Smart community management method and system based on big data

Abstract

The invention discloses a smart community management method and system based on big data, and relates to the technical field of data processing analysis, and the method comprises the steps: carrying out the type analysis of all data of a smart community, and determining a high-frequency accident and a high-frequency accident occurrence position; and performing feature analysis on the high-frequency accident and the high-frequency accident occurrence position to determine high-frequency accident occurrence features. The method comprises the steps of firstly performing type analysis on all data of a smart community to determine a high-frequency accident and a high-frequency accident occurrence position, then performing data analysis on data of all high-frequency accident occurrence positions to determine high-frequency accident occurrence characteristics, and finally, determining the high-frequency accident occurrence characteristics according to the high-frequency accident occurrence characteristics. According to the method, feature matching is performed on all areas of the smart community through the high-frequency accident generation features, and the area needing to be monitored is determined, so that prediction of the high-frequency accident generation position of the smart community is realized, the high-frequency accident generation position is monitored, and after an accident occurs, the accident generation position can be determined in time, and the safety of the accident is improved. And great damage to the smart community is avoided.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing and analysis, and particularly to a smart community management method and system based on big data. Background Art

[0002] A smart community is a model that integrates existing community services through various intelligent technologies and methods to provide residents with convenient services such as government affairs, business, entertainment, education, healthcare, and daily life support. The smart community represents a new concept in community management, and its development can effectively promote economic transformation and the development of the modern service industry.

[0003] Some accidents will occur in smart communities, and some accidents occur frequently. If we do not analyze the frequent accidents, we will not be able to predict the location of the next accident. When an accident occurs, if it is not discovered in time, it will cause a certain degree of damage to the smart community. Summary of the Invention

[0004] In order to solve the above technical problems, a smart community management method and system based on big data is provided. This technical solution solves the problem that some accidents may occur in the smart community proposed in the above background technology, and some accidents occur frequently. If the frequently occurring accidents are not analyzed, it is impossible to predict the location of the next accident. When an accident occurs, if it is not discovered in time, it will cause a certain degree of damage to the smart community.

[0005] In order to achieve the above objects, the technical solution adopted by the present invention is:

[0006] A smart community management method based on big data, comprising:

[0007] Obtain all data from the smart community and, based on the intelligent management terminal, perform type analysis on all data from the smart community to identify high-frequency accidents and their locations.

[0008] Based on the intelligent management terminal, the characteristics of high-frequency accidents and their locations are analyzed to determine the characteristics of high-frequency accidents;

[0009] Based on intelligent management terminals, real-time monitoring of smart communities is carried out with reference to the characteristics of high-frequency accidents.

[0010] Preferably, the acquisition of all data of the smart community, performing type analysis on all data of the smart community based on the intelligent management terminal, and determining high-frequency accidents and locations of high-frequency accidents specifically include the following steps:

[0011] Based on the intelligent management terminal, the database system of the smart community is read and processed to obtain all the data of the smart community;

[0012] Based on the intelligent management terminal, all data of the smart community is read and processed based on the data type, and all accident data of the smart community is obtained;

[0013] Based on the intelligent management terminal, all accident data of the smart community is classified and processed based on the accident type, and a data set of different types of accidents in the smart community is obtained;

[0014] Based on the intelligent management terminal, a comparative analysis is conducted on the data sets of different types of accidents in the smart community to determine the high-frequency accidents and the locations where they occur.

[0015] Preferably, the comparative analysis of data sets of different types of accidents in the smart community based on the intelligent management terminal to determine high-frequency accidents and locations of high-frequency accidents specifically includes the following steps:

[0016] Based on the intelligent management terminal, the data in the data set of different types of accidents in the smart community are counted and processed to determine the number of occurrences of different types of accidents;

[0017] Based on the intelligent management terminal, the number of occurrences of different types of accidents is compared and processed to determine the high-frequency accidents to be verified;

[0018] Based on the intelligent management terminal, the high-frequency accidents to be verified are verified with the data set corresponding to the high-frequency accidents as the characteristics, and the high-frequency accidents and the locations where the high-frequency accidents occur are determined.

[0019] Preferably, the method of comparing the occurrence frequency of different types of accidents based on the intelligent management terminal to determine the high-frequency accidents to be verified specifically includes the following steps:

[0020] Based on the intelligent management terminal, the number of occurrences of different types of accidents and the threshold value of the number of accident occurrences are judged and processed;

[0021] If the number of occurrences of different types of accidents exceeds the set accident occurrence threshold, the type of accident is set as a high-frequency accident to be verified;

[0022] If the number of occurrences of different types of accidents is less than or equal to the set accident occurrence threshold, the data set corresponding to the type of accident is set as the accidental accident data set.

[0023] Preferably, the intelligent management terminal is used to verify the high-frequency accident to be verified by taking the data set corresponding to the high-frequency accident to be verified as a feature, and the high-frequency accident and the location of the high-frequency accident are specifically determined, including the following steps:

[0024] Based on the intelligent management terminal, the data set corresponding to the high-frequency accidents to be verified is extracted and processed to obtain the occurrence time of the high-frequency accidents to be verified;

[0025] Based on the intelligent management terminal, a difference calculation is performed on the occurrence time of the high-frequency accident to be verified to determine a data set of the occurrence time interval of the high-frequency accident to be verified; wherein the difference calculation is performed on the occurrence time of the high-frequency accident to be verified at adjacent times;

[0026] Based on the maximum value function, the data in the data set of the occurrence time interval of the high-frequency accidents to be verified are sorted to obtain the maximum occurrence time interval of the accidents;

[0027] Based on the intelligent management terminal, a comparative analysis of the maximum time intervals between accidents is conducted to determine the high-frequency accidents and their locations.

[0028] Preferably, the comparative analysis of the maximum time intervals between accidents based on the intelligent management terminal to determine the high-frequency accidents and the locations where the high-frequency accidents occur specifically includes the following steps:

[0029] Based on the intelligent management terminal, data extraction and processing are performed on the occurrence time of the high-frequency accident to be verified to obtain the latest occurrence time of the high-frequency accident to be verified; wherein the latest occurrence time of the high-frequency accident to be verified is specifically the data with the latest occurrence time of the accident among the occurrence times of the high-frequency accident to be verified;

[0030] Based on the intelligent management terminal, the data of the timing equipment of the smart community is read and processed to obtain the real-time time of the smart community;

[0031] Based on the intelligent management terminal, the difference between the real-time time of the smart community and the most recent occurrence time of the high-frequency accident to be verified is calculated to determine the real-time time interval of the accident;

[0032] Based on the intelligent management terminal, the real-time time interval of accidents and the maximum time interval of accidents are judged and processed;

[0033] If the real-time interval of an accident is less than or equal to the maximum interval of an accident, the accident is set as a high-frequency accident. Based on the intelligent management terminal, the data sets of different types of accidents in the smart community are matched with the characteristics of high-frequency accidents to determine the location of the high-frequency accident.

[0034] If the real-time occurrence time interval of an accident is greater than the maximum occurrence time interval of an accident, the accident is set as a resolved accident.

[0035] Preferably, the method of performing feature analysis on high-frequency accidents and locations of high-frequency accidents based on the intelligent management terminal to determine the features of high-frequency accidents specifically includes the following steps:

[0036] Based on the intelligent management terminal, the data in the data set corresponding to the high-frequency accidents is read and processed to obtain the occurrence time of the high-frequency accidents;

[0037] Based on the intelligent management terminal, the occurrence time and location of high-frequency accidents are matched and processed to determine the occurrence time of high-frequency accidents at different locations;

[0038] Based on the intelligent management terminal, all accident data of the smart community is extracted and processed based on the accident occurrence time of different high-frequency accident locations to obtain a parameter information set; wherein the parameter information set specifically includes parameter data of different high-frequency accident locations before the accident occurrence time and parameter data of different high-frequency accident locations after the accident occurrence time;

[0039] Based on the intelligent management terminal, data analysis and processing are performed on the parameter information set to determine the characteristics of high-frequency accidents.

[0040] Preferably, the data analysis and processing of the parameter information set based on the intelligent management terminal to determine the characteristics of high-frequency accidents specifically includes the following steps:

[0041] Based on the intelligent management terminal, the data in the parameter information set is classified and processed based on the accident occurrence time of different high-frequency accident locations, and the parameter data of different accident occurrence times are obtained;

[0042] Based on the intelligent management terminal, the parameter data of different accident occurrence times are processed for intersection to determine the same type of parameter data; wherein the same type of parameter data is specifically the same type of parameters of all high-frequency accidents at different occurrence locations;

[0043] Based on the intelligent management terminal, data changes of the same type of parameter data are analyzed to determine the parameter type that causes high-frequency accidents;

[0044] Based on the intelligent management terminal, the parameter type causing high-frequency accidents is set as the high-frequency accident occurrence feature.

[0045] Preferably, the real-time monitoring of the smart community based on the intelligent management terminal and taking the characteristics of high-frequency accidents as a reference specifically includes the following steps:

[0046] Based on the intelligent management terminal, parameters of all areas in the smart community are matched with the characteristics of high-frequency accidents as a reference to determine the locations with high-frequency accident characteristics;

[0047] Based on the intelligent management terminal, data analysis is performed on the characteristics of high-frequency accidents to determine the accident monitoring equipment;

[0048] Based on the intelligent management terminal, accident monitoring equipment is installed at locations with high-frequency accident characteristics to conduct real-time monitoring of the smart community.

[0049] Furthermore, a smart community management system based on big data is proposed, which is used to implement the above-mentioned smart community management method based on big data, including:

[0050] An intelligent management terminal is used to control each module to perform data classification, data comparison, data verification, and location analysis on all data of the smart community, and to monitor the smart community in real time; the intelligent management terminal is used to control data transmission and information exchange between each module;

[0051] A database system, which is used to store all data of the smart community;

[0052] A data classification module is used to classify all data of the smart community and obtain all accident data of the smart community;

[0053] A first accident data verification module, the first accident data verification module is used to compare and judge the number of occurrences of different types of accidents to determine high-frequency accidents to be verified;

[0054] A second accident data verification module, the second accident data verification module is used to perform time comparison and judgment processing on the high-frequency accidents to be verified, and determine the high-frequency accidents;

[0055] A high-frequency accident feature determination module, which is used to perform type analysis on data of high-frequency accident locations to determine the characteristics of high-frequency accidents;

[0056] A monitoring device installation location determination module performs parameter matching on all areas of the smart community based on the characteristics of high-frequency accidents, and determines locations with high-frequency accident characteristics.

[0057] Compared with the existing technology, the present invention provides a smart community management method and system based on big data, which has the following beneficial effects:

[0058] The present invention first performs a type analysis on all data of the smart community to determine high-frequency accidents and the locations where high-frequency accidents occur. Then, by performing data analysis on the data of all high-frequency accident locations, the characteristics of high-frequency accidents are determined. Finally, feature matching is performed on all areas of the smart community based on the characteristics of high-frequency accidents to determine the areas that need to be monitored. The above method not only realizes the prediction of the locations where high-frequency accidents occur in the smart community, but also monitors the locations where high-frequency accidents occur. When an accident occurs, the location of the accident can be determined in time to avoid causing greater damage to the smart community. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] Figure 1 This is a flow chart of steps S100-S300 in a smart community management method based on big data proposed by the present invention;

[0060] Figure 2 This is a structural block diagram of a smart community management system based on big data proposed by the present invention. DETAILED DESCRIPTION

[0061] The following description is intended to disclose the present invention so that those skilled in the art can implement the present invention. The preferred embodiments described below are merely examples, and those skilled in the art may conceive of other obvious variations.

[0062] Reference Figure 1 As shown, a smart community management method based on big data includes:

[0063] S100. Obtain all data of the smart community and, based on the intelligent management terminal, perform type analysis on all data of the smart community to determine high-frequency accidents and their locations.

[0064] S200, based on the intelligent management terminal, perform feature analysis on high-frequency accidents and locations where high-frequency accidents occur, and determine the features of high-frequency accidents;

[0065] S300, based on intelligent management terminals, conducts real-time monitoring of smart communities using the characteristics of high-frequency accidents as a reference;

[0066] It is understandable to those skilled in the art that similar accidents often occur in smart communities. If they are not discovered in time, they may cause certain damage to the smart community. Therefore, by screening all the data of the smart community, accidents that often occur in the smart community (i.e., high-frequency accidents) are determined. However, the locations of these high-frequency accidents may be the same or different. If the locations of the high-frequency accidents are the same, it is only necessary to install monitoring equipment at the locations where the high-frequency accidents occur to monitor the area. When the same type of accident occurs in the area again, it can be discovered in time to avoid causing greater damage to the smart community. If high-frequency accidents occur in different areas, the same type of high-frequency accidents will definitely have the same characteristics. Therefore, the parameters of high-frequency accidents in different areas are compared to determine which parameters cause the occurrence of high-frequency accidents. Subsequently, the entire smart community is matched with the parameters that cause high-frequency accidents to determine the areas where these parameters occur. Finally, corresponding monitoring equipment is installed for these areas to monitor the smart community.

[0067] Example 1

[0068] S100: Obtain all data of the smart community, perform type analysis on all data of the smart community based on the intelligent management terminal, and determine high-frequency accidents and their locations. The specific steps include the following:

[0069] S101. Based on the intelligent management terminal, data is read and processed from the database system of the smart community to obtain all data of the smart community;

[0070] It is understandable that in order to obtain the accident data of the smart community, it is necessary to read the data from the data storage device (i.e., the database system) of the smart community, and then classify the data to determine all the accident data of the smart community;

[0071] S102. Based on the intelligent management terminal, all data of the smart community is read and processed based on the data type to obtain all accident data of the smart community;

[0072] S103. Based on the intelligent management terminal, all accident data of the smart community are classified and processed based on the accident type, and a data set of different types of accidents in the smart community is obtained;

[0073] It is understandable that different types of accidents manifest themselves differently and the parameters that cause them are also different. Therefore, before determining high-frequency accidents, it is necessary to classify all accidents that have occurred within the smart community;

[0074] S104. Based on the intelligent management terminal, comparative analysis is performed on data sets of different types of accidents in the smart community to determine high-frequency accidents and their locations.

[0075] S104, based on the intelligent management terminal, compares and analyzes the data sets of different types of accidents in the smart community to determine the high-frequency accidents and the locations where the high-frequency accidents occur, specifically includes the following steps:

[0076] S1041. Based on the intelligent management terminal, count the data in the data set of different types of accidents in the smart community to determine the number of occurrences of different types of accidents;

[0077] It is understandable that the method of determining high-frequency accidents is based on the number of times an accident occurs. If an accident only occurs once or twice, such an accident cannot be classified as a high-frequency accident. Therefore, before determining a high-frequency accident, it is necessary to first determine the number of times the accident occurs.

[0078] S1042. Compare the occurrence frequency of different types of accidents based on the intelligent management terminal to determine high-frequency accidents to be verified;

[0079] S1043. Based on the intelligent management terminal, verify the high-frequency accident to be verified using the data set corresponding to the high-frequency accident to be verified as a feature, and determine the high-frequency accident and the location where the high-frequency accident occurred;

[0080] S1042, based on the intelligent management terminal, compares the number of occurrences of different types of accidents to determine the high-frequency accidents to be verified, specifically includes the following steps:

[0081] S10421. Based on the intelligent management terminal, determine and process the number of occurrences of different types of accidents and set accident occurrence thresholds;

[0082] S10422. If the number of occurrences of a different type of accident is greater than the set accident occurrence threshold, set the accident of that type as a high-frequency accident to be verified;

[0083] S10423. If the number of occurrences of different types of accidents is less than or equal to the set accident occurrence threshold, set the data set corresponding to the type of accident as the accident data set;

[0084] It is understandable that high-frequency accidents cannot be determined solely by the number of accidents. The number of accidents is judged only to eliminate those accidents with fewer occurrences. Accidents with fewer occurrences generally occur occasionally, and it is impossible to obtain the occurrence pattern of this type of accident. Because the number of occurrences is small, the sample size is too small to obtain the corresponding pattern. In order to reduce the subsequent calculation amount, accidents with fewer occurrences are eliminated and accidents with more occurrences are verified to determine high-frequency accidents. Because accidents with more occurrences are not necessarily high-frequency accidents, they also need to be analyzed in combination with the time of the accident.

[0085] Among them, S1043, based on the intelligent management terminal, verifying the high-frequency accident to be verified with the data set corresponding to the high-frequency accident to be verified as a feature, and determining the high-frequency accident and the location of the high-frequency accident specifically includes the following steps:

[0086] S10431. Based on the intelligent management terminal, extract and process the data set corresponding to the high-frequency accident to be verified to obtain the occurrence time of the high-frequency accident to be verified;

[0087] S10432. Based on the intelligent management terminal, perform a difference calculation on the occurrence time of the high-frequency accident to be verified to determine a data set of occurrence time intervals of the high-frequency accident to be verified; wherein the difference calculation is performed on the occurrence time of the high-frequency accident to be verified at adjacent times;

[0088] S10433. Based on the maximum value function, sort the data in the data set of the occurrence time intervals of the high-frequency accidents to be verified to obtain the maximum occurrence time interval of the accidents;

[0089] It is understandable that in order to improve the accuracy of determining high-frequency accidents, it is necessary to select the accident occurrence interval to verify the high-frequency accidents to be verified. When a type of accident occurs frequently, the time intervals between occurrences are not necessarily the same. Therefore, these time intervals are screened and the accident with the largest time interval is selected as the reference group. This is because when the factors causing this type of accident are resolved, this type of accident will reappear, and the interval between the last occurrence time of the accident and the actual time must be greater than the maximum time interval between the accidents.

[0090] S10434. Based on the intelligent management terminal, conduct comparative analysis of the maximum time intervals between accidents to determine the high-frequency accidents and their locations.

[0091] Among them, S10434, based on the intelligent management terminal, compares and analyzes the maximum time intervals between accidents to determine the high-frequency accidents and the locations where they occur, including the following steps:

[0092] S104341. Based on the intelligent management terminal, extract and process data on the occurrence time of the high-frequency accident to be verified to obtain the latest occurrence time of the high-frequency accident to be verified; wherein the latest occurrence time of the high-frequency accident to be verified is specifically the latest occurrence time of the high-frequency accident to be verified;

[0093] S104342. Based on the intelligent management terminal, read and process data from the timing device of the smart community to obtain the real-time time of the smart community;

[0094] S104343. Based on the intelligent management terminal, calculate the difference between the real-time time of the smart community and the most recent occurrence time of the high-frequency accident to be verified to determine the real-time occurrence time interval of the accident;

[0095] S104344. Based on the intelligent management terminal, determine and process the real-time time interval of accidents and the maximum time interval of accidents;

[0096] S104345. If the real-time occurrence interval of an accident is less than or equal to the maximum occurrence interval of an accident, the accident is set as a high-frequency accident. Based on the intelligent management terminal, the data sets of different types of accidents in the smart community are matched with the high-frequency accident as a feature to determine the location of the high-frequency accident.

[0097] S104346. If the real-time occurrence interval of the accident is greater than the maximum occurrence interval of the accident, the accident is set as a resolved accident;

[0098] It is understandable that accidents that occur frequently are not necessarily high-frequency accidents. For example, some accidents may occur frequently, but there is a significant time interval between the last accident and the current time, and no accidents of the same type have occurred again during this time interval. This indicates that the cause of this type of accident has been resolved, and the accident is no longer a high-frequency accident. For example, an intersection was often congested before, but the number of lanes at the intersection was increased, and traffic jams no longer occurred. However, the number of previous traffic jams is still stored in the database system. Determining high-frequency accidents based solely on the number of accidents is one-sided and will also result in a waste of resources. This is because this type of accident occurred in the past, but the problem has now been resolved and no accidents of this type have occurred again. However, the occurrence records of this type of accident are still stored in the database system. If high-frequency accidents are determined based solely on the number of accidents, subsequent monitoring of this type of accident requires the installation of monitoring equipment. However, since the cause of this type of accident has been resolved, installing monitoring equipment will result in a waste of resources. Therefore, high-frequency accidents that have been resolved are eliminated based on the interval between accidents, which improves the accuracy of high-frequency accident determination.

[0099] Example 2

[0100] S200: Based on the intelligent management terminal, characteristic analysis is performed on high-frequency accidents and their locations, and determining the characteristics of high-frequency accidents specifically includes the following steps:

[0101] S201: Based on the intelligent management terminal, data in the data set corresponding to the high-frequency accidents is read and processed to obtain the occurrence time of the high-frequency accidents;

[0102] S202: Based on the intelligent management terminal, perform information matching processing on the occurrence time and location of high-frequency accidents to determine the occurrence time of high-frequency accidents at different locations;

[0103] It is understandable that when high-frequency accidents occur in different areas, in order to determine the type of high-frequency accident, it is necessary to analyze the parameters of the high-frequency accident of that type. However, the parameters when the high-frequency accident does not occur are of no reference significance. Therefore, according to the accident occurrence time of different high-frequency accident locations, the data in the database system is extracted to determine the parameter changes at the location when the high-frequency accident occurs. Subsequently, by analyzing these parameters, the parameters that caused the high-frequency accident can be determined.

[0104] S203. Based on the intelligent management terminal, extract and process all accident data of the smart community using the accident occurrence time of different high-frequency accident locations as a feature to obtain a parameter information set; wherein the parameter information set specifically includes parameter data of different high-frequency accident locations before the accident occurrence time and parameter data of different high-frequency accident locations after the accident occurrence time;

[0105] S204: Based on the intelligent management terminal, perform data analysis and processing on the parameter information set to determine the characteristics of high-frequency accidents;

[0106] It is understandable that some high-frequency accidents do not occur in the same area, but in different areas. However, the types of these high-frequency accidents are the same. Therefore, the parameters of these high-frequency accidents are processed by intersection to determine the parameters of the same type. Among these parameters of the same type, there will be a parameter that causes the high-frequency accident, and this parameter is the characteristic that causes the high-frequency accident.

[0107] S204, performing data analysis and processing on the parameter information set based on the intelligent management terminal to determine the characteristics of high-frequency accidents, specifically includes the following steps:

[0108] S2041. Based on the intelligent management terminal, classify and process the data in the parameter information set based on the accident occurrence time of different high-frequency accident locations, and obtain parameter data at different accident occurrence times;

[0109] S2042. Based on the intelligent management terminal, perform intersection processing on parameter data at different accident occurrence times to determine parameter data of the same type; wherein the parameter data of the same type specifically refers to parameters of the same type at different occurrence locations of all high-frequency accidents;

[0110] S2043. Based on the intelligent management terminal, perform data change analysis on parameter data of the same type to determine the parameter type causing the high-frequency accident.

[0111] S2044. Based on the intelligent management terminal, the parameter type causing the high-frequency accident is set as a high-frequency accident occurrence characteristic;

[0112] It is understandable that when high-frequency accidents occur in different areas, it is impossible to determine the parameters that caused the high-frequency accidents by only analyzing the accident data of one location. Therefore, the parameters of all locations where high-frequency accidents occur are analyzed, because the occurrence of the same type of high-frequency accidents has the same rules. Therefore, the parameters of all locations where high-frequency accidents occur are first intersection processed to determine the same parameters of these locations where high-frequency accidents occur. Then, according to the time when the high-frequency accident occurs, these same parameters are analyzed, because the parameters that change when the high-frequency accident occurs are the factors that cause the high-frequency accident. Therefore, the parameters of high-frequency accidents at these different locations are analyzed. When the high-frequency accident occurs, the parameters that change at the same time in the data of these locations are the factors that cause the high-frequency accident.

[0113] Example 3

[0114] S300, based on the intelligent management terminal, real-time monitoring of the smart community with reference to the characteristics of high-frequency accidents specifically includes the following steps:

[0115] S301. Based on the intelligent management terminal, parameters of all areas of the smart community are matched with reference to the characteristics of high-frequency accidents, and locations with high-frequency accident characteristics are determined;

[0116] S302. Based on the intelligent management terminal, perform data analysis on the characteristics of high-frequency accidents and determine the accident monitoring equipment;

[0117] S303. Based on the intelligent management terminal, accident monitoring equipment is installed at locations with high-frequency accident occurrence characteristics to conduct real-time monitoring of the smart community;

[0118] It is understandable that after determining the characteristics of high-frequency accidents, the location of this type of accident is uncertain, but the characteristics of this type of accident have been determined. Subsequently, it is only necessary to match the smart community based on this characteristic to determine which areas of the smart community will have this characteristic. The areas with this characteristic are the areas where accidents are likely to occur. Therefore, monitoring equipment is installed in these areas. When this type of accident occurs in these areas, the location of the accident can be determined in a timely manner, reducing damage to the smart community.

[0119] Reference Figure 2 As shown, a smart community management system based on big data is used to implement the above-mentioned smart community management method based on big data, including:

[0120] An intelligent management terminal is used to control each module to perform data classification, data comparison, data verification, and location analysis on all data of the smart community, and to monitor the smart community in real time; the intelligent management terminal is used to control data transmission and information exchange between each module;

[0121] A database system, which is used to store all data of the smart community;

[0122] A data classification module is used to classify all data of the smart community and obtain all accident data of the smart community;

[0123] A first accident data verification module, the first accident data verification module is used to compare and judge the number of occurrences of different types of accidents to determine high-frequency accidents to be verified;

[0124] A second accident data verification module, the second accident data verification module is used to perform time comparison and judgment processing on the high-frequency accidents to be verified, and determine the high-frequency accidents;

[0125] A high-frequency accident feature determination module, which is used to perform type analysis on data of high-frequency accident locations to determine the characteristics of high-frequency accidents;

[0126] A monitoring device installation location determination module performs parameter matching on all areas of the smart community based on the characteristics of high-frequency accidents, and determines locations with high-frequency accident characteristics.

[0127] The above shows and describes the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The above embodiments and descriptions merely illustrate the principles of the present invention. Various changes and modifications may be made to the present invention without departing from the spirit and scope of the present invention. Such changes and modifications are intended to fall within the scope of the present invention. The scope of protection claimed by the present invention is defined by the appended claims and their equivalents.

Claims

1. A smart community management method based on big data, characterized in that: include: Obtain all data from the smart community and, based on the intelligent management terminal, perform type analysis on all data from the smart community to identify high-frequency accidents and their locations. Based on the intelligent management terminal, the characteristics of high-frequency accidents and their locations are analyzed to determine the characteristics of high-frequency accidents; Based on intelligent management terminals, real-time monitoring of smart communities is carried out with reference to the characteristics of high-frequency accidents.

2. A smart community management method based on big data according to claim 1, characterized in that: The steps of obtaining all data of the smart community, performing type analysis on all data of the smart community based on the intelligent management terminal, and determining high-frequency accidents and locations of high-frequency accidents specifically include the following: Based on the intelligent management terminal, the database system of the smart community is read and processed to obtain all the data of the smart community; Based on the intelligent management terminal, all data of the smart community is read and processed based on the data type, and all accident data of the smart community is obtained; Based on the intelligent management terminal, all accident data of the smart community is classified and processed based on the accident type, and a data set of different types of accidents in the smart community is obtained; Based on the intelligent management terminal, a comparative analysis is conducted on the data sets of different types of accidents in the smart community to determine the high-frequency accidents and the locations where they occur.

3. The method for managing a smart community based on big data according to claim 2, characterized in that: The method of comparing and analyzing data sets of different types of accidents in the smart community based on the intelligent management terminal to determine the high-frequency accidents and the locations where the high-frequency accidents occur specifically includes the following steps: Based on the intelligent management terminal, the data in the data set of different types of accidents in the smart community are counted and processed to determine the number of occurrences of different types of accidents; Based on the intelligent management terminal, the number of occurrences of different types of accidents is compared and processed to determine the high-frequency accidents to be verified; Based on the intelligent management terminal, the high-frequency accidents to be verified are verified with the data set corresponding to the high-frequency accidents as the characteristics, and the high-frequency accidents and the locations where the high-frequency accidents occur are determined.

4. The method for managing a smart community based on big data according to claim 3, characterized in that: The method of comparing the occurrence frequency of different types of accidents based on the intelligent management terminal and determining the high-frequency accidents to be verified specifically includes the following steps: Based on the intelligent management terminal, the number of occurrences of different types of accidents and the threshold value of the number of accident occurrences are judged and processed; If the number of occurrences of different types of accidents exceeds the set accident occurrence threshold, the type of accident is set as a high-frequency accident to be verified; If the number of occurrences of different types of accidents is less than or equal to the set accident occurrence threshold, the data set corresponding to the type of accident is set as the accidental accident data set.

5. The method for managing a smart community based on big data according to claim 4, characterized in that: The method of verifying the high-frequency accident to be verified based on the intelligent management terminal using the data set corresponding to the high-frequency accident to be verified as a feature, and determining the high-frequency accident and the location of the high-frequency accident specifically includes the following steps: Based on the intelligent management terminal, the data set corresponding to the high-frequency accidents to be verified is extracted and processed to obtain the occurrence time of the high-frequency accidents to be verified; Based on the intelligent management terminal, a difference calculation is performed on the occurrence time of the high-frequency accident to be verified to determine a data set of the occurrence time interval of the high-frequency accident to be verified; wherein the difference calculation is performed on the occurrence time of the high-frequency accident to be verified at adjacent times; Based on the maximum value function, the data in the data set of the occurrence time interval of the high-frequency accidents to be verified are sorted to obtain the maximum occurrence time interval of the accidents; Based on the intelligent management terminal, a comparative analysis of the maximum time intervals between accidents is conducted to determine the high-frequency accidents and their locations.

6. The method for managing a smart community based on big data according to claim 1, characterized in that: The method of comparing and analyzing the maximum time intervals between accidents based on the intelligent management terminal to determine the high-frequency accidents and the locations where the high-frequency accidents occur specifically includes the following steps: Based on the intelligent management terminal, data extraction and processing are performed on the occurrence time of the high-frequency accident to be verified to obtain the latest occurrence time of the high-frequency accident to be verified; wherein the latest occurrence time of the high-frequency accident to be verified is specifically the data with the latest occurrence time of the accident among the occurrence times of the high-frequency accident to be verified; Based on the intelligent management terminal, the data of the timing equipment of the smart community is read and processed to obtain the real-time time of the smart community; Based on the intelligent management terminal, the difference between the real-time time of the smart community and the most recent occurrence time of the high-frequency accident to be verified is calculated to determine the real-time time interval of the accident; Based on the intelligent management terminal, the real-time time interval of accidents and the maximum time interval of accidents are judged and processed; If the real-time interval of an accident is less than or equal to the maximum interval of an accident, the accident is set as a high-frequency accident. Based on the intelligent management terminal, the data sets of different types of accidents in the smart community are matched with the characteristics of high-frequency accidents to determine the location of the high-frequency accident. If the real-time occurrence time interval of an accident is greater than the maximum occurrence time interval of an accident, the accident is set as a resolved accident.

7. The method for managing a smart community based on big data according to claim 1, characterized in that: The method of performing feature analysis on high-frequency accidents and their locations based on the intelligent management terminal to determine the features of high-frequency accidents specifically includes the following steps: Based on the intelligent management terminal, the data in the data set corresponding to the high-frequency accidents is read and processed to obtain the occurrence time of the high-frequency accidents; Based on the intelligent management terminal, the occurrence time and location of high-frequency accidents are matched and processed to determine the occurrence time of high-frequency accidents at different locations; Based on the intelligent management terminal, all accident data of the smart community is extracted and processed based on the accident occurrence time of different high-frequency accident locations to obtain a parameter information set; wherein the parameter information set specifically includes parameter data of different high-frequency accident locations before the accident occurrence time and parameter data of different high-frequency accident locations after the accident occurrence time; Based on the intelligent management terminal, data analysis and processing are performed on the parameter information set to determine the characteristics of high-frequency accidents.

8. The method for managing a smart community based on big data according to claim 7, characterized in that: The method of performing data analysis and processing on the parameter information set based on the intelligent management terminal to determine the characteristics of high-frequency accidents specifically includes the following steps: Based on the intelligent management terminal, the data in the parameter information set is classified and processed based on the accident occurrence time of different high-frequency accident locations, and the parameter data of different accident occurrence times are obtained; Based on the intelligent management terminal, the parameter data of different accident occurrence times are processed for intersection to determine the same type of parameter data; wherein the same type of parameter data is specifically the same type of parameters of all high-frequency accidents at different occurrence locations; Based on the intelligent management terminal, data changes of the same type of parameter data are analyzed to determine the parameter type that causes high-frequency accidents; Based on the intelligent management terminal, the parameter type causing high-frequency accidents is set as the high-frequency accident occurrence feature.

9. The method for managing a smart community based on big data according to claim 1, characterized in that: The real-time monitoring of the smart community based on the intelligent management terminal and taking the characteristics of high-frequency accidents as a reference specifically includes the following steps: Based on the intelligent management terminal, parameters of all areas in the smart community are matched with the characteristics of high-frequency accidents as a reference to determine the locations with high-frequency accident characteristics; Based on the intelligent management terminal, data analysis is performed on the characteristics of high-frequency accidents to determine the accident monitoring equipment; Based on the intelligent management terminal, accident monitoring equipment is installed at locations with high-frequency accident characteristics to conduct real-time monitoring of the smart community.

10. A smart community management system based on big data, used to implement a smart community management method based on big data as described in any one of claims 1 to 9, characterized in that: include: An intelligent management terminal is used to control each module to perform data classification, data comparison, data verification, and location analysis on all data of the smart community, and to monitor the smart community in real time; the intelligent management terminal is used to control data transmission and information exchange between each module; A database system, which is used to store all data of the smart community; A data classification module is used to classify all data of the smart community and obtain all accident data of the smart community; A first accident data verification module, the first accident data verification module is used to compare and judge the number of occurrences of different types of accidents to determine high-frequency accidents to be verified; A second accident data verification module, the second accident data verification module is used to perform time comparison and judgment processing on the high-frequency accidents to be verified, and determine the high-frequency accidents; A high-frequency accident feature determination module, which is used to perform type analysis on data of high-frequency accident locations to determine the characteristics of high-frequency accidents; A monitoring device installation location determination module performs parameter matching on all areas of the smart community based on the characteristics of high-frequency accidents, and determines locations with high-frequency accident characteristics.

Images