CN114095489A - Smart city data-based monitoring method and system - Google Patents

Abstract

The invention provides a monitoring method and system based on smart city data, and relates to the technical field of smart cities. In the invention, at least one data acquisition terminal device in a plurality of data acquisition terminal devices is controlled to acquire data of a corresponding monitoring area based on the device correlation information among the data acquisition terminal devices, so as to obtain at least one group of corresponding area monitoring data; performing behavior recognition processing on the at least one group of regional monitoring data to obtain a target behavior recognition result corresponding to the at least one group of regional monitoring data; and performing equipment adjustment processing on at least one monitoring area corresponding to at least one data acquisition terminal equipment corresponding to at least one group of area monitoring data based on the target behavior identification result so as to adjust the number of the data acquisition terminal equipment in the at least one monitoring area. Based on the method, the problem of poor monitoring effect in the prior art can be solved.

Description

Smart city data-based monitoring method and system

Technical Field

The invention relates to the technical field of smart cities, in particular to a monitoring method and a monitoring system based on smart city data.

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, in the process of monitoring (or acquiring data) an urban area, the number of deployed data acquisition terminal devices is generally fixed, so that the problem of poor monitoring effect is easily caused.

Disclosure of Invention

In view of the above, the present invention provides a monitoring method and system based on smart city data to improve the problem of poor monitoring effect in the prior art.

In order to achieve the above purpose, the embodiment of the invention adopts the following technical scheme:

the utility model provides a monitoring method based on wisdom city data, 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 control areas, monitoring method includes:

when it is determined that data acquisition is required to be performed on a target area through the plurality of data acquisition terminal devices to realize area monitoring, controlling at least one data acquisition terminal device in the plurality of data acquisition terminal devices to perform data acquisition on the corresponding monitoring area based on device correlation information among the plurality of data acquisition terminal devices to obtain at least one group of corresponding area monitoring data, wherein the target area comprises the plurality of monitoring areas;

performing behavior recognition processing on the at least one group of regional monitoring data to obtain a target behavior recognition result corresponding to the at least one group of regional monitoring data;

and performing equipment adjustment processing on at least one monitoring area corresponding to at least one data acquisition terminal equipment corresponding to the at least one group of area monitoring data based on the target behavior identification result so as to adjust the number of the data acquisition terminal equipment in the at least one monitoring area.

In some preferred embodiments, in the monitoring method based on smart city data, the step of performing behavior recognition processing on the at least one set of regional monitoring data to obtain a target behavior recognition result corresponding to the at least one set of regional monitoring data includes:

and performing behavior recognition processing on the area monitoring data aiming at each group of area monitoring data in the at least one group of area monitoring data to obtain a target behavior recognition result corresponding to the area monitoring data, wherein the target behavior recognition result is used for representing whether a target behavior which does not meet a preset target behavior condition exists in the monitoring area corresponding to the corresponding area monitoring data.

In some preferred embodiments, in the monitoring method based on smart city data, the step of performing behavior recognition processing on the area monitoring data for each group of area monitoring data in the at least one group of area monitoring data to obtain a target behavior recognition result corresponding to the area monitoring data includes:

performing behavior recognition processing on each frame of regional monitoring video frame included in the regional monitoring data aiming at each group of regional monitoring data in the at least one group of regional monitoring data to obtain a behavior recognition result corresponding to each frame of regional monitoring video frame included in the regional monitoring data;

and aiming at each group of regional monitoring data in the at least one group of regional monitoring data, performing fusion processing on the behavior recognition result corresponding to each frame of regional monitoring video frame included in the regional monitoring data to obtain a target behavior recognition result corresponding to the regional monitoring data.

In some preferred embodiments, in the monitoring method based on smart city data, the step of performing fusion processing on the behavior recognition result corresponding to each frame of the regional monitoring video frame included in the regional monitoring data to obtain the target behavior recognition result corresponding to the regional monitoring data, for each group of regional monitoring data in the at least one group of regional monitoring data, includes:

determining whether a behavior identification result corresponding to each frame of regional monitoring video frame included in the regional monitoring data is a first behavior identification result or not aiming at each group of regional monitoring data in the at least one group of regional monitoring data, wherein the first behavior identification result is used for representing that target behaviors which do not meet a preset target behavior condition exist in the corresponding monitoring region;

counting the number of regional monitoring video frames of which the corresponding behavior recognition result is the first behavior recognition result and which are included in the regional monitoring data aiming at each group of regional monitoring data in the at least one group of regional monitoring data to obtain the number of the video frame counting frames corresponding to the regional monitoring data;

and aiming at each group of regional monitoring data in the at least one group of regional monitoring data, obtaining a target behavior identification result corresponding to the regional monitoring data based on the video frame counting frame number corresponding to the regional monitoring data.

In some preferred embodiments, in the monitoring method based on smart city data, the step of obtaining, for each group of area monitoring data in the at least one group of area monitoring data, a target behavior recognition result corresponding to the area monitoring data based on the statistical frame number of the video frames corresponding to the area monitoring data includes:

for each group of regional monitoring data in the at least one group of regional monitoring data, determining the video frame statistical frame number corresponding to the regional monitoring data as a target behavior identification result corresponding to the regional monitoring data, wherein the target behavior identification result is used for representing the number of target behaviors which do not meet a preset target behavior condition and exist in the corresponding monitoring region; or

And determining the video frame number proportional value as a target behavior identification result corresponding to the region monitoring data, wherein the target behavior identification result is used for representing the number of target behaviors which do not meet a preset target behavior condition and exist in the corresponding monitoring region.

In some preferred embodiments, in the monitoring method based on smart city data, the step of performing device adjustment processing on at least one monitoring area corresponding to at least one data acquisition terminal device corresponding to the at least one group of area monitoring data based on the target behavior recognition result to adjust the number of data acquisition terminal devices in the at least one monitoring area includes:

for each group of regional monitoring data in the at least one group of regional monitoring data, determining whether equipment adjustment processing needs to be performed on a monitoring region corresponding to the regional monitoring data based on the target behavior recognition result corresponding to the regional monitoring data;

for each group of regional monitoring data in the at least one group of regional monitoring data, if the fact that equipment adjustment processing is not needed to be carried out on the monitoring region corresponding to the regional monitoring data is determined, the number of the data acquisition terminal equipment in the monitoring region is not adjusted;

and aiming at each group of regional monitoring data in the at least one group of regional monitoring data, if the monitored region corresponding to the regional monitoring data needs to be subjected to equipment adjustment processing, increasing the number of data acquisition terminal equipment in the monitored region.

In some preferred embodiments, in the monitoring method based on smart city data, the step of determining, for each set of area monitoring data in the at least one set of area monitoring data, whether to perform device adjustment processing on a monitoring area corresponding to the area monitoring data based on the target behavior recognition result corresponding to the area monitoring data includes:

sequencing the at least one group of area monitoring data based on a relative magnitude relation between result characteristic values of the target behavior identification result corresponding to each group of area monitoring data to obtain a monitoring data sequence corresponding to the at least one group of area monitoring data, wherein the result characteristic values of the target behavior identification result are used for representing the number or the ratio of the number of target behaviors which do not meet a preset target behavior condition and exist in the corresponding monitoring area;

counting the group number of the regional monitoring data included in the monitoring data sequence to obtain a data counting group number corresponding to the monitoring data sequence, and determining a target screening number based on the data counting group number and a pre-configured screening proportion;

screening the previously sorted target screening quantity group region monitoring data in the monitoring data sequence to serve as first region monitoring data, and taking other region monitoring data except the first region monitoring data as second region monitoring data;

determining, for each group of the first area monitoring data, that a monitoring area corresponding to the area monitoring data needs to be subjected to device adjustment processing, and determining, for each group of the second area monitoring data, a relative magnitude relationship between the result characteristic value of the target behavior recognition result corresponding to the second area monitoring data and a preset characteristic value threshold,

for each group of the second area monitoring data, if the result characteristic value of the target behavior recognition result corresponding to the second area monitoring data is greater than or equal to the characteristic value threshold, it is determined that equipment adjustment processing needs to be performed on the monitoring area corresponding to the area monitoring data, and if the result characteristic value of the target behavior recognition result corresponding to the second area monitoring data is smaller than the characteristic value threshold, it is determined that equipment adjustment processing does not need to be performed on the monitoring area corresponding to the area monitoring data.

The embodiment of the invention also provides a monitoring system based on smart city data, which is applied to a data processing server, wherein the data processing server is in communication connection with a plurality of data acquisition terminal devices, the data acquisition terminal devices are respectively arranged in a plurality of monitoring areas, and the monitoring system comprises:

the data acquisition 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 when determining that the target area needs to be acquired through the plurality of data acquisition terminal devices to realize area monitoring, so as to obtain at least one group of corresponding area monitoring data, wherein the target area comprises the plurality of monitoring areas;

the behavior recognition unit is used for performing behavior recognition processing on the at least one group of regional monitoring data to obtain a target behavior recognition result corresponding to the at least one group of regional monitoring data;

and the equipment adjusting unit is used for carrying out equipment adjustment processing on at least one monitoring area corresponding to at least one data acquisition terminal equipment corresponding to the at least one group of area monitoring data based on the target behavior recognition result so as to adjust the number of the data acquisition terminal equipment in the at least one monitoring area.

In some preferred embodiments, in the monitoring system based on smart city data, the behavior recognition unit is specifically configured to:

and performing behavior recognition processing on the area monitoring data aiming at each group of area monitoring data in the at least one group of area monitoring data to obtain a target behavior recognition result corresponding to the area monitoring data, wherein the target behavior recognition result is used for representing whether a target behavior which does not meet a preset target behavior condition exists in the monitoring area corresponding to the corresponding area monitoring data.

In some preferred embodiments, in the monitoring system based on smart city data, the device adjusting unit is specifically configured to:

for each group of regional monitoring data in the at least one group of regional monitoring data, determining whether equipment adjustment processing needs to be performed on a monitoring region corresponding to the regional monitoring data based on the target behavior recognition result corresponding to the regional monitoring data;

for each group of regional monitoring data in the at least one group of regional monitoring data, if the fact that equipment adjustment processing is not needed to be carried out on the monitoring region corresponding to the regional monitoring data is determined, the number of the data acquisition terminal equipment in the monitoring region is not adjusted;

and aiming at each group of regional monitoring data in the at least one group of regional monitoring data, if the monitored region corresponding to the regional monitoring data needs to be subjected to equipment adjustment processing, increasing the number of data acquisition terminal equipment in the monitored region.

The monitoring method and system based on smart city data provided by the embodiment of the invention can control at least one data acquisition terminal device in the plurality of data acquisition terminal devices to acquire data of a corresponding monitoring area to obtain at least one group of corresponding area monitoring data based on the device correlation information among the plurality of data acquisition terminal devices when determining that the target area needs to be acquired by the plurality of data acquisition terminal devices, then can perform behavior recognition processing on the at least one group of area monitoring data to obtain a target behavior recognition result corresponding to the at least one group of area monitoring data, so that the device adjustment processing can be performed on at least one monitoring area corresponding to the at least one data acquisition terminal device corresponding to the at least one group of area monitoring data based on the target behavior recognition result to adjust the number of the data acquisition terminal devices in the at least one monitoring area, the number of the data acquisition terminal devices in the monitoring area is adjusted according to actual requirements, the monitoring effect is guaranteed, and therefore the problem of poor monitoring effect in the prior art is solved.

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 flowchart illustrating steps included in a monitoring method based on smart city data according to an embodiment of the present invention.

Fig. 3 is a block diagram illustrating units included in a monitoring system based on smart city data 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 monitoring method based on smart city data provided by the 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 monitoring method based on smart city data, which can be applied to the data processing server. The method steps defined by the flow related to the monitoring method based on the smart city data can be realized by the data processing server.

The specific process shown in FIG. 2 will be described in detail below. .

And S100, 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 the embodiment of the present invention, the data processing server is communicatively connected with 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 can control at least one data acquisition terminal device of the plurality of data acquisition terminal devices to perform data acquisition on a corresponding monitoring area based on device correlation information between the plurality of data acquisition terminal devices when it is determined that data acquisition needs to be performed on a target area through the plurality of data acquisition terminal devices to implement area monitoring, so as to obtain at least one group of corresponding area monitoring data. Wherein the target area includes the plurality of monitoring areas.

Step S200, performing behavior recognition processing on the at least one group of regional monitoring data to obtain a target behavior recognition result corresponding to the at least one group of regional monitoring data.

In the embodiment of the present invention, the data processing server may perform behavior recognition processing on the at least one group of area monitoring data to obtain a target behavior recognition result corresponding to the at least one group of area monitoring data.

Step S300, performing equipment adjustment processing on at least one monitoring area corresponding to at least one data acquisition terminal equipment corresponding to the at least one group of area monitoring data based on the target behavior recognition result so as to adjust the number of the data acquisition terminal equipment in the at least one monitoring area.

In this embodiment of the present invention, the data processing server may perform, based on the target behavior recognition result, device adjustment processing on at least one monitoring area corresponding to at least one data acquisition terminal device corresponding to the at least one group of area monitoring data, so as to adjust the number of the data acquisition terminal devices in the at least one monitoring area.

Based on this, through the above steps, when it is determined that the data acquisition needs to be performed on the target area by the plurality of data acquisition terminal devices, 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 information between the plurality of data acquisition terminal devices to obtain at least one set of corresponding area monitoring data, then, the at least one set of area monitoring data may be subjected to behavior recognition processing to obtain a target behavior recognition result corresponding to the at least one set of area monitoring data, so that the at least one monitoring area corresponding to the at least one data acquisition terminal device corresponding to the at least one set of area monitoring data may be subjected to device adjustment processing based on the target behavior recognition result to adjust the number of data acquisition terminal devices in the at least one monitoring area, the number of the data acquisition terminal devices in the monitoring area is adjusted according to actual requirements, the monitoring effect is guaranteed, and therefore the problem of poor monitoring effect in the prior art is solved.

Optionally, in some possible examples, step S100 may include the following steps, such as step S110, step S120, and step S130, which are 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.

Optionally, in some possible examples, step S200 may include the following steps:

and performing behavior recognition processing on the area monitoring data aiming at each group of area monitoring data in the at least one group of area monitoring data to obtain a target behavior recognition result corresponding to the area monitoring data, wherein the target behavior recognition result is used for representing whether a target behavior which does not meet a preset target behavior condition exists in the monitoring area corresponding to the corresponding area monitoring data.

Optionally, in some possible examples, the step of performing behavior recognition processing on the area monitoring data for each group of area monitoring data in the at least one group of area monitoring data to obtain a target behavior recognition result corresponding to the area monitoring data may include the following steps:

firstly, aiming at each group of regional monitoring data in at least one group of regional monitoring data, performing behavior recognition processing on each frame of regional monitoring video frame included in the regional monitoring data to obtain a behavior recognition result corresponding to each frame of regional monitoring video frame included in the regional monitoring data;

and secondly, aiming at each group of regional monitoring data in the at least one group of regional monitoring data, performing fusion processing on the behavior recognition result corresponding to each frame of regional monitoring video frame included in the regional monitoring data to obtain a target behavior recognition result corresponding to the regional monitoring data.

Optionally, in some possible examples, the step of performing fusion processing on the behavior recognition result corresponding to each frame of the regional monitoring video frame included in the regional monitoring data to obtain the target behavior recognition result corresponding to the regional monitoring data according to each group of regional monitoring data in the at least one group of regional monitoring data may include the following steps:

firstly, determining whether a behavior recognition result corresponding to each frame of regional monitoring video frame included in the regional monitoring data is a first behavior recognition result or not aiming at each group of regional monitoring data in the at least one group of regional monitoring data, wherein the first behavior recognition result is used for representing that target behaviors (such as violation) which do not meet a preset target behavior condition exist in the corresponding monitored region;

secondly, counting the number of regional monitoring video frames of which the corresponding behavior recognition results are the first behavior recognition results and which are included in the regional monitoring data aiming at each group of regional monitoring data in the at least one group of regional monitoring data to obtain the number of the video frame counting frames corresponding to the regional monitoring data;

then, for each group of regional monitoring data in the at least one group of regional monitoring data, obtaining a target behavior identification result corresponding to the regional monitoring data based on the video frame counting frame number corresponding to the regional monitoring data.

Optionally, in some possible examples, the step of obtaining, for each group of area monitoring data in the at least one group of area monitoring data, a target behavior identification result corresponding to the area monitoring data based on the video frame statistical frame number corresponding to the area monitoring data includes the following steps:

firstly, aiming at each group of regional monitoring data in at least one group of regional monitoring data, determining the video frame statistical frame number corresponding to the regional monitoring data as a target behavior identification result corresponding to the regional monitoring data, wherein the target behavior identification result is used for representing the number of target behaviors which do not meet a preset target behavior condition and exist in the corresponding monitoring region; or

Secondly, counting the number of regional monitoring video frames included in the regional monitoring data aiming at each group of regional monitoring data in the at least one group of regional monitoring data to obtain the statistical number of video frames corresponding to the regional monitoring data, obtaining the frame number ratio value of the video frames corresponding to the regional monitoring data based on the ratio value between the statistical frame number of the video frames corresponding to the regional monitoring data and the statistical number of the video frames, and determining the frame number ratio value of the video frames as a target behavior identification result corresponding to the regional monitoring data, wherein the target behavior identification result is used for representing the number of target behaviors which do not meet a preset target behavior condition and exist in the corresponding monitoring region.

Optionally, in some possible examples, step S300 may include the following steps:

firstly, aiming at each group of regional monitoring data in at least one group of regional monitoring data, determining whether equipment adjustment processing needs to be carried out on a monitoring region corresponding to the regional monitoring data based on the target behavior identification result corresponding to the regional monitoring data;

secondly, aiming at each group of regional monitoring data in the at least one group of regional monitoring data, if the equipment adjustment processing is determined not to be needed to be carried out on the monitoring region corresponding to the regional monitoring data, the number of the data acquisition terminal equipment in the monitoring region is not adjusted;

then, for each group of regional monitoring data in the at least one group of regional monitoring data, if it is determined that the device adjustment processing needs to be performed on the monitoring region corresponding to the regional monitoring data, the number of the data acquisition terminal devices in the monitoring region is increased.

Optionally, in some possible examples, the step of determining, for each group of area monitoring data in the at least one group of area monitoring data, whether to perform device adjustment processing on a monitoring area corresponding to the area monitoring data based on the target behavior recognition result corresponding to the area monitoring data may further include the following steps:

firstly, sequencing at least one group of area monitoring data based on a relative magnitude relation between result characteristic values of the target behavior identification result corresponding to each group of area monitoring data to obtain a monitoring data sequence corresponding to the at least one group of area monitoring data, wherein the result characteristic values of the target behavior identification result are used for representing the number or the ratio of the number of target behaviors which do not meet a preset target behavior condition and exist in the corresponding monitoring area;

secondly, counting the group number of the regional monitoring data included in the monitoring data sequence to obtain a data counting group number corresponding to the monitoring data sequence, and determining a target screening number based on the data counting group number and a pre-configured screening proportion;

then, screening the previously sorted target screening quantity group region monitoring data in the monitoring data sequence to serve as first region monitoring data, and taking other region monitoring data except the first region monitoring data as second region monitoring data;

then, for each group of the first area monitoring data, determining that equipment adjustment processing needs to be performed on the monitoring area corresponding to the area monitoring data, and for each group of the second area monitoring data, determining a relative size relationship between the result characteristic value of the target behavior identification result corresponding to the second area monitoring data and a preset characteristic value threshold value,

finally, for each group of the second area monitoring data, if the result characteristic value of the target behavior recognition result corresponding to the second area monitoring data is greater than or equal to the characteristic value threshold, it is determined that equipment adjustment processing needs to be performed on the monitoring area corresponding to the area monitoring data, and if the result characteristic value of the target behavior recognition result corresponding to the second area monitoring data is less than the characteristic value threshold, it is determined that equipment adjustment processing does not need to be performed on the monitoring area corresponding to the area monitoring data.

With reference to fig. 3, an embodiment of the present invention further provides a monitoring system based on smart city data, which can be applied to the data processing server. Wherein, the monitoring system based on smart city data may comprise the following functional units (computer program modules):

the data acquisition 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 when determining that the target area needs to be acquired through the plurality of data acquisition terminal devices to realize area monitoring, so as to obtain at least one group of corresponding area monitoring data, wherein the target area comprises the plurality of monitoring areas;

the behavior recognition unit is used for performing behavior recognition processing on the at least one group of regional monitoring data to obtain a target behavior recognition result corresponding to the at least one group of regional monitoring data;

and the equipment adjusting unit is used for carrying out equipment adjustment processing on at least one monitoring area corresponding to at least one data acquisition terminal equipment corresponding to the at least one group of area monitoring data based on the target behavior recognition result so as to adjust the number of the data acquisition terminal equipment in the at least one monitoring area.

Optionally, in some possible examples, the behavior identification unit is specifically configured to:

and performing behavior recognition processing on the area monitoring data aiming at each group of area monitoring data in the at least one group of area monitoring data to obtain a target behavior recognition result corresponding to the area monitoring data, wherein the target behavior recognition result is used for representing whether a target behavior which does not meet a preset target behavior condition exists in the monitoring area corresponding to the corresponding area monitoring data.

Optionally, in some possible examples, the device adjusting unit is specifically configured to:

for each group of regional monitoring data in the at least one group of regional monitoring data, determining whether equipment adjustment processing needs to be performed on a monitoring region corresponding to the regional monitoring data based on the target behavior recognition result corresponding to the regional monitoring data;

for each group of regional monitoring data in the at least one group of regional monitoring data, if the fact that equipment adjustment processing is not needed to be carried out on the monitoring region corresponding to the regional monitoring data is determined, the number of the data acquisition terminal equipment in the monitoring region is not adjusted;

and aiming at each group of regional monitoring data in the at least one group of regional monitoring data, if the monitored region corresponding to the regional monitoring data needs to be subjected to equipment adjustment processing, increasing the number of data acquisition terminal equipment in the monitored region.

In summary, the monitoring method and system based on smart city data provided by the present invention, when it is determined that a target area needs to be acquired by a plurality of data acquisition terminal devices, can control at least one data acquisition terminal device of the plurality of data acquisition terminal devices to acquire data of a corresponding monitored area based on device correlation information between the plurality of data acquisition terminal devices to obtain at least one set of corresponding area monitoring data, and then can perform behavior recognition processing on the at least one set of area monitoring data to obtain a target behavior recognition result corresponding to the at least one set of area monitoring data, so that at least one monitored area corresponding to the at least one data acquisition terminal device corresponding to the at least one set of area monitoring data can be subjected to device adjustment processing based on the target behavior recognition result to adjust the number of data acquisition terminal devices in the at least one monitored area, the number of the data acquisition terminal devices in the monitoring area is adjusted according to actual requirements, the monitoring effect is guaranteed, and therefore the problem of poor monitoring effect in the prior art is solved.

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 monitoring method based on wisdom city data which 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 control areas, monitoring method includes:

when it is determined that data acquisition is required to be performed on a target area through the plurality of data acquisition terminal devices to realize area monitoring, controlling at least one data acquisition terminal device in the plurality of data acquisition terminal devices to perform data acquisition on the corresponding monitoring area based on device correlation information among the plurality of data acquisition terminal devices to obtain at least one group of corresponding area monitoring data, wherein the target area comprises the plurality of monitoring areas;

performing behavior recognition processing on the at least one group of regional monitoring data to obtain a target behavior recognition result corresponding to the at least one group of regional monitoring data;

and performing equipment adjustment processing on at least one monitoring area corresponding to at least one data acquisition terminal equipment corresponding to the at least one group of area monitoring data based on the target behavior identification result so as to adjust the number of the data acquisition terminal equipment in the at least one monitoring area.

2. The smart city data-based monitoring method of claim 1, wherein the step of performing behavior recognition processing on the at least one set of regional monitoring data to obtain a target behavior recognition result corresponding to the at least one set of regional monitoring data includes:

and performing behavior recognition processing on the area monitoring data aiming at each group of area monitoring data in the at least one group of area monitoring data to obtain a target behavior recognition result corresponding to the area monitoring data, wherein the target behavior recognition result is used for representing whether a target behavior which does not meet a preset target behavior condition exists in the monitoring area corresponding to the corresponding area monitoring data.

3. The smart city data-based monitoring method of claim 2, wherein the step of performing behavior recognition processing on the area monitoring data for each set of area monitoring data in the at least one set of area monitoring data to obtain a target behavior recognition result corresponding to the area monitoring data includes:

performing behavior recognition processing on each frame of regional monitoring video frame included in the regional monitoring data aiming at each group of regional monitoring data in the at least one group of regional monitoring data to obtain a behavior recognition result corresponding to each frame of regional monitoring video frame included in the regional monitoring data;

and aiming at each group of regional monitoring data in the at least one group of regional monitoring data, performing fusion processing on the behavior recognition result corresponding to each frame of regional monitoring video frame included in the regional monitoring data to obtain a target behavior recognition result corresponding to the regional monitoring data.

4. The smart city data-based monitoring method of claim 3, wherein the step of performing fusion processing on the behavior recognition result corresponding to each frame of the regional monitoring video frame included in the regional monitoring data to obtain the target behavior recognition result corresponding to the regional monitoring data includes:

determining whether a behavior identification result corresponding to each frame of regional monitoring video frame included in the regional monitoring data is a first behavior identification result or not aiming at each group of regional monitoring data in the at least one group of regional monitoring data, wherein the first behavior identification result is used for representing that target behaviors which do not meet a preset target behavior condition exist in the corresponding monitoring region;

counting the number of regional monitoring video frames of which the corresponding behavior recognition result is the first behavior recognition result and which are included in the regional monitoring data aiming at each group of regional monitoring data in the at least one group of regional monitoring data to obtain the number of the video frame counting frames corresponding to the regional monitoring data;

and aiming at each group of regional monitoring data in the at least one group of regional monitoring data, obtaining a target behavior identification result corresponding to the regional monitoring data based on the video frame counting frame number corresponding to the regional monitoring data.

5. The smart city data-based monitoring method of claim 4, wherein the step of obtaining the target behavior recognition result corresponding to the region monitoring data based on the statistical frame number of the video frames corresponding to the region monitoring data for each group of region monitoring data in the at least one group of region monitoring data comprises:

for each group of regional monitoring data in the at least one group of regional monitoring data, determining the video frame statistical frame number corresponding to the regional monitoring data as a target behavior identification result corresponding to the regional monitoring data, wherein the target behavior identification result is used for representing the number of target behaviors which do not meet a preset target behavior condition and exist in the corresponding monitoring region; or

And determining the video frame number proportional value as a target behavior identification result corresponding to the region monitoring data, wherein the target behavior identification result is used for representing the number of target behaviors which do not meet a preset target behavior condition and exist in the corresponding monitoring region.

6. The smart city data-based monitoring method according to claim 1, wherein the step of performing device adjustment processing on at least one monitoring area corresponding to at least one data acquisition terminal device corresponding to the at least one group of area monitoring data based on the target behavior recognition result to adjust the number of data acquisition terminal devices in the at least one monitoring area includes:

for each group of regional monitoring data in the at least one group of regional monitoring data, determining whether equipment adjustment processing needs to be performed on a monitoring region corresponding to the regional monitoring data based on the target behavior recognition result corresponding to the regional monitoring data;

for each group of regional monitoring data in the at least one group of regional monitoring data, if the fact that equipment adjustment processing is not needed to be carried out on the monitoring region corresponding to the regional monitoring data is determined, the number of the data acquisition terminal equipment in the monitoring region is not adjusted;

and aiming at each group of regional monitoring data in the at least one group of regional monitoring data, if the monitored region corresponding to the regional monitoring data needs to be subjected to equipment adjustment processing, increasing the number of data acquisition terminal equipment in the monitored region.

7. The smart city data-based monitoring method of claim 6, wherein the step of determining whether device adjustment processing needs to be performed on the monitored area corresponding to the area monitoring data based on the target behavior recognition result corresponding to the area monitoring data for each set of area monitoring data in the at least one set of area monitoring data comprises:

sequencing the at least one group of area monitoring data based on a relative magnitude relation between result characteristic values of the target behavior identification result corresponding to each group of area monitoring data to obtain a monitoring data sequence corresponding to the at least one group of area monitoring data, wherein the result characteristic values of the target behavior identification result are used for representing the number or the ratio of the number of target behaviors which do not meet a preset target behavior condition and exist in the corresponding monitoring area;

counting the group number of the regional monitoring data included in the monitoring data sequence to obtain a data counting group number corresponding to the monitoring data sequence, and determining a target screening number based on the data counting group number and a pre-configured screening proportion;

screening the previously sorted target screening quantity group region monitoring data in the monitoring data sequence to serve as first region monitoring data, and taking other region monitoring data except the first region monitoring data as second region monitoring data;

determining, for each group of the first area monitoring data, that a monitoring area corresponding to the area monitoring data needs to be subjected to device adjustment processing, and determining, for each group of the second area monitoring data, a relative magnitude relationship between the result characteristic value of the target behavior recognition result corresponding to the second area monitoring data and a preset characteristic value threshold,

for each group of the second area monitoring data, if the result characteristic value of the target behavior recognition result corresponding to the second area monitoring data is greater than or equal to the characteristic value threshold, it is determined that equipment adjustment processing needs to be performed on the monitoring area corresponding to the area monitoring data, and if the result characteristic value of the target behavior recognition result corresponding to the second area monitoring data is smaller than the characteristic value threshold, it is determined that equipment adjustment processing does not need to be performed on the monitoring area corresponding to the area monitoring data.

8. The utility model provides a monitored control system based on wisdom city data which 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, monitored control system includes:

the data acquisition 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 when determining that the target area needs to be acquired through the plurality of data acquisition terminal devices to realize area monitoring, so as to obtain at least one group of corresponding area monitoring data, wherein the target area comprises the plurality of monitoring areas;

the behavior recognition unit is used for performing behavior recognition processing on the at least one group of regional monitoring data to obtain a target behavior recognition result corresponding to the at least one group of regional monitoring data;

and the equipment adjusting unit is used for carrying out equipment adjustment processing on at least one monitoring area corresponding to at least one data acquisition terminal equipment corresponding to the at least one group of area monitoring data based on the target behavior recognition result so as to adjust the number of the data acquisition terminal equipment in the at least one monitoring area.

9. The smart city data-based monitoring system of claim 8, wherein the behavior recognition unit is specifically configured to:

and performing behavior recognition processing on the area monitoring data aiming at each group of area monitoring data in the at least one group of area monitoring data to obtain a target behavior recognition result corresponding to the area monitoring data, wherein the target behavior recognition result is used for representing whether a target behavior which does not meet a preset target behavior condition exists in the monitoring area corresponding to the corresponding area monitoring data.

10. The smart city data-based monitoring system of claim 8, wherein the device tuning unit is specifically configured to:

for each group of regional monitoring data in the at least one group of regional monitoring data, determining whether equipment adjustment processing needs to be performed on a monitoring region corresponding to the regional monitoring data based on the target behavior recognition result corresponding to the regional monitoring data;

for each group of regional monitoring data in the at least one group of regional monitoring data, if the fact that equipment adjustment processing is not needed to be carried out on the monitoring region corresponding to the regional monitoring data is determined, the number of the data acquisition terminal equipment in the monitoring region is not adjusted;

and aiming at each group of regional monitoring data in the at least one group of regional monitoring data, if the monitored region corresponding to the regional monitoring data needs to be subjected to equipment adjustment processing, increasing the number of data acquisition terminal equipment in the monitored region.

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