A community interaction management system and method based on data analysis technology

Abstract

The present invention discloses a community interactive management system and method based on data analysis technology, belonging to the field of data analysis technology. The present invention uses a community sensor device network to obtain planar data of community distribution and divide monitoring sub-areas, and obtains road condition data for each sub-area; simulates the road condition of each sub-area synchronously through virtual modeling, simulates real-time road condition data for each sub-area, and performs road condition data integration processing for each sub-area; performs real-time vehicle spatial factor influence analysis and time factor influence analysis based on the road condition data of each sub-area, judges abnormal vehicles in each sub-area by comprehensive spatial and temporal analysis, and analyzes the abnormal degree of the sub-area in combination with the judgment results; outputs the road condition data of each sub-area in real time, marks abnormal vehicles and abnormal areas, and feeds back abnormal analysis data.

Description

Technical Field

[0001] The present invention relates to the field of data analysis technology, and in particular to a community interaction management system and method based on data analysis technology. Background Art

[0002] Community interaction management refers to the process of promoting effective communication and interaction among community members through planning, organization, guidance and supervision, while maintaining community order, culture and values ​​to build a positive, healthy and orderly community environment;

[0003] At present, due to the maturity of artificial intelligence technology and Internet of Things technology, they have been introduced into community management systems, making community interactive management more flexible and rapid to a large extent. However, management and regulation in some aspects are still at a relatively low level of intelligence; among them, the monitoring and regulation of temporary vehicle parking in the community is still mostly monitored by manual inspections and manual remote monitoring, and manual regulation and processing are carried out for abnormal vehicles or areas; this method is relatively backward and unintelligent in the current technical environment, and cannot meet the vehicle parking processing and planning needs brought about by the ever-increasing number of vehicles, and is prone to disorderly parking and outdated congested parking, causing vehicle congestion or environmental disorder. Summary of the invention

[0004] The purpose of the present invention is to provide a community interaction management system and method based on data analysis technology to solve the problems raised in the above background technology.

[0005] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0006] A community interaction management method based on data analysis technology, the method comprising the following steps:

[0007] S100, using a community sensor network to acquire planar data on community distribution and divide monitoring sub-areas, and acquiring road condition data for each sub-area;

[0008] S200, simulating the road conditions of each sub-area synchronously through virtual modeling, simulating the real-time road condition data of each sub-area, and integrating the road condition data of each sub-area;

[0009] S300, performing real-time vehicle spatial factor impact analysis and temporal factor impact analysis based on the road condition data of each sub-area, judging abnormal vehicles in each sub-area through comprehensive spatial and temporal analysis, and analyzing the degree of abnormality of the sub-area based on the judgment results;

[0010] S400: Output the road condition data of each sub-area in real time, mark abnormal vehicles and abnormal areas, and feed back abnormal analysis data.

[0011] The S100 uses the community sensor network to acquire planar data of the community distribution and divide the monitoring sub-areas. The specific steps of acquiring road condition data for each sub-area are as follows:

[0012] S101. Obtain monitoring signals of each monitoring sensor device in the corresponding area of ​​the community through the community sensor device control port, and monitor the road condition data of the community by integrating the signal data of each monitoring sensor device to build a monitoring platform; the monitoring sensor device includes an image acquisition sensor device and a video acquisition sensor device; the road condition data includes the area of ​​temporary parking areas on community roads, the number of temporary parked vehicles, the type of temporary vehicles, and the temporary parking time of temporary vehicles;

[0013] S102. The community monitoring area is divided into sub-areas by adopting the same monitoring division method. The location and number of sensor monitoring equipment in the community are coordinated. The community is monitored in non-overlapping areas by using the same number of sensor monitoring equipment. The area monitored by the sensor monitoring equipment in each non-overlapping monitoring area is obtained to achieve sub-area division. Each sub-area is assigned a corresponding area number, and the sensor monitoring equipment in each sub-area is assigned a secondary number.

[0014] The specific steps of S200 for simulating the road conditions of each sub-area synchronously through virtual modeling, simulating the real-time road condition data of each sub-area, and integrating the road condition data of each sub-area are as follows:

[0015] S201, using the virtual cloud 3D modeling platform to synchronously model the real-time traffic data in each sub-area of ​​the community, reducing the data dimension of the virtual model of each sub-area through environmental data noise reduction, and obtaining a cloud monitoring model of the temporary vehicle parking area in each sub-area;

[0016] S202. Integrate and process the real-time traffic data of temporary parking areas for temporary vehicles in each sub-area of ​​the community according to the cloud monitoring model. Generate a self-updated traffic data table for the corresponding sub-area by collecting statistics on the traffic data of each sub-area. Associate the self-updated traffic data table of each sub-area with the corresponding area to realize the integrated processing of real-time monitoring traffic data of each sub-area.

[0017] The specific steps of performing real-time vehicle spatial factor impact analysis and time factor impact analysis based on the road condition data of each sub-area, judging abnormal vehicles in each sub-area by combining spatial and time analysis, and analyzing the abnormality degree of the sub-area based on the judgment results are as follows:

[0018] S301, perform spatial distribution impact analysis on the road condition data of temporary parking areas in each sub-area; obtain the type data of each vehicle in the temporary parking area of ​​the sub-area in real time through the real-time cloud monitoring model, and divide the vehicles into different levels according to the vehicle types; analyze the spatial impact of the temporary parking area of ​​the vehicle according to the vehicle level division results of the temporary parking area of ​​each sub-area and the distribution of vehicle parking space, and the calculation formula is: Among them, Vsi (j,k,n) S is the space occupancy impact value of vehicle number n in the temporary parking area of ​​vehicle number k in the corresponding sub-area number j; (j,k,n) S is the ground area occupied by vehicle number n in the temporary parking area for vehicle number k in the corresponding sub-area number j; (j,k) E is the area of ​​the temporary parking area for vehicle number k in the corresponding sub-area number j; (j,k) S is the effective utilization rate of the temporary parking area for vehicle number k in the corresponding sub-area number j; (j,k,n)Z is the relative area occupied by vehicle numbered n in the temporary parking area of ​​vehicle numbered k in the corresponding sub-area numbered j; the relative area occupied is the space occupied by the target vehicle constructed between the parked vehicle and the surrounding vehicles or the edge of the parking area; the sub-areas, temporary parking areas and vehicle numbers are assigned in sequence; combined with the space occupancy impact value of each vehicle, the comprehensive spatial impact distribution analysis of the temporary parking area of ​​the vehicle in the corresponding sub-area is performed, and the calculation formula is: Among them, Sdi (j,k) is the comprehensive spatial distribution impact value of the temporary parking area numbered k in the corresponding sub-area numbered j; m (j,k) is the real-time number of vehicles parked in the temporary parking area with number k in the corresponding sub-area with number j; M(j,k) is the maximum number of vehicles parked in the temporary parking area with number k in the corresponding sub-area with number j; L is the vehicle classification level; N (j,k,L) Vsi is the number of parked vehicles at level L in the temporary parking area for vehicles numbered k in the corresponding sub-area numbered j; (j,k,n)L is the space occupancy impact value of the vehicle numbered n and level L in the temporary parking area numbered k in the corresponding sub-area numbered j; P(L) is the number of vehicle classification levels;

[0019] S302, analyzing the time series impact of each vehicle in combination with the monitoring period of the temporary parking area corresponding to each sub-area; analyzing the time series factors of the temporary parking area corresponding to each sub-area in the monitoring period in combination with the parking time and the number of parking times of the vehicle in the temporary parking area, and the calculation formula is: Where Vti (j,k,n) is the real-time time occupancy impact value of vehicle number n in the temporary parking area of ​​vehicle number k in the corresponding sub-area number j;(j,k,n) is the parking time of vehicle numbered n in the temporary parking area of ​​vehicle numbered k in the corresponding sub-area numbered j during the monitoring period; c (j,k,n) is the number of parking times of vehicle numbered n in the temporary parking area of ​​vehicle numbered k in the corresponding sub-area numbered j within the monitoring period; T is the monitoring period; C (j,k) is the total number of vehicle parking times in the monitoring period of the temporary parking area numbered k in the corresponding sub-area numbered j; the number of vehicle parking times is at least 1; the time occupancy impact value of each vehicle temporary parking area corresponding to each sub-area is comprehensively analyzed for the comprehensive time occupancy impact of each vehicle temporary parking area, and the calculation formula is: Among them, R (j,k) is the comprehensive time series impact value of the temporary parking area numbered k in the corresponding sub-area numbered j; t (j,k,n)max is the maximum parking time of vehicle numbered n in the temporary parking area of ​​vehicle numbered k in the corresponding sub-area numbered j during the monitoring period; T (j,k)max Vti is the maximum parking time allowed for the temporary parking area for vehicle numbered k in the corresponding sub-area numbered j; (j,k,n)L is the time occupancy impact value of the vehicle with level L numbered n in the temporary parking area with vehicle numbered k in the corresponding sub-area with vehicle numbered j; wherein, the comprehensive spatial impact and time impact of the temporary parking area are analyzed by introducing the vehicle level, because different vehicles have different impacts when parking in the temporary parking area, and introducing it into the calculation makes the calculation result more accurate;

[0020] S303, based on the spatial distribution impact analysis and time series impact analysis of each temporary parking area corresponding to each vehicle in each sub-area, a state analysis is performed on each temporary parking area of ​​the vehicle, and the calculation formula is Osi (j,k) =δ1*Sdi (j,k) +δ2*Rti (j,k) ; Osi (j,k) is the real-time ordered state index of the temporary parking area numbered k in the corresponding sub-area numbered j; where δ1 and δ2 are the spatial distribution influence coefficient and the time series influence coefficient respectively; by introducing the reference threshold Osi (j,k)th and correction parameter γ to judge the status of each vehicle temporary parking area; when Osi (j,k) <Osi (j,k)th -γ, judge that the temporary parking area of ​​the current target vehicle is orderly and normal; when Osi (j,k)th -γ≤Osi (j,k) ≤Osi (j,k)th , it is judged that there is a disorder risk in the temporary parking area of ​​the current target vehicle; when Osi (j,k) ≥Osi (j,k)th, judge that the current target vehicle temporary parking area is in a disordered abnormal state; the reference threshold is a regional ordered index rectification threshold given by combining the ordered index data of historical vehicle temporary parking areas with manual experience. When the threshold is reached, it means that the corresponding vehicle temporary parking area is in a disordered abnormal state due to disordered placement of vehicles or congestion caused by outdated placement of vehicles; the correction parameter is an artificially set warning parameter.

[0021] The specific steps of S400 for outputting the road condition data of each sub-area in real time, marking abnormal vehicles and abnormal areas, and feeding back abnormal analysis data are as follows:

[0022] S401, outputting the real-time vehicle parking data corresponding to the temporary parking area of ​​each sub-area, and feeding back the corresponding area status index;

[0023] S402. Issue corresponding level warnings to areas with disorder risks and disorder anomalies, where disorder risks are primary warnings and disorder anomalies are high-level warnings.

[0024] A community interaction management system based on data analysis technology, the system includes a monitoring area division module, a data virtual processing module, a regional comprehensive status analysis module and a regional data feedback module;

[0025] The monitoring area division module uses the community sensor equipment network to obtain planar data of community distribution and divide monitoring sub-areas, and obtains road condition data for each sub-area; the data virtual processing module synchronously simulates the road condition of each sub-area through virtual modeling, simulates real-time road condition data for each sub-area, and integrates and processes road condition data for each sub-area; the regional comprehensive status analysis module performs real-time vehicle spatial factor impact analysis and time factor impact analysis based on the road condition data of each sub-area, comprehensively analyzes abnormal vehicles in each sub-area through spatial and temporal analysis, and analyzes the degree of abnormality of the sub-area based on the judgment results; the regional data feedback module outputs the road condition data of each sub-area in real time, marks abnormal vehicles and abnormal areas, and feeds back abnormal analysis data.

[0026] The monitoring area division module includes an area data acquisition unit and a sub-area division unit;

[0027] The regional data acquisition unit acquires the monitoring signals of each monitoring sensor device in the corresponding area of ​​the community through the community sensor device control port, and integrates the acquired signal data of each monitoring sensor device to build a monitoring platform to monitor the road condition data of the community; the monitoring sensor device includes an image acquisition sensor device and a video acquisition sensor device; the road condition data includes the area of ​​temporary parking areas for vehicles on community roads, the number of temporary parking vehicles, the type of temporary vehicles and the temporary parking time of temporary vehicles;

[0028] The sub-area division unit divides the community monitoring area into sub-areas by adopting the same-regular monitoring division method. It coordinates the location and quantity of the sensor monitoring equipment in the community, and monitors the non-overlapping areas of the community with the same number of sensor monitoring equipment. The area monitored by the sensor monitoring equipment in each non-overlapping monitoring area is obtained, thereby realizing the sub-area division; each sub-area is assigned a corresponding area number, and the sensor monitoring equipment in each sub-area is assigned a secondary number.

[0029] The data virtual processing module includes a regional cloud monitoring model building unit and a monitoring road condition data integration processing unit;

[0030] The regional cloud monitoring model construction unit uses the virtual cloud 3D modeling platform to synchronously model the real-time traffic data in each sub-area of ​​the community, reduces the data dimension of the virtual model of each sub-area through environmental data noise reduction, and obtains the cloud monitoring model of the temporary vehicle parking area of ​​each sub-area;

[0031] The monitoring road condition data integration processing unit performs real-time road condition data integration processing on temporary parking areas for temporary vehicles in each sub-area of ​​the community according to the cloud monitoring model, generates a self-updating road condition data table for the corresponding sub-area by collecting road condition data statistics for each sub-area, and associates the self-updating road condition data table of each sub-area with the corresponding area to realize real-time monitoring road condition data integration processing for each sub-area.

[0032] The regional comprehensive status analysis module includes a regional spatial distribution impact analysis unit, a regional time series impact analysis unit and a regional status judgment analysis unit;

[0033] The regional spatial distribution impact analysis unit performs spatial distribution impact analysis on the road condition data of the temporary vehicle parking area in each sub-area; obtains the type data of each vehicle in the temporary vehicle parking area in the sub-area in real time through the real-time cloud monitoring model, and divides the vehicle into levels according to the vehicle type; analyzes the spatial impact of the temporary vehicle parking area in combination with the vehicle parking space distribution according to the vehicle level division results of the temporary vehicle parking area in each sub-area; and performs a comprehensive spatial impact distribution analysis on the temporary vehicle parking area in the corresponding sub-area in combination with the space occupancy impact value of each vehicle;

[0034] The regional time series impact analysis unit analyzes the time series impact of each vehicle in combination with the monitoring period of the temporary parking area corresponding to each sub-area; performs time series factor analysis on the temporary parking area corresponding to each vehicle in each sub-area within the monitoring period in combination with the parking time and the number of parking times of the vehicle in the temporary parking area; performs comprehensive time occupancy impact analysis on each temporary parking area according to the time occupancy impact value of each vehicle in the period corresponding to each temporary parking area of ​​each sub-area;

[0035] The regional status judgment and analysis unit comprehensively analyzes the spatial distribution impact analysis and time series impact analysis of each vehicle temporary parking area in each sub-area, and performs status analysis on each vehicle temporary parking area; and judges the status of each vehicle temporary parking area by introducing reference thresholds and correction parameters.

[0036] The regional data feedback module includes a regional data feedback unit and an abnormal area warning unit;

[0037] The regional data feedback unit outputs the real-time vehicle parking data corresponding to the temporary parking area of ​​each sub-area, and feeds back the corresponding regional status index;

[0038] The abnormal area warning unit issues corresponding level warnings to areas with disorder risks and disorder anomalies, wherein disorder risks are primary warnings and disorder anomalies are advanced warnings.

[0039] Compared with the prior art, the beneficial effects achieved by the present invention are as follows: the present invention combines the community monitoring system to acquire regional vehicle parking data, and realizes all-round observation of each area by introducing model construction, and conducts comprehensive analysis of each area in combination with the observation data; combines the spatial occupancy and temporal occupancy impact analysis of the vehicle, conducts comprehensive spatial distribution impact analysis and time series impact analysis of each area, obtains the ordered index of vehicle parking in different areas in combination with the analysis results, and judges the status of each area according to the calculated ordered index results; the present invention can realize real-time monitoring and analysis of the parking conditions and ordered impact of each temporary parking area in the community through comprehensive analysis, and can warn of disorder in each area, which greatly improves the detection and control strategy of the temporary vehicle parking area in the community, and improves the monitoring inaccuracy and control lag caused by traditional manual inspections. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0041] Figure 1 It is a structural schematic diagram of a community interaction management system based on data analysis technology of the present invention. DETAILED DESCRIPTION

[0042] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0043] See also Figure 1 , the present invention provides a technical solution:

[0044] A community interaction management method based on data analysis technology, the method comprising the following steps:

[0045] S100, using a community sensor network to acquire planar data on community distribution and divide monitoring sub-areas, and acquiring road condition data for each sub-area;

[0046] S200, simulating the road conditions of each sub-area synchronously through virtual modeling, simulating the real-time road condition data of each sub-area, and integrating the road condition data of each sub-area;

[0047] S300, performing real-time vehicle spatial factor impact analysis and temporal factor impact analysis based on the road condition data of each sub-area, judging abnormal vehicles in each sub-area through comprehensive spatial and temporal analysis, and analyzing the degree of abnormality of the sub-area based on the judgment results;

[0048] S400: Output the road condition data of each sub-area in real time, mark abnormal vehicles and abnormal areas, and feed back abnormal analysis data.

[0049] The S100 uses the community sensor network to acquire planar data of the community distribution and divide the monitoring sub-areas. The specific steps of acquiring road condition data for each sub-area are as follows:

[0050] S101. Obtain monitoring signals of each monitoring sensor device in the corresponding area of ​​the community through the community sensor device control port, and monitor the road condition data of the community by integrating the signal data of each monitoring sensor device to build a monitoring platform; the monitoring sensor device includes an image acquisition sensor device and a video acquisition sensor device; the road condition data includes the area of ​​temporary parking areas on community roads, the number of temporary parked vehicles, the type of temporary vehicles, and the temporary parking time of temporary vehicles;

[0051] S102. The community monitoring area is divided into sub-areas by adopting the same monitoring division method. The location and number of sensor monitoring equipment in the community are coordinated. The community is monitored in non-overlapping areas by using the same number of sensor monitoring equipment. The area monitored by the sensor monitoring equipment in each non-overlapping monitoring area is obtained to achieve sub-area division. Each sub-area is assigned a corresponding area number, and the sensor monitoring equipment in each sub-area is assigned a secondary number.

[0052] The specific steps of S200 for simulating the road conditions of each sub-area synchronously through virtual modeling, simulating the real-time road condition data of each sub-area, and integrating the road condition data of each sub-area are as follows:

[0053] S201, using the virtual cloud 3D modeling platform to synchronously model the real-time traffic data in each sub-area of ​​the community, reducing the data dimension of the virtual model of each sub-area through environmental data noise reduction, and obtaining a cloud monitoring model of the temporary vehicle parking area in each sub-area;

[0054] S202. Integrate and process the real-time traffic data of temporary parking areas for temporary vehicles in each sub-area of ​​the community according to the cloud monitoring model. Generate a self-updated traffic data table for the corresponding sub-area by collecting statistics on the traffic data of each sub-area. Associate the self-updated traffic data table of each sub-area with the corresponding area to realize the integrated processing of real-time monitoring traffic data of each sub-area.

[0055] The specific steps of performing real-time vehicle spatial factor impact analysis and time factor impact analysis based on the road condition data of each sub-area, judging abnormal vehicles in each sub-area by combining spatial and time analysis, and analyzing the abnormality degree of the sub-area based on the judgment results are as follows:

[0056] S301, perform spatial distribution impact analysis on the road condition data of temporary parking areas in each sub-area; obtain the type data of each vehicle in the temporary parking area of ​​the sub-area in real time through the real-time cloud monitoring model, and divide the vehicles into different levels according to the vehicle types; analyze the spatial impact of the temporary parking area of ​​the vehicle according to the vehicle level division results of the temporary parking area of ​​each sub-area and the distribution of vehicle parking space, and the calculation formula is: Among them, Vsi (j,k,n) S is the space occupancy impact value of vehicle number n in the temporary parking area of ​​vehicle number k in the corresponding sub-area number j; (j,k,n) S is the ground area occupied by vehicle number n in the temporary parking area for vehicle number k in the corresponding sub-area number j; (j,k) E is the area of ​​the temporary parking area for vehicle number k in the corresponding sub-area number j; (j,k) S is the effective utilization rate of the temporary parking area for vehicle number k in the corresponding sub-area number j; (j,k,n)Z is the relative area occupied by vehicle number n in the temporary parking area of ​​vehicle number k in the corresponding sub-area number j; combined with the space occupancy impact value of each vehicle, a comprehensive spatial impact distribution analysis is performed on the temporary parking area of ​​vehicles in the corresponding sub-area, and the calculation formula is: Among them, Sdi (j,k) is the comprehensive spatial distribution impact value of the temporary parking area numbered k in the corresponding sub-area numbered j; m (j,k) is the real-time number of vehicles parked in the temporary parking area with number k in the corresponding sub-area with number j; M(j,k) is the maximum number of vehicles parked in the temporary parking area with number k in the corresponding sub-area with number j; L is the vehicle classification level; N (j,k,L)Vsi is the number of parked vehicles at level L in the temporary parking area for vehicles numbered k in the corresponding sub-area numbered j; (j,k,n)L is the space occupancy impact value of the vehicle numbered n and level L in the temporary parking area numbered k in the corresponding sub-area numbered j; P(L) is the number of vehicle classification levels;

[0057] S302, analyzing the time series impact of each vehicle in combination with the monitoring period of the temporary parking area corresponding to each sub-area; analyzing the time series factors of the temporary parking area corresponding to each sub-area in the monitoring period in combination with the parking time and the number of parking times of the vehicle in the temporary parking area, and the calculation formula is: Where Vti (j,k,n) is the real-time time occupancy impact value of vehicle number n in the temporary parking area of ​​vehicle number k in the corresponding sub-area number j; (j,k,n) is the parking time of vehicle numbered n in the temporary parking area of ​​vehicle numbered k in the corresponding sub-area numbered j during the monitoring period; c (j,k,n) is the number of parking times of vehicle numbered n in the temporary parking area of ​​vehicle numbered k in the corresponding sub-area numbered j within the monitoring period; T is the monitoring period; C (j,k) is the total number of vehicle parking times in the monitoring period of the temporary parking area numbered k in the corresponding sub-area numbered j; the time occupancy impact values ​​of vehicles in each sub-area corresponding to each temporary parking area period are comprehensively analyzed to analyze the comprehensive time occupancy impact of each temporary parking area, and the calculation formula is: Among them, R (j,k) is the comprehensive time series impact value of the temporary parking area numbered k in the corresponding sub-area numbered j; t (j,k,n)max is the maximum parking time of vehicle numbered n in the temporary parking area of ​​vehicle numbered k in the corresponding sub-area numbered j during the monitoring period; T (j,k)max Vti is the maximum parking time allowed for the temporary parking area for vehicle number k in the corresponding sub-area number j; (j,k,n)L is the time occupancy impact value of the vehicle numbered n and level L in the temporary parking area numbered k in the corresponding sub-area numbered j;

[0058] S303, based on the spatial distribution impact analysis and time series impact analysis of each temporary parking area corresponding to each vehicle in each sub-area, a state analysis is performed on each temporary parking area of ​​the vehicle, and the calculation formula is Osi (j,k) =δ1*Sdi (j,k) +δ2*Rti (j,k) ; Osi (j,k) is the real-time ordered state index of the temporary parking area numbered k in the corresponding sub-area numbered j; where δ1 and δ2 are the spatial distribution influence coefficient and the time series influence coefficient respectively; by introducing the reference threshold Osi(j,k)th and correction parameter γ to judge the status of each vehicle temporary parking area; when Osi (j,k) <Osi (j,k)th -γ, judge that the temporary parking area of ​​the current target vehicle is orderly and normal; when Osi (j,k)th -γ≤Osi (j,k) ≤Osi (j,k)th , it is judged that there is a disorder risk in the temporary parking area of ​​the current target vehicle; when Osi (j,k) ≥Osi (j,k)th , it is judged that the current temporary parking area of ​​the target vehicle is in a disordered and abnormal state.

[0059] The specific steps of S400 for outputting the road condition data of each sub-area in real time, marking abnormal vehicles and abnormal areas, and feeding back abnormal analysis data are as follows:

[0060] S401, outputting the real-time vehicle parking data corresponding to the temporary parking area of ​​each sub-area, and feeding back the corresponding area status index;

[0061] S402. Issue corresponding level warnings to areas with disorder risks and disorder anomalies, where disorder risks are primary warnings and disorder anomalies are high-level warnings.

[0062] A community interaction management system based on data analysis technology, the system includes a monitoring area division module, a data virtual processing module, a regional comprehensive status analysis module and a regional data feedback module;

[0063] The monitoring area division module uses the community sensor equipment network to obtain planar data of community distribution and divide monitoring sub-areas, and obtains road condition data for each sub-area; the data virtual processing module synchronously simulates the road condition of each sub-area through virtual modeling, simulates real-time road condition data for each sub-area, and integrates and processes road condition data for each sub-area; the regional comprehensive status analysis module performs real-time vehicle spatial factor impact analysis and time factor impact analysis based on the road condition data of each sub-area, comprehensively analyzes abnormal vehicles in each sub-area through spatial and temporal analysis, and analyzes the degree of abnormality of the sub-area based on the judgment results; the regional data feedback module outputs the road condition data of each sub-area in real time, marks abnormal vehicles and abnormal areas, and feeds back abnormal analysis data.

[0064] The monitoring area division module includes an area data acquisition unit and a sub-area division unit;

[0065] The regional data acquisition unit acquires the monitoring signals of each monitoring sensor device in the corresponding area of ​​the community through the community sensor device control port, and integrates the acquired signal data of each monitoring sensor device to build a monitoring platform to monitor the road condition data of the community; the monitoring sensor device includes an image acquisition sensor device and a video acquisition sensor device; the road condition data includes the area of ​​temporary parking areas for vehicles on community roads, the number of temporary parking vehicles, the type of temporary vehicles and the temporary parking time of temporary vehicles;

[0066] The sub-area division unit divides the community monitoring area into sub-areas by adopting the same-regular monitoring division method. It coordinates the location and quantity of the sensor monitoring equipment in the community, and monitors the non-overlapping areas of the community with the same number of sensor monitoring equipment. The area monitored by the sensor monitoring equipment in each non-overlapping monitoring area is obtained, thereby realizing the sub-area division; each sub-area is assigned a corresponding area number, and the sensor monitoring equipment in each sub-area is assigned a secondary number.

[0067] The data virtual processing module includes a regional cloud monitoring model building unit and a monitoring road condition data integration processing unit;

[0068] The regional cloud monitoring model construction unit uses the virtual cloud 3D modeling platform to synchronously model the real-time traffic data in each sub-area of ​​the community, reduces the data dimension of the virtual model of each sub-area through environmental data noise reduction, and obtains the cloud monitoring model of the temporary vehicle parking area of ​​each sub-area;

[0069] The monitoring road condition data integration processing unit performs real-time road condition data integration processing on temporary parking areas for temporary vehicles in each sub-area of ​​the community according to the cloud monitoring model, generates a self-updating road condition data table for the corresponding sub-area by collecting road condition data statistics for each sub-area, and associates the self-updating road condition data table of each sub-area with the corresponding area to realize real-time monitoring road condition data integration processing for each sub-area.

[0070] The regional comprehensive status analysis module includes a regional spatial distribution impact analysis unit, a regional time series impact analysis unit and a regional status judgment analysis unit;

[0071] The regional spatial distribution impact analysis unit performs spatial distribution impact analysis on the road condition data of the temporary vehicle parking area in each sub-area; obtains the type data of each vehicle in the temporary vehicle parking area in the sub-area in real time through the real-time cloud monitoring model, and divides the vehicle into levels according to the vehicle type; analyzes the spatial impact of the temporary vehicle parking area in combination with the vehicle parking space distribution according to the vehicle level division results of the temporary vehicle parking area in each sub-area; and performs a comprehensive spatial impact distribution analysis on the temporary vehicle parking area in the corresponding sub-area in combination with the space occupancy impact value of each vehicle;

[0072] The regional time series impact analysis unit analyzes the time series impact of each vehicle in combination with the monitoring period of the temporary parking area corresponding to each sub-area; performs time series factor analysis on the temporary parking area corresponding to each vehicle in each sub-area within the monitoring period in combination with the parking time and the number of parking times of the vehicle in the temporary parking area; performs comprehensive time occupancy impact analysis on each temporary parking area according to the time occupancy impact value of each vehicle in the period corresponding to each temporary parking area of ​​each sub-area;

[0073] The regional status judgment and analysis unit comprehensively analyzes the spatial distribution impact analysis and time series impact analysis of each vehicle temporary parking area in each sub-area, and performs status analysis on each vehicle temporary parking area; and judges the status of each vehicle temporary parking area by introducing reference thresholds and correction parameters.

[0074] The regional data feedback module includes a regional data feedback unit and an abnormal area warning unit;

[0075] The regional data feedback unit outputs the real-time vehicle parking data corresponding to the temporary parking area of ​​each sub-area, and feeds back the corresponding regional status index;

[0076] The abnormal area warning unit issues corresponding level warnings to areas with disorder risk and disorder anomaly, wherein disorder risk is a primary warning and disorder anomaly is a high-level warning;

[0077] In the example:

[0078] A community is in urgent need of rectifying the disorderly parking phenomenon in the temporary parking area of ​​the community. It introduces the community interactive management system based on data analysis technology of the present invention, and collects data by connecting to the monitoring system of the community; it obtains the monitoring signals of each monitoring sensor device in the corresponding area of ​​the community through the community sensor device control port, and integrates the signal data of each monitoring sensor device to build a monitoring platform to monitor the road condition data of the community; the monitoring sensor device includes an image acquisition sensor device and a video acquisition sensor device; the road condition data includes the area of ​​the temporary parking area of ​​the community road, the number of temporary parking vehicles, the type of temporary vehicles and the temporary parking time of temporary vehicles;

[0079] The community monitoring area is divided into sub-areas by adopting the same monitoring division method. The location and quantity of the sensor monitoring equipment in the community are coordinated. By using the same number of sensor monitoring equipment to monitor the non-overlapping areas of the community, the area monitored by the sensor monitoring equipment in each non-overlapping monitoring area is obtained, and the sub-area division is realized; the corresponding area number is assigned to each sub-area, and the sensor monitoring equipment in each sub-area is assigned a secondary number;

[0080] The real-time traffic data in each sub-area of ​​the community is synchronously modeled and processed using the virtual cloud 3D modeling platform. The virtual model of each sub-area is subjected to data dimension reduction through environmental data noise reduction to obtain the cloud monitoring model of the temporary vehicle parking area in each sub-area. The real-time traffic data of the temporary vehicle parking area in each sub-area of ​​the community is integrated and processed according to the cloud monitoring model. The self-updated traffic data table of the corresponding sub-area is generated by collecting traffic data statistics for each sub-area. The self-updated traffic data table of each sub-area is associated with the corresponding area to realize the integrated processing of the real-time monitoring traffic data of each sub-area.

[0081] The spatial distribution impact analysis is performed on the road condition data of the temporary parking area in each sub-area. The type data of each vehicle in the temporary parking area of ​​the sub-area is obtained in real time through the real-time cloud monitoring model, and the vehicle level is divided according to the vehicle type. According to the vehicle level division results of the temporary parking area of ​​each sub-area, the spatial impact of the temporary parking area of ​​the vehicle is analyzed in combination with the vehicle parking space distribution. The calculation formula is: Combined with the space occupancy impact value of each vehicle, a comprehensive spatial impact distribution analysis is performed on the temporary parking area of ​​the vehicle in the corresponding sub-area. The calculation formula is: Combined with the monitoring period of the temporary parking area corresponding to each sub-area, the time series impact of each vehicle is analyzed; combined with the parking time and number of vehicles in the temporary parking area, the time series factor analysis of the temporary parking area corresponding to each sub-area within the monitoring period is performed, and the calculation formula is: Among them, the number of parking times of the vehicle is at least 1; the time occupancy impact value of each sub-area corresponding to each vehicle temporary parking area period is comprehensively analyzed for the comprehensive time occupancy impact of each vehicle temporary parking area, and the calculation formula is: Based on the spatial distribution impact analysis and time series impact analysis of each temporary parking area in each sub-area, the status analysis of each temporary parking area is carried out, and the calculation formula is Osi (j,k) =δ1*Sdi (j,k) +δ2*Rti (j,k) ; By introducing the reference threshold Osi (j,k)th and correction parameter γ to judge the status of each vehicle temporary parking area; when Osi (j,k) <Osi (j,k)th -γ, judge that the temporary parking area of ​​the current target vehicle is orderly and normal; when Osi (j,k)th -γ≤Osi (j,k) ≤Osi (j,k)th , it is judged that there is a disorder risk in the temporary parking area of ​​the current target vehicle; when Osi (j,k) ≥Osi (j,k)th , it is judged that the current temporary parking area of ​​the target vehicle is in a disorderly and abnormal state;

[0082] The real-time vehicle parking data of the corresponding temporary parking areas in each sub-area is output, and the corresponding regional status index is fed back; corresponding level warnings are issued for areas with disorder risks and disorder anomalies, among which disorder risks are primary warnings and disorder anomalies are advanced warnings.

[0083] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0084] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art can still modify the technical solutions described in the aforementioned embodiments or replace some of the technical features therein by equivalents. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A community interaction management method based on data analysis technology, characterized in that: The method comprises the following steps: S100, using a community sensor network to acquire planar data on community distribution and divide monitoring sub-areas, and acquiring road condition data for each sub-area; S200, simulating the road conditions of each sub-area synchronously through virtual modeling, simulating the real-time road condition data of each sub-area, and integrating the road condition data of each sub-area; S300, performing real-time vehicle spatial factor impact analysis and temporal factor impact analysis based on the road condition data of each sub-area, judging abnormal vehicles in each sub-area through comprehensive spatial and temporal analysis, and analyzing the degree of abnormality of the sub-area based on the judgment results; The specific steps of S300 are as follows: S301, perform spatial distribution impact analysis on the road condition data of temporary parking areas in each sub-area; obtain the type data of each vehicle in the temporary parking area of ​​the sub-area in real time through the real-time cloud monitoring model, and divide the vehicles into different levels according to the vehicle types; analyze the spatial impact of the temporary parking area of ​​the vehicle according to the vehicle level division results of the temporary parking area of ​​each sub-area and the distribution of vehicle parking space, and the calculation formula is: Among them, Vsi (j,k,n) S is the space occupancy impact value of vehicle number n in the temporary parking area of ​​vehicle number k in the corresponding sub-area number j; (j,k,n) S is the ground area occupied by vehicle number n in the temporary parking area for vehicle number k in the corresponding sub-area number j; (j,k) E is the area of ​​the temporary parking area for vehicle number k in the corresponding sub-area number j; (j,k) S is the effective utilization rate of the temporary parking area for vehicle number k in the corresponding sub-area number j; (j,k,n)Z is the relative area occupied by vehicle number n in the temporary parking area of ​​vehicle number k in the corresponding sub-area number j; combined with the space occupancy impact value of each vehicle, a comprehensive spatial impact distribution analysis is performed on the temporary parking area of ​​vehicles in the corresponding sub-area, and the calculation formula is: Among them, Sdi (j,k) is the comprehensive spatial distribution impact value of the temporary parking area numbered k in the corresponding sub-area numbered j; m (j,k) is the real-time number of vehicles parked in the temporary parking area with number k in the corresponding sub-area with number j; M(j,k) is the maximum number of vehicles parked in the temporary parking area with number k in the corresponding sub-area with number j; L is the vehicle classification level; N (j,k,L) Vsi is the number of parked vehicles at level L in the temporary parking area for vehicles numbered k in the corresponding sub-area numbered j; (j,k,n)L is the space occupancy impact value of the vehicle numbered n and level L in the temporary parking area numbered k in the corresponding sub-area numbered j; P(L) is the number of vehicle classification levels; S302, analyzing the time series impact of each vehicle in combination with the monitoring period of the temporary parking area corresponding to each sub-area; analyzing the time series factors of the temporary parking area corresponding to each sub-area in the monitoring period in combination with the parking time and the number of parking times of the vehicle in the temporary parking area, and the calculation formula is: Where Vti (j,k,n) is the real-time time occupancy impact value of vehicle number n in the temporary parking area of ​​vehicle number k in the corresponding sub-area number j; (j,k,n) is the parking time of vehicle numbered n in the temporary parking area of ​​vehicle numbered k in the corresponding sub-area numbered j during the monitoring period; c (j,k,n) is the number of parking times of vehicle numbered n in the temporary parking area of ​​vehicle numbered k in the corresponding sub-area numbered j within the monitoring period; T is the monitoring period; C (j,k) is the total number of vehicle parking times in the monitoring period of the temporary parking area numbered k in the corresponding sub-area numbered j; the time occupancy impact values ​​of vehicles in each sub-area corresponding to each temporary parking area period are comprehensively analyzed to analyze the comprehensive time occupancy impact of each temporary parking area, and the calculation formula is: Among them, R (j,k) is the comprehensive time series impact value of the temporary parking area numbered k in the corresponding sub-area numbered j; t (j,k,n)max is the maximum parking time of vehicle numbered n in the temporary parking area of ​​vehicle numbered k in the corresponding sub-area numbered j during the monitoring period; T (j,k)max Vti is the maximum parking time allowed for the temporary parking area for vehicle number k in the corresponding sub-area number j; (j,k,n)L is the time occupancy impact value of the vehicle numbered n and level L in the temporary parking area numbered k in the corresponding sub-area numbered j; S303, based on the spatial distribution impact analysis and time series impact analysis of each temporary parking area corresponding to each vehicle in each sub-area, a state analysis is performed on each temporary parking area of ​​the vehicle, and the calculation formula is Osi (j,k) =δ1*Sdi (j,k) +δ2*Rti (j,k) ; Osi (j,k) is the real-time ordered state index of the temporary parking area numbered k in the corresponding sub-area numbered j; where δ1 and δ2 are the spatial distribution influence coefficient and the time series influence coefficient respectively; by introducing the reference threshold Osi (j,k)th and correction parameter γ to judge the status of each vehicle temporary parking area; when Osi (j,k) <Osi (j,k)th -γ, judge that the temporary parking area of ​​the current target vehicle is orderly and normal; when Osi (j,k)th -γ≤Osi (j,k) ≤Osi (j,k)th , it is judged that there is a disorder risk in the temporary parking area of ​​the current target vehicle; when Osi (j,k) ≥Osi (j,k)th , it is judged that the current temporary parking area of ​​the target vehicle is in a disorderly and abnormal state; S400: Output the road condition data of each sub-area in real time, mark abnormal vehicles and abnormal areas, and feed back abnormal analysis data.

2. According to claim 1, a community interaction management method based on data analysis technology is characterized by: The S100 uses the community sensor network to acquire planar data of the community distribution and divide the monitoring sub-areas. The specific steps of acquiring road condition data for each sub-area are as follows: S101. Obtain monitoring signals of each monitoring sensor device in the corresponding area of ​​the community through the community sensor device control port, and monitor the road condition data of the community by integrating the signal data of each monitoring sensor device to build a monitoring platform; the monitoring sensor device includes an image acquisition sensor device and a video acquisition sensor device; the road condition data includes the area of ​​temporary parking areas on community roads, the number of temporary parked vehicles, the type of temporary vehicles, and the temporary parking time of temporary vehicles; S102. The community monitoring area is divided into sub-areas by adopting the same monitoring division method. The location and number of sensor monitoring equipment in the community are coordinated. The community is monitored in non-overlapping areas by using the same number of sensor monitoring equipment. The area monitored by the sensor monitoring equipment in each non-overlapping monitoring area is obtained to achieve sub-area division. Each sub-area is assigned a corresponding area number, and the sensor monitoring equipment in each sub-area is assigned a secondary number.

3. The community interaction management method based on data analysis technology according to claim 2 is characterized by: The specific steps of S200 for simulating the road conditions of each sub-area synchronously through virtual modeling, simulating the real-time road condition data of each sub-area, and integrating the road condition data of each sub-area are as follows: S201, using the virtual cloud 3D modeling platform to synchronously model the real-time traffic data in each sub-area of ​​the community, reducing the data dimension of the virtual model of each sub-area through environmental data noise reduction, and obtaining a cloud monitoring model of the temporary vehicle parking area in each sub-area; S202. Integrate the real-time traffic data of temporary parking areas for temporary vehicles in each sub-area of ​​the community according to the cloud monitoring model, and generate a self-updated traffic data table for the corresponding sub-area by collecting statistics on the traffic data of each sub-area; the self-updated traffic data table is synchronously updated according to the real-time collected data; associate the self-updated traffic data table of each sub-area with the corresponding area to realize the integrated processing of real-time monitoring traffic data of each sub-area.

4. The community interaction management method based on data analysis technology according to claim 3 is characterized by: The specific steps of S400 for outputting the road condition data of each sub-area in real time, marking abnormal vehicles and abnormal areas, and feeding back abnormal analysis data are as follows: S401, outputting the real-time vehicle parking data corresponding to the temporary parking area of ​​each sub-area, and feeding back the corresponding area status index; S402. Issue corresponding level warnings to areas with disorder risks and disorder anomalies, where disorder risks are primary warnings and disorder anomalies are high-level warnings.

5. A community interaction management system based on data analysis technology, applying a community interaction management method based on data analysis technology as described in any one of claims 1 to 4, characterized in that: The system includes a monitoring area division module, a data virtual processing module, a regional comprehensive status analysis module and a regional data feedback module; The monitoring area division module uses the community sensor equipment network to obtain planar data of community distribution and divide monitoring sub-areas, and obtains road condition data for each sub-area; the data virtual processing module synchronously simulates the road condition of each sub-area through virtual modeling, simulates real-time road condition data for each sub-area, and integrates and processes road condition data for each sub-area; the regional comprehensive status analysis module performs real-time vehicle spatial factor impact analysis and time factor impact analysis based on the road condition data of each sub-area, comprehensively analyzes abnormal vehicles in each sub-area through spatial and temporal analysis, and analyzes the degree of abnormality of the sub-area based on the judgment results; the regional data feedback module outputs the road condition data of each sub-area in real time, marks abnormal vehicles and abnormal areas, and feeds back abnormal analysis data.

6. A community interaction management system based on data analysis technology according to claim 5, characterized in that: The monitoring area division module includes an area data acquisition unit and a sub-area division unit; The regional data acquisition unit acquires the monitoring signals of each monitoring sensor device in the corresponding area of ​​the community through the community sensor device control port, and integrates the acquired signal data of each monitoring sensor device to build a monitoring platform to monitor the road condition data of the community; the monitoring sensor device includes an image acquisition sensor device and a video acquisition sensor device; the road condition data includes the area of ​​temporary parking areas for vehicles on community roads, the number of temporary parking vehicles, the type of temporary vehicles and the temporary parking time of temporary vehicles; The sub-area division unit divides the community monitoring area into sub-areas by adopting the same-regular monitoring division method. It coordinates the location and quantity of the sensor monitoring equipment in the community, and monitors the non-overlapping areas of the community with the same number of sensor monitoring equipment. The area monitored by the sensor monitoring equipment in each non-overlapping monitoring area is obtained, thereby realizing the sub-area division; each sub-area is assigned a corresponding area number, and the sensor monitoring equipment in each sub-area is assigned a secondary number.

7. A community interaction management system based on data analysis technology according to claim 6, characterized in that: The data virtual processing module includes a regional cloud monitoring model building unit and a monitoring road condition data integration processing unit; The regional cloud monitoring model construction unit uses the virtual cloud 3D modeling platform to synchronously model the real-time traffic data in each sub-area of ​​the community, reduces the data dimension of the virtual model of each sub-area through environmental data noise reduction, and obtains the cloud monitoring model of the temporary vehicle parking area of ​​each sub-area; The monitoring road condition data integration processing unit performs real-time road condition data integration processing on temporary parking areas for temporary vehicles in each sub-area of ​​the community according to the cloud monitoring model, generates a self-updating road condition data table for the corresponding sub-area by collecting road condition data statistics for each sub-area, and associates the self-updating road condition data table of each sub-area with the corresponding area to realize real-time monitoring road condition data integration processing for each sub-area.

8. The community interaction management system based on data analysis technology according to claim 7, characterized in that: The regional comprehensive status analysis module includes a regional spatial distribution impact analysis unit, a regional time series impact analysis unit and a regional status judgment analysis unit; The regional spatial distribution impact analysis unit performs spatial distribution impact analysis on the road condition data of the temporary vehicle parking area in each sub-area; obtains the type data of each vehicle in the temporary vehicle parking area in the sub-area in real time through the real-time cloud monitoring model, and divides the vehicle into levels according to the vehicle type; analyzes the spatial impact of the temporary vehicle parking area in combination with the vehicle parking space distribution according to the vehicle level division results of the temporary vehicle parking area in each sub-area; and performs a comprehensive spatial impact distribution analysis on the temporary vehicle parking area in the corresponding sub-area in combination with the space occupancy impact value of each vehicle; The regional time series impact analysis unit analyzes the time series impact of each vehicle in combination with the monitoring period of the temporary parking area corresponding to each sub-area; performs time series factor analysis on the temporary parking area corresponding to each vehicle in each sub-area within the monitoring period in combination with the parking time and the number of parking times of the vehicle in the temporary parking area; performs comprehensive time occupancy impact analysis on each temporary parking area according to the time occupancy impact value of each vehicle in the period corresponding to each temporary parking area of ​​each sub-area; The regional status judgment and analysis unit comprehensively analyzes the spatial distribution impact analysis and time series impact analysis of each vehicle temporary parking area in each sub-area, and performs status analysis on each vehicle temporary parking area; and judges the status of each vehicle temporary parking area by introducing reference thresholds and correction parameters.

9. A community interaction management system based on data analysis technology according to claim 8, characterized in that: The regional data feedback module includes a regional data feedback unit and an abnormal area warning unit; The regional data feedback unit outputs the real-time vehicle parking data corresponding to the temporary parking area of ​​each sub-area, and feeds back the corresponding regional status index; The abnormal area warning unit issues corresponding level warnings to areas with disorder risks and disorder anomalies, wherein disorder risks are primary warnings and disorder anomalies are advanced warnings.

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