CN112528838A - Smart city monitoring method and device based on data analysis - Google Patents
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Abstract
The invention discloses a smart city monitoring method and device based on data analysis. According to the method, whether monitoring equipment corresponding to a monitoring first control area fails or not can be accurately judged through biological characteristic information and environmental information of the first monitoring area, and further, if the monitoring equipment corresponding to the first control area fails, a time node of the failure is determined according to current biological characteristic information of other areas of a target smart city, so that a safety accident image of the target smart city can be timely and quickly recorded according to the time node of the failure, and further, a safety accident image can be comprehensively monitored in a second monitoring area of the target smart city, and thus intelligent development of the city can be realized.
Description
Technical Field
The disclosure relates to the technical field of smart city monitoring, in particular to a smart city monitoring method and device based on data analysis.
Background
Smart city (SmartCity) is a city that uses various information technologies or innovative concepts to connect and integrate the systems and services of the city to improve the efficiency of resource utilization, thereby optimizing city management and services and improving the quality of life of citizens. The smart city is a city informatization advanced form which fully applies a new generation of information technology to various industries in the city and is based on the innovation of the next generation of knowledge society, realizes the deep integration of informatization, industrialization and urbanization, is beneficial to relieving the large urban diseases, improves the urbanization quality, realizes the fine and dynamic management, improves the urban management effect and improves the quality of life of citizens.
The construction phase is still handled in development in present wisdom city, and is limited to each regional monitoring ability in city, and the supervisory equipment in certain region in the city breaks down and this moment the regional incident has appeared again, can lead to at this moment can't monitor the incident picture, and then can't realize the intelligent development in city.
Disclosure of Invention
In order to solve the technical problems in the related art, the disclosure provides a smart city monitoring method and device based on data analysis.
The invention provides a smart city monitoring method based on data analysis, which comprises the following steps:
acquiring monitoring video information in a monitoring state corresponding to a first monitoring area of one area in a target smart city; the monitoring video information is used for recording each piece of biological characteristic information in the monitoring video;
determining whether monitoring equipment corresponding to the first monitoring area fails or not based on the biological characteristic information and the environmental information of the first monitoring area;
responding to the monitoring equipment corresponding to the first monitoring area to have a fault, and determining a time node of the monitoring equipment corresponding to the first monitoring area to have the fault according to target biological characteristic information in the first monitoring area and current biological characteristic information in other areas of the target smart city; wherein the target biometric information and the current biometric information refer to the same monitored object;
responding to the monitoring video information, recording a safety accident image of the target smart city based on each piece of biological characteristic information, and entering a monitoring state corresponding to a second monitoring area of the target smart city; wherein an initial time node of the second monitoring area is determined based on the current biometric information and the failed time node.
Preferably, the determining, according to the target biometric information in the first monitoring area and the current biometric information appearing in other areas of the target smart city, a time node at which the monitoring device corresponding to the first monitoring area fails includes: and calculating the feature similarity of the target biological feature information of the first monitoring area and the current biological feature information of other areas of the target smart city to obtain a fault time node corresponding to the first monitoring area.
Preferably, the determining whether the monitoring device corresponding to the first monitoring area has a fault based on the biometric information and the environmental information of the first monitoring area includes: if the fusion degree of the biological characteristic information of the first monitoring area and the environmental information is matched with a preset fusion degree, determining that the monitoring equipment corresponding to the first monitoring area has a fault; and if the fusion degree of the biological characteristic information of the first monitoring area and the environmental information is not matched with the preset fusion degree, determining that the monitoring equipment corresponding to the first monitoring area does not have a fault.
Preferably, the recording of the security accident image of the target smart city based on each piece of biometric information and the entering of the monitoring state corresponding to the second monitoring area of the target smart city specifically include: and correcting the biological characteristic information of the first monitoring area, responding to the monitoring video information, recording the safety accident image of the target smart city based on each piece of biological characteristic information, and entering a monitoring state corresponding to a second monitoring area of the target smart city.
Preferably, after entering a monitoring state corresponding to a second monitoring area of the target smart city, the method further includes:
acquiring a target monitoring video stream corresponding to a to-be-detected block;
and realizing traffic flow route guidance for the to-be-detected block based on the target monitoring video stream.
The invention also provides a smart city monitoring device based on data analysis, which comprises:
the monitoring video information acquisition module is used for acquiring monitoring video information in a monitoring state corresponding to a first monitoring area of one area in a target smart city; the monitoring video information is used for recording each piece of biological characteristic information in the monitoring video;
the monitoring equipment fault judging module is used for determining whether the monitoring equipment corresponding to the first monitoring area has a fault or not based on the biological characteristic information and the environmental information of the first monitoring area;
a failure time node determining module, configured to determine, in response to a failure of a monitoring device corresponding to the first monitoring area, a time node at which the monitoring device corresponding to the first monitoring area fails according to target biometric information in the first monitoring area and current biometric information in other areas of the target smart city; wherein the target biometric information and the current biometric information refer to the same monitored object;
the monitoring state entering module is used for responding to the monitoring video information, recording a safety accident image of the target smart city based on each piece of biological characteristic information, and entering a monitoring state corresponding to a second monitoring area of the target smart city; wherein an initial time node of the second monitoring area is determined based on the current biometric information and the failed time node.
Preferably, the failure time node determining module is specifically configured to: and calculating the feature similarity of the target biological feature information of the first monitoring area and the current biological feature information of other areas of the target smart city to obtain a fault time node corresponding to the first monitoring area.
Preferably, the monitoring device fault determining module is specifically configured to: if the fusion degree of the biological characteristic information of the first monitoring area and the environmental information is matched with a preset fusion degree, determining that the monitoring equipment corresponding to the first monitoring area has a fault; and if the fusion degree of the biological characteristic information of the first monitoring area and the environmental information is not matched with the preset fusion degree, determining that the monitoring equipment corresponding to the first monitoring area does not have a fault.
Preferably, the monitoring state entering module is specifically configured to: and correcting the biological characteristic information of the first monitoring area, responding to the monitoring video information, recording the safety accident image of the target smart city based on each piece of biological characteristic information, and entering a monitoring state corresponding to a second monitoring area of the target smart city.
Preferably, after entering a monitoring state corresponding to a second monitoring area of the target smart city, the apparatus further includes: the monitoring video stream acquisition module is specifically configured to:
acquiring a target monitoring video stream corresponding to a to-be-detected block;
and realizing traffic flow route guidance for the to-be-detected block based on the target monitoring video stream.
The technical scheme provided by the embodiment of the disclosure can have the following beneficial effects.
The utility model provides a smart city monitoring method and device based on data analysis, firstly obtain the monitoring video information in the target smart city, secondly confirm whether the monitoring equipment corresponding to the first monitoring area breaks down based on the biological characteristic information and the environmental information of the first monitoring area, if the monitoring equipment breaks down, then confirm the current biological characteristic information and the time node of breaking down that appear in other areas of the target smart city, further record the security accident image of the target smart city, then enter the monitoring state corresponding to the second monitoring area of the target smart city.
Therefore, whether the monitoring equipment corresponding to the first control area fails or not can be accurately judged through the biological characteristic information and the environmental information of the first monitoring area, if the monitoring equipment corresponding to the first control area fails, the time node of the failure is determined according to the current biological characteristic information of other areas of the target smart city, so that the safety accident image of the target smart city can be timely and quickly recorded according to the time node of the failure, and further the safety accident image can be comprehensively monitored in the second monitoring area of the target smart city, so that the intelligent development of the city can be realized.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and together with the description, serve to explain the principles of the invention.
Fig. 1 is a flowchart of a smart city monitoring method based on data analysis according to an embodiment of the present invention.
Fig. 2 is a block diagram of a smart city monitoring apparatus based on data analysis according to an embodiment of the present invention.
Fig. 3 is a schematic diagram of a hardware structure of a monitoring center according to an embodiment of the present invention.
Detailed Description
Reference will now be made in detail to the exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, like numbers in different drawings represent the same or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present application. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the present application, as detailed in the appended claims.
The method aims to solve the problem that when monitoring equipment in a certain area in a city breaks down and a safety accident occurs in the area, the safety accident picture cannot be monitored.
To achieve the above objective, please refer to fig. 1, the present invention provides a flow chart of a smart city monitoring method based on data analysis, which specifically executes the contents described in the following steps S110 to S140.
Step S110, obtaining monitoring video information in a monitoring state corresponding to a first monitoring area of one area in the target smart city.
In this embodiment, the monitoring video information is used to record each piece of biometric information in the monitoring video.
Step S120, determining whether the monitoring device corresponding to the first monitoring area is faulty or not based on the biometric information and the environmental information of the first monitoring area.
Step S130, in response to the monitoring device corresponding to the first monitoring area failing, determining a time node at which the monitoring device corresponding to the first monitoring area fails according to the target biometric information in the first monitoring area and the current biometric information in other areas of the target smart city.
In this embodiment, the target biometric information and the current biometric information refer to the same monitored object.
Step S140, in response to the monitoring video information, recording a security accident image of the target smart city based on each piece of biometric information, and entering a monitoring state corresponding to a second monitoring area of the target smart city.
In this embodiment, the initial time node of the second monitoring area is determined based on the current biometric information and the failed time node.
In the process of executing the above steps S110 to S140, the following advantages can be achieved: the method comprises the steps of firstly obtaining monitoring video information in a target smart city, secondly determining whether monitoring equipment corresponding to a first monitoring area fails or not based on biological characteristic information and environmental information of the first monitoring area, if so, determining current biological characteristic information and time nodes of failure in other areas of the target smart city, further recording safety accident images of the target smart city, and then entering a monitoring state corresponding to a second monitoring area of the target smart city.
Therefore, whether the monitoring equipment corresponding to the first control area fails or not can be accurately judged through the biological characteristic information and the environmental information of the first monitoring area, if the monitoring equipment corresponding to the first control area fails, the time node of the failure is determined according to the current biological characteristic information of other areas of the target smart city, so that the safety accident image of the target smart city can be timely and quickly recorded according to the time node of the failure, and further the safety accident image can be comprehensively monitored in the second monitoring area of the target smart city, so that the intelligent development of the city can be realized.
Optionally, the determining, according to the target biometric information in the first monitoring area and the current biometric information appearing in other areas of the target smart city, a time node at which the monitoring device corresponding to the first monitoring area fails includes: and calculating the feature similarity of the target biological feature information of the first monitoring area and the current biological feature information of other areas of the target smart city to obtain a fault time node corresponding to the first monitoring area.
Optionally, the determining whether the monitoring device corresponding to the first monitoring area fails based on the biometric information and the environmental information of the first monitoring area includes: if the fusion degree of the biological characteristic information of the first monitoring area and the environmental information is matched with a preset fusion degree, determining that the monitoring equipment corresponding to the first monitoring area has a fault; if the fusion degree of the biological characteristic information of the first monitoring area and the environmental information is not matched with a preset fusion degree, determining that the monitoring equipment corresponding to the first monitoring area does not have a fault;
optionally, recording a security accident image of the target smart city based on each piece of biometric information, and entering a monitoring state corresponding to a second monitoring area of the target smart city, specifically including: and correcting the biological characteristic information of the first monitoring area, responding to the monitoring video information, recording the safety accident image of the target smart city based on each piece of biological characteristic information, and entering a monitoring state corresponding to a second monitoring area of the target smart city.
Optionally, after entering a monitoring state corresponding to a second monitoring area of the target smart city, the method further includes:
acquiring a target monitoring video stream corresponding to a to-be-detected block;
and realizing traffic flow route guidance for the to-be-detected block based on the target monitoring video stream.
In an alternative embodiment, implementing traffic route guidance for the block to be detected based on the target surveillance video stream may further include the following step S300: acquiring target traffic information in a to-be-detected block; extracting road traffic information from the target traffic information; determining a first matching degree between the road traffic flow information and preset traffic flow planning information; the traffic flow planning information is used for representing road environment information which does not change along with time change; when a first matching degree between target road traffic flow information in the road traffic flow information and preset traffic flow planning information meets a congestion scheduling condition, planning a traffic flow route guidance strategy corresponding to the target road traffic flow information in the target traffic information; and sending the traffic flow route guiding strategy to a target user terminal.
In this way, firstly, the road traffic flow information is extracted from the acquired target traffic information, then when a first matching degree between the target road traffic flow information in the road traffic flow information and preset traffic flow planning information meets a congestion scheduling condition, a traffic flow route guidance strategy corresponding to the target road traffic flow information is planned in the target traffic information, and the traffic flow route guidance strategy is sent to a target user terminal.
In this way, by extracting the road traffic information from the acquired target traffic information, the traffic information of the road and the driving rate of the vehicle can be quickly analyzed from the road traffic information. When the first matching degree between the target road traffic flow information and the preset traffic flow planning information is further judged to meet the congestion scheduling condition, a corresponding traffic flow route guiding strategy is planned for the target road traffic flow information, so that a user does not need to listen to a broadcast to know the traffic problem, the traffic flow route guiding strategy is actively pushed to a mobile phone, the accurate guiding of the traffic flow can be timely realized, the problem of traffic congestion can be effectively avoided, and convenience can be brought to the user for going out.
Further, the air conditioner is provided with a fan,
the extracting of the road traffic information from the target traffic information includes:
preprocessing the target traffic information;
performing first feature extraction on the preprocessed target traffic information to obtain current road traffic information;
and performing secondary feature extraction on the current road traffic flow information to obtain road traffic flow information corresponding to the target traffic information.
Therefore, the current road traffic flow information is obtained by performing the first characteristic extraction on the processed target traffic information, and the second characteristic extraction is further performed on the current road traffic flow information, so that the road traffic flow information can be accurately obtained through multiple times of characteristic extraction.
The current road traffic information comprises initial road traffic information, first current road traffic information and second current road traffic information; the first feature extraction is performed on the preprocessed target traffic information to obtain current road traffic information, and the method comprises the following steps:
carrying out information identification processing on the preprocessed target traffic information through a plain text identification unit in a preset information extraction thread to obtain initial road traffic information;
performing first feature extraction on the initial road traffic flow information through a first feature extraction unit in the preset information extraction thread to obtain first current road traffic flow information;
and performing first feature extraction on the first current road traffic flow information through a second feature extraction unit in the preset information extraction thread to obtain second current road traffic flow information.
The second feature extraction is performed on the current road traffic flow information to obtain the road traffic flow information corresponding to the target traffic information, and the method comprises the following steps:
performing second feature extraction on the second current road traffic flow information through a first feature extraction unit in the preset information extraction thread to obtain second road traffic flow information;
performing secondary feature extraction on the second road traffic flow information through a second feature extraction unit in the preset information extraction thread to obtain actual road traffic flow information;
and fusing the initial road traffic flow information, the second road traffic flow information, the actual road traffic flow information and the traffic flow corresponding to each road in the preprocessed target traffic information to obtain road traffic flow information.
Further, on the basis of step 300, the method further includes:
performing road state analysis on the first current road traffic flow information through a first thread state list in the preset information extraction thread to obtain first passing vehicle state information;
performing secondary feature extraction on the second road traffic flow information to obtain actual road traffic flow information;
performing second feature extraction on fusion processing information consisting of the first passing vehicle state information and the second road traffic flow information to obtain target road traffic flow information;
performing road state analysis on the initial road traffic flow information through a second thread state list of the preset information extraction thread to obtain target initial road traffic flow information;
the fusing the initial road traffic information, the second road traffic information, the actual road traffic information and the preprocessed traffic flow corresponding to each road in the target traffic information comprises: and fusing the target initial road traffic flow information, the actual road traffic flow information, the target road traffic flow information and the traffic flow corresponding to each road in the preprocessed target traffic information.
Further, the step of configuring the preset information extraction thread includes:
acquiring road information to be planned; mapping the road information to the preset information extraction thread, and extracting the configuration road traffic flow information of each lane in the road information;
calculating the matching degree of the configured road traffic flow information and the road information to be planned to obtain a second matching degree; the road information to be planned belongs to thread information in the preset information extraction thread;
calculating a deviation value according to the determined second matching degree; and verifying the information of each thread in the preset information extraction threads based on the calculated deviation values, and stopping configuration until the verified deviation value corresponding to the preset information extraction threads is smaller than the preset deviation value.
In this way, the acquired road information to be planned is firstly mapped into the preset information extraction thread, so that the configured road traffic flow information of each lane in the road information can be efficiently extracted. And further calculating a deviation value according to the determined second matching degree, and checking each thread information in the preset information extraction thread based on the deviation value, so that the accuracy of each thread information in the preset information extraction thread can be determined.
Further, on the basis of step 300, the method further includes: and when the preset information extraction thread is stopped being configured, taking the road information to be planned corresponding to the configured preset information extraction thread as the traffic flow planning information.
Further, the air conditioner is provided with a fan,
the determining a first matching degree between the road traffic flow information and preset traffic flow planning information includes: inputting the road traffic flow information into a two-dimensional matrix to obtain a road traffic flow information distribution track; and calculating a first matching degree between the road traffic information distribution track and preset traffic planning information.
The step of inputting the road traffic flow information into a two-dimensional matrix to obtain a road traffic flow information distribution track comprises the following steps: and performing information identification processing on the road traffic flow information by adopting thread interface information of a preset information extraction thread to obtain a road traffic flow information distribution track of a two-dimensional matrix.
The calculating a first matching degree between the road traffic information distribution track and preset traffic planning information includes:
calculating a difference threshold value between the road traffic flow information and preset traffic flow planning information;
calculating the sudden change incidence rate of the flow sudden change mode of each lane in the target traffic information in the corresponding traffic flow interval by using the difference threshold;
summing the calculated mutation occurrence rates to obtain mutation occurrence rates and values; performing stage matching proportion analysis on the mutation occurrence rate and the mutation value to obtain a matching proportion;
and determining the matching ratio as a first matching degree between the road traffic information and the corresponding traffic planning information.
Further, planning a traffic route guidance strategy corresponding to the target road traffic information in the target traffic information includes:
determining first traffic jam information corresponding to first target traffic information and second traffic jam information corresponding to second target traffic information, wherein the first traffic jam information and the second traffic jam information respectively comprise a plurality of jam duration records with different weights; extracting initial traffic flow information of the first target traffic information recorded in any blocking time of the first traffic blocking information, and determining a blocking time record with the minimum weight in the second traffic blocking information as a target blocking time record; loading the initial traffic flow information into the target blocking time length record according to a preset loading mode and a preset loading protocol, obtaining an initial loading list in the target blocking time length record, and determining a first traffic list relation between the first target traffic information and the second target traffic information based on the initial traffic flow information and the initial loading list;
acquiring target road traffic information in the target blocking time record based on the initial loading list, loading the target road traffic information into the blocking time record of the initial traffic flow information according to a second traffic list relation corresponding to the first traffic list relation, acquiring key road traffic information corresponding to the target road traffic information in the blocking time record of the initial traffic flow information, and determining key traffic section information of the key road traffic information as target traffic flow information; acquiring actual traffic state information loaded into the target blocking time length record by the initial traffic flow information;
according to the association degrees between the key road traffic flow information and the traffic flow information to be detected corresponding to a plurality of lists to be associated on the actual traffic state information, sequentially acquiring road map information corresponding to the target traffic flow information from the second traffic jam information until the list of the congestion duration record of the acquired road map information is consistent with the list of the target traffic flow information in the first traffic jam information, and stopping acquiring the road map information in the next congestion duration record;
receiving a road map information analysis instruction; the road map information analysis instruction comprises a road name search mode of road map information to be analyzed, wherein the road name search mode refers to a road name search mode stored by a server; acquiring corresponding static road information according to the received road map information analysis instruction; the static road information is reference road information of each road map information to be analyzed; determining a plurality of sub-road information to be analyzed of the road map information to be analyzed according to the road name searching mode; each piece of sub-road information to be analyzed comprises a plurality of road interference factors;
acquiring road comprehensive information corresponding to each piece of sub-road information to be analyzed, and coding the plurality of pieces of sub-road information to be analyzed according to the road comprehensive information; identifying specific characteristic information of each road interference factor in the first sub-road information to be analyzed according to the road comprehensive information of the first sub-road information to be analyzed for the first sub-road information to be analyzed in the encoded plurality of sub-road information to be analyzed;
identifying road interference factors of corresponding characteristic information from the static road information based on the specific characteristic information, and determining each road interference factor according to the road interference factor corresponding to the corresponding characteristic information and a preset road determination function so as to determine first target road information; the preset road determining function is used for defining a determining method of each road interference factor in the road map information to be analyzed;
de-noising the first target road information, deleting the noise information in the de-noised first target road information, and loading the noise information into a road planning text which accords with the road name searching mode and is stored in a server; the denoising processing comprises redundant information eliminating processing; and according to the determining, denoising and adding modes of the first target road information, sequentially performing determining, denoising and adding operations on other sub-road information to be analyzed in the encoded sub-road information to be analyzed until a corresponding traffic flow route guidance strategy is determined.
In this way, the extracted initial traffic flow information is loaded into the determined target blocking time duration record, so that an initial loading list can be recorded in the target blocking time duration record in real time, a first traffic list relation between first target traffic information and second target traffic information is further determined based on the initial traffic flow information and the initial loading list, so that key road traffic flow information can be obtained quickly, further, key traffic flow section information of the key road traffic flow information is used as the target traffic flow information, actual traffic state information obtained after the target traffic flow information is determined and loaded into the target blocking time duration record by the initial traffic flow information is obtained, and therefore the accuracy of traffic state judgment can be improved. And then the road map information corresponding to the target traffic flow information is acquired from the second traffic jam information, so that the road information of each road can be found out intuitively and clearly according to the road map information. And analyzing first target road information after the road map information is determined, and then sequentially analyzing a plurality of pieces of target road information. And then a traffic route guidance strategy is analyzed according to the information of the plurality of target roads, so that the traffic road can be accurately regulated and controlled through the traffic route guidance strategy, and the traffic road is effectively prevented from being jammed.
On the basis of the above, please refer to fig. 2, the present invention further provides a block diagram of a smart city monitoring apparatus 200 based on data analysis, the apparatus includes:
the monitoring video information acquiring module 210 is configured to acquire monitoring video information in a monitoring state corresponding to a first monitoring area of one area in a target smart city; the monitoring video information is used for recording each piece of biological characteristic information in the monitoring video;
a monitoring device failure determining module 220, configured to determine whether a monitoring device corresponding to the first monitoring area fails based on the biometric information and the environmental information of the first monitoring area;
a failure time node determining module 230, configured to determine, in response to a failure of a monitoring device corresponding to the first monitoring area, a time node at which the monitoring device corresponding to the first monitoring area fails according to target biometric information in the first monitoring area and current biometric information in other areas of the target smart city; wherein the target biometric information and the current biometric information refer to the same monitored object;
a monitoring state entering module 240, configured to record, in response to the monitoring video information, a security accident image of the target smart city based on each piece of biometric information, and enter a monitoring state corresponding to a second monitoring area of the target smart city; wherein an initial time node of the second monitoring area is determined based on the current biometric information and the failed time node.
Optionally, the failure time node determining module 230 is specifically configured to: and calculating the feature similarity of the target biological feature information of the first monitoring area and the current biological feature information of other areas of the target smart city to obtain a fault time node corresponding to the first monitoring area.
Optionally, the monitoring device failure determining module 220 is specifically configured to: if the fusion degree of the biological characteristic information of the first monitoring area and the environmental information is matched with a preset fusion degree, determining that the monitoring equipment corresponding to the first monitoring area has a fault; if the fusion degree of the biological characteristic information of the first monitoring area and the environmental information is not matched with a preset fusion degree, determining that the monitoring equipment corresponding to the first monitoring area does not have a fault;
optionally, the monitoring state entering module 240 is specifically configured to: and correcting the biological characteristic information of the first monitoring area, responding to the monitoring video information, recording the safety accident image of the target smart city based on each piece of biological characteristic information, and entering a monitoring state corresponding to a second monitoring area of the target smart city.
Optionally, after entering a monitoring state corresponding to a second monitoring area of the target smart city, the apparatus further includes: the surveillance video stream obtaining module 250 is specifically configured to:
acquiring a target monitoring video stream corresponding to a to-be-detected block;
and realizing traffic flow route guidance for the to-be-detected block based on the target monitoring video stream.
On the basis of the above, please refer to fig. 3 in combination, which provides a monitoring center 300, including a processor 310, a memory 320 connected to the processor 310, and a bus 330; wherein, the processor 310 and the memory 320 communicate with each other through the bus 330; the processor 310 is used to call the program instructions in the memory 320 to execute the above-mentioned method.
Further, a readable storage medium is provided, on which a program is stored, which when executed by a processor implements the method described above.
It will be understood that the invention is not limited to the precise arrangements described above and shown in the drawings and that various modifications and changes may be made without departing from the scope thereof. The scope of the invention is limited only by the appended claims.
Claims (10)
1. A smart city monitoring method based on data analysis is characterized by comprising the following steps:
acquiring monitoring video information in a monitoring state corresponding to a first monitoring area of one area in a target smart city; the monitoring video information is used for recording each piece of biological characteristic information in the monitoring video;
determining whether monitoring equipment corresponding to the first monitoring area fails or not based on the biological characteristic information and the environmental information of the first monitoring area;
responding to the monitoring equipment corresponding to the first monitoring area to have a fault, and determining a time node of the monitoring equipment corresponding to the first monitoring area to have the fault according to target biological characteristic information in the first monitoring area and current biological characteristic information in other areas of the target smart city; wherein the target biometric information and the current biometric information refer to the same monitored object;
responding to the monitoring video information, recording a safety accident image of the target smart city based on each piece of biological characteristic information, and entering a monitoring state corresponding to a second monitoring area of the target smart city; wherein an initial time node of the second monitoring area is determined based on the current biometric information and the failed time node.
2. The method of claim 1, wherein the determining, according to the target biometric information in the first monitoring area and the current biometric information in other areas of the target smart city, a time node at which the monitoring device corresponding to the first monitoring area fails includes: and calculating the feature similarity of the target biological feature information of the first monitoring area and the current biological feature information of other areas of the target smart city to obtain a fault time node corresponding to the first monitoring area.
3. The method of claim 1, wherein the determining whether the monitoring device corresponding to the first monitoring area is faulty based on the biometric information and the environmental information of the first monitoring area comprises: if the fusion degree of the biological characteristic information of the first monitoring area and the environmental information is matched with a preset fusion degree, determining that the monitoring equipment corresponding to the first monitoring area has a fault; and if the fusion degree of the biological characteristic information of the first monitoring area and the environmental information is not matched with the preset fusion degree, determining that the monitoring equipment corresponding to the first monitoring area does not have a fault.
4. The method according to claim 1, wherein recording the security incident image of the target smart city based on each piece of biometric information, and entering a monitoring state corresponding to a second monitoring area of the target smart city, specifically comprises: and correcting the biological characteristic information of the first monitoring area, responding to the monitoring video information, recording the safety accident image of the target smart city based on each piece of biological characteristic information, and entering a monitoring state corresponding to a second monitoring area of the target smart city.
5. The method of claim 1, wherein after entering a monitoring state corresponding to a second monitoring area of the target smart city, the method further comprises:
acquiring a target monitoring video stream corresponding to a to-be-detected block;
and realizing traffic flow route guidance for the to-be-detected block based on the target monitoring video stream.
6. A smart city monitoring device based on data analysis, the device comprising:
the monitoring video information acquisition module is used for acquiring monitoring video information in a monitoring state corresponding to a first monitoring area of one area in a target smart city; the monitoring video information is used for recording each piece of biological characteristic information in the monitoring video;
the monitoring equipment fault judging module is used for determining whether the monitoring equipment corresponding to the first monitoring area has a fault or not based on the biological characteristic information and the environmental information of the first monitoring area;
a failure time node determining module, configured to determine, in response to a failure of a monitoring device corresponding to the first monitoring area, a time node at which the monitoring device corresponding to the first monitoring area fails according to target biometric information in the first monitoring area and current biometric information in other areas of the target smart city; wherein the target biometric information and the current biometric information refer to the same monitored object;
the monitoring state entering module is used for responding to the monitoring video information, recording a safety accident image of the target smart city based on each piece of biological characteristic information, and entering a monitoring state corresponding to a second monitoring area of the target smart city; wherein an initial time node of the second monitoring area is determined based on the current biometric information and the failed time node.
7. The apparatus according to claim 6, wherein the failure time node determining module is specifically configured to: and calculating the feature similarity of the target biological feature information of the first monitoring area and the current biological feature information of other areas of the target smart city to obtain a fault time node corresponding to the first monitoring area.
8. The apparatus according to claim 6, wherein the monitoring device failure determination module is specifically configured to: if the fusion degree of the biological characteristic information of the first monitoring area and the environmental information is matched with a preset fusion degree, determining that the monitoring equipment corresponding to the first monitoring area has a fault; and if the fusion degree of the biological characteristic information of the first monitoring area and the environmental information is not matched with the preset fusion degree, determining that the monitoring equipment corresponding to the first monitoring area does not have a fault.
9. The apparatus according to claim 6, wherein the monitoring state entering module is specifically configured to: and correcting the biological characteristic information of the first monitoring area, responding to the monitoring video information, recording the safety accident image of the target smart city based on each piece of biological characteristic information, and entering a monitoring state corresponding to a second monitoring area of the target smart city.
10. The apparatus of claim 6, wherein after entering the monitoring state corresponding to the second monitoring area of the target smart city, the apparatus further comprises: the monitoring video stream acquisition module is specifically configured to:
acquiring a target monitoring video stream corresponding to a to-be-detected block;
and realizing traffic flow route guidance for the to-be-detected block based on the target monitoring video stream.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011427226.1A CN112528838A (en) | 2020-12-09 | 2020-12-09 | Smart city monitoring method and device based on data analysis |
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| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011427226.1A CN112528838A (en) | 2020-12-09 | 2020-12-09 | Smart city monitoring method and device based on data analysis |
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| CN112528838A true CN112528838A (en) | 2021-03-19 |
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| CN202011427226.1A Withdrawn CN112528838A (en) | 2020-12-09 | 2020-12-09 | Smart city monitoring method and device based on data analysis |
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| Country | Link |
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| CN (1) | CN112528838A (en) |
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2020
- 2020-12-09 CN CN202011427226.1A patent/CN112528838A/en not_active Withdrawn
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