state, the system comprising: the video identification module is used for acquiring a current traffic signal lamp image at the current moment, processing the current traffic signal lamp image by using a machine vision model and acquiring the state information of the current traffic signal lamp; and the model updating module is used for updating the corresponding traffic signal lamp state in the digital twin model according to the current traffic signal lamp state information.
The embodiment of the disclosure provides computer equipment, which comprises a processor, a memory and an input/output interface; the processor is connected with the memory and the input/output interface respectively, wherein the input/output interface is used for receiving data and outputting the data, the memory is used for storing a computer program, and the processor is used for calling the computer program so as to enable the computer device comprising the processor to execute the method for updating the digital twin model state in the embodiment of the disclosure.
An aspect of the disclosed embodiments provides a computer-readable storage medium storing a computer program adapted to be loaded and executed by a processor, so as to cause a computer device having the processor to execute the method for updating a digital twin model state in the disclosed embodiments.
An aspect of an embodiment of the present disclosure provides a computer program product or a computer program, which includes computer instructions stored in a computer-readable storage medium. The processor of the computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, causing the computer device to perform the method provided in the various alternatives in the embodiments of the present disclosure.
Drawings
Fig. 1 is a network interaction architecture diagram of a method for updating a digital twin model state according to an embodiment of the present disclosure.
FIG. 2 schematically illustrates a flow diagram of a method of updating a digital twin model state according to an embodiment of the disclosure.
FIG. 3 schematically illustrates a flow diagram of a method of updating a digital twin model state according to another embodiment of the present disclosure.
FIG. 4 is a block diagram of an apparatus for updating a digital twin model state according to an embodiment of the present disclosure.
Fig. 5 is a schematic structural diagram of a computer device according to an embodiment of the present disclosure.
Detailed Description
Example embodiments will now be described more fully with reference to the accompanying drawings. Example embodiments may, however, be embodied in many different forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of example embodiments to those skilled in the art.
In the description of the present disclosure, "/" denotes "or" means, for example, a/B may denote a or B, unless otherwise specified. "and/or" herein is merely an association describing an associated object, and means that there may be three relationships, e.g., a and/or B, which may mean: a exists alone, A and B exist simultaneously, and B exists alone. Further, "at least one" means one or more, "a plurality" means two or more. The terms "first", "second", and the like do not necessarily limit the number and execution order, and the terms "first", "second", and the like do not necessarily limit the difference.
Fig. 1 is a network interaction architecture diagram of a method for updating a digital twin model state according to an embodiment of the present disclosure.
In the embodiment of the present disclosure, please refer to fig. 1, fig. 1 is a network interaction architecture diagram for updating a digital twin model state provided by the embodiment of the present disclosure, and the embodiment of the present disclosure may be implemented by a user device and/or a computer device. The computer device 101 may acquire data from a user device, process and display the data, where the computer device 101 may perform data interaction with the user device, and the computer device 101 may be a server where an application program is located, may also belong to the user device (i.e., a background of the user device), and the like, which is not limited herein. Wherein the user equipment may be the user equipment 102a, the user equipment 102b, the user equipment 102c, or the like.
The disclosed embodiments may be implemented by a computer device 101. Specifically, the computer device 101 acquires the current traffic signal light image at the current moment from the user device (for example, the user device 102a, or an application installed on the user device 102a, or an opened web page); processing the current traffic signal lamp image by using a machine vision model to obtain the current traffic signal lamp state information; and updating the corresponding traffic signal lamp state in the digital twin model according to the current traffic signal lamp state information.
The embodiments of the present disclosure may be implemented by any one of the user equipment 102a, the user equipment 102b, the user equipment 102c, or the like. Specifically, taking the user equipment 102a as an example, the user equipment 102a acquires a current traffic signal lamp image at the current moment; processing the current traffic signal lamp image by using a machine vision model to obtain the current traffic signal lamp state information; and updating the corresponding traffic signal lamp state in the digital twin model according to the current traffic signal lamp state information.
The user device may be a mobile phone (e.g., the user device 102c) or a laptop (e.g., the user device 102b), or may also be a playback device in a vehicle (e.g., the user device 102a), and the like, which is not limited herein. The user device 102a may be regarded as a playing device in the vehicle 103, and the user devices 102a, 102b, and 102c may display an application program, a web page, or the like. The user equipment in fig. 1 is only an exemplary part of the equipment, and in the present disclosure, the user equipment is not limited to the equipment illustrated in fig. 1. The application program in the present disclosure may be any application program having display data.
It is understood that the user equipment mentioned in the embodiments of the present disclosure may be a computer device, and the computer device in the embodiments of the present disclosure includes, but is not limited to, a terminal or a server. In other words, the computer device may be a server or a terminal, or a system of a server and a terminal. The above-mentioned terminal may be an electronic device, including but not limited to a mobile phone, a tablet computer, a desktop computer, a notebook computer, a palm computer, a vehicle-mounted device, an Augmented Reality/Virtual Reality (AR/VR) device, a helmet display, a smart television, a wearable device, a smart speaker, a digital camera, a camera, and other Mobile Internet Devices (MID) with network access capability, or a terminal in a scene such as a train, a ship, or a flight, etc.
The above-mentioned server may be an independent physical server, a server cluster or a distributed system formed by a plurality of physical servers, or a cloud server providing basic cloud computing services such as cloud service, a cloud database, cloud computing, a cloud function, cloud storage, Network service, cloud communication, middleware service, domain name service, security service, vehicle-road cooperation, a Content Delivery Network (CDN), a big data and artificial intelligence platform, and the like.
Optionally, the data related to the embodiments of the present disclosure may be stored in a computer device, or the data may be stored based on a cloud storage technology, which is not limited herein.
FIG. 2 is a flowchart of a method for updating a digital twin model state according to an embodiment of the disclosure. The method provided by the embodiment of fig. 2 executed by the computer device 101 and/or the user equipment in fig. 1 is taken as an example for illustration, but the disclosure is not limited thereto.
As shown in fig. 2, the method provided by the embodiment of the present disclosure may include the following steps.
In step S210, the current traffic light image at the current time is acquired.
In an exemplary embodiment, acquiring the current traffic signal image at the current time may include: acquiring a configuration file, wherein the configuration file comprises intersection position relation information between each camera and a corresponding traffic signal lamp; calling the cameras of the corresponding traffic signal lamps according to the configuration files to acquire video streams of the corresponding traffic signal lamps shot by the cameras; and acquiring the current traffic light image of the corresponding traffic light at the current moment from the video stream.
In an exemplary embodiment, the intersection position relationship information includes a mapping relationship between an Identifier (ID) of each camera and an identifier of a traffic signal of the corresponding intersection.
In the embodiment of the disclosure, one or more corresponding cameras are arranged for the traffic lights arranged at each intersection, and the one or more cameras are used for shooting the traffic lights at the corresponding intersection to obtain a video stream of the traffic lights, and the traffic light images at various moments can be intercepted from the video stream, that is, the traffic light image at the current moment is called as a current traffic light image, and the traffic light image at the previous moment is called as a previous traffic light image. The corresponding camera or cameras can be installed at any suitable position as long as clear shooting of the corresponding traffic signal lamp can be guaranteed.
The traffic signal light may be a traffic light, but the disclosure is not limited thereto.
After the corresponding cameras are set for the traffic lights at each intersection, the mapping relationship between one or more cameras and one or more corresponding traffic lights can be configured in the user equipment, and according to the mapping relationship, which traffic light corresponds to each video stream can be known. Specifically, each traffic signal lamp can be numbered to obtain a traffic signal lamp ID, each camera can be numbered to obtain a camera ID, and the traffic signal lamp ID and the corresponding camera ID are stored in a configuration file in an associated manner.
In step S220, the current traffic light image is processed by using a machine vision model to obtain current traffic light state information.
In the embodiment of the disclosure, the traffic light image at each time can be processed by using any machine vision model to obtain the traffic light state information at each time, and the traffic light state information at the current time is referred to as current traffic light state information. For example, a machine vision model may be trained in advance using a training sample, where the training sample includes training traffic signal images and labels thereof, the labels represent actual traffic signal state information of each training traffic signal image, such as red light, green light, yellow light, and the like, the machine vision model processes the training traffic signal images to obtain predicted traffic signal state information, a loss function is constructed according to the actual traffic signal state information and the corresponding predicted traffic signal state information to obtain a trained machine vision model, and then the trained machine vision model is used to identify a current traffic signal image and predict and output the current traffic signal state information.
In step S230, the corresponding traffic light state in the digital twin model is updated according to the current traffic light state information.
In an exemplary embodiment, updating the corresponding traffic signal status in the digital twin model according to the current traffic signal status information may include: acquiring the previous traffic signal lamp state information corresponding to the traffic signal lamp at the previous moment; comparing the previous traffic signal lamp state information with the current traffic signal lamp state information; and if the current traffic signal lamp state information is changed compared with the last traffic signal lamp state information, updating the corresponding traffic signal lamp state in the digital twin model according to the current traffic signal lamp state information.
In an exemplary embodiment, if the current traffic signal status information is changed from the previous traffic signal status information, updating the corresponding traffic signal status in the digital twin model according to the current traffic signal status information may include: if the current traffic signal lamp state information is changed compared with the last traffic signal lamp state information, acquiring a preset configuration format; packaging the current traffic signal lamp state information and the corresponding relation between the traffic signal lamp and the model component into a signal lamp state data message according to the preset format; and updating the corresponding traffic signal lamp state in the digital twin model according to the signal lamp state data message.
For example, a preset format of the configuration may be stored in the configuration file in advance, but the present disclosure is not limited thereto.
In the embodiment of the present disclosure, the model component refers to a model component in which, in the digital twin model of the digital road, a digital object corresponding to a physical entity of the physical world, for example, a traffic light on a real road, is mapped into a traffic light in the digital twin model.
In some embodiments, the user device may pre-store a correspondence between each traffic light and each model component in the digital twin model, and may encapsulate the correspondence between the traffic light and the model component when encapsulating the signal light status data packet. In other embodiments, the correspondence between the traffic signal lamp and the model component may also be stored in a computer device, for example, in a cloud server (hereinafter, referred to as a cloud), at this time, when the signal lamp status data packet is encapsulated, the current traffic signal lamp status information and the ID of the corresponding traffic signal lamp may be encapsulated, when the cloud receives the signal lamp status data packet, the cloud analyzes the signal lamp status data packet to obtain the ID of the corresponding traffic signal lamp, and the correspondence between the traffic signal lamp and the model component is queried according to the ID of the traffic signal lamp, so that which traffic signal lamp in the digital twin model corresponds to the current traffic signal lamp status information may be known.
In an exemplary embodiment, updating the corresponding traffic signal light status in the digital twin model according to the signal light status data message may include: sending the signal lamp state data message to a cloud end; receiving and analyzing the signal lamp state data message through the cloud end to obtain the current traffic signal lamp state information and the corresponding relation between the traffic signal lamp and the model component; and updating the state of the corresponding traffic signal lamp in the digital twin model according to the current traffic signal lamp state information and the corresponding relation between the traffic signal lamp and the model component.
For example, at the previous time, the traffic signal 1 is red, and at the current time, the traffic signal 1 changes to green, and the traffic signal 1 in the digital twin model may be changed from red to green.
According to the method provided by the embodiment of the disclosure, the traffic signal lamp images at all times acquired by the camera corresponding to the traffic signal lamp are identified through the machine vision model, so that the traffic signal lamp state information at all times is obtained, the digital twin state can be updated according to the traffic signal lamp state at all times identified and obtained by the machine vision model, the real-time synchronization of the digital twin model and the physical world is realized, and the method can be applied to the field of smart cities.
The method provided by the embodiment of the present disclosure is illustrated in fig. 3, but the present disclosure is not limited thereto. As shown in fig. 3, the method provided by the embodiment of the present disclosure may include the following steps.
In step S301, a configuration file is obtained, where the configuration file includes intersection position relationship information between each camera and a corresponding traffic signal lamp.
In step S302, according to the configuration file, a camera of a corresponding traffic signal lamp is called to obtain a video stream of the corresponding traffic signal lamp captured by the camera.
In step S303, a current traffic light image of the corresponding traffic light at the current time is obtained from the video stream.
In step S304, the previous traffic signal status information corresponding to the traffic signal at the previous time is obtained.
In step S305, the previous traffic signal status information and the current traffic signal status information are compared.
In step S306, if the current traffic light status information is changed from the previous traffic light status information, a preset format of configuration is obtained.
In step S307, the current traffic signal status information and the correspondence between the traffic signal and the model component are encapsulated into a signal lamp status data packet according to the preset format.
In step S308, the signal lamp status data packet is sent to the cloud.
In step S309, the signal lamp status data packet is received and analyzed by the cloud, so as to obtain the current traffic signal lamp status information and the correspondence between the traffic signal lamp and the model component.
In step S310, the state of the corresponding traffic light in the digital twin model is updated according to the current traffic light state information and the correspondence between the traffic light and the model component.
The specific implementation of the embodiment of fig. 3 may refer to the description of the above embodiment.
Further, an embodiment of the present disclosure also provides a system for updating a digital twin model state, where the system may include: the video identification module can be used for acquiring a current traffic signal lamp image at the current moment, processing the current traffic signal lamp image by using a machine vision model and acquiring the state information of the current traffic signal lamp; and the model updating module can be used for updating the corresponding traffic signal lamp state in the digital twin model according to the current traffic signal lamp state information.
In an exemplary embodiment, the system may further include: the configuration management module is used for storing intersection position relation information between each camera and the corresponding traffic signal lamp in a city in a configuration file mode; the video identification module is further used for calling a corresponding camera according to the configuration file to obtain a video stream corresponding to the traffic signal lamp and obtaining the current traffic signal lamp image from the video stream; the state monitoring module is used for monitoring the current traffic signal lamp state information obtained by the video identification module in real time and triggering the data conversion module when the current traffic signal lamp state information is monitored to be changed compared with the last traffic signal lamp state information at the last moment; the data conversion module is used for packaging the current traffic signal lamp state information and the corresponding relation between the traffic signal lamp and the model component into a signal lamp state data message according to a preset configuration format; the data sending module is used for sending the signal lamp state data message to a cloud end; the data receiving module is used for receiving and analyzing the signal lamp state data message, acquiring the current traffic signal lamp state information and the corresponding relation between the traffic signal lamp and the model component, and sending the current traffic signal lamp state information and the corresponding relation between the traffic signal lamp and the model component, which are acquired through analysis, to the model updating module; the model updating module is further used for updating the state of the corresponding traffic signal lamp in the digital twin model according to the current traffic signal lamp state information and the corresponding relation between the traffic signal lamp and the model component.
The embodiment of the disclosure builds and exerts data element value around a digital twin city, and the method and the system for updating the state of a digital twin model based on machine vision recognition of a traffic signal lamp recognize the state of a traffic light at a road junction through a road side camera, transmit the recognized information to a cloud in real time, synchronize the state of a digital twin body in real time after the cloud acquires the state, and realize the quasi-real-time consistency of the state of the digital twin body and the state of a physical object.
For example, as shown in fig. 4, the system may include: a configuration management module 410, a video recognition module 420, a status monitoring module 430, a data conversion module 440, a data transmission module 450, a data reception module 460, and a model update module 470.
The configuration management module 410 may be configured to store intersection position relationship information of the cameras and the traffic lights in the city in a configuration file manner.
The video identification module 420 may be configured to invoke camera capabilities to obtain signal light status information in real-time from the video stream using a machine vision algorithm.
The status monitoring module 430 may be configured to monitor the acquired status information in real time, and trigger the data conversion module when the status changes.
The data conversion module 440 may be configured to encapsulate the signal lamp status data packet according to a preset format according to the information of the configuration management module 410 and the status monitoring module 430.
The data sending module 450 may be configured to send the encapsulated packet to the cloud data receiving module 460 in real time.
The data receiving module 460 may be configured to receive the encapsulated signal lamp status data message, parse the message, and send the message to the model updating module 470.
The model updating module 470 may be configured to receive the traffic light status information (i.e., the current traffic light status information), and update the corresponding traffic light status in the digital twin according to the correspondence between the message and the model component.
The system flow is shown in fig. 4:
a. the configuration management module 410 stores configuration information in a configuration file, which may include: the camera ID is related to the monitored intersection traffic light ID, but the disclosure is not limited thereto.
b. The video recognition module 420 invokes the camera capabilities to obtain the signal light status information from the video stream in real time with the machine vision model.
c. The status monitoring module 430 monitors the acquired status information in real time, and triggers the data conversion module 440 when the status changes.
d. The data conversion module 440 obtains information from the configuration management module 410 and the status monitoring module 430.
e. The data conversion module 440 encapsulates the signal lamp status data message according to a preset format, and inputs the signal lamp status data message to the data transmission module 450.
f. The data receiving module 460 receives the encapsulated status messages such as signals, analyzes the messages and sends the messages to the model updating module 470.
g. The model updating module 470 receives the signal lamp status information and updates the corresponding signal lamp status in the digital twin according to the corresponding relationship between the message and the model component.
Fig. 5 is a schematic structural diagram of a computer device according to an embodiment of the present disclosure.
Referring to fig. 5, fig. 5 is a schematic structural diagram of a computer device according to an embodiment of the present disclosure. As shown in fig. 5, the computer device in the embodiment of the present disclosure may include: one or more processors 1001, memory 1002, and input-output interface 1003. The processor 1001, the memory 1002, and the input/output interface 1003 are connected by a bus 1004. The memory 1002 is used for storing a computer program, which includes program instructions, and the input/output interface 1003 is used for receiving data and outputting data, for example, for data interaction between a host and a computer device, or for data interaction between virtual machines in the host; the processor 1001 is used to execute program instructions stored by the memory 1002.
Among other things, the processor 1001 may perform the following operations: acquiring a current traffic signal lamp image at the current moment; processing the current traffic signal lamp image by using a machine vision model to obtain the current traffic signal lamp state information; and updating the corresponding traffic signal lamp state in the digital twin model according to the current traffic signal lamp state information.
In some possible embodiments, the processor 1001 may be a Central Processing Unit (CPU), and the processor may be other general purpose processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf programmable gate array (FPGA) or other programmable logic device, a discrete gate or transistor logic device, a discrete hardware component, or the like. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The memory 1002 may include both read-only memory and random-access memory, and provides instructions and data to the processor 1001 and the input/output interface 1003. A portion of the memory 1002 may also include non-volatile random access memory. For example, the memory 1002 may also store device type information.
In a specific implementation, the computer device may execute, through each built-in functional module thereof, the implementation manner provided in each step in fig. 2, fig. 3, or fig. 4, which may specifically refer to the implementation manner provided in each step in fig. 2, fig. 3, or fig. 4, and is not described herein again.
The disclosed embodiments provide a computer device, including: the system comprises a processor, an input/output interface and a memory, wherein the processor acquires a computer program in the memory, and executes each step of the method shown in the figure 2 or the figure 3 or the figure 4 to perform data processing operation.
An embodiment of the present disclosure further provides a computer-readable storage medium, where a computer program is stored, where the computer program is suitable for being loaded by the processor and executing the method for updating the digital twin model state provided in each step in fig. 2, fig. 3, or fig. 4, and for specific reference, implementation manners provided in each step in fig. 2, fig. 3, or fig. 4 may be mentioned, and are not described herein again. In addition, the beneficial effects of the same method are not described in detail. For technical details not disclosed in embodiments of the computer-readable storage medium to which the present disclosure relates, refer to the description of embodiments of the method of the present disclosure. By way of example, a computer program can be deployed to be executed on one computer device or on multiple computer devices at one site or distributed across multiple sites and interconnected by a communication network.
The computer readable storage medium may be the apparatus for updating the digital twin model state provided in any of the foregoing embodiments or an internal storage unit of the computer device, such as a hard disk or a memory of the computer device. The computer readable storage medium may also be an external storage device of the computer device, such as a plug-in hard disk, a Smart Memory Card (SMC), a Secure Digital (SD) card, a flash card (flash card), and the like, provided on the computer device. Further, the computer-readable storage medium may also include both an internal storage unit and an external storage device of the computer device. The computer-readable storage medium is used for storing the computer program and other programs and data required by the computer device. The computer readable storage medium may also be used to temporarily store data that has been output or is to be output.
Embodiments of the present disclosure also provide a computer program product or computer program comprising computer instructions stored in a computer readable storage medium. The computer instructions are read by a processor of a computer device from a computer-readable storage medium, and the computer instructions are executed by the processor to cause the computer device to perform the methods provided in the various alternatives of fig. 2 or fig. 3 or fig. 4.
The terms "first," "second," and the like in the description and in the claims and the drawings of the embodiments of the present disclosure are used for distinguishing between different objects and not for describing a particular order. Furthermore, the terms "comprises" and any variations thereof, are intended to cover non-exclusive inclusions. For example, a process, method, apparatus, product, or apparatus that comprises a list of steps or elements is not limited to the listed steps or modules, but may alternatively include other steps or modules not listed or inherent to such process, method, apparatus, product, or apparatus.
Those of ordinary skill in the art will appreciate that the elements and algorithm steps of the examples described in connection with the embodiments disclosed herein may be embodied in electronic hardware, computer software, or combinations of both, and that the components and steps of the examples have been described in a functional general in the specification for the purpose of clearly illustrating the interchangeability of hardware and software. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. 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 disclosure.
The method and the related apparatus provided by the embodiments of the present disclosure are described with reference to the flowchart and/or the structural diagram of the method provided by the embodiments of the present disclosure, and each flow and/or block of the flowchart and/or the structural diagram of the method, and the combination of the flow and/or block in the flowchart and/or the block diagram can be specifically implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block or blocks of the block diagram. These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block or blocks of the block diagram. These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block or blocks.
The disclosure of the present invention is not intended to be limited to the particular embodiments disclosed, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (10)
1. A method of updating a digital twin model state, comprising:
acquiring a current traffic signal lamp image at the current moment;
processing the current traffic signal lamp image by using a machine vision model to obtain the current traffic signal lamp state information;
and updating the corresponding traffic signal lamp state in the digital twin model according to the current traffic signal lamp state information.
2. The method of claim 1, wherein obtaining the current traffic light image at the current time comprises:
acquiring a configuration file, wherein the configuration file comprises intersection position relation information between each camera and a corresponding traffic signal lamp;
calling the cameras of the corresponding traffic signal lamps according to the configuration files to acquire video streams of the corresponding traffic signal lamps shot by the cameras;
and acquiring the current traffic light image of the corresponding traffic light at the current moment from the video stream.
3. The method according to claim 2, wherein the intersection position relationship information includes a mapping relationship between the identification of each camera and the identification of the traffic signal lamp of the corresponding intersection.
4. The method of claim 1, wherein updating a corresponding traffic signal state in a digital twin model based on the current traffic signal state information comprises:
acquiring the previous traffic signal lamp state information corresponding to the traffic signal lamp at the previous moment;
comparing the previous traffic signal lamp state information with the current traffic signal lamp state information;
and if the current traffic signal lamp state information is changed compared with the last traffic signal lamp state information, updating the corresponding traffic signal lamp state in the digital twin model according to the current traffic signal lamp state information.
5. The method of claim 4, wherein updating the corresponding traffic signal status in the digital twin model based on the current traffic signal status information if the current traffic signal status information changes from the previous traffic signal status information comprises:
if the current traffic signal lamp state information is changed compared with the last traffic signal lamp state information, acquiring a preset configuration format;
packaging the current traffic signal lamp state information and the corresponding relation between the traffic signal lamp and the model component into a signal lamp state data message according to the preset format;
and updating the corresponding traffic signal lamp state in the digital twin model according to the signal lamp state data message.
6. The method of claim 5, wherein updating the corresponding traffic light status in the digital twin model based on the signal light status data message comprises:
sending the signal lamp state data message to a cloud end;
receiving and analyzing the signal lamp state data message through the cloud end to obtain the current traffic signal lamp state information and the corresponding relation between the traffic signal lamp and the model component;
and updating the state of the corresponding traffic signal lamp in the digital twin model according to the current traffic signal lamp state information and the corresponding relation between the traffic signal lamp and the model component.
7. A system for updating a digital twin model state, comprising:
the video identification module is used for acquiring a current traffic signal lamp image at the current moment, processing the current traffic signal lamp image by using a machine vision model and acquiring the state information of the current traffic signal lamp;
and the model updating module is used for updating the corresponding traffic signal lamp state in the digital twin model according to the current traffic signal lamp state information.
8. The method of claim 7, further comprising:
the configuration management module is used for storing intersection position relation information between each camera and the corresponding traffic signal lamp in a city in a configuration file mode;
the video identification module is further used for calling a corresponding camera according to the configuration file to obtain a video stream corresponding to the traffic signal lamp and obtaining the current traffic signal lamp image from the video stream;
the state monitoring module is used for monitoring the current traffic signal lamp state information obtained by the video identification module in real time and triggering the data conversion module when the current traffic signal lamp state information is monitored to be changed compared with the last traffic signal lamp state information at the last moment;
the data conversion module is used for packaging the current traffic signal lamp state information and the corresponding relation between the traffic signal lamp and the model component into a signal lamp state data message according to a preset configuration format;
the data sending module is used for sending the signal lamp state data message to a cloud end;
the data receiving module is used for receiving and analyzing the signal lamp state data message, acquiring the current traffic signal lamp state information and the corresponding relation between the traffic signal lamp and the model component, and sending the current traffic signal lamp state information and the corresponding relation between the traffic signal lamp and the model component, which are acquired through analysis, to the model updating module;
the model updating module is further used for updating the state of the corresponding traffic signal lamp in the digital twin model according to the current traffic signal lamp state information and the corresponding relation between the traffic signal lamp and the model component.
9. A computer device comprising a processor, a memory, an input output interface;
the processor is connected to the memory and the input/output interface respectively, wherein the input/output interface is used for receiving data and outputting data, the memory is used for storing a computer program, and the processor is used for calling the computer program to enable the computer device to execute the method of any one of claims 1 to 6.
10. A computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, carries out the method according to any one of claims 1 to 6.
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| CN114419896A (en) * | 2022-03-29 | 2022-04-29 | 海尔数字科技(青岛)有限公司 | Traffic signal lamp control method, device, equipment and medium based on digital twins |
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| CN114419896A (en) * | 2022-03-29 | 2022-04-29 | 海尔数字科技(青岛)有限公司 | Traffic signal lamp control method, device, equipment and medium based on digital twins |
| CN114419896B (en) * | 2022-03-29 | 2022-07-12 | 海尔数字科技(青岛)有限公司 | Traffic signal lamp control method, device, equipment and medium based on digital twins |
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