CN111222784A - Security monitoring method and system based on population big data - Google Patents
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Abstract
The invention discloses a security monitoring method and system based on population big data. The method comprises the following steps: acquiring community personnel information to be evaluated in a first period in a current community; inputting the community personnel information to be evaluated into a risk evaluation model to obtain risk personnel information; the risk assessment model is generated by training through a random forest algorithm; and carrying out key monitoring treatment on the risk personnel. The invention can strengthen security monitoring of resident people in the community, discover abnormal factors, perform early warning and timely processing before adverse events occur, and ensure the safety and stability of the community.
Description
Technical Field
The invention relates to the technical field of security, in particular to a security monitoring method and system based on population big data.
Background
With the development of social economy, population flows more and more frequently, logistics, food delivery and other home-going services also become normal, and therefore great hidden dangers are brought to community safety. In order to guarantee the community safety, security personnel are arranged at the entrance and exit of many communities for monitoring, and entrance guard equipment is arranged to control the entrance and exit of the personnel. And the function of entrance guard's equipment is also constantly strengthening and improving, and not only comparatively original and traditional "punch the card" discrepancy, but also diversified functions such as facial equipment, ID card discernment have been increased, through the interaction with backstage server to control the discrepancy of personnel outside the community more effectively.
However, the existing measures focus on controlling temporary access of people outside the community, and neglect the possible security risks of residents in the community. The development of urbanization makes the community scale of centralized living larger and larger, the frequent population in a community not only has the living of buying rooms, but also has a plurality of residents living in renting rooms, and the complexity and mobility of personnel make the residents in the community engage in illegal criminal behaviors or have violent conflict among the residents. In order to avoid the situations, a large amount of public security personnel needs to be invested in the conventional community management means to patrol in the community, the management cost is very high, and the effect is difficult to guarantee. The existing security monitoring systems arranged in communities all need security personnel to monitor in real time through a monitor screen, but risk events cannot be timely discovered and processed due to fatigue or negligence and the like.
Disclosure of Invention
The invention aims to provide a security monitoring method and a security monitoring system based on population big data, which can find out the potential safety hazard of a community by summarizing and analyzing the big data so as to pay attention to the community or take measures in advance, thereby solving the problems in the background technology.
In a first aspect of the present invention, a security monitoring method based on population big data is provided, including:
acquiring community personnel information to be evaluated in a first period in a current community;
inputting the community personnel information to be evaluated into a risk evaluation model to obtain risk personnel information; the risk assessment model is generated by training through a random forest algorithm;
and carrying out key monitoring treatment on the risk personnel.
Further, the community personnel information to be evaluated comprises personal information of community personnel to be evaluated, an access record in a first period, an intra-community action event and/or an intra-community action track.
Further, the risk assessment model is generated as follows:
the method comprises the steps of obtaining community security historical data of a first area in a second period, wherein the second period is larger than the first period, and the first area comprises a plurality of communities;
and training by adopting a random forest algorithm according to the community security historical data to obtain the risk assessment model.
Further, before inputting the community personnel information to be evaluated into a risk evaluation model, the method further comprises the following steps:
and judging the information integrity of each community person in the community person information to be evaluated, and identifying the corresponding community person with incomplete information as a risk person.
Further, the judging the information integrity of each community person includes:
determining missing information in the community personnel information by comparing with a preset template;
if the missing information is key information, the information is determined to be incomplete;
otherwise, calculating the occupation ratio of the missing information in all the information, and if the occupation ratio is greater than a preset threshold value, determining that the information is incomplete.
Further, the key monitoring processing of the risk personnel comprises the following steps:
submitting to security personnel for manual processing; and/or
And sending the risk personnel information to a cloud server, and carrying out secondary screening by the cloud server according to a public security database.
In a second aspect of the present invention, a security monitoring system based on population big data is provided, including:
the cloud server is used for training by adopting a random forest algorithm according to the community security historical data to obtain a risk evaluation model and sending the risk evaluation model to the community server;
the community server is used for acquiring community personnel information to be evaluated in a first period in the current community, inputting the community personnel information to be evaluated into a risk evaluation model to obtain risk personnel information, and performing key monitoring processing on the risk personnel.
Further, the performing of the key monitoring processing on the risk personnel comprises:
submitting security personnel for manual processing and/or sending the risk personnel information to a cloud server;
and the cloud server is also used for receiving the information of the risk personnel and carrying out secondary screening according to a public security database.
In a third aspect of the invention, an apparatus is presented, comprising:
one or more processors;
storage means for storing one or more programs;
when executed by the one or more processors, cause the one or more processors to implement a method as described in the first aspect.
In a fourth aspect of the present invention a computer-readable storage medium is presented, on which a computer program is stored, which program, when being executed by a processor, carries out the method according to the first aspect.
According to the security monitoring method and system based on population big data, the random forest algorithm is used for training historical security data of at least a plurality of communities to obtain the risk evaluation model, and the information of community personnel to be evaluated is input into the risk evaluation model to identify potential risk personnel, so that security monitoring of resident personnel in the communities can be enhanced, abnormal factors can be found, early warning and timely processing can be carried out before adverse events occur, and safety and stability of the communities are guaranteed. Further, the cloud server is accessed into the public security database, so that the conditions of risk personnel can be screened again, and the probability of criminal crime is reduced.
Drawings
FIG. 1 shows a flow diagram of a security monitoring method based on demographic big data according to an embodiment of the present invention;
FIG. 2 illustrates an architecture diagram of a security monitoring system based on demographic big data according to an embodiment of the present invention;
FIG. 3 illustrates a block diagram of an exemplary electronic device capable of implementing embodiments of the present disclosure.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The safety events in the community comprise domestic internal conflicts, upstairs and downstairs neighborhood conflicts, criminal behaviors of internal residents, single-living personnel accidents and the like. These events are difficult to be found in the existing security monitoring system, but before serious consequences occur, some symptoms often appear, for example, before serious conflict occurs in the neighborhood, the events occur to a lower extent but are quarreling frequently, community access of criminals is lack of regularity, and the like. With the improvement of the community monitoring system, the improvement of the image recognition technology and the like and the popularization of the cloud computing and big data technology, various information of community personnel can be accurately and comprehensively obtained. Based on this, embodiments of the present invention are presented.
Fig. 1 shows a security monitoring method 100 based on population big data according to an embodiment of the present invention, including:
s101, community personnel information to be evaluated in a first period in a current community is obtained;
the community personnel refer to community residents and include residents with house property rights and tenants living in rents. The community person registers personal information such as a name, an identification number, a work unit, and the like when entering a community area, and the personal information is stored in the community server. Optionally, some entrance guard equipment has face identification and ID card function of punching the card to can all record its information to any personnel of cominging in and going out the community.
The system may initiate the evaluation of community personnel periodically at a set period and/or on a set date. The set period is, for example, weekly or monthly, and the set time point is, for example, a date having a special meaning, such as ten days before the spring festival, ten days before the national day festival, and the like. When the evaluation of community personnel is started according to a set period, the first period is optionally not less than the set period.
The community personnel to be evaluated comprise all personnel or part of personnel in the community. Because the number of community personnel is huge, in order to reduce the processing load, optionally only part of the community personnel are evaluated at each time, and the community personnel to be evaluated are obtained by screening from all the community personnel according to a preset rule. The preset rules comprise persons who live in the community for a time length less than a first preset threshold value, persons who live in the community for a time length more than a second preset threshold value from the last time of evaluation, and the like.
The community personnel information to be evaluated comprises personal information of community personnel to be evaluated, access records in the first period, behavior events in the community and/or action tracks in the community and the like. Because entrance guard and supervisory equipment are installed to the community, and at present a lot of supervisory equipment all have face identification and gesture recognition function, consequently can accurately take notes personnel's the discrepancy condition and the activity condition in the community to form discrepancy record and action orbit. The intra-community behavioral events may be generated from a log of property daily management. The first time period is optionally greater than or equal to one month, so that the recent situation of community people can be reasonably reflected, and the influence of accidental factors is reduced.
S102, inputting the community personnel information to be evaluated into a risk evaluation model to obtain risk personnel information; the risk assessment model is generated by training through a random forest algorithm;
wherein the risk assessment model is generated as follows:
the method comprises the steps of obtaining community security historical data of a first area in a second period, wherein the second period is larger than the first period, and the first area comprises a plurality of communities;
and training by adopting a random forest algorithm according to the community security historical data to obtain the risk assessment model.
Optionally, the risk assessment model is generated by a cloud server based on big data. The community security history data may be collected over a longer period and a wider range, for example, the duration of the second period is 10 times that of the first period, and the first area is one or more cities. And the community server of each community sends the data of the community personnel information to the cloud server for summarizing. The data of the community personnel information also comprises risk data related to personnel, such as neighborhood conflicts, disturbed public security, criminal behaviors, accidents and the like.
The random forest is used as an integrated learning method for forming Bagging integration by taking a decision tree as a base learner, the decision tree with randomly selected features and sample sets is used as a weak learner, and the final classification result is obtained by adopting a mode of voting by all the decision trees.
Specifically, the collected community personnel information data is used as a sample set, feature extraction is carried out on the data according to expert experience, for example, sex, age interval, native place, work type, access time regularity, action track consistency, property complaint condition and the like are selected as sample features, and risk personnel and non-risk personnel are used as classification labels;
then, dividing historical data of community personnel information into a training set and a testing set according to a certain division ratio, wherein the training set is used for training data to obtain a plurality of decision trees to form a random forest classification model, and the testing set is used for testing the prediction accuracy of the model; in this embodiment, a segmentation ratio of 0.2, that is, 80% of the historical data is set for training of the model, and the rest of the data is used for testing.
Setting input parameters of a random forest prediction model for model training, wherein the input parameters comprise: the number t of decision trees, the depth depe of each decision tree, the accident-related risk factor dimension n and the feature selection number f of each node are integers, wherein f is the square root of n or the logarithm of n with 2 as the base;
randomly extracting m samples from the training set by adopting a bagging integration algorithm to form a new training set;
randomly selecting k features (k < d) from all the features d, and then selecting the optimal segmentation attribute from the k features as a node to establish a CART decision tree;
in the example, the sex, the age interval, the native place, the work type, the regular degree of the access time, the consistency of the action track and the complaint situation of the property are shared by 7 characteristics, when the decision tree is established, k characteristics (k <7) are randomly selected, and then the best segmentation attribute is selected from the k characteristics to be used as a node to establish the CART decision tree;
repeating the two steps for 120 times, and establishing 120 CART decision trees to form a random forest prediction model;
and inputting the test set into the random forest prediction model to judge the prediction precision of the random forest prediction model, and adjusting the parameters (the number t of decision trees and the number f of node feature choices) of the random forest prediction model to ensure that the precision meets the requirement.
The community personnel information to be socially evaluated is input into a trained random forest prediction model, each decision tree in the random forest gives the risk condition of the personnel, the random forest outputs the risk condition with more votes as a final prediction result according to the principle that a small number obeys a majority, and therefore the prediction of the risk personnel is finished with high precision.
The risk personnel information comprises personnel names, identity card numbers, residences, contact information, risk outlines and the like.
S103, performing key monitoring processing on the risk personnel.
The method comprises the steps of submitting security personnel for manual processing and/or sending risk personnel information to a cloud server, and carrying out secondary screening by the cloud server according to a public security database. Optionally, after the information of the risk personnel is obtained, the information is sent to security personnel, and the security personnel can pay attention to and monitor the information of the risk personnel when patrolling in an entrance and a community. And the secondary screening comprises the steps that the cloud server inquires whether the criminal information of the risk personnel exists from a public security database, and feeds back the inquiry result to the community server. And sending the risk personnel information to community security personnel under the condition that the criminal information exists. It should be noted that, considering the privacy rights of the persons, the specific crime information is not fed back to the community server and sent to the community security personnel, that is, the content of the risk personnel information known by the community security personnel is the same as that without secondary screening, but the risk personnel who have undergone secondary screening are marked specifically.
According to the embodiment, the random forest algorithm is used for training historical security data of at least a plurality of communities to obtain the risk assessment model, and the information of community personnel to be assessed is input into the risk assessment model to identify potential risk personnel, so that security monitoring of resident personnel in the communities can be enhanced, abnormal factors can be found, early warning and timely processing can be performed before adverse events occur, and safety and stability of the communities are guaranteed. Further, the cloud server is accessed into the public security database, so that the conditions of risk personnel can be screened again, and the probability of criminal crime is reduced.
In addition, because collection and entry of community personnel information inevitably require manual participation, the condition that the registration of the community personnel information is incomplete may be caused, for example, names, identification numbers and face information of community personnel can be recorded through access control equipment, but because the personnel do not go to property registration, the community server cannot acquire information such as check-in time and resident addresses.
For this, before inputting the community personnel information to be evaluated into a risk evaluation model, the method further includes:
and judging the information integrity of each community person in the community person information to be evaluated, and identifying the corresponding community person with incomplete information as a risk person.
Wherein, the judging of the information integrity of each community person comprises the following steps:
determining missing information items in community personnel information by comparing with a preset template;
if the missing information item is key information, the information is determined to be incomplete; otherwise, calculating the occupation ratio of the missing information in all the information, and if the occupation ratio is greater than a preset threshold value, determining that the information is incomplete. The key information is set according to experience and has a large influence on judging the risk of community personnel, such as check-in time, resident addresses and the like. If key information is not lost, for example, partial access records, discontinuous action tracks and the like are lost, the community personnel with excessive information loss are determined as risk personnel by calculating the information loss proportion.
Fig. 2 shows a security monitoring system 200 based on population big data according to an embodiment of the present invention, which includes a cloud server 201 and a plurality of community servers 202, wherein:
the cloud server 201 is used for training by adopting a random forest algorithm according to the community security historical data to obtain a risk evaluation model, and sending the risk evaluation model to the community server; the community security historical data is community security historical data of a first area in a second period, the second period is greater than the first period, and the first area comprises a plurality of communities;
the community server 202 is configured to obtain community staff information to be evaluated in a first period in a current community, input the community staff information to be evaluated into a risk evaluation model, obtain risk staff information, and perform key monitoring processing on the risk staff. The community personnel information to be evaluated comprises personal information of community personnel to be evaluated, access records in a first period, behavior events in the community and/or action tracks in the community and the like.
Further, the community server 202 is further configured to, before inputting the information of the community people to be evaluated into the risk evaluation model, determine the information integrity of each community person in the information of the community people to be evaluated, and identify the corresponding community person whose information is incomplete as a risk person. The step of judging the information integrity of each community person comprises the following steps:
determining missing information in the community personnel information by comparing with a preset template;
if the missing information is key information, the information is determined to be incomplete;
otherwise, calculating the occupation ratio of the missing information in all the information, and if the occupation ratio is greater than a preset threshold value, determining that the information is incomplete.
Further, the performing of the key monitoring processing on the risk personnel comprises:
submitting to security personnel for manual processing; and/or sending the risk personnel information to a cloud server.
And the cloud server is also used for receiving the information of the risk personnel and carrying out secondary screening according to a public security database.
For details that are not disclosed in the embodiment of the system of the present invention, please refer to the above-mentioned embodiment of the method of the present invention, and details thereof are not described herein again, because the system of the exemplary embodiment of the present invention can be used to implement the steps of the above-mentioned embodiment of the method described in fig. 1.
The following describes, in connection with a specific example, a security monitoring method process based on population big data executed by the system of the present invention:
in a city, be equipped with the cloud ware platform, be equipped with the community server in every community, through the mode that equipment collection information such as the existing entrance guard of community, surveillance camera and registered by the property management personnel is artifical, acquire the community personnel's of living information and save in the community server in the community, the community server of each community regularly with community personnel information send to the cloud ware gathers, forms the big data relevant with the community security protection, community security protection historical data promptly. The community security history data includes, for example, community personnel information of all communities in the city in the last three years.
And the cloud server adopts a random forest algorithm to train according to the community security historical data to obtain a risk evaluation model, and the risk evaluation model is sent to the community server. Based on big data, some situations which are easy to cause security risks can be found, such as no access to a cell for a long time, abnormal access time, abnormal action track, excessive number of visitors and the like; whether the person to be evaluated meets the situations can be judged through the rules in the risk evaluation model.
The community server periodically and/or on a set date acquires all community personnel information in the community from an internal storage device, wherein the community personnel information comprises names, identification numbers, sexes, community entrance duration, last evaluation time and the like, and then the personnel with the community entrance duration smaller than a first preset threshold and the personnel with the distance from the last evaluated duration larger than a second preset threshold are selected as the personnel to be evaluated.
And judging the information integrity of the personnel to be evaluated one by one, including determining missing information items in the community personnel information. If no missing information exists, the next step is carried out; if the missing information exists, further judging whether the missing information is key information, if so, determining that the information is incomplete, and marking the person to be evaluated as a risk person; otherwise, calculating the occupation ratio of the missing information in all the information, if the occupation ratio is larger than a preset threshold value, determining that the information is incomplete, and marking the person to be evaluated as a risk person; if the ratio is smaller than a preset threshold value, entering the next step;
and inputting the information of the personnel to be evaluated into the risk evaluation model to obtain the information of the risk personnel, wherein the information of the risk personnel comprises personnel names, identity card numbers, residences, contact ways, risk outlines and the like.
Submitting the information of the risk personnel to security personnel through interface presentation or message sending and the like;
meanwhile, the risk personnel information is sent to a cloud server;
the cloud server is connected with the public security database, whether criminal information exists is inquired according to names and identity card numbers of risk personnel, the risk personnel information with the criminal information is specially marked, and the criminal information is sent to security personnel.
Community security personnel focus on the risk personnel to discover possible criminal activity, or potential neighborhood conflicts.
In summary, the embodiment of the invention collects the community personnel information of a plurality of communities or even a city through a plurality of ways, and generates the risk assessment model by using the random forest algorithm so as to identify potential abnormal risks such as conflicts and crimes according to the performance of resident personnel in the community within a period of time, thereby improving the safety of the community.
FIG. 3 shows a schematic block diagram of an electronic device 300 that may be used to implement embodiments of the present disclosure. The device 300 may be used to implement the community server 202 in FIG. 2. As shown, device 300 includes a Central Processing Unit (CPU)301 that may perform various appropriate actions and processes in accordance with computer program instructions stored in a Read Only Memory (ROM)302 or loaded from a storage unit 308 into a Random Access Memory (RAM) 303. In the RAM 303, various programs and data necessary for the operation of the device 300 can also be stored. The CPU 301, ROM 302, and RAM 303 are connected to each other via a bus 304. An input/output (I/O) interface 305 is also connected to bus 304.
Various components in device 300 are connected to I/O interface 305, including: an input unit 306 such as a keyboard, a mouse, or the like; an output unit 307 such as various types of displays, speakers, and the like; a storage unit 308 such as a magnetic disk, optical disk, or the like; and a communication unit 309 such as a network card, modem, wireless communication transceiver, etc. The communication unit 309 allows the device 300 to exchange information/data with other devices via a computer network such as the internet and/or various telecommunication networks.
The processing unit 301 performs the various methods and processes described above. For example, in some embodiments, the method may be implemented as a computer software program tangibly embodied in a machine-readable medium, such as storage unit 308. In some embodiments, part or all of the computer program may be loaded and/or installed onto device 300 via ROM 302 and/or communication unit 309. When the computer program is loaded into RAM 303 and executed by CPU 301, one or more steps of the method described above may be performed. Alternatively, in other embodiments, the CPU 301 may be configured to perform the method by any other suitable means (e.g., by way of firmware).
The functions described herein above may be performed, at least in part, by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: a Field Programmable Gate Array (FPGA), an Application Specific Integrated Circuit (ASIC), an Application Specific Standard Product (ASSP), a system on a chip (SOC), a load programmable logic device (CPLD), and the like.
Program code for implementing the methods of the present disclosure may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the program codes, when executed by the processor or controller, cause the functions/operations specified in the flowchart and/or block diagram to be performed. The program code may execute entirely on the machine, partly on the machine, as a stand-alone software package partly on the machine and partly on a remote machine or entirely on the remote machine or server.
In the context of this disclosure, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
Further, while operations are depicted in a particular order, this should be understood as requiring that such operations be performed in the particular order shown or in sequential order, or that all illustrated operations be performed, to achieve desirable results. Under certain circumstances, multitasking and parallel processing may be advantageous. Likewise, while several specific implementation details are included in the above discussion, these should not be construed as limitations on the scope of the disclosure. Certain features that are described in the context of separate embodiments can also be implemented in combination in a single implementation. Conversely, various features that are described in the context of a single implementation can also be implemented in multiple implementations separately or in any suitable subcombination.
Although the subject matter has been described in language specific to structural features and/or methodological acts, it is to be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or acts described above. Rather, the specific features and acts described above are disclosed as example forms of implementing the claims.
Claims (10)
1. A security monitoring method based on population big data is characterized by comprising the following steps:
acquiring community personnel information to be evaluated in a first period in a current community;
inputting the community personnel information to be evaluated into a risk evaluation model to obtain risk personnel information; the risk assessment model is generated by training through a random forest algorithm;
and carrying out key monitoring treatment on the risk personnel.
2. The security monitoring method according to claim 1, wherein the community personnel information to be evaluated comprises personal information of community personnel to be evaluated, an entrance and exit record in a first period, a behavior event in the community and/or a behavior track in the community.
3. The security monitoring method of claim 2, wherein the risk assessment model is generated as follows:
the method comprises the steps of obtaining community security historical data of a first area in a second period, wherein the second period is larger than the first period, and the first area comprises a plurality of communities;
and training by adopting a random forest algorithm according to the community security historical data to obtain the risk assessment model.
4. The security monitoring method according to claim 3, wherein before inputting the community personnel information to be evaluated into a risk evaluation model, the method further comprises:
and judging the information integrity of each community person in the community person information to be evaluated, and identifying the corresponding community person with incomplete information as a risk person.
5. The security monitoring method according to claim 4, wherein the judging of the information integrity of each community person comprises:
determining missing information in the community personnel information by comparing with a preset template;
if the missing information is key information, the information is determined to be incomplete;
otherwise, calculating the occupation ratio of the missing information in all the information, and if the occupation ratio is greater than a preset threshold value, determining that the information is incomplete.
6. The security monitoring method according to claim 5, wherein the key monitoring processing of the risk personnel comprises:
submitting to security personnel for manual processing; and/or
And sending the risk personnel information to a cloud server, and carrying out secondary screening by the cloud server according to a public security database.
7. A security monitoring system based on population big data is characterized by comprising:
the cloud server is used for training by adopting a random forest algorithm according to the community security historical data to obtain a risk evaluation model and sending the risk evaluation model to the community server;
the community server is used for acquiring community personnel information to be evaluated in a first period in the current community, inputting the community personnel information to be evaluated into a risk evaluation model to obtain risk personnel information, and performing key monitoring processing on the risk personnel.
8. The system of claim 7, wherein said focus monitoring process for said at-risk person comprises:
submitting security personnel for manual processing and/or sending the risk personnel information to a cloud server;
and the cloud server is also used for receiving the information of the risk personnel and carrying out secondary screening according to a public security database.
9. An apparatus, comprising:
one or more processors;
storage means for storing one or more programs;
when executed by the one or more processors, cause the one or more processors to implement the method of any one of claims 1-6.
10. A computer-readable storage medium, on which a computer program is stored, which program, when being executed by a processor, carries out the method of any one of claims 1 to 6.
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