Smart community service management method and system

Abstract

The invention relates to the technical field of community personnel management, and particularly discloses an intelligent community service management method and system, and the method comprises the steps: querying road information in a community and an installed public camera, and determining a community passing network containing a station according to the road information and the installed public camera; receiving a monitoring video acquired by a public camera in real time, identifying the monitoring video, and determining traffic data of each station; and determining the personnel state of each area according to the passing data, constructing a personnel distribution map, and sending the personnel distribution map to the user side. The stations connected with all the areas are determined according to the road information of the community and the monitoring areas of the public cameras, the personnel states of all the areas are obtained in real time based on the stations and sent to the user side, so that the user can inquire which area the service personnel are located in, at the moment, the service personnel can directly go to the corresponding area to seek for the service, and the user experience is improved. And the dynamic community service is realized through the intelligent equipment.

Description

Technical Field

[0001] This invention relates to the field of community personnel management technology, specifically a smart community service management method and system. Background Technology

[0002] A smart community refers to a model that integrates various existing service resources in a community by utilizing various intelligent technologies and methods to provide residents with a variety of convenient services such as government affairs, business, entertainment, education, medical care, and mutual assistance in daily life.

[0003] Most existing community services are fixed-location services, requiring people seeking services to go to designated locations to obtain them. This means that staff spend most of their time in the office, and if they go out, some people seeking services may end up making wasted trips. This fixed service actually limits the service scope of staff. How to make staff's office locations dynamic while reducing the phenomenon of users making wasted trips is the technical problem that this application aims to solve. Summary of the Invention

[0004] The purpose of this invention is to provide a smart community service management method and system to solve the problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention provides the following technical solution:

[0006] A smart community service management method, the method comprising:

[0007] Query the road information and installed public cameras within the community, and determine the community access network containing the stations based on the road information and installed public cameras;

[0008] The community access network is used to divide and number the various areas;

[0009] The system receives surveillance videos from public cameras in real time, identifies the surveillance videos, and determines the access data for each station.

[0010] Based on the access data, the status of people in each area is determined, a personnel distribution map is constructed, and sent to the user terminal.

[0011] As a further aspect of the present invention: the step of querying road information and installed public cameras within the community, and determining the community access network containing stations based on the road information and installed public cameras includes:

[0012] Query road information within the community and construct the community road network;

[0013] Establish a connection channel with public cameras, query the working parameters of public cameras, and mark the road segments corresponding to public cameras in the community road network according to the working parameters;

[0014] Traverse the marked road segments in the community road network to identify the missing segments;

[0015] Query the endpoints of the community road network and the endpoints of missing segments, and use them as stations. Count the community road networks containing stations to obtain the community access network containing stations.

[0016] As a further aspect of the present invention: the step of receiving surveillance video acquired by public cameras in real time, identifying the surveillance video, and determining the access data for each station includes:

[0017] It receives surveillance video from public cameras in real time, identifies the surveillance video, locates the target, and records the target's characteristics.

[0018] In the community access network, query the road segment corresponding to the public camera. When the road segment contains a station, determine the movement trajectory based on the target characteristics, determine the movement direction of each station based on the movement trajectory, and determine the passage data based on the movement direction.

[0019] The passage data contains target features, including entry data and exit data; when the direction of movement is towards the station, it is determined to be exit data, and when the direction of movement is away from the station, it is determined to be entry data.

[0020] As a further aspect of the present invention: the steps of receiving surveillance video acquired by a public camera in real time, identifying the surveillance video, locating the target, and recording the target features include:

[0021] It receives surveillance video from public cameras in real time and converts the surveillance video into image frames;

[0022] The image frame is converted into a grayscale image, and then logarithmic and exponential transformations are performed on the grayscale image.

[0023] Contour recognition is performed on the logarithmically transformed image to determine the head contour;

[0024] Contour recognition is performed on the image after exponential transformation to determine facial contours;

[0025] The target is located in the original image based on the facial and head contours, and the contour features of the target are extracted as target features.

[0026] The logarithmic transformation rule is as follows: The rules for exponential transformation are as follows: In the formula, a1, b1, c1, a2, b2 and c2 are parameters preset by the management; g(i,j) is the value of point (i,j) after transformation, and f(i,j) is the value of point (i,j) before transformation.

[0027] As a further aspect of the present invention: the step of determining the personnel status of each area based on the access data, constructing a personnel distribution map, and sending it to the user terminal includes:

[0028] The traversal order is determined by the region number, and the stations and their access data for each region are queried sequentially based on the traversal order.

[0029] When the passage data is outbound data, add target features to this area;

[0030] When the data is inbound data, remove the target features in this area;

[0031] Periodically analyze target characteristics across all areas, construct personnel distribution maps, and send them to user terminals.

[0032] As a further aspect of the present invention, the method further includes:

[0033] The system receives area labeling information input from the management unit and labels target features within the area based on the area labeling information; the area labeling information is used to characterize the identity information of each area.

[0034] When constructing a personnel distribution map, the labeling information of target features is retained in real time.

[0035] The present invention also provides a smart community service management system, the system comprising:

[0036] The access network construction module is used to query road information and installed public cameras within the community, and determine the community access network containing stations based on the road information and installed public cameras;

[0037] The region segmentation module is used to segment and number each region based on the community access network;

[0038] The video recognition module is used to receive surveillance videos acquired by public cameras in real time, recognize the surveillance videos, and determine the access data of each station.

[0039] The personnel status determination module is used to determine the personnel status in each area based on the access data, construct a personnel distribution map, and send it to the user terminal.

[0040] As a further aspect of the present invention: the access network construction module includes:

[0041] The road network construction unit is used to query road information within the community and construct the community road network.

[0042] The road segment marking unit is used to establish a connection channel with the public camera, query the working parameters of the public camera, and mark the road segment corresponding to the public camera in the community road network according to the working parameters;

[0043] The road segment calculation unit is used to traverse the marked road segments in the community road network and determine the missing segments;

[0044] The station marking unit is used to query the endpoints of the community road network and the endpoints of missing segments as stations, and to count the community road networks containing stations to obtain the community access network containing stations.

[0045] As a further aspect of the present invention: the video recognition module includes:

[0046] The feature recognition unit is used to receive surveillance video from public cameras in real time, identify the surveillance video, locate the target, and record the target features.

[0047] The passage data determination unit is used to query the road segment corresponding to the public camera in the community passage network. When the road segment contains a station, it determines the movement trajectory based on the target characteristics, determines the movement direction of each station based on the movement trajectory, and determines the passage data based on the movement direction.

[0048] The passage data contains target features, including entry data and exit data; when the direction of movement is towards the station, it is determined to be exit data, and when the direction of movement is away from the station, it is determined to be entry data.

[0049] As a further aspect of the present invention: the personnel status determination module includes:

[0050] The data traversal unit is used to determine the traversal order based on the number of each region, and to query the stations and their passage data of each region in turn based on the traversal order.

[0051] The data addition unit is used to add target features to the area when the passage data is outbound data;

[0052] The data removal unit is used to remove target features in this area when the data is inbound data.

[0053] The identification execution unit is used to periodically collect target features in all areas, construct personnel distribution maps, and send them to the user terminal.

[0054] Compared with the prior art, the beneficial effects of the present invention are: the present invention determines the stations connected to each area based on the community's road information and the monitoring area of ​​public cameras, obtains the status of personnel in each area in real time based on the stations, and sends it to the user terminal, so that the user can find out which area the service personnel are in. At this time, when seeking services, they can go directly to the corresponding area, thus realizing the dynamicization of community services through smart devices. Attached Figure Description

[0055] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention.

[0056] Figure 1 A flowchart for smart community service management methods.

[0057] Figure 2 The first sub-process flowchart for the service management methodology of smart communities.

[0058] Figure 3 The second sub-process flowchart for the smart community service management method.

[0059] Figure 4 The third sub-process flowchart for smart community service management methods.

[0060] Figure 5 A structural diagram of the smart community service management system. Detailed Implementation

[0061] To make the technical problems to be solved, the technical solutions, and the beneficial effects of the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present invention and are not intended to limit the present invention.

[0062] Figure 1 The flowchart illustrates a smart community service management method. In this embodiment of the invention, a smart community service management method includes:

[0063] Step S100: Query the road information and installed public cameras in the community, and determine the community access network containing stations based on the road information and installed public cameras;

[0064] Road information within the community is generally determined and filed in advance during construction. Considering pedestrian traffic habits, there will be some other roads in addition to the original filings. These roads are also included in the road information mentioned above. Therefore, the management needs to upload and supplement road information regularly.

[0065] Furthermore, by querying the public cameras installed within the community, we can identify cameras that monitor public areas and can be used to monitor various roads. By combining road information with the public camera data, a community access network can be constructed. Stations can be set up within this network, which can be understood as entrances and exits to various areas, mostly buildings (non-public areas), where public cameras do not have the corresponding monitoring permissions.

[0066] Step S200: Divide and number each area based on the community access network;

[0067] In layman's terms, the resulting community access network consists of various roads and points on those roads that allow access to other areas. By analyzing the community access network, the accessible areas can be segmented and numbered.

[0068] Step S300: Receive surveillance video from public cameras in real time, identify the surveillance video, and determine the access data for each station;

[0069] By establishing a connection channel with public cameras and acquiring surveillance video through these cameras, and by identifying the surveillance video, the passage data of each station can be determined. The passage data of each station refers to how many people pass through each station and their passage status, which includes leaving the area and entering the area.

[0070] Step S400: Determine the personnel status of each area based on the access data, construct a personnel distribution map, and send it to the user terminal;

[0071] By analyzing the traffic data, we can determine the status of people in each area. The status of people includes the number of people and their identity information. By statistically analyzing the status of people in each area, we can construct a population distribution map, which represents how many people are in each area. Sending the population distribution map to the user terminal allows the user to know how many people are in each area in real time.

[0072] Figure 2 The first sub-process flowchart of the smart community service management method includes the steps of querying road information and installed public cameras within the community, and determining the community access network containing stations based on the road information and installed public cameras:

[0073] Step S101: Query road information within the community and construct the community road network;

[0074] Step S102: Establish a connection channel with the public camera, query the working parameters of the public camera, and mark the road segment corresponding to the public camera in the community road network according to the working parameters;

[0075] Step S103: Traverse the marked road segments in the community road network to determine the missing segments;

[0076] Step S104: Query the endpoints of the community road network and the endpoints of the missing segments as stations, count the community road networks containing stations, and obtain the community access network containing stations.

[0077] By querying road information within the community, a community road network can be constructed, which can be regarded as a map containing only roads. Then, by establishing a connection channel with public cameras and querying the monitoring angles of public cameras, their monitoring areas can be determined. By querying the roads contained in the monitoring area, and based on the same scale, the corresponding road segments can be marked in the community road network.

[0078] The number of public cameras is limited, and few communities can achieve full-scale monitoring. Therefore, the marked road segments in the community road network are separate and not necessarily connected to each other. Based on this, unmarked road segments are considered missing segments. Finally, by querying the endpoints of the missing segments and the endpoints of each road, these can be used as stations. These stations are then inserted into the community road network, which is called the community access network.

[0079] Figure 3 The second sub-process flowchart of the smart community service management method includes the following steps: receiving real-time surveillance video from public cameras, identifying the surveillance video, and determining the access data for each station.

[0080] Step S301: Receive surveillance video from public cameras in real time, identify the surveillance video, locate the target, and record the target features;

[0081] Step S302: Query the road segment corresponding to the public camera in the community access network. When the road segment contains a station, determine the movement trajectory based on the target characteristics, determine the movement direction of each station based on the movement trajectory, and determine the access data based on the movement direction.

[0082] The passage data contains target features, including entry data and exit data; when the direction of movement is towards the station, it is determined to be exit data, and when the direction of movement is away from the station, it is determined to be entry data.

[0083] Establish connection channels with various public cameras to receive surveillance videos in real time. Identify pedestrians in the videos and record their target features simultaneously. Since it is video data, identify each image in the video data to obtain a motion trajectory, which is the position of the pedestrian at each moment. Analyze the motion trajectory to determine the passage data.

[0084] Furthermore, the steps of receiving surveillance video from public cameras in real time, identifying the surveillance video, locating the target, and recording the target's features include:

[0085] It receives surveillance video from public cameras in real time and converts the surveillance video into image frames;

[0086] The image frame is converted into a grayscale image, and then logarithmic and exponential transformations are performed on the grayscale image.

[0087] Contour recognition is performed on the logarithmically transformed image to determine the head contour;

[0088] Contour recognition is performed on the image after exponential transformation to determine facial contours;

[0089] The target is located in the original image based on the facial and head contours, and the contour features of the target are extracted as target features.

[0090] The logarithmic transformation rule is as follows: The rules for exponential transformation are as follows: In the formula, a1, b1, c1, a2, b2 and c2 are parameters preset by the management; g(i,j) is the value of point (i,j) after transformation, and f(i,j) is the value of point (i,j) before transformation.

[0091] In one embodiment of the technical solution of this invention, the target localization process and the target feature determination process are specifically defined. First, the surveillance video is converted into image frames, and their order is preserved during the conversion process. Then, each image is converted into a grayscale image. The grayscale image is equivalent to a simplification of the original image, which can simplify the calculation in the contour recognition process. However, while simplifying the calculation, some information is lost. Therefore, the grayscale image needs to be adjusted before contour recognition. The purpose of this application is to locate pedestrians. The most obvious features of pedestrians are their hair and face areas. The hair area is mostly black, and its grayscale value is relatively small. Logarithmic transformation can be used to extend it, making the difference between it and the surrounding environment more obvious, thereby making the contour recognition process more accurate. Furthermore, the face area is generally light-colored, and its grayscale value is relatively large. Exponential transformation can be used to extend it, making the difference between it and the surrounding environment more obvious, thereby making the contour recognition process more accurate.

[0092] In layman's terms, the process of recognizing dark contours is optimized by logarithmic transformation, and the process of recognizing light contours is optimized by exponential transformation.

[0093] Finally, based on the contour recognition results, the pedestrian can be located, and the contour information of the pedestrian in the original image can be extracted as the target feature.

[0094] Specifically, the functions of the logarithmic and exponential transformations mentioned above are explained as follows:

[0095] The function of logarithmic transformation is to stretch the low grayscale areas of an image more significantly and compress the high grayscale areas. In this case, the grayscale difference between pixels in the low grayscale areas will increase, making the contour recognition of dark areas more accurate. Conversely, the function of exponential transformation is to stretch the high grayscale areas of an image more significantly. The grayscale difference between pixels in the high grayscale areas will increase, making the contour recognition of bright areas more accurate.

[0096] Figure 4 The third sub-process flowchart of the smart community service management method includes the following steps: determining the personnel status of each area based on the access data, constructing a personnel distribution map, and sending it to the user terminal.

[0097] Step S401: Determine the traversal order based on the area number, and query the stations and their access data for each area in turn based on the traversal order;

[0098] Step S402: When the passage data is outbound data, add target features to this area;

[0099] Step S403: When the passage data is inbound data, remove the target feature in this area;

[0100] Step S404: Periodically collect target features in all areas, construct a personnel distribution map, and send it to the user terminal.

[0101] In one example of the technical solution of this invention, the traversal order of each region is determined according to the numbering result, and the stations and passage data of each region are queried in turn according to the traversal order. The meaning of this process is that a region may correspond to multiple stations (multiple roads point to the same region). Only by statistically analyzing the passage data of all stations can the actual situation in a region be obtained. If the passage data at a certain station is entry data, the target feature is added to the region. If the passage data at a certain station is exit data, the target feature is removed from the region. In this way, the target features of each region can be obtained, and then a personnel distribution map can be constructed.

[0102] Specifically, regarding the personnel distribution map, it is used to represent the situation of people in different areas. The simplest way is to only record the number of people in each area. On this basis, if the target features can be identified and their identities determined, then the identity information can be filled into each area.

[0103] The principle of identifying target features is generally to identify work clothes. The identity information of personnel in certain special uniforms is easy to determine. The identity information identified in this application is only the personnel's occupation and does not include the personnel's personal information.

[0104] As a preferred embodiment of the technical solution of the present invention, the method further includes:

[0105] The system receives area labeling information input from the management unit and labels target features within the area based on the area labeling information; the area labeling information is used to characterize the identity information of each area.

[0106] When constructing a personnel distribution map, the labeling information of target features is retained in real time.

[0107] The above content provides a specific application process in which the management marks personnel in each area. The marking process generally occurs during working hours. At this time, all personnel in the service center can be marked as staff. Then, when constructing the personnel distribution map later, staff will be marked in each area, which is equivalent to further limiting the personnel distribution map.

[0108] Figure 5 The diagram shows the structural composition of a smart community service management system. In this embodiment of the invention, a smart community service management system 10 includes:

[0109] The access network construction module 11 is used to query road information and installed public cameras within the community, and determine the community access network containing stations based on the road information and installed public cameras;

[0110] The region segmentation module 12 is used to segment and number each region based on the community access network;

[0111] The video recognition module 13 is used to receive surveillance videos acquired by public cameras in real time, recognize the surveillance videos, and determine the access data of each station.

[0112] The personnel status determination module 14 is used to determine the personnel status of each area based on the passage data, construct a personnel distribution map, and send it to the user terminal.

[0113] Furthermore, the access network construction module 11 includes:

[0114] The road network construction unit is used to query road information within the community and construct the community road network.

[0115] The road segment marking unit is used to establish a connection channel with the public camera, query the working parameters of the public camera, and mark the road segment corresponding to the public camera in the community road network according to the working parameters;

[0116] The road segment calculation unit is used to traverse the marked road segments in the community road network and determine the missing segments;

[0117] The station marking unit is used to query the endpoints of the community road network and the endpoints of missing segments as stations, and to count the community road networks containing stations to obtain the community access network containing stations.

[0118] Specifically, the video recognition module 13 includes:

[0119] The feature recognition unit is used to receive surveillance video from public cameras in real time, identify the surveillance video, locate the target, and record the target features.

[0120] The passage data determination unit is used to query the road segment corresponding to the public camera in the community passage network. When the road segment contains a station, it determines the movement trajectory based on the target characteristics, determines the movement direction of each station based on the movement trajectory, and determines the passage data based on the movement direction.

[0121] The passage data contains target features, including entry data and exit data; when the direction of movement is towards the station, it is determined to be exit data, and when the direction of movement is away from the station, it is determined to be entry data.

[0122] Furthermore, the personnel status determination module 14 includes:

[0123] The data traversal unit is used to determine the traversal order based on the number of each region, and to query the stations and their passage data of each region in turn based on the traversal order.

[0124] The data addition unit is used to add target features to the area when the passage data is outbound data;

[0125] The data removal unit is used to remove target features in this area when the data is inbound data.

[0126] The identification execution unit is used to periodically collect target features in all areas, construct personnel distribution maps, and send them to the user terminal.

[0127] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A smart community service management method, characterized in that, The method includes: Query the road information and installed public cameras within the community, and determine the community access network containing the stations based on the road information and installed public cameras; The community access network is used to divide and number the various areas; The system receives surveillance videos from public cameras in real time, identifies the surveillance videos, and determines the access data for each station. Based on the access data, the status of people in each area is determined, a personnel distribution map is constructed, and sent to the user terminal.

2. The smart community service management method according to claim 1, characterized in that, The steps of querying road information and installed public cameras within the community, and determining the community access network containing stations based on the road information and installed public cameras, include: Query road information within the community and construct the community road network; Establish a connection channel with public cameras, query the working parameters of public cameras, and mark the road segments corresponding to public cameras in the community road network according to the working parameters; Traverse the marked road segments in the community road network to identify the missing segments; Query the endpoints of the community road network and the endpoints of missing segments, and use them as stations. Count the community road networks containing stations to obtain the community access network containing stations.

3. The smart community service management method according to claim 2, characterized in that, The steps of receiving surveillance video from public cameras in real time, identifying the surveillance video, and determining the access data for each station include: It receives surveillance video from public cameras in real time, identifies the surveillance video, locates the target, and records the target's characteristics. In the community access network, query the road segment corresponding to the public camera. When the road segment contains a station, determine the movement trajectory based on the target characteristics, determine the movement direction of each station based on the movement trajectory, and determine the passage data based on the movement direction. The passage data contains target features, including entry data and exit data; when the direction of movement is towards the station, it is determined to be exit data, and when the direction of movement is away from the station, it is determined to be entry data.

4. The smart community service management method according to claim 3, characterized in that, The steps of receiving surveillance video from public cameras in real time, identifying the surveillance video, locating the target, and recording the target's features include: It receives surveillance video from public cameras in real time and converts the surveillance video into image frames; The image frame is converted into a grayscale image, and then logarithmic and exponential transformations are performed on the grayscale image. Contour recognition is performed on the logarithmically transformed image to determine the head contour; Contour recognition is performed on the image after exponential transformation to determine facial contours; The target is located in the original image based on the facial and head contours, and the contour features of the target are extracted as target features. The logarithmic transformation rule is as follows: The rules for exponential transformation are as follows: In the formula, a1, b1, c1, a2, b2 and c2 are parameters preset by the management; g(i,j) is the value of point (i,j) after transformation, and f(i,j) is the value of point (i,j) before transformation.

5. The smart community service management method according to claim 3, characterized in that, The steps of determining the personnel status of each area based on the access data, constructing a personnel distribution map, and sending it to the user terminal include: The traversal order is determined by the region number, and the stations and their access data for each region are queried sequentially based on the traversal order. When the passage data is outbound data, add target features to this area; When the data is inbound data, remove the target features in this area; Periodically analyze target characteristics across all areas, construct personnel distribution maps, and send them to user terminals.

6. The smart community service management method according to claim 5, characterized in that, The method further includes: The system receives area labeling information input from the management unit and labels target features within the area based on the area labeling information; the area labeling information is used to characterize the identity information of each area. When constructing a personnel distribution map, the labeling information of target features is retained in real time.

7. A smart community service management system, characterized in that, The system includes: The access network construction module is used to query road information and installed public cameras within the community, and determine the community access network containing stations based on the road information and installed public cameras; The region segmentation module is used to segment and number each region based on the community access network; The video recognition module is used to receive surveillance videos acquired by public cameras in real time, recognize the surveillance videos, and determine the access data of each station. The personnel status determination module is used to determine the personnel status in each area based on the access data, construct a personnel distribution map, and send it to the user terminal.

8. The smart community service management system according to claim 7, characterized in that, The access network construction module includes: The road network construction unit is used to query road information within the community and construct the community road network. The road segment marking unit is used to establish a connection channel with the public camera, query the working parameters of the public camera, and mark the road segment corresponding to the public camera in the community road network according to the working parameters; The road segment calculation unit is used to traverse the marked road segments in the community road network and determine the missing segments; The station marking unit is used to query the endpoints of the community road network and the endpoints of missing segments as stations, and to count the community road networks containing stations to obtain the community access network containing stations.

9. The smart community service management system according to claim 8, characterized in that, The video recognition module includes: The feature recognition unit is used to receive surveillance video from public cameras in real time, identify the surveillance video, locate the target, and record the target features. The passage data determination unit is used to query the road segment corresponding to the public camera in the community passage network. When the road segment contains a station, it determines the movement trajectory based on the target characteristics, determines the movement direction of each station based on the movement trajectory, and determines the passage data based on the movement direction. The passage data contains target features, including entry data and exit data; when the direction of movement is towards the station, it is determined to be exit data, and when the direction of movement is away from the station, it is determined to be entry data.

10. The smart community service management system according to claim 9, characterized in that, The personnel status determination module includes: The data traversal unit is used to determine the traversal order based on the number of each region, and to query the stations and their passage data of each region in turn based on the traversal order. The data addition unit is used to add target features to the area when the transit data is outbound data; The data removal unit is used to remove target features in this area when the data is inbound data. The identification execution unit is used to periodically collect target features in all areas, construct personnel distribution maps, and send them to the user terminal.

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