CN115546725A

CN115546725A - Scene recognition early warning method, system, device, equipment and storage medium

Info

Publication number: CN115546725A
Application number: CN202211284808.8A
Authority: CN
Inventors: 李鹏斐; 吉开轩
Original assignee: Agricultural Bank of China
Current assignee: Agricultural Bank of China
Priority date: 2022-10-17
Filing date: 2022-10-17
Publication date: 2022-12-30

Abstract

The invention discloses a scene recognition early warning method, a system, a device, equipment and a storage medium. The method comprises the following steps: acquiring each image acquisition device and each edge node related to at least one scene to be identified; generating a configuration file corresponding to the corresponding scene to be identified according to the incidence relation between each image acquisition device and each edge node and each scene to be identified; determining video image data and a scene model of a corresponding scene to be identified according to the configuration file; determining a scene recognition result of a corresponding scene to be recognized based on the scene model according to the video image data; and carrying out early warning processing on the corresponding scene to be identified according to the video image data and the scene identification result. The embodiment of the invention improves the efficiency and accuracy of scene recognition and early warning.

Description

Scene recognition early warning method, system, device, equipment and storage medium

Technical Field

The invention relates to the field of data processing, in particular to a scene recognition early warning method, a scene recognition early warning system, a scene recognition early warning device, scene recognition early warning equipment and a storage medium.

Background

For the analysis of the intelligent video of the website, the video data volume involved in the analysis and calculation of the video data of the current cloud is large, and the requirement on real-time performance is high, so that the requirement on a large-scale website scene is difficult to support. In the prior art, when the problem of processing a large-scale scene video is solved, an analysis platform based on edge calculation is adopted, but the processing process in other aspects is not continuous, so that the quality and the efficiency of scene identification processing are low.

Disclosure of Invention

The invention provides a scene recognition early warning method, a system, a device, equipment and a storage medium, which are used for improving the efficiency and the accuracy of scene recognition early warning.

According to an aspect of the present invention, a scene recognition early warning method is provided, the method comprising:

acquiring each image acquisition device and each edge node related to at least one scene to be identified;

generating a configuration file corresponding to the corresponding scene to be identified according to the incidence relation between each image acquisition device and each edge node and each scene to be identified;

determining video image data and a scene model of a corresponding scene to be identified according to the configuration file;

determining a scene recognition result of a corresponding scene to be recognized based on the scene model according to the video image data;

and carrying out early warning processing on the corresponding scene to be identified according to the video image data and the scene identification result.

According to another aspect of the present invention, there is provided a scene recognition early warning system, the system comprising:

the system comprises an edge equipment management platform, at least one edge node and a service management system; the edge device management platform is in communication connection with each edge node; each edge node is in communication connection with the service management system;

the edge device management platform is used for acquiring each image acquisition device and each edge node related to at least one scene to be identified; generating a configuration file corresponding to the corresponding scene to be identified according to the incidence relation between each image acquisition device and each edge node and each scene to be identified;

each edge node is used for determining video image data and a scene model of a corresponding scene to be identified according to the configuration file; determining a scene recognition result of a corresponding scene to be recognized based on the scene model according to the video image data;

and the service management system is used for carrying out early warning processing on the corresponding scene to be identified according to the video image data and the scene identification result.

According to another aspect of the present invention, there is provided a scene recognition early warning apparatus, including:

the edge node acquisition module is used for acquiring each image acquisition device and each edge node related to at least one scene to be identified;

the configuration file generation module is used for generating configuration files corresponding to the corresponding scenes to be identified according to the incidence relation between each image acquisition device, each edge node and each scene to be identified;

the scene model determining module is used for determining video image data and a scene model of a corresponding scene to be identified according to the configuration file;

the recognition result determining module is used for determining a scene recognition result of a corresponding scene to be recognized based on the scene model according to the video image data;

and the early warning processing module is used for carrying out early warning processing on the corresponding scene to be recognized according to the video image data and the scene recognition result.

According to another aspect of the present invention, there is provided an electronic apparatus including:

at least one processor; and

a memory communicatively coupled to the at least one processor; wherein, the first and the second end of the pipe are connected with each other,

the memory stores a computer program executable by the at least one processor, the computer program being executable by the at least one processor to enable the at least one processor to perform the method of scene recognition pre-warning according to any of the embodiments of the present invention.

According to another aspect of the present invention, there is provided a computer-readable storage medium storing computer instructions for causing a processor to implement a scene recognition early warning method according to any one of the embodiments of the present invention when the computer instructions are executed.

According to the scheme of the embodiment of the invention, the configuration file corresponding to the corresponding scene to be identified is generated according to the incidence relation between each image acquisition device and each edge node and each scene to be identified; determining video image data and a scene model of a corresponding scene to be identified according to the configuration file; determining a scene recognition result of a corresponding scene to be recognized based on the scene model according to the video image data; and carrying out early warning processing on the corresponding scene to be recognized according to the video image data and the scene recognition result. According to the scheme, the whole processes of algorithm starting, processing and early warning and the like are carried out through video acquisition, generation and issuing of configuration files, edge nodes, intelligent analysis and whole-process management of video data are achieved, and efficiency and accuracy of scene recognition and early warning are improved.

It should be understood that the statements in this section are not intended to identify key or critical features of the embodiments of the present invention, nor are they intended to limit the scope of the invention. Other features of the present invention will become apparent from the following description.

Drawings

In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the description below are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without creative efforts.

Fig. 1 is a flowchart of a scene recognition method according to an embodiment of the present invention;

fig. 2A is a schematic structural diagram of a scene recognition early warning system according to a second embodiment of the present invention;

fig. 2B is a schematic structural diagram of a scene recognition and early warning system according to a second embodiment of the present invention.

Fig. 2C is an interaction diagram of a scene recognition method according to a second embodiment of the present invention.

Fig. 3 is a schematic structural diagram of a scene recognition apparatus according to a third embodiment of the present invention;

fig. 4 is a schematic structural diagram of an electronic device implementing the scene recognition method according to the embodiment of the present invention.

Detailed Description

In order to make the technical solutions of the present invention better understood, 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.

It should be noted that the terms "first," "second," and the like in the description and claims of the present invention and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the invention described herein are capable of operation in other sequences than those illustrated or described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.

Example one

Fig. 1 is a flowchart of a scene recognition and early warning method according to an embodiment of the present invention, where the method is applicable to a situation of recognizing and early warning a scene to be recognized, and the method may be executed by a scene recognition and early warning device, where the scene recognition and early warning device may be implemented in a form of hardware and/or software, and the scene recognition and early warning device may be configured in an electronic device. As shown in fig. 1, the method includes:

and S110, acquiring each image acquisition device and each edge node related to at least one scene to be identified.

The scene to be recognized may be a scene to be early-warning recognized. For example, the scene to be recognized may be a traffic recognition scene.

The image acquisition device may be a device deployed in each scene to be identified and configured to acquire video image data in the scene to be identified. For example, the image capture device may be a camera.

Wherein the edge node may be an edge box for performing edge calculation at the edge. For scene recognition of any scene to be recognized, at least one edge box can be adopted to perform edge calculation on the scene. The edge box can be used for analyzing and processing video image data acquired by the image acquisition equipment.

Illustratively, an image capture device and an edge node associated with at least one scene to be identified may be obtained by an edge device management platform. Optionally, each edge node may register in advance on the edge device management platform, specifically, each edge node may register in advance on the edge device management platform based on its own device identifier and device address, and enter its own information into the edge device management platform.

And S120, generating a configuration file corresponding to the corresponding scene to be identified according to the incidence relation between each image acquisition device and each edge node and each scene to be identified.

For example, the image capture device to be docked with each edge node may be determined by the edge device management platform based on each edge node registered in the platform, and the scene to be recognized that each edge node needs to process and the video image data that needs to be acquired by the scene to be recognized. For example, if there are a scene a to be identified and a scene B to be identified; an image acquisition device a, an image acquisition device b and an image acquisition device c exist; there are edge nodes m and edge nodes n. If the edge node used for performing scene recognition early warning on the scene A to be recognized is the edge node m, and the video image data required to be acquired for recognizing the scene A to be recognized is from the image acquisition equipment a and the image acquisition equipment b, an association relationship exists among the scene A to be recognized, the edge node m, the image acquisition equipment a and the image acquisition equipment b. If the edge node used for performing scene recognition early warning on the scene B to be recognized is the edge node n and the video image data required to be acquired for recognizing the scene B to be recognized is from the image acquisition equipment c, an incidence relation exists among the scene B to be recognized, the edge node n and the image acquisition equipment c.

For example, the edge device management platform may generate a configuration file corresponding to each scene to be identified according to an association relationship between each image capture device and each edge node and each scene to be identified. It should be noted that, for the same scene to be identified, the scene configuration parameters in the configuration files in different regions or regions are different. And different scene models are required to be used for different scenes to be identified. Therefore, the corresponding configuration files are different for different scenes to be identified. After the edge device management generates the configuration file corresponding to the scene to be identified, the configuration file is issued to the edge node corresponding to the scene to be identified.

In an optional embodiment, generating a configuration file corresponding to each scene to be identified according to an association relationship between each image acquisition device and each edge node and each scene to be identified includes: generating scene configuration parameters of corresponding scenes to be identified according to the incidence relation between each image acquisition device and each edge node and each scene to be identified; determining scene model identifications corresponding to scenes to be identified; and taking the scene configuration parameters and the scene model identifications of the corresponding scenes to be recognized as configuration files corresponding to the corresponding scenes to be recognized.

The scene configuration parameters may include a time parameter, a region parameter, a device identifier of the image capturing device, and a series of parameters for scene recognition.

The scene model identification can be used for uniquely representing a scene model of a scene to be identified; the scene models corresponding to different scenes to be recognized are different, and specifically, relevant technicians train the scene models of the different scenes to be recognized in advance and store the scene models in an algorithm warehouse of the edge device management platform.

Illustratively, according to a scene to be recognized, a scene model identifier corresponding to a scene model for performing early warning recognition on the scene to be recognized may be determined, and the scene configuration parameters and the scene model identifier of the scene to be recognized are used as configuration files corresponding to the corresponding scene to be recognized, and the configuration files are fed back to the corresponding edge nodes.

And S130, determining video image data and a scene model of the corresponding scene to be identified according to the configuration file.

Illustratively, after the configuration file is obtained, the corresponding edge node performs file analysis on the configuration file, and obtains video image data and a scene model of a corresponding scene to be identified through the analyzed file.

In an optional embodiment, the scene configuration parameters comprise a device identification of the image capture device; correspondingly, according to the configuration file, determining video image data and a scene model of a corresponding scene to be identified comprises the following steps: acquiring video image data from image acquisition equipment corresponding to the equipment identifier according to the equipment identifier; and acquiring a scene model identifier from the configuration file, and acquiring a corresponding scene model based on the scene model identifier.

The scene configuration parameters comprise equipment identification of the image acquisition equipment and are used for uniquely representing the image acquisition equipment.

Illustratively, after the edge node acquires the scene configuration parameters, the device identifier of the image capturing device is acquired from the scene configuration parameters, so that based on the device identifier, a connection between the image capturing device and the corresponding image capturing device is established, and video image data acquired by the image capturing device is acquired. The edge node can also obtain a scene model identifier from the scene configuration parameters, and based on the scene model identifier, pull the scene model corresponding to the scene model identifier from an algorithm warehouse of the edge device management platform.

And S140, determining a scene recognition result of the corresponding scene to be recognized based on the scene model according to the video image data.

For example, the edge node may input the acquired video image data to the scene model, and obtain a scene recognition result of the corresponding to-be-recognized scene output by the scene model.

In an optional embodiment, the scene configuration parameters include scene characteristic parameters corresponding to a scene to be identified; correspondingly, the method for determining the scene recognition result of the corresponding scene to be recognized based on the scene model according to the video image data comprises the following steps: inputting video image data and scene characteristic parameters into a scene model to obtain a scene model output result; and outputting the result of the scene model as a scene recognition result of the corresponding scene to be recognized.

Illustratively, the edge node inputs the video image data and the scene characteristic parameters into a scene model to obtain a scene model output result, and the scene model output result is used as a scene identification result of a corresponding scene to be identified. The scene characteristic parameters may include time parameters, regional parameters, and the like.

For example, for a scene to be identified of the human traffic, due to external factors, for an area a, the human traffic is gathered around ten am, and therefore, the corresponding scene identification time may be 9-12 am; for area B, the pedestrian traffic is concentrated around four pm, so its corresponding scene identification time may be 15-18 pm. Therefore, for the scene to be identified of the pedestrian volume, the corresponding scene characteristic parameters are different for different regions.

And S150, performing early warning processing on the corresponding scene to be recognized according to the video image data and the scene recognition result.

For example, each edge node may send video image data and a scene recognition result corresponding to the video image data to a service management system, the service management system performs early warning processing on the scene recognition result, and sends the video image data and the scene recognition result corresponding to the video image data to a model algorithm training platform through the service management system to optimize a corresponding scene model.

In an optional embodiment, the early warning processing is performed on the corresponding scene to be recognized according to the video image data and the scene recognition result, and the method comprises the following steps: if the scene recognition result meets the preset early warning rule, carrying out scene early warning on the corresponding scene to be recognized; and updating the model of the scene model corresponding to the corresponding scene to be recognized according to the video image data and the scene recognition result.

The early warning rules can be preset by related technicians. For example, for a pedestrian volume scene, the corresponding early warning rule may be that the pedestrian volume identified by the scene identification result is greater than a preset pedestrian volume threshold.

Illustratively, after the service management system obtains the scene recognition result, the service management system may perform early warning processing on the corresponding scene to be recognized based on the corresponding early warning rule. Different scenes to be identified can correspond to different early warning rules. Meanwhile, the model of the scene model corresponding to the corresponding scene to be recognized can be updated according to the video image data and the scene recognition result. Optionally, the service management system may further send the video image data and the scene recognition result to the model algorithm training platform, and the model algorithm training platform performs model updating on the scene model corresponding to the corresponding scene to be recognized, so as to store the updated scene model.

In an optional embodiment, the edge node comprises a node device identification and a node device address; after carrying out early warning processing to the corresponding scene to be identified, the method further comprises the following steps: and monitoring each edge node in real time according to the node equipment identifier and the node equipment address.

For example, the edge device management platform may store in advance a device identifier and a node device address corresponding to the edge node, and specifically, each edge node may register in advance in the edge device management platform. Each edge node can synchronize the operation information to the edge device management platform, and the edge device management platform monitors each edge node in real time according to the node device identification and the node device address, so that whether the edge node with a fault or an error is present or not is judged, and the abnormal edge node can be quickly processed.

Example two

On the basis of the foregoing embodiment, the present embodiment further provides a scene recognition and early warning system for implementing the scene recognition and early warning method of the foregoing embodiment, as shown in fig. 2A, which is a schematic structural diagram of the scene recognition and early warning system. The scene recognition early warning system comprises an edge device management platform 201, at least one edge node 202 and a service management system 203. The edge device management platform 201 is in communication connection with each edge node 202; each edge node 202 is communicatively coupled to a traffic management system 203.

The edge device management platform 201 is configured to acquire each image acquisition device and each edge node related to at least one scene to be identified; and generating a configuration file corresponding to the corresponding scene to be identified according to the incidence relation between each image acquisition device and each edge node and each scene to be identified.

Each edge node 202 is used for determining video image data and a scene model of a corresponding scene to be identified according to the configuration file; and determining a scene recognition result of the corresponding scene to be recognized based on the scene model according to the video image data.

And the service management system 203 is used for performing early warning processing on the corresponding scene to be recognized according to the video image data and the scene recognition result.

In another optional embodiment, the scene recognition early warning system may further include an image acquisition device 204 and a model algorithm training platform 205. Fig. 2B is a schematic structural diagram of a scene recognition early warning system. The image capturing device 204 may be a camera, and is configured to capture video image data in each scene to be identified. The model algorithm training platform is used for carrying out model training and model optimization on the scene model corresponding to each scene to be recognized.

In a specific embodiment, an interaction diagram of a scene recognition method is shown in fig. 2C. And logging in the user on the edge device management platform. For example, the relevant operator with access right can log in the user on the edge device management platform. And the image acquisition equipment acquires video image data and sends the video image data to the edge node, and the edge node performs information input and registration in the edge equipment management platform. And the edge device management platform selects a scene model from an algorithm warehouse thereof and generates a configuration file. And the edge device management platform issues the configuration file to an edge node, the edge node analyzes the configuration file to obtain a model identifier of a corresponding scene model, and pulls the scene model from an algorithm warehouse of the edge device management platform and starts the scene model based on the model identifier. And the edge node determines a scene recognition result of a corresponding scene to be recognized based on the scene model according to the video image data, feeds the scene recognition result back to the service management system, and carries out early warning processing on the scene recognition result by the service management system. And the service management system feeds back information such as video image data, scene recognition results and the like to the model algorithm training platform, and model optimization is carried out on the scene model in the model algorithm training platform. In addition, each edge node synchronizes the own device information to the edge device management platform, and the edge device management platform monitors the devices of each edge node.

In the scheme of the embodiment, a scene recognition system with cloud, edge and end cooperation is provided based on edge computing, edge node device management cloudization is achieved, namely, a network point edge computing node and an image are collected, a set of cloud architecture is formed with an edge device management platform, and system construction is carried out in a resource virtualization mode. The intelligent video analysis full-flow management from algorithm training, video acquisition, configuration file generation and distribution, starting of edge equipment algorithm to service system processing early warning and equipment monitoring is realized through system integration. In the process, system loose coupling, high availability, hierarchical deployment and unified parameterized configuration management aiming at the network point environment are realized. Meanwhile, the edge device management platform is used for butting the network point camera video acquisition end, the network point deployment edge node and the cloud model algorithm training platform, so that the problem of insufficient computing power of cloud video analysis is solved. The intelligent management capability of the whole flow of the network points is improved.

EXAMPLE III

Fig. 3 is a schematic structural diagram of a scene recognition early warning apparatus according to a second embodiment of the present invention. The scene recognition early warning device provided by the embodiment of the invention can be suitable for the situation of recognizing and early warning a scene to be recognized, can be realized in a hardware and/or software mode, and specifically comprises the following components as shown in fig. 3: the system comprises an edge node acquisition module 301, a configuration file generation module 302, a scene model determination module 303, a recognition result determination module 304 and an early warning processing module 305. Wherein, the first and the second end of the pipe are connected with each other,

an edge node obtaining module 301, configured to obtain each image acquisition device and each edge node related to at least one scene to be identified;

a configuration file generating module 302, configured to generate a configuration file corresponding to each scene to be identified according to an association relationship between each image acquisition device and each edge node and each scene to be identified;

a scene model determining module 303, configured to determine, according to the configuration file, video image data and a scene model of a corresponding scene to be identified;

an identification result determining module 304, configured to determine, according to the video image data, a scene identification result of a corresponding scene to be identified based on the scene model;

and the early warning processing module 305 is configured to perform early warning processing on a corresponding scene to be recognized according to the video image data and the scene recognition result.

According to the scheme of the embodiment of the invention, the configuration file corresponding to the corresponding scene to be identified is generated according to the incidence relation between each image acquisition device and each edge node and each scene to be identified; determining video image data and a scene model of a corresponding scene to be identified according to the configuration file; determining a scene recognition result of a corresponding scene to be recognized based on the scene model according to the video image data; and carrying out early warning processing on the corresponding scene to be identified according to the video image data and the scene identification result. According to the scheme, the whole processes of algorithm starting, processing early warning and the like are carried out through video acquisition, generation and issuing of configuration files, edge nodes, intelligent analysis and full-process management of video data are achieved, and the efficiency and accuracy of scene recognition early warning are improved.

Optionally, the configuration file generating module 302 includes:

the configuration parameter generating unit is used for generating scene configuration parameters of corresponding scenes to be identified according to the incidence relation between each image acquisition device, each edge node and each scene to be identified;

the model identification determining unit is used for determining scene model identifications corresponding to the scenes to be identified;

and the configuration file generating unit is used for taking the scene configuration parameters and the scene model identifications of the corresponding scenes to be recognized as the configuration files corresponding to the corresponding scenes to be recognized.

Optionally, the scene configuration parameter includes a device identifier of the image capturing device;

accordingly, the scene model determining module 303 includes:

the video image data acquisition unit is used for acquiring video image data from the image acquisition equipment corresponding to the equipment identification according to the equipment identification;

and the scene model determining unit is used for acquiring a scene model identifier from the configuration file and acquiring a corresponding scene model based on the scene model identifier.

Optionally, the scene configuration parameters include scene characteristic parameters corresponding to a scene to be identified;

accordingly, the recognition result determining module 304 includes:

the output result determining unit is used for inputting the video image data and the scene characteristic parameters into the scene model to obtain the scene model output result;

and the recognition result determining unit is used for outputting the scene model as a scene recognition result of the corresponding scene to be recognized.

Optionally, the early warning processing module 305 includes:

the scene early warning unit is used for carrying out scene early warning on the corresponding scene to be recognized if the scene recognition result meets a preset early warning rule; and (c) a second step of,

and the model updating unit is used for updating the model of the scene model corresponding to the corresponding scene to be identified according to the video image data and the scene identification result.

Optionally, the edge node includes a node device identifier and a node device address;

the device further comprises:

and the real-time monitoring module is used for monitoring each edge node in real time according to the node equipment identifier and the node equipment address after the early warning processing is carried out on the corresponding scene to be recognized.

The scene recognition early warning device provided by the embodiment of the invention can execute the scene recognition early warning method provided by any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the execution method.

Example four

FIG. 4 shows a schematic block diagram of an electronic device 40 that may be used to implement an embodiment of the invention. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device may also represent various forms of mobile devices, such as personal digital assistants, cellular phones, smart phones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the inventions described and/or claimed herein.

As shown in fig. 4, the electronic device 40 includes at least one processor 41, and a memory communicatively connected to the at least one processor 41, such as a Read Only Memory (ROM) 42, a Random Access Memory (RAM) 43, and the like, wherein the memory stores a computer program executable by the at least one processor, and the processor 41 may perform various appropriate actions and processes according to the computer program stored in the Read Only Memory (ROM) 42 or the computer program loaded from the storage unit 48 into the Random Access Memory (RAM) 43. In the RAM 43, various programs and data necessary for the operation of the electronic apparatus 40 can also be stored. The processor 41, the ROM 42, and the RAM 43 are connected to each other via a bus 44. An input/output (I/O) interface 45 is also connected to bus 44.

A number of components in the electronic device 40 are connected to the I/O interface 45, including: an input unit 46 such as a keyboard, a mouse, or the like; an output unit 47 such as various types of displays, speakers, and the like; a storage unit 48 such as a magnetic disk, an optical disk, or the like; and a communication unit 49 such as a network card, modem, wireless communication transceiver, etc. The communication unit 49 allows the electronic device 40 to exchange information/data with other devices via a computer network such as the internet and/or various telecommunication networks.

Processor 41 may be a variety of general and/or special purpose processing components with processing and computing capabilities. Some examples of processor 41 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various dedicated Artificial Intelligence (AI) computing chips, various processors running machine learning model algorithms, a Digital Signal Processor (DSP), and any suitable processor, controller, microcontroller, and so forth. Processor 41 performs the various methods and processes described above, such as the scene recognition early warning method.

In some embodiments, the scene recognition pre-warning method may be implemented as a computer program tangibly embodied in a computer-readable storage medium, such as storage unit 48. In some embodiments, part or all of the computer program may be loaded and/or installed onto the electronic device 40 via the ROM 42 and/or the communication unit 49. When the computer program is loaded into the RAM 43 and executed by the processor 41, one or more steps of the scene recognition pre-warning method described above may be performed. Alternatively, in other embodiments, processor 41 may be configured to perform the scene recognition early warning method by any other suitable means (e.g., by way of firmware).

Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuitry, field Programmable Gate Arrays (FPGAs), application Specific Integrated Circuits (ASICs), application Specific Standard Products (ASSPs), system on a chip (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs that are executable and/or interpretable on a programmable system including at least one programmable processor, which may be special or general purpose, receiving data and instructions from, and transmitting data and instructions to, a storage system, at least one input device, and at least one output device.

A computer program for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the computer programs, when executed by the processor, cause the functions/acts specified in the flowchart and/or block diagram block or blocks to be performed. A computer program can execute entirely on a machine, partly on a machine, as a stand-alone software package partly on a machine and partly on a remote machine or entirely on a remote machine or server.

In the context of the present invention, a computer-readable storage medium may be a tangible medium that can contain, or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. A computer readable storage medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. Alternatively, the computer readable storage medium may be a machine readable signal medium. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

To provide for interaction with a user, the systems and techniques described here can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) by which a user can provide input to the electronic device. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic, speech, or tactile input.

The systems and techniques described here can be implemented in a computing system that includes a back-end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back-end, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), wide Area Networks (WANs), blockchain networks, and the Internet.

The computing system may include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server can be a cloud server, also called a cloud computing server or a cloud host, and is a host product in a cloud computing service system, so that the defects of high management difficulty and weak service expansibility in the traditional physical host and VPS service are overcome.

It should be understood that various forms of the flows shown above may be used, with steps reordered, added, or deleted. For example, the steps described in the present invention may be executed in parallel, sequentially, or in different orders, and are not limited herein as long as the desired results of the technical solution of the present invention can be achieved.

The above-described embodiments should not be construed as limiting the scope of the invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions may be made, depending on design requirements and other factors. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A scene recognition early warning method is characterized by comprising the following steps:

2. The method according to claim 1, wherein the generating a configuration file corresponding to each scene to be identified according to an association relationship between each image capturing device and each edge node and each scene to be identified comprises:

generating scene configuration parameters of corresponding scenes to be identified according to the incidence relation between each image acquisition device and each edge node and each scene to be identified;

determining scene model identifications corresponding to the scenes to be recognized;

and taking the scene configuration parameters and the scene model identifications of the corresponding scenes to be recognized as configuration files corresponding to the corresponding scenes to be recognized.

3. The method of claim 2, wherein the scene configuration parameters include a device identification of an image capture device;

correspondingly, the determining the video image data and the scene model of the corresponding scene to be recognized according to the configuration file includes:

acquiring video image data from image acquisition equipment corresponding to the equipment identifier according to the equipment identifier;

and acquiring a scene model identifier from the configuration file, and acquiring a corresponding scene model based on the scene model identifier.

4. The method according to claim 2, wherein the scene configuration parameters include scene feature parameters corresponding to the scene to be identified;

correspondingly, the determining a scene recognition result of the corresponding scene to be recognized based on the scene model according to the video image data includes:

inputting the video image data and the scene characteristic parameters into the scene model to obtain an output result of the scene model;

and outputting a result of the scene model as a scene recognition result of the corresponding scene to be recognized.

5. The method according to any one of claims 1 to 4, wherein the performing of the early warning processing on the corresponding scene to be recognized according to the video image data and the scene recognition result comprises:

if the scene recognition result meets a preset early warning rule, carrying out scene early warning on the corresponding scene to be recognized; and (c) a second step of,

and updating the model of the scene model corresponding to the corresponding scene to be recognized according to the video image data and the scene recognition result.

6. The method according to any of claims 1-4, wherein the edge node comprises a node device identification and a node device address;

after the early warning processing is performed on the corresponding scene to be recognized, the method further comprises the following steps:

and monitoring each edge node in real time according to the node equipment identifier and the node equipment address.

7. A scene recognition early warning system, comprising:

8. A scene recognition early warning device, characterized by, includes:

9. An electronic device, characterized in that the electronic device comprises:

at least one processor; and

a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,

the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-7.

10. A computer-readable storage medium having stored thereon computer instructions for causing a processor to implement the method of any one of claims 1-7 when executed.