CN112991130A - Artificial intelligence-based city management event processing method and device - Google Patents
Artificial intelligence-based city management event processing method and device Download PDFInfo
- Publication number
- CN112991130A CN112991130A CN202110279064.XA CN202110279064A CN112991130A CN 112991130 A CN112991130 A CN 112991130A CN 202110279064 A CN202110279064 A CN 202110279064A CN 112991130 A CN112991130 A CN 112991130A
- Authority
- CN
- China
- Prior art keywords
- image data
- target
- image
- urban
- area
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000013473 artificial intelligence Methods 0.000 title claims abstract description 39
- 238000003672 processing method Methods 0.000 title claims abstract description 15
- 238000012545 processing Methods 0.000 claims abstract description 109
- 238000000034 method Methods 0.000 claims abstract description 21
- 238000004458 analytical method Methods 0.000 claims abstract description 19
- 238000010276 construction Methods 0.000 claims description 47
- 230000015654 memory Effects 0.000 claims description 17
- 230000006378 damage Effects 0.000 claims description 15
- 230000008859 change Effects 0.000 claims description 5
- 238000009430 construction management Methods 0.000 claims description 5
- 239000000428 dust Substances 0.000 claims description 5
- 230000000694 effects Effects 0.000 abstract description 6
- 230000010485 coping Effects 0.000 abstract 1
- 238000010586 diagram Methods 0.000 description 4
- 238000003062 neural network model Methods 0.000 description 4
- 230000004044 response Effects 0.000 description 4
- 238000012549 training Methods 0.000 description 4
- 230000008901 benefit Effects 0.000 description 3
- 238000005516 engineering process Methods 0.000 description 3
- 230000002265 prevention Effects 0.000 description 2
- 230000008569 process Effects 0.000 description 2
- 206010063385 Intellectualisation Diseases 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000006870 function Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000012544 monitoring process Methods 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
- G06Q50/10—Services
- G06Q50/26—Government or public services
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/067—Enterprise or organisation modelling
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V20/00—Scenes; Scene-specific elements
- G06V20/40—Scenes; Scene-specific elements in video content
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V20/00—Scenes; Scene-specific elements
- G06V20/40—Scenes; Scene-specific elements in video content
- G06V20/44—Event detection
Abstract
The invention discloses a city management event processing method and a device based on artificial intelligence, wherein the method comprises the following steps: acquiring target image data of a target area in a target time period, and judging whether a city management event occurs in the target area or not based on an image recognition algorithm according to the target image data; when the urban management event occurs in the target area, acquiring all related image data of the area near the target area in the target time period from a related image block chain; and analyzing the target image data and all related image data based on a city management event analysis model, and determining processing information and a processing scheme corresponding to the city management event. Therefore, the invention realizes the automatic coping and processing of the urban management events through the artificial intelligence algorithm, and compared with the existing mode of using manual patrol or citizen supervision, the invention has higher processing efficiency and better processing effect.
Description
Technical Field
The invention relates to the technical field of smart cities, in particular to a city management event processing method and device based on artificial intelligence.
Background
Along with the intellectualization of cities, the prevention of urban treatment events by government departments is also gradually improved, and along with the rise and the density of urban population, the prevention of the urban treatment events is more and more important. How to efficiently and timely acquire urban management events becomes a key technical difficulty for intelligent urban management.
The existing urban management event processing technology still stays at the aspect of utilizing manual urban patrol or playing the monitoring function of citizens, reporting and processing when the urban management event is found, the advantage of target image data brought by cameras which are more and more spread in the city is not realized, meanwhile, when the processing scheme of the urban management event is determined, a manual decision-making mode is still adopted, the efficiency is low, and when some emergency urban management events are to be sent, the processing effect is unsatisfactory.
Disclosure of Invention
The technical problem to be solved by the invention is to provide an artificial intelligence-based urban management event processing method and device, which can judge urban management events in a target area according to target image data, and automatically obtain corresponding processing information and processing schemes based on an analysis model by combining with related image data of other nearby areas, thereby realizing automatic handling and processing of urban management events through an artificial intelligence algorithm.
In order to solve the technical problem, the first aspect of the invention discloses an artificial intelligence-based urban treatment event processing method, which comprises the following steps:
acquiring target image data of a target area in a target time period, and judging whether a city management event occurs in the target area or not based on an image recognition algorithm according to the target image data;
when the urban management event occurs in the target area, acquiring all related image data of the area near the target area in the target time period from a related image block chain;
analyzing the target image data and all related image data based on a city management event analysis model, and determining processing information and a processing scheme corresponding to the city management event; the processing information comprises one or more of information of a liability object, information of responsibility proportion of each object, information of a victim object and information of a responsible department corresponding to the urban management event.
As an alternative embodiment, in the first aspect of the invention, the urban management event comprises one or more of a nighttime construction event, a facility destruction event, a traffic congestion event and an illegal allocation event; the related image block chain comprises one or more of a vehicle event data recorder image block chain, a street camera image block chain and a shop camera image block chain; the relevant image data comprises one or more of a tachograph image of a vehicle of the vicinity, a street camera image of the vicinity, a shop camera image of the vicinity; the responsible department information includes one or more of a construction management department, a facility management department, a traffic management department, and a city law enforcement management department.
As an optional implementation manner, in the first aspect of the present invention, the determining, according to the target image data and based on an image recognition algorithm, whether the target area has an urban management event includes:
identifying current time information and a construction image in the target image data based on an image identification algorithm; the construction image comprises one or more of a construction worker image, a construction equipment movement image, a construction building change image and a construction dust image;
and when the current time information is judged to be at night and the construction image exists, judging that the target area has a night construction event.
As an optional implementation manner, in the first aspect of the present invention, the determining, according to the target image data and based on an image recognition algorithm, whether the target area has an urban management event includes:
judging whether the target image data contains urban facility images or not based on an image recognition algorithm; the urban facility image comprises one or more of an electric power facility image, a drainage facility image, a traffic facility image and a convenience facility image;
matching the urban facility image with a preset urban facility template, and judging whether the urban facility image has a missing image condition;
when the situation that the urban facility image has a missing image is judged, judging that a facility damage event occurs in the target area;
and/or the presence of a gas in the gas,
judging the city scene of the target image data based on an image recognition algorithm and a preset city scene template; the urban scene is a street scene, a market area scene or a traffic facility area scene;
matching the target image data with a city facility template corresponding to the city scene, and judging whether the target image data lacks the city facility image or not;
and when the target image data is judged to have the condition of missing the urban facility image, judging that a facility damage event occurs in the target area.
As an optional implementation manner, in the first aspect of the present invention, the determining, according to the target image data and based on an image recognition algorithm, whether the target area has an urban management event includes:
judging whether the urban scene of the target image data is a traffic road scene or not based on an image recognition algorithm and a preset urban scene template;
when the urban scene of the target image data is judged to be a traffic road scene, judging whether a congested vehicle image set exists in the target image data; the average advancing speed of all vehicle images in the congested vehicle image set is lower than a preset speed threshold;
and when the image set of the congested vehicle exists in the target image data, judging that a traffic congestion event occurs in the target area.
As an optional implementation manner, in the first aspect of the present invention, the determining, according to the target image data and based on an image recognition algorithm, whether the target area has an urban management event includes:
judging whether the city scene of the target image data is a commercial area street scene or not based on an image recognition algorithm and a preset city scene template;
when the target image data is judged to belong to a street scene of a business area, judging whether an illegal booth image set exists in the target image data; the position of a booth image in the illegal booth image set is outside a preset compliance area;
when the fact that the image set of the illegal booth exists in the target image data is judged, judging that an illegal booth arrangement event occurs in the target area;
and/or the presence of a gas in the gas,
judging whether the city scene of the target image data is a commercial area street scene or not based on an image recognition algorithm and a preset city scene template;
when the target image data is judged to belong to a street scene of a business area, judging whether the street of the business area to which the target image data belongs is a street area prohibited to be shared or not and whether a booth image set exists in the target image data or not;
and when the business area street to which the target image data belongs is judged to be a street area prohibited to be shared and the target image data contains a booth image set, judging that an illegal sharing event occurs in the target area.
As an optional implementation manner, in the first aspect of the present invention, the acquiring, from the related image block chain, all related image data of the vicinity of the target region in the target time period includes:
acquiring marks of all camera shooting main bodies in the area near the target area; the camera shooting main body comprises one or more of a vehicle driving recorder, a street camera and a shop camera;
and downloading all the related image data recorded by the camera main body in the target time period from the related image block chain according to the identification of the camera main body.
As an alternative implementation, in the first aspect of the present invention, the method further includes:
sending the processing information and the processing scheme corresponding to the urban management event to corresponding processing equipment; the processing device comprises one or more of a government department terminal device, a regional broadcast device and a mobile terminal device of a target user.
The second aspect of the invention discloses an artificial intelligence-based urban management event processing device, which comprises:
the judging module is used for acquiring target image data of a target area in a target time period and judging whether a city management event occurs in the target area or not based on an image recognition algorithm according to the target image data;
the acquisition module is used for acquiring all relevant image data of the area near the target area in the target time period from a relevant image block chain when the urban management event of the target area is judged to occur;
the analysis module is used for analyzing the target image data and all the related image data based on an urban management event analysis model and determining processing information and a processing scheme corresponding to the urban management event; the processing information comprises one or more of information of a accountability object, information of a victim object and information of a responsible department corresponding to the urban management event.
As an alternative embodiment, in the second aspect of the invention, the urban management event comprises one or more of a night construction event, a facility destruction event, a traffic jam event and an illegal allocation event; the related image block chain comprises one or more of a vehicle event data recorder image block chain, a street camera image block chain and a shop camera image block chain; the relevant image data comprises one or more of a tachograph image of a vehicle of the vicinity, a street camera image of the vicinity, a shop camera image of the vicinity; the responsible department information includes one or more of a construction management department, a facility management department, a traffic management department, and a city law enforcement management department.
As an optional implementation manner, in the second aspect of the present invention, a specific manner of determining, by the determining module, whether an urban management event occurs in the target area based on an image recognition algorithm according to the target image data includes:
identifying current time information and a construction image in the target image data based on an image identification algorithm; the construction image comprises one or more of a construction worker image, a construction equipment movement image, a construction building change image and a construction dust image;
and when the current time information is judged to be at night and the construction image exists, judging that the target area has a night construction event.
As an optional implementation manner, in the second aspect of the present invention, a specific manner of determining, by the determining module, whether an urban management event occurs in the target area based on an image recognition algorithm according to the target image data includes:
judging whether the target image data contains urban facility images or not based on an image recognition algorithm; the urban facility image comprises one or more of an electric power facility image, a drainage facility image, a traffic facility image and a convenience facility image;
matching the urban facility image with a preset urban facility template, and judging whether the urban facility image has a missing image condition;
when the situation that the urban facility image has a missing image is judged, judging that a facility damage event occurs in the target area;
and/or the presence of a gas in the gas,
judging the city scene of the target image data based on an image recognition algorithm and a preset city scene template; the urban scene is a street scene, a market area scene or a traffic facility area scene;
matching the target image data with a city facility template corresponding to the city scene, and judging whether the target image data lacks the city facility image or not;
and when the target image data is judged to have the condition of missing the urban facility image, judging that a facility damage event occurs in the target area.
As an optional implementation manner, in the second aspect of the present invention, a specific manner of determining, by the determining module, whether an urban management event occurs in the target area based on an image recognition algorithm according to the target image data includes:
judging whether the urban scene of the target image data is a traffic road scene or not based on an image recognition algorithm and a preset urban scene template;
when the urban scene of the target image data is judged to be a traffic road scene, judging whether a congested vehicle image set exists in the target image data; the average advancing speed of all vehicle images in the congested vehicle image set is lower than a preset speed threshold;
and when the image set of the congested vehicle exists in the target image data, judging that a traffic congestion event occurs in the target area.
As an optional implementation manner, in the second aspect of the present invention, a specific manner of determining, by the determining module, whether an urban management event occurs in the target area based on an image recognition algorithm according to the target image data includes:
judging whether the city scene of the target image data is a commercial area street scene or not based on an image recognition algorithm and a preset city scene template;
when the target image data is judged to belong to a street scene of a business area, judging whether an illegal booth image set exists in the target image data; the position of a booth image in the illegal booth image set is outside a preset compliance area;
when the fact that the image set of the illegal booth exists in the target image data is judged, judging that an illegal booth arrangement event occurs in the target area;
and/or the presence of a gas in the gas,
judging whether the city scene of the target image data is a commercial area street scene or not based on an image recognition algorithm and a preset city scene template;
when the target image data is judged to belong to a street scene of a business area, judging whether the street of the business area to which the target image data belongs is a street area prohibited to be shared or not and whether a booth image set exists in the target image data or not;
and when the business area street to which the target image data belongs is judged to be a street area prohibited to be shared and the target image data contains a booth image set, judging that an illegal sharing event occurs in the target area.
As an optional implementation manner, in the second aspect of the present invention, a specific manner of acquiring, by the acquisition module, all relevant image data of the vicinity of the target region in the target time period from the relevant image block chain includes:
acquiring marks of all camera shooting main bodies in the area near the target area; the camera shooting main body comprises one or more of a vehicle driving recorder, a street camera and a shop camera;
and downloading all the related image data recorded by the camera main body in the target time period from the related image block chain according to the identification of the camera main body.
As an alternative embodiment, in the second aspect of the present invention, the apparatus further comprises:
the sending module is used for sending the processing information and the processing scheme corresponding to the urban management event to corresponding processing equipment; the processing device comprises one or more of a government department terminal device, a regional broadcast device and a mobile terminal device of a target user.
The third aspect of the invention discloses another artificial intelligence-based urban treatment event processing device, which comprises:
a memory storing executable program code;
a processor coupled with the memory;
the processor calls the executable program code stored in the memory to execute part or all of the steps of the artificial intelligence-based urban management event processing method disclosed by the first aspect of the embodiment of the invention.
The fourth aspect of the embodiments of the present invention discloses a computer storage medium, where the computer storage medium stores computer instructions, and when the computer instructions are called, the computer instructions are used to execute part or all of the steps in the artificial intelligence based city management event processing method disclosed in the first aspect of the embodiments of the present invention.
Compared with the prior art, the embodiment of the invention has the following beneficial effects:
in the embodiment of the invention, target image data of a target area in a target time period is obtained, and whether a city management event occurs in the target area is judged based on an image recognition algorithm according to the target image data; when the urban management event occurs in the target area, acquiring all related image data of the area near the target area in the target time period from a related image block chain; analyzing the target image data and all related image data based on a city management event analysis model, and determining processing information and a processing scheme corresponding to the city management event; the processing information comprises one or more of information of a liability object, information of responsibility proportion of each object, information of a victim object and information of a responsible department corresponding to the urban management event. Therefore, the method and the device can judge the urban management event in the target area according to the target image data, and automatically obtain the corresponding processing information and processing scheme based on the analysis model by combining the relevant image data of other nearby areas, so that the urban management event can be automatically responded and processed by an artificial intelligence algorithm.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
FIG. 1 is a schematic flow chart of a method for processing an urban management event based on artificial intelligence, which is disclosed by the embodiment of the invention;
FIG. 2 is a schematic structural diagram of an artificial intelligence-based urban management event processing device according to an embodiment of the present invention;
fig. 3 is a schematic structural diagram of another artificial intelligence-based urban treatment event processing device disclosed in 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.
The terms "first," "second," and the like in the description and claims of the present invention and in the above-described drawings are used for distinguishing between different objects and not for describing a particular order. Furthermore, the terms "include" and "have," as well as any variations thereof, are intended to cover non-exclusive inclusions. For example, a process, method, apparatus, article, or article that comprises a list of steps or elements is not limited to only those steps or elements listed, but may alternatively include other steps or elements not listed, or inherent to such process, method, article, or article.
Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the invention. The appearances of the phrase in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. It is explicitly and implicitly understood by one skilled in the art that the embodiments described herein can be combined with other embodiments.
The invention discloses a city management event processing method and device based on artificial intelligence, which can judge a city management event in a target area according to target image data, and automatically obtain corresponding processing information and a processing scheme based on an analysis model by combining related image data of other nearby areas, thereby realizing automatic handling and processing of the city management event through an artificial intelligence algorithm. The following are detailed below.
Example one
Referring to fig. 1, fig. 1 is a schematic flow chart of a method for processing an urban management event based on artificial intelligence according to an embodiment of the present invention. As shown in fig. 1, the artificial intelligence based city management event processing method may include the following operations:
101. and acquiring target image data of the target area in a target time period, and judging whether the target area has an urban management event or not based on an image recognition algorithm according to the target image data.
In an embodiment of the invention, the urban management event comprises one or more of a night construction event, a facility destruction event, a traffic jam event and an illegal allocation event.
102. And when the urban management event occurs in the target area, acquiring all related image data of the area near the target area in the target time period from the related image block chain.
In an embodiment of the invention, the related image block chain includes one or more of a car recorder image block chain, a street camera image block chain, and a shop camera image block chain.
In an embodiment of the invention, the relevant image data comprises one or more of a tachograph image of a vehicle of said vicinity, a street camera image of said vicinity, a shop camera image of said vicinity.
103. And analyzing the target image data and all related image data based on the urban management event analysis model, and determining processing information and a processing scheme corresponding to the urban management event.
In the embodiment of the invention, the processing information comprises one or more of information of a liability object, information of responsibility proportion of each object, information of a victim object and information of a responsible department corresponding to the urban management event. Optionally, the information of the responsible department includes one or more of a construction management department, a facility management department, a traffic management department and a city law enforcement management department.
Therefore, by implementing the embodiment of the invention, the urban management event in the target area can be judged according to the target image data, and the corresponding processing information and processing scheme can be automatically obtained based on the analysis model by combining the related image data of other nearby areas, so that the urban management event can be automatically responded and processed by an artificial intelligence algorithm.
As an alternative embodiment, in step 101, determining whether the city management event occurs in the target area based on the image recognition algorithm according to the target image data includes:
identifying current time information and a construction image in the target image data based on an image identification algorithm; the construction image comprises one or more of a construction worker image, a construction equipment movement image, a construction building change image and a construction dust image;
and when the current time information is judged to be at night and the construction image exists, judging that the night construction event occurs in the target area.
Therefore, by implementing the optional embodiment, the night construction event in the target area can be judged according to the target image data, so that the urban management event can be automatically identified through an algorithm, and compared with the existing mode of using manual identification response, the method has the advantages of higher identification efficiency and better identification effect.
As an alternative embodiment, in step 101, determining whether the city management event occurs in the target area based on the image recognition algorithm according to the target image data includes:
judging whether the target image data contains urban facility images or not based on an image recognition algorithm; the city facility image comprises one or more of an electric power facility image, a drainage facility image, a traffic facility image and a convenience facility image;
matching the urban facility image with a preset urban facility template, and judging whether the urban facility image has the condition of missing images;
and when the urban facility image is judged to have the condition of missing images, judging that a facility damage event occurs in the target area.
Therefore, by implementing the optional embodiment, whether facilities in the target area are damaged or not can be judged according to the target image data, so that the urban management event is automatically identified through an algorithm, and compared with the existing mode of using manual identification response, the method has higher identification efficiency and better identification effect.
As an alternative embodiment, in step 101, determining whether the city management event occurs in the target area based on the image recognition algorithm according to the target image data includes:
judging the city scene of the target image data based on an image recognition algorithm and a preset city scene template; the urban scene is a street scene, a market area scene or a traffic facility area scene;
matching the target image data with a city facility template corresponding to a city scene, and judging whether the target image data has a condition of missing city facility images;
and when the target image data is judged to have the condition of missing urban facility images, judging that a facility damage event occurs in the target area.
Therefore, by implementing the optional embodiment, whether facilities in the target area are lost or not, such as whether a manhole cover is lost or not, can be judged according to the target image data, so that the urban management event is automatically identified through an algorithm.
As an alternative embodiment, in step 101, determining whether the city management event occurs in the target area based on the image recognition algorithm according to the target image data includes:
judging whether the urban scene of the target image data is a traffic road scene or not based on an image recognition algorithm and a preset urban scene template;
when the urban scene of the target image data is judged to be a traffic road scene, judging whether a congested vehicle image set exists in the target image data; the average advancing speed of all vehicle images in the jammed vehicle image set is lower than a preset speed threshold;
and when the target image data is judged to have the image set of the congested vehicle, judging that the traffic congestion event occurs in the target area.
Therefore, by implementing the optional embodiment, the traffic jam event in the target area can be judged according to the target image data, so that the urban management event can be automatically identified through an algorithm.
As an alternative embodiment, in step 101, determining whether the city management event occurs in the target area based on the image recognition algorithm according to the target image data includes:
judging whether the city scene of the target image data is a commercial area street scene or not based on an image recognition algorithm and a preset city scene template;
when the target image data is judged to belong to a street scene of a business area, judging whether an illegal booth image set exists in the target image data; the position of a booth image in the illegal booth image set is outside a preset compliance area;
and when the image set of the illegal stall exists in the target image data, judging that the illegal stall event occurs in the target area.
Therefore, by implementing the optional embodiment, the illegal allocation event in the target area can be judged according to the target image data, so that the urban management event can be automatically identified through an algorithm.
As an alternative embodiment, in step 101, determining whether the city management event occurs in the target area based on the image recognition algorithm according to the target image data includes:
judging whether the city scene of the target image data is a commercial area street scene or not based on an image recognition algorithm and a preset city scene template;
when the target image data is judged to belong to a street scene of a business area, judging whether the street of the business area to which the target image data belongs is a street area prohibited to be shared or not and whether a booth image set exists in the target image data or not;
and when the business area street to which the target image data belongs is judged to be a street area prohibited to be shared and the target image data has a booth image set, judging that an illegal sharing event occurs in the target area.
Therefore, by implementing the optional embodiment, the illegal allocation event in the target area can be judged according to the target image data, so that the urban management event can be automatically identified through an algorithm.
As an alternative embodiment, in step 102, acquiring all relevant image data of the vicinity of the target region in the target time period from the relevant image block chain includes:
acquiring marks of all camera shooting main bodies in a region near a target region;
and downloading all the recorded related image data of the shooting subject in the target time period from the related image block chain according to the identification of the shooting subject.
In the embodiment of the invention, the camera body comprises one or more of a vehicle driving recorder, a street camera and a shop camera.
Therefore, by implementing the optional embodiment, all the related image data recorded by the camera main body in the target time period can be downloaded from the related image block chain, so that the authenticity and reliability of the target image data which is accidentally removed are ensured by utilizing the block chain technology, and compared with the existing mode of calling image data, the authenticity of the target image data is higher.
As an alternative embodiment, the method further comprises:
and sending the processing information and the processing scheme corresponding to the urban management event to corresponding processing equipment.
In the embodiment of the invention, the processing equipment comprises one or more of government department terminal equipment, regional broadcast equipment and mobile terminal equipment of target users.
Therefore, by implementing the optional embodiment, the processing information and the processing scheme corresponding to the urban management event can be sent to the corresponding processing equipment, so that the processing equipment can inform relevant departments of the acquisition, recording and emergency treatment of the urban management event, or inform relevant personnel of the site evacuation.
As an optional embodiment, the city management event analysis model in step 103 may include a relational database of correspondence between various city management events and processing information and processing schemes, or may be a predictive neural network model obtained by training in advance using training data including city management event information and corresponding processing information and processing schemes, where the city management event information may include information such as types, scales, times, and locations of the city management events.
In a further embodiment, the analyzing the target image data and all the related image data in step 103 to determine the processing information and the processing scheme corresponding to the urban management event may include:
processing the target image data and all related image data based on an image recognition algorithm, and recognizing the types of city management events in the target image data and all related image data;
and determining processing information and a processing scheme corresponding to the urban management event according to the type of the urban management event and the relational database and/or the prediction neural network model.
Therefore, by implementing the further embodiment, the target image data and all related image data can be analyzed based on the image recognition algorithm, and the processing information and the processing scheme corresponding to the urban management event are determined, so that the processing information of the urban management event is automatically determined through the artificial intelligence algorithm.
Example two
Referring to fig. 2, fig. 2 is a schematic structural diagram of an artificial intelligence-based urban management event processing device according to an embodiment of the present invention. As shown in fig. 2, the apparatus may include:
the determining module 201 is configured to obtain target image data of a target area in a target time period, and determine whether a city management event occurs in the target area based on an image recognition algorithm according to the target image data.
In an embodiment of the invention, the urban management event comprises one or more of a night construction event, a facility destruction event, a traffic jam event and an illegal allocation event.
The obtaining module 202 is configured to obtain all relevant image data of a region near the target region in the target time period from the relevant image block chain when it is determined that the city management event occurs in the target region.
In an embodiment of the invention, the related image block chain includes one or more of a car recorder image block chain, a street camera image block chain, and a shop camera image block chain.
In an embodiment of the invention, the relevant image data comprises one or more of a tachograph image of a vehicle of said vicinity, a street camera image of said vicinity, a shop camera image of said vicinity.
And the analysis module 203 is configured to analyze the target image data and all related image data based on the urban management event analysis model, and determine processing information and a processing scheme corresponding to the urban management event.
In the embodiment of the invention, the processing information comprises one or more of information of a liability object, information of responsibility proportion of each object, information of a victim object and information of a responsible department corresponding to the urban management event. Optionally, the information of the responsible department includes one or more of a construction management department, a facility management department, a traffic management department and a city law enforcement management department.
Therefore, by implementing the embodiment of the invention, the urban management event in the target area can be judged according to the target image data, and the corresponding processing information and processing scheme can be automatically obtained based on the analysis model by combining the related image data of other nearby areas, so that the urban management event can be automatically responded and processed by an artificial intelligence algorithm.
As an optional embodiment, the specific manner for determining, by the determining module 201, whether the target area has the urban management event based on the image recognition algorithm according to the target image data includes:
identifying current time information and a construction image in the target image data based on an image identification algorithm; the construction image comprises one or more of a construction worker image, a construction equipment movement image, a construction building change image and a construction dust image;
and when the current time information is judged to be at night and the construction image exists, judging that the night construction event occurs in the target area.
Therefore, by implementing the optional embodiment, the night construction event in the target area can be judged according to the target image data, so that the urban management event can be automatically identified through an algorithm, and compared with the existing mode of using manual identification response, the method has the advantages of higher identification efficiency and better identification effect.
As an optional embodiment, the specific manner for determining, by the determining module 201, whether the target area has the urban management event based on the image recognition algorithm according to the target image data includes:
judging whether the target image data contains urban facility images or not based on an image recognition algorithm; the city facility image comprises one or more of an electric power facility image, a drainage facility image, a traffic facility image and a convenience facility image;
matching the urban facility image with a preset urban facility template, and judging whether the urban facility image has the condition of missing images;
and when the urban facility image is judged to have the condition of missing images, judging that a facility damage event occurs in the target area.
Therefore, by implementing the optional embodiment, whether facilities in the target area are damaged or not can be judged according to the target image data, so that the urban management event is automatically identified through an algorithm, and compared with the existing mode of using manual identification response, the method has higher identification efficiency and better identification effect.
As an optional embodiment, the specific manner for determining, by the determining module 201, whether the target area has the urban management event based on the image recognition algorithm according to the target image data includes:
judging the city scene of the target image data based on an image recognition algorithm and a preset city scene template; the urban scene is a street scene, a market area scene or a traffic facility area scene;
matching the target image data with a city facility template corresponding to a city scene, and judging whether the target image data has a condition of missing city facility images;
and when the target image data is judged to have the condition of missing urban facility images, judging that a facility damage event occurs in the target area.
Therefore, by implementing the optional embodiment, whether facilities in the target area are lost or not, such as whether a manhole cover is lost or not, can be judged according to the target image data, so that the urban management event is automatically identified through an algorithm.
As an optional embodiment, the specific manner for determining, by the determining module 201, whether the target area has the urban management event based on the image recognition algorithm according to the target image data includes:
judging whether the urban scene of the target image data is a traffic road scene or not based on an image recognition algorithm and a preset urban scene template;
when the urban scene of the target image data is judged to be a traffic road scene, judging whether a congested vehicle image set exists in the target image data; the average advancing speed of all vehicle images in the jammed vehicle image set is lower than a preset speed threshold;
and when the target image data is judged to have the image set of the congested vehicle, judging that the traffic congestion event occurs in the target area.
Therefore, by implementing the optional embodiment, the traffic jam event in the target area can be judged according to the target image data, so that the urban management event can be automatically identified through an algorithm.
As an optional embodiment, the specific manner for determining, by the determining module 201, whether the target area has the urban management event based on the image recognition algorithm according to the target image data includes:
judging whether the city scene of the target image data is a commercial area street scene or not based on an image recognition algorithm and a preset city scene template;
when the target image data is judged to belong to a street scene of a business area, judging whether an illegal booth image set exists in the target image data; the position of a booth image in the illegal booth image set is outside a preset compliance area;
and when the image set of the illegal stall exists in the target image data, judging that the illegal stall event occurs in the target area.
Therefore, by implementing the optional embodiment, the illegal allocation event in the target area can be judged according to the target image data, so that the urban management event can be automatically identified through an algorithm.
As an optional embodiment, the specific manner for determining, by the determining module 201, whether the target area has the urban management event based on the image recognition algorithm according to the target image data includes:
judging whether the city scene of the target image data is a commercial area street scene or not based on an image recognition algorithm and a preset city scene template;
when the target image data is judged to belong to a street scene of a business area, judging whether the street of the business area to which the target image data belongs is a street area prohibited to be shared or not and whether a booth image set exists in the target image data or not;
and when the business area street to which the target image data belongs is judged to be a street area prohibited to be shared and the target image data has a booth image set, judging that an illegal sharing event occurs in the target area.
Therefore, by implementing the optional embodiment, the illegal allocation event in the target area can be judged according to the target image data, so that the urban management event can be automatically identified through an algorithm.
As an alternative embodiment, the specific manner of acquiring all the relevant image data of the vicinity of the target region in the target time period from the relevant image block chain by the acquiring module 202 includes:
acquiring marks of all camera shooting main bodies in a region near a target region;
and downloading all the recorded related image data of the shooting subject in the target time period from the related image block chain according to the identification of the shooting subject.
In the embodiment of the invention, the camera body comprises one or more of a vehicle driving recorder, a street camera and a shop camera.
Therefore, by implementing the optional embodiment, all the related image data recorded by the camera main body in the target time period can be downloaded from the related image block chain, so that the authenticity and reliability of the target image data which is accidentally removed are ensured by utilizing the block chain technology, and compared with the existing mode of calling image data, the authenticity of the target image data is higher.
As an alternative embodiment, the apparatus further comprises:
and the sending module is used for sending the processing information and the processing scheme corresponding to the urban management event to the corresponding processing equipment.
In the embodiment of the invention, the processing equipment comprises one or more of government department terminal equipment, regional broadcast equipment and mobile terminal equipment of target users.
Therefore, by implementing the optional embodiment, the processing information and the processing scheme corresponding to the urban management event can be sent to the corresponding processing equipment, so that the processing equipment can inform relevant departments of the acquisition, recording and emergency treatment of the urban management event, or inform relevant personnel of the site evacuation.
As an optional embodiment, the analysis model of the urban management event in the analysis module 103 may include a relational database of correspondence between various urban management events and the processing information and the processing scheme, or may be a predictive neural network model obtained by training in advance using training data including urban management event information and the corresponding processing information and the processing scheme, where the urban management event information may include information such as the type, scale, time, and location of the urban management event.
In a further embodiment, the analyzing module 103 analyzes the target image data and all related image data to determine a specific manner of processing information and a processing scheme corresponding to the urban management event, which may include:
processing the target image data and all related image data based on an image recognition algorithm, and recognizing the types of city management events in the target image data and all related image data;
and determining processing information and a processing scheme corresponding to the urban management event according to the type of the urban management event and the relational database and/or the prediction neural network model.
Therefore, by implementing the further embodiment, the target image data and all related image data can be analyzed based on the image recognition algorithm, and the processing information and the processing scheme corresponding to the urban management event are determined, so that the processing information of the urban management event is automatically determined through the artificial intelligence algorithm.
EXAMPLE III
Referring to fig. 3, fig. 3 is a schematic structural diagram of another artificial intelligence-based urban management event processing device according to an embodiment of the present invention. As shown in fig. 3, the apparatus may include:
a memory 301 storing executable program code;
a processor 302 coupled to the memory 301;
the processor 302 calls the executable program code stored in the memory 301 to execute part or all of the steps of the artificial intelligence based city management event processing method disclosed in the embodiment of the invention.
Example four
The embodiment of the invention discloses a computer storage medium, which stores computer instructions, and when the computer instructions are called, the computer instructions are used for executing part or all of the steps in the artificial intelligence-based urban management event processing method disclosed by the embodiment of the invention.
The above-described embodiments of the apparatus are merely illustrative, and the modules described as separate components may or may not be physically separate, and the components shown as modules may or may not be physical modules, may be located in one place, or may be distributed on a plurality of network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment. One of ordinary skill in the art can understand and implement it without inventive effort.
Through the above detailed description of the embodiments, those skilled in the art will clearly understand that the embodiments may be implemented by software plus a necessary general hardware platform, and may also be implemented by hardware. Based on such understanding, the above technical solutions may be embodied in the form of a software product, which may be stored in a computer-readable storage medium, where the storage medium includes a Read-Only Memory (ROM), a Random Access Memory (RAM), a Programmable Read-Only Memory (PROM), an Erasable Programmable Read-Only Memory (EPROM), a One-time Programmable Read-Only Memory (OTPROM), an Electrically Erasable Programmable Read-Only Memory (EEPROM), a Compact Disc-Read-Only Memory (CD-ROM), or other disk memories, CD-ROMs, or other magnetic disks, A tape memory, or any other medium readable by a computer that can be used to carry or store data.
Finally, it should be noted that: the method and the device for processing the urban management event based on the artificial intelligence disclosed by the embodiment of the invention are only the preferred embodiment of the invention, and are only used for explaining the technical scheme of the invention, but not limiting the technical scheme; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those skilled in the art; the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and the modifications or the substitutions do not make the essence of the corresponding technical solutions depart from the spirit and scope of the technical solutions of the embodiments of the present invention.
Claims (10)
1. A city treatment event processing method based on artificial intelligence is characterized by comprising the following steps:
acquiring target image data of a target area in a target time period, and judging whether a city management event occurs in the target area or not based on an image recognition algorithm according to the target image data;
when the urban management event occurs in the target area, acquiring all related image data of the area near the target area in the target time period from a related image block chain;
analyzing the target image data and all related image data based on a city management event analysis model, and determining processing information and a processing scheme corresponding to the city management event; the processing information comprises one or more of information of a liability object, information of responsibility proportion of each object, information of a victim object and information of a responsible department corresponding to the urban management event.
2. The artificial intelligence based city management event processing method according to claim 1, wherein the city management event comprises one or more of a night construction event, a facility destruction event, a traffic jam event and a disbursement event; the related image block chain comprises one or more of a vehicle event data recorder image block chain, a street camera image block chain and a shop camera image block chain; the relevant image data comprises one or more of a tachograph image of a vehicle of the vicinity, a street camera image of the vicinity, a shop camera image of the vicinity; the responsible department information includes one or more of a construction management department, a facility management department, a traffic management department, and a city law enforcement management department.
3. The method for processing the urban management event based on the artificial intelligence of claim 2, wherein the step of judging whether the urban management event occurs in the target area based on an image recognition algorithm according to the target image data comprises the following steps:
identifying current time information and a construction image in the target image data based on an image identification algorithm; the construction image comprises one or more of a construction worker image, a construction equipment movement image, a construction building change image and a construction dust image;
and when the current time information is judged to be at night and the construction image exists, judging that the target area has a night construction event.
4. The method for processing the urban management event based on the artificial intelligence of claim 2, wherein the step of judging whether the urban management event occurs in the target area based on an image recognition algorithm according to the target image data comprises the following steps:
judging whether the target image data contains urban facility images or not based on an image recognition algorithm; the urban facility image comprises one or more of an electric power facility image, a drainage facility image, a traffic facility image and a convenience facility image;
matching the urban facility image with a preset urban facility template, and judging whether the urban facility image has a missing image condition;
when the situation that the urban facility image has a missing image is judged, judging that a facility damage event occurs in the target area;
and/or the presence of a gas in the gas,
judging the city scene of the target image data based on an image recognition algorithm and a preset city scene template; the urban scene is a street scene, a market area scene or a traffic facility area scene;
matching the target image data with a city facility template corresponding to the city scene, and judging whether the target image data lacks the city facility image or not;
and when the target image data is judged to have the condition of missing the urban facility image, judging that a facility damage event occurs in the target area.
5. The method for processing the urban management event based on the artificial intelligence of claim 2, wherein the step of judging whether the urban management event occurs in the target area based on an image recognition algorithm according to the target image data comprises the following steps:
judging whether the urban scene of the target image data is a traffic road scene or not based on an image recognition algorithm and a preset urban scene template;
when the urban scene of the target image data is judged to be a traffic road scene, judging whether a congested vehicle image set exists in the target image data; the average advancing speed of all vehicle images in the congested vehicle image set is lower than a preset speed threshold;
and when the image set of the congested vehicle exists in the target image data, judging that a traffic congestion event occurs in the target area.
6. The method for processing the urban management event based on the artificial intelligence of claim 2, wherein the step of judging whether the urban management event occurs in the target area based on an image recognition algorithm according to the target image data comprises the following steps:
judging whether the city scene of the target image data is a commercial area street scene or not based on an image recognition algorithm and a preset city scene template;
when the target image data is judged to belong to a street scene of a business area, judging whether an illegal booth image set exists in the target image data; the position of a booth image in the illegal booth image set is outside a preset compliance area;
when the fact that the image set of the illegal booth exists in the target image data is judged, judging that an illegal booth arrangement event occurs in the target area;
and/or the presence of a gas in the gas,
judging whether the city scene of the target image data is a commercial area street scene or not based on an image recognition algorithm and a preset city scene template;
when the target image data is judged to belong to a street scene of a business area, judging whether the street of the business area to which the target image data belongs is a street area prohibited to be shared or not and whether a booth image set exists in the target image data or not;
and when the business area street to which the target image data belongs is judged to be a street area prohibited to be shared and the target image data contains a booth image set, judging that an illegal sharing event occurs in the target area.
7. The method according to claim 2, wherein the obtaining all relevant image data of the vicinity of the target area in the target time period from the relevant image block chain comprises:
acquiring marks of all camera shooting main bodies in the area near the target area; the camera shooting main body comprises one or more of a vehicle driving recorder, a street camera and a shop camera;
and downloading all the related image data recorded by the camera main body in the target time period from the related image block chain according to the identification of the camera main body.
8. The artificial intelligence based city governance event handling method according to claim 1, further comprising:
sending the processing information and the processing scheme corresponding to the urban management event to corresponding processing equipment; the processing device comprises one or more of a government department terminal device, a regional broadcast device and a mobile terminal device of a target user.
9. The utility model provides a city treatment event processing apparatus based on artificial intelligence which characterized in that includes:
the judging module is used for acquiring target image data of a target area in a target time period and judging whether a city management event occurs in the target area or not based on an image recognition algorithm according to the target image data;
the acquisition module is used for acquiring all relevant image data of the area near the target area in the target time period from a relevant image block chain when the urban management event of the target area is judged to occur;
the analysis module is used for analyzing the target image data and all the related image data based on an urban management event analysis model and determining processing information and a processing scheme corresponding to the urban management event; the processing information comprises one or more of information of a accountability object, information of a victim object and information of a responsible department corresponding to the urban management event.
10. An artificial intelligence based urban treatment event processing device, characterized in that the device comprises:
a memory storing executable program code;
a processor coupled with the memory;
the processor calls the executable program code stored in the memory to perform the artificial intelligence based city management event processing method of any one of claims 1 to 8.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110279064.XA CN112991130A (en) | 2021-03-16 | 2021-03-16 | Artificial intelligence-based city management event processing method and device |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110279064.XA CN112991130A (en) | 2021-03-16 | 2021-03-16 | Artificial intelligence-based city management event processing method and device |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN112991130A true CN112991130A (en) | 2021-06-18 |
Family
ID=76335679
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110279064.XA Pending CN112991130A (en) | 2021-03-16 | 2021-03-16 | Artificial intelligence-based city management event processing method and device |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112991130A (en) |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112991129A (en) * | 2021-03-16 | 2021-06-18 | 广东省电信规划设计院有限公司 | Public security event processing method and device based on artificial intelligence |
| CN113326812A (en) * | 2021-07-01 | 2021-08-31 | 安徽金晥泵业科技股份有限公司 | Regional burst regulation and storage treatment drainage system based on internet |
| CN114118933A (en) * | 2021-08-24 | 2022-03-01 | 泰华智慧产业集团股份有限公司 | Recording method and system for city management event |
| WO2023045602A1 (en) * | 2021-09-27 | 2023-03-30 | 杭州海康威视系统技术有限公司 | Image recognition method and electronic device |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110020755A (en) * | 2019-04-12 | 2019-07-16 | 城云科技（中国）有限公司 | City management system based on man-machine coordination |
| CN110363426A (en) * | 2019-07-15 | 2019-10-22 | 软通动力信息技术有限公司 | A kind of long-range enforcement approach of cloud platform, device, server and storage medium |
| CN110443727A (en) * | 2019-08-15 | 2019-11-12 | 刘德建 | A method of identification construction site safety behavior |
| CN112070004A (en) * | 2020-09-07 | 2020-12-11 | 北京软通智慧城市科技有限公司 | Intelligent city violation supervision method, system, server and storage medium |
| CN112488659A (en) * | 2020-12-11 | 2021-03-12 | 浪潮云信息技术股份公司 | Urban event intelligent discovery and automatic order distribution method based on remote sensing |
-
2021
- 2021-03-16 CN CN202110279064.XA patent/CN112991130A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110020755A (en) * | 2019-04-12 | 2019-07-16 | 城云科技（中国）有限公司 | City management system based on man-machine coordination |
| CN110363426A (en) * | 2019-07-15 | 2019-10-22 | 软通动力信息技术有限公司 | A kind of long-range enforcement approach of cloud platform, device, server and storage medium |
| CN110443727A (en) * | 2019-08-15 | 2019-11-12 | 刘德建 | A method of identification construction site safety behavior |
| CN112070004A (en) * | 2020-09-07 | 2020-12-11 | 北京软通智慧城市科技有限公司 | Intelligent city violation supervision method, system, server and storage medium |
| CN112488659A (en) * | 2020-12-11 | 2021-03-12 | 浪潮云信息技术股份公司 | Urban event intelligent discovery and automatic order distribution method based on remote sensing |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112991129A (en) * | 2021-03-16 | 2021-06-18 | 广东省电信规划设计院有限公司 | Public security event processing method and device based on artificial intelligence |
| CN113326812A (en) * | 2021-07-01 | 2021-08-31 | 安徽金晥泵业科技股份有限公司 | Regional burst regulation and storage treatment drainage system based on internet |
| CN114118933A (en) * | 2021-08-24 | 2022-03-01 | 泰华智慧产业集团股份有限公司 | Recording method and system for city management event |
| WO2023045602A1 (en) * | 2021-09-27 | 2023-03-30 | 杭州海康威视系统技术有限公司 | Image recognition method and electronic device |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN112991130A (en) | Artificial intelligence-based city management event processing method and device | |
| CN111429726B (en) | Monitoring video illegal parking vehicle detection and management method and corresponding system | |
| CN106971552B (en) | Fake plate phenomenon detection method and system | |
| KR102122850B1 (en) | Solution for analysis road and recognition vehicle license plate employing deep-learning | |
| CN109714576B (en) | Vehicle identification method, device and system based on video monitoring and server | |
| CN112016735B (en) | Patrol route planning method and system based on traffic violation hotspot prediction and readable storage medium | |
| CN110070061A (en) | A kind of passengers quantity projectional technique and device | |
| CN107886595A (en) | Recognition methods violating the regulations, drive recorder and storage medium based on drive recorder | |
| CN113011833A (en) | Safety management method and device for construction site, computer equipment and storage medium | |
| CN114023064B (en) | Community gridding community public security management system and management method | |
| CN114677617A (en) | Intelligent street lamp control method and device based on intelligent illumination | |
| CN112380892B (en) | Image recognition method, device, equipment and medium | |
| CN114332778B (en) | Intelligent alarm work order generation method and device based on people stream density and related medium | |
| CN113487877A (en) | Road vehicle illegal parking monitoring method | |
| CN113569899A (en) | Intelligent classification method for fee stealing and evading behaviors, storage medium and terminal | |
| CN110443312B (en) | Urban management method and system based on human body posture | |
| CN116863708A (en) | Smart city scheduling distribution system | |
| CN115272656A (en) | Environment detection alarm method and device, computer equipment and storage medium | |
| CN111754767A (en) | Highway traffic early warning method, device and system based on intelligent lamp pole | |
| CN112419774B (en) | Road monitoring method and system convenient for emergency ambulance passing and storage medium | |
| Wang et al. | Vision-based highway traffic accident detection | |
| CN112863195A (en) | Vehicle state determination method and device | |
| CN113408319A (en) | Urban road abnormity perception processing method, device, system and storage medium | |
| CN112991129A (en) | Public security event processing method and device based on artificial intelligence | |
| CN114913447B (en) | Police intelligent command room system and method based on scene recognition |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |