CN114117121A - Data acquisition method for smart city - Google Patents
Data acquisition method for smart city Download PDFInfo
- Publication number
- CN114117121A CN114117121A CN202111240029.3A CN202111240029A CN114117121A CN 114117121 A CN114117121 A CN 114117121A CN 202111240029 A CN202111240029 A CN 202111240029A CN 114117121 A CN114117121 A CN 114117121A
- Authority
- CN
- China
- Prior art keywords
- monitoring
- area
- data acquisition
- acquisition terminal
- areas
- 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.)
- Withdrawn
Links
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/70—Information retrieval; Database structures therefor; File system structures therefor of video data
- G06F16/73—Querying
- G06F16/735—Filtering based on additional data, e.g. user or group profiles
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/70—Information retrieval; Database structures therefor; File system structures therefor of video data
- G06F16/78—Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually
- G06F16/783—Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually using metadata automatically derived from the content
- G06F16/7837—Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually using metadata automatically derived from the content using objects detected or recognised in the video content
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/70—Information retrieval; Database structures therefor; File system structures therefor of video data
- G06F16/78—Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually
- G06F16/787—Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually using geographical or spatial information, e.g. location
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Multimedia (AREA)
- Data Mining & Analysis (AREA)
- Databases & Information Systems (AREA)
- Physics & Mathematics (AREA)
- General Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Library & Information Science (AREA)
- Computational Linguistics (AREA)
- Telephonic Communication Services (AREA)
Abstract
The invention provides a data acquisition method of a smart city, and relates to the technical field of smart cities. Firstly, determining whether data acquisition needs to be carried out on a target area through a plurality of data acquisition terminal devices, wherein the target area comprises a plurality of monitoring areas; secondly, if it is determined that data acquisition needs to be carried out on the target area through the plurality of data acquisition terminal devices, determining device correlation relationship information among the plurality of data acquisition terminal devices based on the area position relationship among the plurality of monitoring areas; and then, controlling at least one data acquisition terminal device in the plurality of data acquisition terminal devices to acquire data of the corresponding monitoring area based on the device correlation information among the plurality of data acquisition terminal devices. Based on the method, the problem that the control precision of the data acquisition terminal equipment is not high in the data acquisition process in the prior art can be solved.
Description
Technical Field
The invention relates to the technical field of smart cities, in particular to a data acquisition method of a smart city.
Background
The smart city is a new theory and a new mode for promoting city planning, construction, management and service intellectualization by applying new generation information integration technologies such as internet of things, cloud computing, big data and space geographic information integration. For example, the corresponding monitoring data may be obtained by monitoring the urban area, and then, area management and the like are performed on the urban area based on the monitoring data. However, in the prior art, when monitoring (or data acquisition) is performed on an urban area, generally, synchronous start-up or stop control is performed on each data acquisition terminal device to perform synchronous monitoring, and thus, there is a problem that control accuracy of the data acquisition terminal device is not high.
Disclosure of Invention
In view of the above, the present invention provides a data acquisition method for a smart city, so as to solve the problem in the prior art that the control accuracy of a data acquisition terminal device is not high in the data acquisition process.
In order to achieve the above purpose, the embodiment of the invention adopts the following technical scheme:
the utility model provides a data acquisition method in wisdom city, is applied to data processing server, data processing server communication connection has a plurality of data acquisition terminal equipment, a plurality of data acquisition terminal equipment set up respectively in a plurality of monitoring areas, the data acquisition method includes:
determining whether data acquisition needs to be carried out on a target area through the plurality of data acquisition terminal devices to realize area monitoring, wherein the target area comprises the plurality of monitoring areas;
if it is determined that data acquisition needs to be performed on the target area through the plurality of data acquisition terminal devices to realize area monitoring, determining device correlation relationship information among the plurality of data acquisition terminal devices based on the area position relationship among the plurality of monitoring areas;
and controlling at least one data acquisition terminal device in the plurality of data acquisition terminal devices to acquire data of a corresponding monitoring area based on the device correlation relation information among the plurality of data acquisition terminal devices to obtain at least one group of corresponding area monitoring data.
In some preferred embodiments, in the data collecting method for a smart city, the step of determining whether data collection of a target area by the plurality of data collecting terminal devices is required to achieve area monitoring includes:
acquiring current time to obtain corresponding current time information;
determining whether the current time information belongs to a preset target time interval or not;
if the current time information is determined to belong to the target time interval, determining that data acquisition needs to be carried out on the target area through the plurality of data acquisition terminal devices to realize area monitoring, and if the current time information is determined not to belong to the target time interval, determining that the data acquisition does not need to be carried out on the target area through the plurality of data acquisition terminal devices to realize the area monitoring.
In some preferred embodiments, in the data collecting method for a smart city, the step of determining whether data collection of a target area by the plurality of data collecting terminal devices is required to achieve area monitoring includes:
judging whether an area monitoring instruction is acquired;
if the area monitoring instruction is judged to be acquired, determining that data acquisition needs to be carried out on the target area through the plurality of data acquisition terminal devices so as to realize area monitoring;
if the area monitoring instruction is judged not to be acquired, acquiring current time information and determining whether the current time information belongs to a preset target time interval;
if the current time information is determined to belong to the target time interval, determining that data acquisition needs to be carried out on the target area through the plurality of data acquisition terminal devices to realize area monitoring, and if the current time information is determined not to belong to the target time interval, determining that the data acquisition does not need to be carried out on the target area through the plurality of data acquisition terminal devices to realize the area monitoring.
In some preferred embodiments, in the data collecting method for a smart city, if it is determined that data collection of the target area by the plurality of data collecting terminal devices is required to achieve area monitoring, the step of determining device correlation relationship information between the plurality of data collecting terminal devices based on an area position relationship between the plurality of monitored areas includes:
if it is determined that data acquisition needs to be performed on the target area through the plurality of data acquisition terminal devices to realize area monitoring, acquiring area position information of each monitoring area in the plurality of monitoring areas, and determining an area position relationship among the plurality of monitoring areas based on the area position information of each monitoring area;
and determining equipment correlation relation information among the plurality of data acquisition terminal equipment corresponding to the plurality of monitoring areas based on the area position relation among the plurality of monitoring areas.
In some preferred embodiments, in the data collecting method for a smart city, if it is determined that data collection of the target area by the plurality of data collecting terminal devices is required to realize area monitoring, the step of obtaining area location information of each of the plurality of monitoring areas and determining an area location relationship between the plurality of monitoring areas based on the area location information of each of the plurality of monitoring areas includes:
if it is determined that data acquisition needs to be performed on the target area through the plurality of data acquisition terminal devices to achieve area monitoring, acquiring area position information of each monitoring area in the plurality of monitoring areas, and determining a first-dimension position relationship between every two monitoring areas based on the area position information of every two monitoring areas;
acquiring regional path map data of the target region, and determining a second dimension position relationship between every two monitoring regions in the monitoring regions based on the path connection relationship of the monitoring regions on the regional path map;
determining the area position relationship between every two of the plurality of monitoring areas based on the first dimension position relationship and the second dimension position relationship between every two of the plurality of monitoring areas.
In some preferred embodiments, in the data collecting method for a smart city, if it is determined that data collection of the target area by the multiple data collecting terminal devices is required to achieve area monitoring, the step of obtaining area position information of each of the multiple monitoring areas and determining a first-dimension position relationship between every two monitoring areas based on the area position information of every two monitoring areas includes:
if it is determined that data acquisition needs to be performed on the target area through the plurality of data acquisition terminal devices to realize area monitoring, acquiring area position information of each monitoring area in the plurality of monitoring areas, and determining area distance information between the two monitoring areas based on the area position information of the two monitoring areas for each two monitoring areas in the plurality of monitoring areas;
determining the maximum value in the regional distance information between every two monitoring regions in the plurality of monitoring regions to obtain corresponding target regional distance information;
calculating the ratio between the region distance information between the two monitoring regions and the target region distance information aiming at each two monitoring regions in the plurality of monitoring regions to obtain region distance characterization values corresponding to the two monitoring regions;
and for each two monitoring areas in the plurality of monitoring areas, determining a first-dimension position relationship between the two monitoring areas based on the area distance characteristic values corresponding to the two monitoring areas, wherein the first-dimension position relationship between the two monitoring areas with smaller area distance characteristic values is closer in characteristic position relationship than the first-dimension position relationship between the two monitoring areas with larger area distance characteristic values.
In some preferred embodiments, in the data collecting method for a smart city, the step of acquiring regional path map data of the target region and determining a second-dimensional position relationship between each two monitoring regions in the monitoring regions based on path connection relationships of the monitoring regions on the regional path map includes:
acquiring regional path map data of the target region;
for each two monitoring areas in the multiple monitoring areas, determining each connecting path connected between the two monitoring areas based on the area path map data, and determining path length information of each connecting path, wherein each connecting path comprises at least one road section, and each road section is connected between two adjacent road intersections;
calculating an average value of path length information of each connection path between the two monitoring areas aiming at each two monitoring areas in the multiple monitoring areas to obtain a path length average value between the two monitoring areas, determining the path length average value with the maximum value as a target path length average value, and calculating a ratio between the path length average value between the two monitoring areas and the target path length average value aiming at each two monitoring areas in the multiple monitoring areas to obtain a path length characterization value between the two monitoring areas;
for each two monitoring areas in the multiple monitoring areas, determining the number of paths of connecting paths between the two monitoring areas based on the area path map data, determining the number of paths with the maximum value as a target number of paths, and for each two monitoring areas in the multiple monitoring areas, calculating the ratio between the number of paths between the two monitoring areas and the target number of paths to obtain a path number representation value between the two monitoring areas;
and for each two monitoring areas in the multiple monitoring areas, fusing to obtain a second-dimension position relationship between the two monitoring areas based on the path length characteristic value and the path number characteristic value between the two monitoring areas, wherein the second-dimension position relationship between the two monitoring areas with the path length characteristic value and the path number characteristic value having a smaller fusion value is closer in the characteristic position relationship than the second-dimension position relationship between the two monitoring areas with the path length characteristic value and the path number characteristic value having a larger fusion value.
In some preferred embodiments, in the data collecting method for a smart city, the step of determining, based on the first-dimension positional relationship and the second-dimension positional relationship between each two of the plurality of monitoring areas, an area positional relationship between each two of the plurality of monitoring areas includes:
acquiring a first fusion coefficient and a second fusion coefficient corresponding to the first dimension position relation and the second dimension position relation, wherein the first fusion coefficient is smaller than the second fusion coefficient;
and for each two monitoring areas in the plurality of monitoring areas, based on the first fusion coefficient and the second fusion coefficient, performing fusion processing on the first dimension position relationship and the second dimension position relationship between the two monitoring areas to obtain an area position relationship between the two monitoring areas, wherein the area position relationship with a larger value is closer to the represented position relationship than the area position relationship with a smaller value.
In some preferred embodiments, in the data collecting method for a smart city, the step of determining device correlation relationship information between the plurality of data collecting terminal devices corresponding to the plurality of monitoring areas based on the area position relationship between the plurality of monitoring areas includes:
and determining device correlation relationship information between two data acquisition terminal devices corresponding to the two monitoring areas based on the area position relationship between the two monitoring areas for each two monitoring areas in the plurality of monitoring areas, wherein the device correlation relationship information between the two data acquisition terminal devices corresponding to the two monitoring areas with the closer area position relationship has higher device correlation degree compared with the device correlation relationship information between the two data acquisition terminal devices corresponding to the two monitoring areas with the farther area position relationship.
In some preferred embodiments, in the data collecting method for a smart city, the step of controlling at least one data collecting terminal device of the plurality of data collecting terminal devices to collect data of a corresponding monitored area based on the device correlation information between the plurality of data collecting terminal devices to obtain at least one set of corresponding area monitoring data includes:
clustering the data acquisition terminal equipment based on the equipment correlation relation information among the data acquisition terminal equipment to obtain at least one equipment cluster corresponding to the data acquisition terminal equipment, wherein each equipment cluster in the at least one equipment cluster comprises at least one data acquisition terminal equipment;
for each equipment cluster, selecting at least one data acquisition terminal equipment from the data acquisition terminal equipment included in the equipment cluster as target data acquisition terminal equipment;
and respectively controlling each target data acquisition terminal device to acquire data of the corresponding monitoring area to obtain at least one group of corresponding area monitoring data.
According to the data acquisition method for the smart city, provided by the embodiment of the invention, when the region monitoring is determined to be realized by acquiring data of the target region, the device correlation relationship information among the corresponding multiple data acquisition terminal devices can be determined based on the region position relationship among the multiple monitoring regions included in the target region, so that at least one data acquisition terminal device in the multiple data acquisition terminal devices can be controlled to acquire data of the corresponding monitoring region based on the device correlation relationship information among the multiple data acquisition terminal devices, thus the basis of whether the data acquisition terminal device is controlled to acquire data of the corresponding monitoring region is increased, and the problem of low control accuracy of the data acquisition terminal device in the data acquisition process in the prior art can be improved.
In order to make the aforementioned and other objects, features and advantages of the present invention comprehensible, preferred embodiments accompanied with figures are described in detail below.
Drawings
Fig. 1 is a block diagram of a data processing server according to an embodiment of the present invention.
Fig. 2 is a schematic flowchart illustrating steps included in a data acquisition method for a smart city according to an embodiment of the present invention.
Fig. 3 is a block diagram illustrating units included in a data acquisition system of a smart city according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, 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. The components of embodiments of the present invention generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations.
Thus, the following detailed description of the embodiments of the present invention, presented in the figures, is not intended to limit the scope of the invention, as claimed, but is merely representative of selected embodiments of the invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
As shown in fig. 1, an embodiment of the present invention provides a data processing server. Wherein the data processing server may include a memory and a processor.
In detail, the memory and the processor are electrically connected directly or indirectly to realize data transmission or interaction. For example, they may be electrically connected to each other via one or more communication buses or signal lines. The memory can have stored therein at least one software function (computer program) which can be present in the form of software or firmware. The processor may be configured to execute the executable computer program stored in the memory, so as to implement the data collection method for the smart city according to an embodiment of the present invention (as described later).
Alternatively, in some possible examples, the Memory may be, but is not limited to, a Random Access Memory (RAM), a Read Only Memory (ROM), a Programmable Read-Only Memory (PROM), an Erasable Read-Only Memory (EPROM), an electrically Erasable Read-Only Memory (EEPROM), and the like.
Alternatively, in some possible examples, the Processor may be a general-purpose Processor including a Central Processing Unit (CPU), a Network Processor (NP), a System on Chip (SoC), and the like; but may also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other programmable logic device, discrete gate or transistor logic device, discrete hardware components.
Alternatively, in some possible examples, the structure shown in fig. 1 is only an illustration, and the data processing server may further include more or fewer components than those shown in fig. 1, or have a different configuration from that shown in fig. 1, for example, may include a communication unit for information interaction with other devices (such as a data acquisition terminal device for data acquisition or monitoring, and the like).
With reference to fig. 2, an embodiment of the present invention further provides a data collecting method for a smart city, which can be applied to the data processing server. The method steps defined by the relevant flow of the data acquisition method of the smart city can be realized by the data processing server.
The specific process shown in FIG. 2 will be described in detail below.
Step S110, determining whether data acquisition needs to be performed on the target area through the plurality of data acquisition terminal devices to realize area monitoring.
In the embodiment of the present invention, the data processing server is communicatively connected to a plurality of data acquisition terminal devices, and the plurality of data acquisition terminal devices are respectively disposed in a plurality of monitoring areas, so that the data processing server may determine whether it is necessary to perform data acquisition on a target area through the plurality of data acquisition terminal devices to realize area monitoring. Wherein the target area may include the plurality of monitoring areas.
Step S120, if it is determined that the target area needs to be subjected to data acquisition by the plurality of data acquisition terminal devices to realize area monitoring, determining device correlation information between the plurality of data acquisition terminal devices based on the area position relationship between the plurality of monitored areas.
In the embodiment of the present invention, when it is determined that the target area needs to be subjected to data acquisition by the plurality of data acquisition terminal devices to realize area monitoring, the data processing server may determine device correlation information between the plurality of data acquisition terminal devices based on an area position relationship between the plurality of monitoring areas
Step S130, based on the device correlation information among the multiple data acquisition terminal devices, controlling at least one data acquisition terminal device among the multiple data acquisition terminal devices to perform data acquisition on a corresponding monitoring area, so as to obtain at least one group of corresponding area monitoring data.
The data processing server may control at least one data acquisition terminal device of the plurality of data acquisition terminal devices to perform data acquisition on a corresponding monitored area based on the device correlation information among the plurality of data acquisition terminal devices, so as to obtain at least one set of area monitoring data (e.g., at least one area monitoring video, etc.) corresponding to the at least one data acquisition terminal device.
Based on this, through the above steps, when it is determined that the area monitoring is to be achieved by acquiring data of the target area, the device correlation relationship information between the corresponding plurality of data acquisition terminal devices may be determined based on the area position relationship between the plurality of monitoring areas included in the target area, so that at least one data acquisition terminal device of the plurality of data acquisition terminal devices may be controlled to acquire data of the corresponding monitoring area based on the device correlation relationship information between the plurality of data acquisition terminal devices.
Optionally, in some possible examples, step S110 may include the following steps:
firstly, acquiring current time to obtain corresponding current time information;
secondly, determining whether the current time information belongs to a preset target time interval;
and then, if the current time information is determined to belong to the target time interval, determining that data acquisition needs to be carried out on the target area through the plurality of data acquisition terminal devices to realize area monitoring, and if the current time information is determined not to belong to the target time interval, determining that the data acquisition does not need to be carried out on the target area through the plurality of data acquisition terminal devices to realize the area monitoring.
Optionally, in other possible examples, step S110 may include the following steps:
firstly, judging whether an area monitoring instruction is acquired;
secondly, if the area monitoring instruction is judged to be acquired, determining that data acquisition needs to be carried out on the target area through the plurality of data acquisition terminal devices so as to realize area monitoring;
then, if the area monitoring instruction is judged not to be acquired, acquiring current time information and determining whether the current time information belongs to a preset target time interval;
and finally, if the current time information is determined to belong to the target time interval, determining that data acquisition needs to be carried out on the target area through the plurality of data acquisition terminal devices to realize area monitoring, and if the current time information is determined not to belong to the target time interval, determining that the data acquisition does not need to be carried out on the target area through the plurality of data acquisition terminal devices to realize the area monitoring.
Optionally, in some possible examples, step S120 may include the following steps:
firstly, if it is determined that data acquisition needs to be performed on the target area through the plurality of data acquisition terminal devices to realize area monitoring, acquiring area position information of each monitoring area in the plurality of monitoring areas, and determining an area position relationship among the plurality of monitoring areas based on the area position information of each monitoring area;
secondly, determining the device correlation relationship information among the plurality of data acquisition terminal devices corresponding to the plurality of monitoring areas based on the area position relationship among the plurality of monitoring areas.
Optionally, in some possible examples, if it is determined that data acquisition needs to be performed on the target area through the multiple data acquisition terminal devices to implement area monitoring, the step of obtaining area location information of each of the multiple monitoring areas and determining an area location relationship between the multiple monitoring areas based on the area location information of each of the multiple monitoring areas may further include the following steps:
firstly, if it is determined that data acquisition needs to be performed on the target area through the plurality of data acquisition terminal devices to realize area monitoring, acquiring area position information of each monitoring area in the plurality of monitoring areas, and determining a first-dimension position relationship between every two monitoring areas based on the area position information of every two monitoring areas;
secondly, acquiring regional path map data of the target region, and determining a second dimension position relationship between every two monitoring regions in the monitoring regions based on the path connection relationship of the monitoring regions on the regional path map;
then, based on the first-dimension positional relationship and the second-dimension positional relationship between each two of the plurality of monitoring areas, determining an area positional relationship between each two of the plurality of monitoring areas.
Optionally, in some possible examples, if it is determined that data acquisition of the target area by the multiple data acquisition terminal devices is required to achieve area monitoring, the step of obtaining area location information of each of the multiple monitoring areas and determining the first-dimension location relationship between every two monitoring areas based on the area location information of every two monitoring areas may further include the following steps:
firstly, if it is determined that data acquisition needs to be performed on the target area through the plurality of data acquisition terminal devices to realize area monitoring, acquiring area position information of each of the plurality of monitoring areas, and determining area distance information (which may be a minimum straight line distance between two areas) between the two monitoring areas based on the area position information of the two monitoring areas for each two monitoring areas;
secondly, determining the maximum value in the regional distance information between every two monitoring regions in the plurality of monitoring regions to obtain corresponding target regional distance information;
then, for each two monitoring areas in the plurality of monitoring areas, calculating the proportion between the area distance information between the two monitoring areas and the target area distance information to obtain area distance characterization values corresponding to the two monitoring areas;
and finally, for each two monitoring areas in the plurality of monitoring areas, determining a first-dimension position relationship between the two monitoring areas based on the area distance characteristic values corresponding to the two monitoring areas, wherein the first-dimension position relationship between the two monitoring areas with smaller area distance characteristic values is closer in characteristic position relationship than the first-dimension position relationship between the two monitoring areas with larger area distance characteristic values.
Optionally, in some possible examples, the step of obtaining area route map data of the target area and determining a second-dimensional position relationship between each two monitoring areas in the multiple monitoring areas based on the route connection relationships of the multiple monitoring areas on the area route map may further include the following steps:
firstly, acquiring regional path map data of the target region;
secondly, for each two monitoring areas in the multiple monitoring areas, determining each connecting path connected between the two monitoring areas based on the area path map data, and determining path length information of each connecting path, wherein each connecting path comprises at least one road section, and each road section is connected between two adjacent road intersections;
then, for each two monitoring areas in the multiple monitoring areas, calculating an average value of path length information of each connection path between the two monitoring areas to obtain a path length average value between the two monitoring areas, determining a path length average value with a maximum value as a target path length average value, and for each two monitoring areas in the multiple monitoring areas, calculating a ratio between the path length average value between the two monitoring areas and the target path length average value to obtain a path length characterization value between the two monitoring areas;
then, for each two monitoring areas in the multiple monitoring areas, determining the number of paths of connecting paths between the two monitoring areas based on the area path map data, and determining the number of paths with the maximum value as a target number of paths, and for each two monitoring areas in the multiple monitoring areas, calculating the ratio between the number of paths between the two monitoring areas and the target number of paths to obtain a path number representation value between the two monitoring areas;
finally, for each two monitoring areas in the multiple monitoring areas, based on the path length characterizing value and the path number characterizing value between the two monitoring areas, a second-dimension position relationship between the two monitoring areas is obtained through fusion (e.g., calculating a weighted sum value of the path length characterizing value and the path number characterizing value, etc.), where the second-dimension position relationship between the two monitoring areas with the smaller fusion value of the path length characterizing value and the path number characterizing value is closer than the second-dimension position relationship between the two monitoring areas with the larger fusion value of the path length characterizing value and the path number characterizing value.
Optionally, in some possible examples, the step of determining the area position relationship between each two of the plurality of monitoring areas based on the first-dimension position relationship and the second-dimension position relationship between each two of the plurality of monitoring areas may further include the following steps:
first, a first fusion coefficient and a second fusion coefficient corresponding to the first dimension position relationship and the second dimension position relationship are obtained, wherein the first fusion coefficient is smaller than the second fusion coefficient (in other applications, the size relationship may be different);
secondly, for each two monitoring areas in the multiple monitoring areas, based on the first fusion coefficient and the second fusion coefficient, performing fusion processing (such as weighted summation calculation) on the first dimension position relationship and the second dimension position relationship between the two monitoring areas to obtain an area position relationship between the two monitoring areas, wherein the area position relationship with a larger value is closer to the position relationship represented by the area position relationship with a smaller value.
Optionally, in some possible examples, the step of determining device correlation relationship information between the plurality of data acquisition terminal devices corresponding to the plurality of monitoring areas based on the area position relationship between the plurality of monitoring areas may further include the following steps:
firstly, for each two monitoring areas in the plurality of monitoring areas, determining device correlation relationship information between two data acquisition terminal devices corresponding to the two monitoring areas based on the area position relationship between the two monitoring areas, wherein the device correlation relationship information between the two data acquisition terminal devices corresponding to the two monitoring areas with the closer area position relationship has a higher device correlation degree compared with the device correlation relationship information between the two data acquisition terminal devices corresponding to the two monitoring areas with the farther area position relationship.
Optionally, in some possible examples, step S130 may include the following steps:
firstly, clustering the data acquisition terminal devices based on the device correlation relationship information among the data acquisition terminal devices to obtain at least one device cluster corresponding to the data acquisition terminal devices, wherein each device cluster in the at least one device cluster comprises at least one data acquisition terminal device;
secondly, aiming at each equipment cluster (in the at least one equipment cluster), selecting at least one data acquisition terminal device from the data acquisition terminal devices included in the equipment cluster as a target data acquisition terminal device;
and then, respectively controlling each target data acquisition terminal device to acquire data of the corresponding monitoring area to obtain at least one group of corresponding area monitoring data.
Optionally, in some possible examples, for each device cluster, the step of selecting at least one data acquisition terminal device from the data acquisition terminal devices included in the device cluster as a target data acquisition terminal device may further include the following steps:
firstly, determining each equipment cluster with the number of included data acquisition terminal equipment larger than or equal to a preset target equipment number threshold (such as 2) as a target equipment cluster;
secondly, for each target equipment cluster, performing target equipment screening operation on the target equipment cluster to select at least one data acquisition terminal equipment from the data acquisition terminal equipment included in the target equipment cluster as target data acquisition terminal equipment.
Wherein, in some possible examples, the target device screening operation may include:
firstly, carrying out grouping processing (arbitrary) on data acquisition terminal equipment included in the target equipment cluster to obtain at least one group of equipment combination, wherein each group of equipment combination comprises two data acquisition terminal equipment, and the data acquisition terminal equipment corresponding to different equipment combinations is different;
secondly, adjusting the at least one group of equipment combination, and forming an equipment combination set based on the at least one group of equipment combination and the adjusted equipment combination, wherein the equipment combination set comprises a plurality of groups of equipment combinations, and the plurality of groups of equipment combinations comprise the at least one group of equipment combination;
then, counting the number of the data acquisition terminal equipment included in the target equipment cluster to obtain the equipment counting number corresponding to the target equipment cluster;
then, calculating an average value of the device correlation degrees represented by the device correlation relationship information between every two pieces of data acquisition terminal equipment in the target device cluster, obtaining a device correlation degree average value corresponding to the target device cluster, and determining target screening proportion information corresponding to the target device cluster based on the device correlation degree average value, wherein the target screening proportion information and the device correlation degree average value have a negative correlation relationship;
and finally, determining the target screening number based on the target screening proportion information and the device statistical number, screening at least one group of device combinations in the device combination set, wherein the device correlation degree of the representations corresponding to the device correlation relationship information among the device combinations is minimum, and the number of the corresponding data acquisition terminal devices is less than or equal to the target screening number, and taking the data acquisition terminal devices corresponding to the at least one group of device combinations as the target data acquisition terminal devices.
With reference to fig. 3, an embodiment of the present invention further provides a data acquisition system for a smart city, which can be applied to the data processing server. Wherein, the data acquisition system in wisdom city can include:
the area monitoring determining unit is used for determining whether data acquisition is required to be carried out on a target area through the plurality of data acquisition terminal devices so as to realize area monitoring, wherein the target area comprises the plurality of monitoring areas;
the device correlation determining unit is used for determining device correlation information among the plurality of data acquisition terminal devices based on the regional position relationship among the plurality of monitoring regions if it is determined that the target region needs to be subjected to data acquisition through the plurality of data acquisition terminal devices to realize regional monitoring;
and the data acquisition control unit is used for controlling at least one data acquisition terminal device in the plurality of data acquisition terminal devices to acquire data of a corresponding monitoring area based on the device correlation information among the plurality of data acquisition terminal devices to obtain at least one group of corresponding area monitoring data.
In summary, according to the data acquisition method for the smart city provided by the present invention, when it is determined that the area monitoring needs to be implemented by acquiring data of the target area, the device correlation relationship information between the plurality of corresponding data acquisition terminal devices may be determined based on the area position relationship between the plurality of monitoring areas included in the target area, so that at least one data acquisition terminal device of the plurality of data acquisition terminal devices may be controlled to perform data acquisition on the corresponding monitoring area based on the device correlation relationship information between the plurality of data acquisition terminal devices, and thus, a basis for controlling the data acquisition terminal device to perform data acquisition on the corresponding monitoring area is increased, and a problem in the prior art that the control accuracy of the data acquisition terminal device is not high in the data acquisition process can be improved.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. The utility model provides a data acquisition method in wisdom city, is characterized in that, is applied to data processing server, data processing server communication connection has a plurality of data acquisition terminal equipment, a plurality of data acquisition terminal equipment set up respectively in a plurality of monitoring areas, the data acquisition method includes:
determining whether data acquisition needs to be carried out on a target area through the plurality of data acquisition terminal devices to realize area monitoring, wherein the target area comprises the plurality of monitoring areas;
if it is determined that data acquisition needs to be performed on the target area through the plurality of data acquisition terminal devices to realize area monitoring, determining device correlation relationship information among the plurality of data acquisition terminal devices based on the area position relationship among the plurality of monitoring areas;
and controlling at least one data acquisition terminal device in the plurality of data acquisition terminal devices to acquire data of a corresponding monitoring area based on the device correlation relation information among the plurality of data acquisition terminal devices to obtain at least one group of corresponding area monitoring data.
2. The method for collecting data of a smart city according to claim 1, wherein the step of determining whether data collection of a target area by the plurality of data collection terminal devices is required for area monitoring comprises:
acquiring current time to obtain corresponding current time information;
determining whether the current time information belongs to a preset target time interval or not;
if the current time information is determined to belong to the target time interval, determining that data acquisition needs to be carried out on the target area through the plurality of data acquisition terminal devices to realize area monitoring, and if the current time information is determined not to belong to the target time interval, determining that the data acquisition does not need to be carried out on the target area through the plurality of data acquisition terminal devices to realize the area monitoring.
3. The method for collecting data of a smart city according to claim 1, wherein the step of determining whether data collection of a target area by the plurality of data collection terminal devices is required for area monitoring comprises:
judging whether an area monitoring instruction is acquired;
if the area monitoring instruction is judged to be acquired, determining that data acquisition needs to be carried out on the target area through the plurality of data acquisition terminal devices so as to realize area monitoring;
if the area monitoring instruction is judged not to be acquired, acquiring current time information and determining whether the current time information belongs to a preset target time interval;
if the current time information is determined to belong to the target time interval, determining that data acquisition needs to be carried out on the target area through the plurality of data acquisition terminal devices to realize area monitoring, and if the current time information is determined not to belong to the target time interval, determining that the data acquisition does not need to be carried out on the target area through the plurality of data acquisition terminal devices to realize the area monitoring.
4. The method according to claim 1, wherein if it is determined that the target area needs to be acquired by the data acquisition terminal devices for area monitoring, the step of determining the device correlation information between the data acquisition terminal devices based on the area location relationship between the monitored areas comprises:
if it is determined that data acquisition needs to be performed on the target area through the plurality of data acquisition terminal devices to realize area monitoring, acquiring area position information of each monitoring area in the plurality of monitoring areas, and determining an area position relationship among the plurality of monitoring areas based on the area position information of each monitoring area;
and determining equipment correlation relation information among the plurality of data acquisition terminal equipment corresponding to the plurality of monitoring areas based on the area position relation among the plurality of monitoring areas.
5. The method according to claim 4, wherein if it is determined that data acquisition of the target area by the plurality of data acquisition terminal devices is required to achieve area monitoring, the step of obtaining area location information of each of the plurality of monitored areas and determining an area location relationship between the plurality of monitored areas based on the area location information of each of the monitored areas comprises:
if it is determined that data acquisition needs to be performed on the target area through the plurality of data acquisition terminal devices to achieve area monitoring, acquiring area position information of each monitoring area in the plurality of monitoring areas, and determining a first-dimension position relationship between every two monitoring areas based on the area position information of every two monitoring areas;
acquiring regional path map data of the target region, and determining a second dimension position relationship between every two monitoring regions in the monitoring regions based on the path connection relationship of the monitoring regions on the regional path map;
determining the area position relationship between every two of the plurality of monitoring areas based on the first dimension position relationship and the second dimension position relationship between every two of the plurality of monitoring areas.
6. The method according to claim 5, wherein if it is determined that data acquisition of the target area by the plurality of data acquisition terminal devices is required to achieve area monitoring, the step of obtaining area location information of each of the plurality of monitored areas and determining a first-dimension location relationship between each two of the monitored areas based on the area location information of each two of the monitored areas comprises:
if it is determined that data acquisition needs to be performed on the target area through the plurality of data acquisition terminal devices to realize area monitoring, acquiring area position information of each monitoring area in the plurality of monitoring areas, and determining area distance information between the two monitoring areas based on the area position information of the two monitoring areas for each two monitoring areas in the plurality of monitoring areas;
determining the maximum value in the regional distance information between every two monitoring regions in the plurality of monitoring regions to obtain corresponding target regional distance information;
calculating the ratio between the region distance information between the two monitoring regions and the target region distance information aiming at each two monitoring regions in the plurality of monitoring regions to obtain region distance characterization values corresponding to the two monitoring regions;
and for each two monitoring areas in the plurality of monitoring areas, determining a first-dimension position relationship between the two monitoring areas based on the area distance characteristic values corresponding to the two monitoring areas, wherein the first-dimension position relationship between the two monitoring areas with smaller area distance characteristic values is closer in characteristic position relationship than the first-dimension position relationship between the two monitoring areas with larger area distance characteristic values.
7. The method of claim 5, wherein the step of obtaining the regional path map data of the target region and determining the second-dimensional position relationship between each two monitoring regions in the monitoring regions based on the path connection relationship of the monitoring regions on the regional path map comprises:
acquiring regional path map data of the target region;
for each two monitoring areas in the multiple monitoring areas, determining each connecting path connected between the two monitoring areas based on the area path map data, and determining path length information of each connecting path, wherein each connecting path comprises at least one road section, and each road section is connected between two adjacent road intersections;
calculating an average value of path length information of each connection path between the two monitoring areas aiming at each two monitoring areas in the multiple monitoring areas to obtain a path length average value between the two monitoring areas, determining the path length average value with the maximum value as a target path length average value, and calculating a ratio between the path length average value between the two monitoring areas and the target path length average value aiming at each two monitoring areas in the multiple monitoring areas to obtain a path length characterization value between the two monitoring areas;
for each two monitoring areas in the multiple monitoring areas, determining the number of paths of connecting paths between the two monitoring areas based on the area path map data, determining the number of paths with the maximum value as a target number of paths, and for each two monitoring areas in the multiple monitoring areas, calculating the ratio between the number of paths between the two monitoring areas and the target number of paths to obtain a path number representation value between the two monitoring areas;
and for each two monitoring areas in the multiple monitoring areas, fusing to obtain a second-dimension position relationship between the two monitoring areas based on the path length characteristic value and the path number characteristic value between the two monitoring areas, wherein the second-dimension position relationship between the two monitoring areas with the path length characteristic value and the path number characteristic value having a smaller fusion value is closer in the characteristic position relationship than the second-dimension position relationship between the two monitoring areas with the path length characteristic value and the path number characteristic value having a larger fusion value.
8. The method of claim 5, wherein the step of determining the area location relationship between each two of the plurality of monitored areas based on the first dimension location relationship and the second dimension location relationship between each two of the plurality of monitored areas comprises:
acquiring a first fusion coefficient and a second fusion coefficient corresponding to the first dimension position relation and the second dimension position relation, wherein the first fusion coefficient is smaller than the second fusion coefficient;
and for each two monitoring areas in the plurality of monitoring areas, based on the first fusion coefficient and the second fusion coefficient, performing fusion processing on the first dimension position relationship and the second dimension position relationship between the two monitoring areas to obtain an area position relationship between the two monitoring areas, wherein the area position relationship with a larger value is closer to the represented position relationship than the area position relationship with a smaller value.
9. The method for acquiring data of a smart city according to claim 4, wherein the step of determining device correlation information between the plurality of data acquisition terminal devices corresponding to the plurality of monitored areas based on the area location relationship between the plurality of monitored areas includes:
and determining device correlation relationship information between two data acquisition terminal devices corresponding to the two monitoring areas based on the area position relationship between the two monitoring areas for each two monitoring areas in the plurality of monitoring areas, wherein the device correlation relationship information between the two data acquisition terminal devices corresponding to the two monitoring areas with the closer area position relationship has higher device correlation degree compared with the device correlation relationship information between the two data acquisition terminal devices corresponding to the two monitoring areas with the farther area position relationship.
10. The method according to any one of claims 1 to 9, wherein the step of controlling at least one of the plurality of data acquisition terminal devices to acquire data of a corresponding monitored area based on the device correlation information between the plurality of data acquisition terminal devices to obtain at least one set of area monitoring data includes:
clustering the data acquisition terminal equipment based on the equipment correlation relation information among the data acquisition terminal equipment to obtain at least one equipment cluster corresponding to the data acquisition terminal equipment, wherein each equipment cluster in the at least one equipment cluster comprises at least one data acquisition terminal equipment;
for each equipment cluster, selecting at least one data acquisition terminal equipment from the data acquisition terminal equipment included in the equipment cluster as target data acquisition terminal equipment;
and respectively controlling each target data acquisition terminal device to acquire data of the corresponding monitoring area to obtain at least one group of corresponding area monitoring data.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111240029.3A CN114117121A (en) | 2021-10-25 | 2021-10-25 | Data acquisition method for smart city |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111240029.3A CN114117121A (en) | 2021-10-25 | 2021-10-25 | Data acquisition method for smart city |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN114117121A true CN114117121A (en) | 2022-03-01 |
Family
ID=80377091
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202111240029.3A Withdrawn CN114117121A (en) | 2021-10-25 | 2021-10-25 | Data acquisition method for smart city |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114117121A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116582213A (en) * | 2023-06-07 | 2023-08-11 | 中电山河数字科技(南通)有限公司 | Intelligent data acquisition device based on time synchronization processing and time synchronization processing |
-
2021
- 2021-10-25 CN CN202111240029.3A patent/CN114117121A/en not_active Withdrawn
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116582213A (en) * | 2023-06-07 | 2023-08-11 | 中电山河数字科技(南通)有限公司 | Intelligent data acquisition device based on time synchronization processing and time synchronization processing |
| CN116582213B (en) * | 2023-06-07 | 2023-10-17 | 中电山河数字科技(南通)有限公司 | Intelligent data acquisition device based on time synchronization processing and time synchronization processing |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US20120130625A1 (en) | Systems and methods for determining traffic intensity using information obtained through crowdsourcing | |
| CN115524554A (en) | Comprehensive operation management method and system for intelligent park | |
| CN111738484B (en) | Method and device for selecting address of bus stop and computer readable storage medium | |
| CN109995611B (en) | Traffic classification model establishing and traffic classification method, device, equipment and server | |
| CN114117121A (en) | Data acquisition method for smart city | |
| CN113949881A (en) | Service processing method and system based on smart city data | |
| CN114117122A (en) | Data analysis method and system based on smart city data | |
| CN116821777B (en) | Novel basic mapping data integration method and system | |
| CN114173088A (en) | Service adjusting method and system based on smart city monitoring | |
| CN114189535A (en) | Service request method and system based on smart city data | |
| CN113961780A (en) | Resident cell acquisition method and device, electronic equipment and storage medium | |
| CN112541047A (en) | Mass data aggregation method suitable for multistage gridding management system | |
| CN113094325A (en) | Device deployment method, apparatus, computer system, and computer-readable storage medium | |
| CN113392903A (en) | Method, system and device for identifying construction site area | |
| CN116227929B (en) | Communication data analysis method, device, equipment and storage medium | |
| CN105678456B (en) | Method and system for automatically evaluating running state of electric energy metering device | |
| Chandio et al. | Towards adaptable and tunable cloud-based map-matching strategy for GPS trajectories | |
| CN111199650A (en) | Cloud lane control method and system based on 5G network | |
| CN116566766A (en) | Intelligent power gateway management and control method and system | |
| CN116010543A (en) | Lane information determination method, lane information determination device, electronic equipment and storage medium | |
| CN114095489A (en) | Smart city data-based monitoring method and system | |
| CN115639578A (en) | Beidou navigation positioning monitoring processing method and system | |
| CN114266317A (en) | Clustering method and device and server | |
| CN114170455A (en) | Object gathering method and device, electronic equipment and storage medium | |
| CN111105617B (en) | Intelligent traffic prediction system based on matrix stability analysis |
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 | ||
| WW01 | Invention patent application withdrawn after publication |
Application publication date: 20220301 |
|
| WW01 | Invention patent application withdrawn after publication |