CN110633915A - High-risk place identification method and device - Google Patents
High-risk place identification method and device Download PDFInfo
- Publication number
- CN110633915A CN110633915A CN201910903237.3A CN201910903237A CN110633915A CN 110633915 A CN110633915 A CN 110633915A CN 201910903237 A CN201910903237 A CN 201910903237A CN 110633915 A CN110633915 A CN 110633915A
- Authority
- CN
- China
- Prior art keywords
- monitoring
- place
- determining
- risk
- information
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0635—Risk analysis of enterprise or organisation activities
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
- G06Q10/06393—Score-carding, benchmarking or key performance indicator [KPI] analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
- G06Q50/10—Services
- G06Q50/26—Government or public services
Abstract
The application provides a high-risk place identification method and device, comprising the following steps: acquiring activity information of at least one monitored object; determining access information of the monitored object contained in each monitoring place based on the monitoring places corresponding to the access point information in the activity information of the monitored object; and determining a high-risk score of each monitoring place based on the access information, and determining a high-risk place from the monitoring places based on the high-risk score, wherein the high-risk score is used for representing the high-risk degree of the monitoring places. By the method, the efficiency and the accuracy of high-risk place prediction can be improved.
Description
Technical Field
The application relates to the technical field of information processing, in particular to a high-risk place identification method and device.
Background
The abuse of drugs is extremely harmful, and the social security is seriously influenced. Drug abuse not only brings serious harm to the drug addict and the family, but also induces a series of illegal criminal activities such as robbery and cheating. Therefore, the related departments now invest a great deal of manpower and material resources to fight against drug taking and drug selling activities every year.
At present, target objects are mainly monitored manually, high-risk places are predicted according to the entrance and exit conditions of the target objects in public places, and the high-risk places are monitored in a key mode, so that the monitoring of drug taking and drug selling behaviors is achieved. However, on one hand, the method wastes a large amount of human resources, and on the other hand, the prediction of high-risk places is not accurate, so that the drug taking and drug selling behaviors cannot be accurately attacked.
Disclosure of Invention
In view of this, an object of the present application is to provide a high-risk location identification method and apparatus, so as to improve efficiency and accuracy of high-risk location identification.
In a first aspect, an embodiment of the present application provides a high-risk location identification method, including:
acquiring activity information of at least one monitored object;
determining access information of the monitored object contained in each monitoring place based on the monitoring places corresponding to the access point information in the activity information of the monitored object;
and determining a high-risk score of each monitoring place based on the access information, and determining a high-risk place from the monitoring places based on the high-risk score, wherein the high-risk score is used for representing the high-risk degree of the monitoring places.
In a possible embodiment, the activity information further comprises at least one of the following information:
communication information, traffic information, and logistics information.
In one possible embodiment, before determining a high risk score for each monitored location based on the access information, the method further comprises:
determining the per-person consumption time of each monitoring place;
and determining the monitoring place with the average time length of people consumption being less than the preset value as a first monitoring place, and determining the monitoring place with the average time length of people consumption being not less than the preset value as a second monitoring place.
In one possible embodiment, for a first monitoring location, the determining a high risk score for each monitoring location based on the access information includes:
determining a first monitoring place into which more than a first preset number of target monitoring objects enter in a first preset time interval as a first target monitoring place, wherein the target monitoring objects are partial monitoring objects;
determining a scoring weight of each target monitoring object based on the access times of each target monitoring object in the first target monitoring place within a preset time from the current moment;
and determining the high-risk score of the first target monitoring place based on the high-risk score and the scoring weight of each target monitoring object.
In one possible embodiment, the determining a high risk score for each monitoring location based on the access information for a second monitoring location includes:
determining a second monitoring place into which more than a second preset number of target monitoring objects enter within a second preset time interval as a second target monitoring place, wherein the second preset time interval is longer than the first preset time interval;
determining the scoring weight of each target monitoring object based on the access times of each target monitoring object in the second target monitoring place within a preset time from the current moment;
and determining the high-risk score of the second target monitoring place based on the high-risk score and the scoring weight of each target monitoring object.
In a possible embodiment, the target monitoring object is determined according to the following method:
inputting the activity information of each monitored object into a pre-trained high-risk detection model to obtain a high-risk score of each monitored object;
and determining the monitoring object with the high risk score exceeding the preset score as a target monitoring object.
In one possible embodiment, after determining a high risk location from the monitoring locations based on the high risk score, the method further comprises:
receiving place retrieval information input by a user, wherein the place retrieval information carries identification information of any monitoring place in the monitoring places;
and searching for a high-risk condition of the monitoring place associated with the place retrieval information based on the place retrieval information, wherein the high-risk condition is used for representing whether the monitoring place is a high-risk place.
In a second aspect, an embodiment of the present application further provides a high-risk location identification device, including:
the acquisition module is used for acquiring the activity information of at least one monitoring object;
a first determining module, configured to determine access information of the monitored object included in each monitoring location based on a monitoring location corresponding to access point information in the activity information of the monitored object;
and the second determination module is used for determining a high-risk score of each monitoring place based on the access information, and determining a high-risk place from the monitoring places based on the high-risk score, wherein the high-risk score is used for representing the high-risk degree of the monitoring places.
In a possible embodiment, the activity information further comprises at least one of the following information:
communication information, traffic information, and logistics information.
In one possible embodiment, the second determining module, before determining the high risk score for each monitored location based on the access information, is further configured to:
determining the per-person consumption time of each monitoring place;
and determining the monitoring place with the average time length of people consumption being less than the preset value as a first monitoring place, and determining the monitoring place with the average time length of people consumption being not less than the preset value as a second monitoring place.
In a possible implementation manner, for a first monitoring location, the second determining module, when determining the high risk score of each monitoring location based on the access information, is specifically configured to:
determining a first monitoring place into which more than a first preset number of target monitoring objects enter in a first preset time interval as a first target monitoring place, wherein the target monitoring objects are partial monitoring objects;
determining a scoring weight of each target monitoring object based on the access times of each target monitoring object in the first target monitoring place within a preset time from the current moment;
and determining the high-risk score of the first target monitoring place based on the high-risk score and the scoring weight of each target monitoring object.
In a possible implementation manner, for a second monitoring location, the second determining module, when determining the high risk score of each monitoring location based on the access information, is specifically configured to:
determining a second monitoring place into which more than a second preset number of target monitoring objects enter within a second preset time interval as a second target monitoring place, wherein the second preset time interval is longer than the first preset time interval;
determining the scoring weight of each target monitoring object based on the access times of each target monitoring object in the second target monitoring place within a preset time from the current moment;
and determining the high-risk score of the second target monitoring place based on the high-risk score and the scoring weight of each target monitoring object.
In a possible embodiment, the apparatus further comprises:
a third determining module, configured to determine the target monitored object according to the following method:
inputting the activity information of each monitored object into a pre-trained high-risk detection model to obtain a high-risk score of each monitored object;
and determining the monitoring object with the high risk score exceeding the preset score as a target monitoring object.
In a possible embodiment, the apparatus further comprises:
the retrieval module is used for receiving place retrieval information input by a user after determining a high-risk place from the monitoring places based on the high-risk score, wherein the place retrieval information carries identification information of any monitoring place in the monitoring places; and searching for a high-risk condition of the monitoring place associated with the place retrieval information based on the place retrieval information, wherein the high-risk condition is used for representing whether the monitoring place is a high-risk place.
In a third aspect, an embodiment of the present application further provides an electronic device, including: a processor, a memory and a bus, the memory storing machine-readable instructions executable by the processor, the processor and the memory communicating via the bus when the electronic device is running, the machine-readable instructions when executed by the processor performing the steps of the first aspect described above, or any possible implementation of the first aspect.
In a fourth aspect, this application further provides a computer-readable storage medium, on which a computer program is stored, where the computer program is executed by a processor to perform the steps in the first aspect or any one of the possible implementation manners of the first aspect.
According to the high-risk place identification method and device provided by the embodiment of the application, the activity information of at least one monitoring object is obtained, the access information of the monitoring object in each monitoring place is determined based on the monitoring place corresponding to the access point information in the activity information of the monitoring object, the high-risk score of each monitoring place is determined based on the access information, and the high-risk place is determined from the monitoring places based on the high-risk score. Compared with the method for predicting the high-risk places manually, the method combines the activity information of all monitored objects, and the predicted high-risk places are higher in efficiency and accuracy.
In order to make the aforementioned objects, features and advantages of the present application more comprehensible, preferred embodiments accompanied with figures are described in detail below.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are required to be used in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present application and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings can be obtained from the drawings without inventive effort.
Fig. 1 shows a schematic flow chart of a high-risk location identification method provided in an embodiment of the present application;
fig. 2 is a schematic flow chart illustrating a method for determining a high-risk score of a first monitoring location according to an embodiment of the present application;
fig. 3 illustrates a determination method of a high risk score of a second monitoring location according to an embodiment of the present application;
fig. 4 shows a schematic architecture diagram of a high-risk site identification device provided in an embodiment of the present application;
fig. 5 shows a schematic structural diagram of an electronic device 500 provided in an embodiment of the present application.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all the embodiments. The components of the embodiments of the present application, generally described and illustrated in the figures herein, can be arranged and designed in a wide variety of different configurations. Thus, the following detailed description of the embodiments of the present application, presented in the accompanying drawings, is not intended to limit the scope of the claimed application, but is merely representative of selected embodiments of the application. All other embodiments, which can be derived by a person skilled in the art from the embodiments of the present application without making any creative effort, shall fall within the protection scope of the present application.
For the convenience of understanding the present embodiment, a detailed description will be first given of a high-risk location identification method disclosed in the embodiments of the present application.
Example one
Referring to fig. 1, a schematic flow chart of a high-risk location identification method provided in the embodiment of the present application includes:
In one possible embodiment, the activity information of the monitoring object includes at least one of the following information:
communication information, traffic information, and logistics information.
The communication information comprises communication records of communication tools used by the monitored object, such as call records of a mobile phone, short message records, or chat records of various social software; the traffic information includes information of a vehicle on which the monitoring object is seated, such as a riding record and the like; the logistics information comprises information of the mail items of the monitoring object, such as recipient information, sender information, mail item types and the like.
And 102, determining access information of the monitored object contained in each monitored place based on the monitored place corresponding to the access point information in the activity information of the monitored object.
In a specific implementation, the activity information of the monitored object may further include access point information, where the access point information includes access information of the monitored object at each monitoring location. Whether a monitored object comes in and goes out in each monitored place or not can be determined based on the monitored place corresponding to the access point information in the activity information of the monitored object, and corresponding access information when the monitored object comes in and goes out can be determined.
In practical application, the activity information of all monitored objects can be obtained first, and the identifier of each activity information, such as an identity card number, a mobile phone number, a passport number, a military officer license number, a Media access control Address (MAC Address), and the like, is determined, and then the activity information is classified and stored.
Specifically, the monitoring places corresponding to the access point information in the activity information may be sorted according to the initials of the names of the monitoring places, and then the access point information of the monitored objects accessing the same monitoring place is divided into the same file.
Illustratively, the monitoring object a enters the monitoring place 1 at 17 th in 9 th in 2019 at 10:00, leaves the monitoring place 1 at 17 th in 9 th in 2019 at 11:30, enters the monitoring place 2 at 18 th in 9 th in 2019 at 10:00, and leaves the monitoring place 2 at 18 th in 9 th in 2019 at 13: 00; if the monitoring object B enters the monitoring place at 17 th in 2019, 14:00, and leaves the monitoring place 1 at 17 th in 2019, 17 th in month 15:00, the stored data in the monitoring place 1 are that the monitoring object a enters the monitoring place 1 at 10:00 in 2019, 17 th in month 9, 11:30 in 2019, 17 th in month 17, and leaves the monitoring place 1 at 14:00 in 2019, 9, 17 th in month 9, 15:00 in month 9, and leaves the monitoring place 1.
By this storage method, for each monitoring place, it is possible to obtain whether each monitoring object enters, and when the entering and leaving times are if the monitoring object enters the monitoring place.
In one possible application scenario of the present application, after the activity information is classified according to the monitoring location, the activity information may be classified according to the date, for example, the activity information may be stored in a directory format of year and month and a file name of date, for example, the data of 2017 to 2019 may be stored in a manner of storing directories 2019, 2018 and 2017, the data of each year is divided into 12-month storage directories under the data of each year, the data of each day is stored under the directory of each month, and the data naming format of each day may be location personnel data dd _ a _ B, where dd represents the date, such as the data of 17 th of the 9 th of the 2019, and may be stored in the directory of the 9 months under the directory of 2019, the data naming format is location personnel data 17_ a _ B, a represents the first letter of the first word of the monitoring location name, B represents the first letter of the last word of the monitoring location name, the naming format of the data of the hotel as wealthy in 2019, 9, 17 and is place personnel data 17_ F _ G.
And 103, determining a high-risk score of each monitoring place based on the access information, and determining the high-risk places from the monitoring places based on the high-risk scores.
Wherein, the high risk score is used for guaranteeing the high risk degree of the monitoring place.
In consideration of the possibility of a change in a high-risk location, that is, the monitoring location may be a high-risk location in a certain time period, but the monitoring location may not be a high-risk location in other time periods, in a specific implementation, after the data is classified and stored by the method described in step 102, when determining the high-risk score of each monitoring location based on the access information, the access information in a certain time period may be obtained first, and then the high-risk score of each monitoring location may be determined based on the access information in the time period.
In practical application, the methods for determining the high risk scores in different monitoring places are different, so that the monitoring places can be divided into a first monitoring place and a second monitoring place according to the per-person consumption time of each monitoring place.
For example, when determining the time duration of the personal consumption of each monitoring place, in one possible implementation, the time duration of the personal consumption of each monitoring place may be determined according to the access information of the monitoring objects in the monitoring places, and in another possible implementation, the time duration of the personal consumption of each monitoring place may be set according to the experience of people.
Specifically, when the first monitoring place and the second monitoring place are divided, the monitoring place with the average time length of people consumption less than the preset value can be determined as the first monitoring place, and the monitoring place with the average time length of people consumption not less than the preset value can be determined as the second monitoring place. Illustratively, the first monitoring location may be a supermarket, a bar, a restaurant, etc., and the second monitoring location may be a hotel, a private establishment, etc.
Referring to fig. 2, a schematic flow chart of a method for determining a high risk score of a first monitoring location provided by an embodiment of the present application is shown, where the method for determining a high risk score of different monitoring locations is different, and includes:
In another embodiment, if it is detected that there are more than a first preset number of target monitoring objects in the first monitoring place, the first monitoring place is determined as the first target monitoring place.
The target monitoring objects are part of monitoring objects, specifically, when the target monitoring objects are determined from the monitoring objects, the activity information of each monitoring object can be input into a pre-trained high-risk detection model to obtain the high-risk score of each monitoring object, and the monitoring objects with the high-risk scores exceeding the preset score are determined as the target monitoring objects.
And step 203, determining the high risk score of the first target monitoring place based on the high risk score and the scoring weight of each target monitoring object.
For the second monitoring location, referring to fig. 3, an embodiment of the present application provides a method for determining a high risk score of the second monitoring location, including the following steps:
It should be noted that the second preset time interval is greater than the first preset time interval.
And step 303, determining a high-risk score of the second target monitoring place based on the high-risk score and the scoring weight of each target monitoring object.
In specific implementation, after the high-risk place is identified, in a possible application scene, the high-risk place can be identified on the map so as to remind a worker to monitor the high-risk place, and the force of illegal behavior supervision is improved.
In another possible application scenario, after determining a high-risk place from monitoring places based on a high-risk score, the method may further receive place retrieval information input by a user, where the place retrieval information carries identification information of any monitoring place in the monitoring places, and then, based on the place retrieval information, search for a high-risk situation of the monitoring places associated with the place retrieval information, where the high-risk situation is used to characterize whether the monitoring places are high-risk places.
According to the high-risk place identification method provided by the embodiment of the application, the activity information of at least one monitoring object is obtained, the access information of the monitoring object in each monitoring place is determined based on the monitoring places corresponding to the access point information in the activity information of the monitoring object, the high-risk score of each monitoring place is determined based on the access information, and the high-risk place is determined from the monitoring places based on the high-risk score. Compared with the method for predicting the high-risk places manually, the method combines the activity information of all monitored objects, and the predicted high-risk places are higher in efficiency and accuracy.
Example two
Based on the same concept, an embodiment of the present application further provides a high-risk location identification device, as shown in fig. 4, which is an architecture schematic diagram of the high-risk location identification device provided in the embodiment of the present application, and includes an obtaining module 401, a first determining module 402, a second determining module 403, a third determining module 404, and a retrieving module 405, specifically:
an obtaining module 401, configured to obtain activity information of at least one monitoring object;
a first determining module 402, configured to determine access information of the monitored object included in each monitoring location based on a monitoring location corresponding to access point information in the activity information of the monitored object;
a second determining module 403, configured to determine a high-risk score for each monitoring location based on the access information, and determine a high-risk location from the monitoring locations based on the high-risk score, where the high-risk score is used to represent a high-risk degree of the monitoring location.
In one possible design, the activity information further includes at least one of:
communication information, traffic information, and logistics information.
In one possible design, the second determining module 403, before determining the high risk score for each monitored location based on the access information, is further configured to:
determining the per-person consumption time of each monitoring place;
and determining the monitoring place with the average time length of people consumption being less than the preset value as a first monitoring place, and determining the monitoring place with the average time length of people consumption being not less than the preset value as a second monitoring place.
In one possible design, for a first monitoring location, the second determining module 403, when determining the high risk score of each monitoring location based on the access information, is specifically configured to:
determining a first monitoring place into which more than a first preset number of target monitoring objects enter in a first preset time interval as a first target monitoring place, wherein the target monitoring objects are partial monitoring objects;
determining a scoring weight of each target monitoring object based on the access times of each target monitoring object in the first target monitoring place within a preset time from the current moment;
and determining the high-risk score of the first target monitoring place based on the high-risk score and the scoring weight of each target monitoring object.
In one possible design, for a second monitoring location, the second determining module 403, when determining the high risk score of each monitoring location based on the access information, is specifically configured to:
determining a second monitoring place into which more than a second preset number of target monitoring objects enter within a second preset time interval as a second target monitoring place, wherein the second preset time interval is longer than the first preset time interval;
determining the scoring weight of each target monitoring object based on the access times of each target monitoring object in the second target monitoring place within a preset time from the current moment;
and determining the high-risk score of the second target monitoring place based on the high-risk score and the scoring weight of each target monitoring object.
In one possible design, the apparatus further includes:
a third determining module 404, configured to determine the target monitoring object according to the following method:
inputting the activity information of each monitored object into a pre-trained high-risk detection model to obtain a high-risk score of each monitored object;
and determining the monitoring object with the high risk score exceeding the preset score as a target monitoring object.
In one possible design, the apparatus further includes:
a retrieval module 405, configured to receive location retrieval information input by a user after determining a high-risk location from the monitoring locations based on the high-risk score, where the location retrieval information includes identification information of any monitoring location in the monitoring locations; and searching for a high-risk condition of the monitoring place associated with the place retrieval information based on the place retrieval information, wherein the high-risk condition is used for representing whether the monitoring place is a high-risk place.
The high-risk place recognition device provided by the embodiment of the application determines the access information of the monitoring object in each monitoring place by acquiring the activity information of at least one monitoring object and then based on the monitoring place corresponding to the access point information in the activity information of the monitoring object, and finally determines the high-risk score of each monitoring place based on the access information and determines the high-risk place from the monitoring places based on the high-risk score. Compared with the manual prediction of high-risk places, the device combines the activity information of all monitored objects, and the efficiency and accuracy of the predicted high-risk places are higher.
EXAMPLE III
Based on the same technical concept, the embodiment of the application also provides the electronic equipment. Referring to fig. 5, a schematic structural diagram of an electronic device 500 provided in the embodiment of the present application includes a processor 501, a memory 502, and a bus 503. The memory 502 is used for storing execution instructions and includes a memory 5021 and an external memory 5022; the memory 5021 is also referred to as an internal memory, and is used for temporarily storing operation data in the processor 501 and data exchanged with an external storage 5022 such as a hard disk, the processor 501 exchanges data with the external storage 5022 through the memory 5021, and when the electronic device 500 operates, the processor 501 communicates with the storage 502 through the bus 503, so that the processor 501 executes the following instructions:
acquiring activity information of at least one monitored object;
determining access information of the monitored object contained in each monitoring place based on the monitoring places corresponding to the access point information in the activity information of the monitored object;
and determining a high-risk score of each monitoring place based on the access information, and determining a high-risk place from the monitoring places based on the high-risk score, wherein the high-risk score is used for representing the high-risk degree of the monitoring places.
In one possible design, in the instructions executed by processor 501, the activity information further includes at least one of the following information:
communication information, traffic information, and logistics information.
In one possible design, the processor 501 executes instructions that, prior to determining a high risk score for each monitored location based on the access information, further include:
determining the per-person consumption time of each monitoring place;
and determining the monitoring place with the average time length of people consumption being less than the preset value as a first monitoring place, and determining the monitoring place with the average time length of people consumption being not less than the preset value as a second monitoring place.
In one possible design, the determining, by the processor 501, a high risk score for each monitoring location based on the access information for a first monitoring location includes:
determining a first monitoring place into which more than a first preset number of target monitoring objects enter in a first preset time interval as a first target monitoring place, wherein the target monitoring objects are partial monitoring objects;
determining a scoring weight of each target monitoring object based on the access times of each target monitoring object in the first target monitoring place within a preset time from the current moment;
and determining the high-risk score of the first target monitoring place based on the high-risk score and the scoring weight of each target monitoring object.
In one possible design, the determining, by the processor 501, a high risk score for each monitoring location based on the access information for a second monitoring location includes:
determining a second monitoring place into which more than a second preset number of target monitoring objects enter within a second preset time interval as a second target monitoring place, wherein the second preset time interval is longer than the first preset time interval;
determining the scoring weight of each target monitoring object based on the access times of each target monitoring object in the second target monitoring place within a preset time from the current moment;
and determining the high-risk score of the second target monitoring place based on the high-risk score and the scoring weight of each target monitoring object.
In one possible design, the processor 501 executes instructions to determine the target monitoring object according to the following method:
inputting the activity information of each monitored object into a pre-trained high-risk detection model to obtain a high-risk score of each monitored object;
and determining the monitoring object with the high risk score exceeding the preset score as a target monitoring object.
In one possible design, after determining a high risk location from the monitoring locations based on the high risk score, the processor 501 executes instructions that further include:
receiving place retrieval information input by a user, wherein the place retrieval information carries identification information of any monitoring place in the monitoring places;
and searching for a high-risk condition of the monitoring place associated with the place retrieval information based on the place retrieval information, wherein the high-risk condition is used for representing whether the monitoring place is a high-risk place.
Example four
The embodiment of the present application further provides a computer-readable storage medium, where a computer program is stored on the computer-readable storage medium, and when the computer program is executed by a processor, the steps of the high-risk location identification method described in any of the above embodiments are performed.
Specifically, the storage medium can be a general-purpose storage medium, such as a removable disk, a hard disk, or the like, and when a computer program on the storage medium is executed, the steps of the high-risk location identification method can be executed, so as to improve the efficiency and accuracy of high-risk location identification.
The computer program product for performing the high-risk location identification method provided in the embodiment of the present application includes a computer-readable storage medium storing a nonvolatile program code executable by a processor, where instructions included in the program code may be used to execute the method described in the foregoing method embodiment, and specific implementation may refer to the method embodiment, which is not described herein again.
It is clear to those skilled in the art that, for convenience and brevity of description, the specific working processes of the above-described systems, apparatuses and units may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
In the several embodiments provided in the present application, it should be understood that the disclosed system, apparatus and method may be implemented in other ways. The above-described embodiments of the apparatus are merely illustrative, and for example, the division of the units is only one logical division, and there may be other divisions when actually implemented, and for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection of devices or units through some communication interfaces, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit.
The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a non-volatile computer-readable storage medium executable by a processor. Based on such understanding, the technical solution of the present application or portions thereof that substantially contribute to the prior art may be embodied in the form of a software product stored in a storage medium and including instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: various media capable of storing program codes, such as a usb disk, a removable hard disk, a Read-only Memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disk.
Finally, it should be noted that: the above-mentioned embodiments are only specific embodiments of the present application, and are used for illustrating the technical solutions of the present application, but not limiting the same, and the scope of the present application is not limited thereto, and although the present application is described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: any person skilled in the art can modify or easily conceive the technical solutions described in the foregoing embodiments or equivalent substitutes for some technical features within the technical scope disclosed in the present application; such modifications, changes or substitutions do not depart from the spirit and scope of the exemplary embodiments of the present application, and are intended to be covered by the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (10)
1. A high-risk place identification method is characterized by comprising the following steps:
acquiring activity information of at least one monitored object;
determining access information of the monitored object contained in each monitoring place based on the monitoring places corresponding to the access point information in the activity information of the monitored object;
and determining a high-risk score of each monitoring place based on the access information, and determining a high-risk place from the monitoring places based on the high-risk score, wherein the high-risk score is used for representing the high-risk degree of the monitoring places.
2. The method of claim 1, wherein the activity information further comprises at least one of:
communication information, traffic information, and logistics information.
3. The method of claim 1, wherein prior to determining a high risk score for each monitored location based on the access information, the method further comprises:
determining the per-person consumption time of each monitoring place;
and determining the monitoring place with the average time length of people consumption being less than the preset value as a first monitoring place, and determining the monitoring place with the average time length of people consumption being not less than the preset value as a second monitoring place.
4. The method of claim 3, wherein determining a high risk score for each monitoring location based on the access information for a first monitoring location comprises:
determining a first monitoring place into which more than a first preset number of target monitoring objects enter in a first preset time interval as a first target monitoring place, wherein the target monitoring objects are partial monitoring objects;
determining a scoring weight of each target monitoring object based on the access times of each target monitoring object in the first target monitoring place within a preset time from the current moment;
and determining the high-risk score of the first target monitoring place based on the high-risk score and the scoring weight of each target monitoring object.
5. The method of claim 3, wherein determining a high risk score for each monitoring location based on the access information for a second monitoring location comprises:
determining a second monitoring place into which more than a second preset number of target monitoring objects enter within a second preset time interval as a second target monitoring place, wherein the second preset time interval is longer than the first preset time interval;
determining the scoring weight of each target monitoring object based on the access times of each target monitoring object in the second target monitoring place within a preset time from the current moment;
and determining the high-risk score of the second target monitoring place based on the high-risk score and the scoring weight of each target monitoring object.
6. The method according to claim 4 or claim 5, wherein the target monitoring object is determined according to the following method:
inputting the activity information of each monitored object into a pre-trained high-risk detection model to obtain a high-risk score of each monitored object;
and determining the monitoring object with the high risk score exceeding the preset score as a target monitoring object.
7. The method of claim 1, wherein after determining a high risk site from the monitoring sites based on the high risk score, the method further comprises:
receiving place retrieval information input by a user, wherein the place retrieval information carries identification information of any monitoring place in the monitoring places;
and searching for a high-risk condition of the monitoring place associated with the place retrieval information based on the place retrieval information, wherein the high-risk condition is used for representing whether the monitoring place is a high-risk place.
8. A high-risk place recognition device, comprising:
the acquisition module is used for acquiring the activity information of at least one monitoring object;
a first determining module, configured to determine access information of the monitored object included in each monitoring location based on a monitoring location corresponding to access point information in the activity information of the monitored object;
and the second determination module is used for determining a high-risk score of each monitoring place based on the access information, and determining a high-risk place from the monitoring places based on the high-risk score, wherein the high-risk score is used for representing the high-risk degree of the monitoring places.
9. An electronic device, comprising: a processor, a memory and a bus, the memory storing machine readable instructions executable by the processor, the processor and the memory communicating via the bus when the electronic device is running, the machine readable instructions when executed by the processor performing the steps of the high risk location identification method according to any one of claims 1 to 7.
10. A computer-readable storage medium, having stored thereon a computer program which, when being executed by a processor, performs the steps of the high risk location identification method according to any one of claims 1 to 7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910903237.3A CN110633915A (en) | 2019-09-24 | 2019-09-24 | High-risk place identification method and device |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910903237.3A CN110633915A (en) | 2019-09-24 | 2019-09-24 | High-risk place identification method and device |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN110633915A true CN110633915A (en) | 2019-12-31 |
Family
ID=68973996
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910903237.3A Pending CN110633915A (en) | 2019-09-24 | 2019-09-24 | High-risk place identification method and device |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110633915A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113554261A (en) * | 2021-05-24 | 2021-10-26 | 浙江大华技术股份有限公司 | Abnormal object identification method, abnormal object identification device, electronic device and storage medium |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20160314298A1 (en) * | 2015-04-27 | 2016-10-27 | Iboss, Inc. | Malicious program identification based on program behavior |
| CN107645482A (en) * | 2016-07-22 | 2018-01-30 | 阿里巴巴集团控股有限公司 | A kind of risk control method and device for business operation |
| CN107730124A (en) * | 2017-10-20 | 2018-02-23 | 安厦系统科技成都有限责任公司 | A kind of method for carrying out security risk assessment for enterprise or project |
| CN109033973A (en) * | 2018-06-26 | 2018-12-18 | 北京旷视科技有限公司 | Monitoring and early warning method, apparatus and electronic equipment |
| CN110009022A (en) * | 2019-03-26 | 2019-07-12 | 第四范式（北京）技术有限公司 | Prediction technique, device and the calculating equipment of drug addict's information |
| CN110209709A (en) * | 2019-06-06 | 2019-09-06 | 四川九洲电器集团有限责任公司 | A method of concern human behavior analysis |
-
2019
- 2019-09-24 CN CN201910903237.3A patent/CN110633915A/en active Pending
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20160314298A1 (en) * | 2015-04-27 | 2016-10-27 | Iboss, Inc. | Malicious program identification based on program behavior |
| CN107645482A (en) * | 2016-07-22 | 2018-01-30 | 阿里巴巴集团控股有限公司 | A kind of risk control method and device for business operation |
| CN107730124A (en) * | 2017-10-20 | 2018-02-23 | 安厦系统科技成都有限责任公司 | A kind of method for carrying out security risk assessment for enterprise or project |
| CN109033973A (en) * | 2018-06-26 | 2018-12-18 | 北京旷视科技有限公司 | Monitoring and early warning method, apparatus and electronic equipment |
| CN110009022A (en) * | 2019-03-26 | 2019-07-12 | 第四范式（北京）技术有限公司 | Prediction technique, device and the calculating equipment of drug addict's information |
| CN110209709A (en) * | 2019-06-06 | 2019-09-06 | 四川九洲电器集团有限责任公司 | A method of concern human behavior analysis |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113554261A (en) * | 2021-05-24 | 2021-10-26 | 浙江大华技术股份有限公司 | Abnormal object identification method, abnormal object identification device, electronic device and storage medium |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN107358158B (en) | Group partner plan early warning method and device | |
| CN108269116A (en) | A kind of advertisement safety monitoring method and device | |
| CN109896374B (en) | Elevator screen display method and system | |
| CN110674879B (en) | Identification matching method and device, electronic equipment and readable storage medium | |
| CN110659335A (en) | Analysis method for recessive drug addicts | |
| CN110191097B (en) | Method, system, equipment and storage medium for detecting security of login page | |
| CN110633915A (en) | High-risk place identification method and device | |
| CN111221722A (en) | Behavior detection method and device, electronic equipment and storage medium | |
| CN110825893A (en) | Target searching method, device, system and storage medium | |
| CN112070458A (en) | Account identification method and device | |
| CN110196924B (en) | Method and device for constructing characteristic information base and method and device for tracking target object | |
| CN107403322A (en) | Determination, method for authenticating user identity, device and the computing device of operating reliability | |
| CN111291150B (en) | Method and device for determining information to be searched and readable storage medium | |
| CN111050293A (en) | Data processing method and device and electronic equipment | |
| CN113642926B (en) | Method and device for risk early warning, electronic equipment and storage medium | |
| CN107491332B (en) | Method and device for identifying false installation of application program and server | |
| CN112035726B (en) | Trademark registration method and device | |
| CN112218046B (en) | Object monitoring method and device | |
| CN113327336A (en) | Method and device for identifying man-vehicle relationship and electronic equipment | |
| CN113468948A (en) | View data based security and protection control method, module, equipment and storage medium | |
| CN112416922A (en) | Group partner association data mining method, device, equipment and storage medium | |
| CN112017307A (en) | License plate detection method and device, storage medium and electronic equipment | |
| CN106682612B (en) | Alarm method, terminal, server and system based on image recognition | |
| CN111263351A (en) | Service processing method, service processing device, electronic device and storage medium | |
| CN112347151B (en) | Suspicion degree determination method and data analysis equipment |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| TA01 | Transfer of patent application right |
Effective date of registration: 20210915 Address after: 100000 room 650, 6th floor, building 11, Huashan Garden Cultural Media Industrial Park, 1376 folk culture street, Gaobeidian village, Gaobeidian Township, Chaoyang District, Beijing Applicant after: Beijing Zhizhi Heshu Technology Co.,Ltd. Address before: No.310, building 4, courtyard 8, Dongbei Wangxi Road, Haidian District, Beijing Applicant before: MININGLAMP SOFTWARE SYSTEMS Co.,Ltd. |
|
| TA01 | Transfer of patent application right |