Corrected object activity early warning method and system, computer and storage medium

Abstract

The invention provides a correction object activity early warning method and system, a computer and a storage medium. The method comprises the following steps: acquiring shared data to establish an information platform; analyzing the historical behavior data of the correction object to construct a behavior mode portrait of the correction object; acquiring real-time travel data of the correction object based on the information platform; and continuously monitoring and acquiring the matching degree of the position information and the behavior mode portrait, and if the matching degree is lower than a preset threshold value, sending an early warning prompt based on the information platform. By establishing an information platform, all-directional and real-time information mastering of community correction objects is realized, the problem of information lag and inaccuracy caused by dependence on manual recording in the past is solved, supervisors can quickly obtain accurate positions, behavior dynamics and various associated information of the objects, and the accuracy of the information is improved. Therefore, the supervision work can be more efficiently planned, the supervision resources are reasonably allocated, and the timeliness and accuracy of supervision are greatly improved.

Description

Technical Field

[0001] The present invention relates to the technical field of correction object management, and particularly to a method, system, computer, and storage medium for warning of the activities of correction objects. Background Art

[0002] With the rapid development of technology, the transportation system in modern society has become increasingly diversified, and various new transportation methods such as high-speed trains, online car-hailing, and shared bicycles have emerged continuously. They not only have a significant increase in speed but also have achieved a qualitative leap in terms of convenience and efficiency. While this transformation has brought great convenience to the travel of ordinary people, it has also brought challenges to community correction work.

[0003] In the prior art, the ways to track the whereabouts of correction objects usually rely on correction objects in the community to actively submit applications for going out. However, this method completely depends on the consciousness and integrity of the objects, and there are obvious limitations; or it is through on-site visits for investigation. However, this method not only consumes a large amount of human, material, and time costs but also has low efficiency. It can often only obtain information at a specific time point and cannot achieve real-time dynamic tracking, resulting in low efficiency and poor effect in correction management work. Summary of the Invention

[0004] Aiming at the deficiencies of the prior art, the purpose of the present invention is to provide a method, system, computer, and storage medium for warning of the activities of correction objects, aiming to solve the technical problems of low efficiency and poor effect in correction management work in the prior art.

[0005] To achieve the above purpose, in the first aspect, the present invention provides a method for warning of the activities of correction objects, including the following steps: Obtain shared data based on a standardized data protocol to establish an information platform based on the shared data, where the shared data includes real-time travel data and historical behavior data corresponding to correction objects; Analyze the historical behavior data of the correction object to determine the activity area and activity route of the correction object, so as to construct a behavior pattern portrait of the correction object; Obtain the real-time travel data of the correction object based on the information platform to determine the location information of the correction object based on the real-time travel data; Continuously monitor and obtain the matching degree between the location information and the behavior pattern portrait. If the matching degree is lower than a preset threshold, send a warning prompt based on the information platform.

[0006] According to one aspect of the above technical solution, the real-time travel data includes first positioning data obtained based on a mobile device and second positioning data obtained based on a means of transportation; The steps of determining the location information of the correction object based on the real-time travel data specifically include: Based on the first positioning data obtained by the mobile device, determine the first location information of the correction object; Based on the second positioning data obtained by the means of transportation, determine the second location information of the correction object; Based on the image data obtained by the camera in the monitoring area corresponding to the first location information and / or the second location information, verify the personal information of the correction object to determine the location information of the correction object.

[0007] According to one aspect of the above technical solution, the historical behavior data includes daily travel data, and the daily travel data includes travel time and travel destination; The steps of continuously monitoring and obtaining the matching degree between the location information and the behavior pattern portrait specifically include: Based on the daily travel data, obtain the predicted location data corresponding to the behavior pattern portrait at the current time; Compare the predicted location data with the location information to obtain the matching degree between the location information and the behavior pattern portrait.

[0008] According to one aspect of the above technical solution, the shared data further includes real-time consumption data, the historical behavior data further includes consumption habit data, and the consumption habit data includes consumption location, consumption amount, and consumption type; The steps of continuously monitoring and obtaining the matching degree between the location information and the behavior pattern portrait specifically include: Based on the consumption habit data, obtain the predicted consumption data corresponding to the behavior pattern portrait at the current consumption location; Compare the predicted consumption data with the real-time consumption data to obtain the matching degree between the location information and the behavior pattern portrait.

[0009] According to one aspect of the above technical solution, the shared data further includes communication data, the historical behavior data further includes social data, and the social data includes common contact data and contact time data; The method further includes: Obtain the communication data and determine whether the contact corresponding to the communication data is a contact in the common contact data; If the contact is an abnormal contact, send a warning prompt based on the information platform.

[0010] In a second aspect, the present application further provides a correction object activity warning system, including: An information platform module for obtaining shared data based on a standardized data protocol to establish an information platform based on the shared data, where the shared data includes real-time travel data and historical behavior data corresponding to correction objects; A behavior analysis module for analyzing the historical behavior data of the correction object to determine the activity area and activity route of the correction object, so as to construct a behavior pattern portrait of the correction object; A location module for obtaining the real-time travel data of the correction object based on the information platform to determine the location information of the correction object based on the real-time travel data; An early warning module for continuously monitoring and obtaining the matching degree between the location information and the behavior pattern portrait. If the matching degree is lower than a preset threshold, an early warning prompt is sent based on the information platform.

[0011] According to one aspect of the above technical solution, the real-time travel data includes first positioning data obtained based on a mobile device and second positioning data obtained based on a means of transportation; The location module is specifically used for: The steps of determining the location information of the correction object based on the real-time travel data specifically include: Based on the first positioning data obtained by the mobile device, determine the first location information of the correction object; Based on the second positioning data obtained by the means of transportation, determine the second location information of the correction object; Based on the image data obtained by the camera in the monitoring area corresponding to the first location information and / or the second location information, verify the personal information of the correction object to determine the location information of the correction object.

[0012] According to one aspect of the above technical solution, the historical behavior data includes daily travel data, and the daily travel data includes travel time and travel destination; The early warning module is specifically used for: The steps of continuously monitoring and obtaining the matching degree between the location information and the behavior pattern portrait specifically include: Based on the daily travel data, obtain the predicted location data corresponding to the behavior pattern portrait at the current time; Compare the predicted location data with the location information to obtain the matching degree between the location information and the behavior pattern portrait.

[0013] According to one aspect of the above technical solution, the shared data further includes communication data, the historical behavior data further includes social data, and the social data includes common contact data and contact time data; The system further includes: A communication module, configured to obtain the communication data and determine whether the contact corresponding to the communication data is a contact in the common contact data; If the contact is an abnormal contact, a warning prompt is sent based on the information platform.

[0014] In a third aspect, an embodiment of the present application provides a computer, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the computer program, it implements a correction object activity warning method as described in the first aspect.

[0015] In a fourth aspect, an embodiment of the present application provides a storage medium, on which a computer program is stored. When the program is executed by a processor, it implements a correction object activity warning method as described in the first aspect.

[0016] Compared with the prior art, the beneficial effects of the present invention are as follows: By summarizing various types of shared data through a standardized data protocol to establish an information platform, it realizes the comprehensive and real-time information mastery of community correction objects, solves the problems of information lag and inaccuracy caused by relying on manual records in the past. Supervisors can quickly obtain the precise location, behavior dynamics, and various associated information of the objects, so as to plan supervision work more efficiently, allocate supervision resources reasonably, conduct investigation and verification and take corresponding measures in the shortest time, greatly improving the timeliness and accuracy of supervision, and effectively reducing supervision loopholes caused by information errors or delays; By obtaining the analysis and generation of real-time positions and behavior pattern portraits, it greatly enhances the control of the activity range and behavior rules of community correction objects. Whether they exceed the specified area or show abnormal behavior patterns, they can be detected and warned in time, improving the effect of correction work. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 It is a flowchart of the correction object activity warning method in the first embodiment of the present invention; Figure 2 It is a structural block diagram of the correction object activity warning system in the second embodiment of the present invention; Figure 3 It is a schematic hardware structure diagram of the computer in the third embodiment of the present application; The following specific embodiments will further illustrate the present invention in conjunction with the above-mentioned drawings. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0018] For the convenience of understanding the present invention, the present invention will be described more comprehensively below with reference to the relevant drawings. Several embodiments of the present invention are shown in the drawings. However, the present invention can be implemented in many different forms and is not limited to the embodiments described herein. On the contrary, these embodiments are provided to make the disclosure of the present invention more thorough and comprehensive.

[0019] It should be noted that when an element is referred to as being "fixed to" another element, it can be directly on the other element or there can also be an intermediate element. When an element is considered to be "connected" to another element, it can be directly connected to the other element or there may be an intermediate element at the same time. The terms "vertical", "horizontal", "left", "right" and similar expressions used herein are for illustrative purposes only.

[0020] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those of ordinary skill in the technical field to which the present invention belongs. The terms used in the description of the present invention herein are only for the purpose of describing specific embodiments and are not intended to limit the present invention. The term "and / or" used herein includes any and all combinations of one or more of the related listed items.

[0021] Embodiment 1 Please refer to Figure 1 , which shows a flowchart of the correction object activity warning method in the first embodiment of the present invention. As shown in the figure, the method includes the following steps: Step S100, obtaining shared data based on a standardized data protocol to establish an information platform based on the shared data. The shared data includes real-time travel data and historical behavior data corresponding to the correction object. The above real-time travel data includes ticket data such as railway, aviation, and highway passenger transport, including ticket purchase time, departure place, destination, train number, flight number, etc., or the driving track of a self-driving vehicle and a vehicle driven by others within the scope of traffic monitoring, as well as mobile phone positioning information, etc.; the above shared data also includes personal information, supervision data, etc. of the correction object. Inside the platform, a data classification storage and management system is constructed to classify and store data from different sources according to categories such as personal information, supervision records, behavior data, and traffic travel data of community correction objects, so as to facilitate subsequent data query, analysis, and processing. At the same time, a data quality monitoring module is established to perform real-time verification and cleaning on the collected data to ensure the accuracy and integrity of the data.

[0022] Step S200: Analyze the historical behavior data of the correction object to determine the activity area and activity route of the correction object, so as to construct a behavioral pattern portrait of the correction object. Specifically, in this embodiment, the above historical behavior data includes daily travel data, and the daily travel data includes travel time and travel destination; the above historical behavior data also includes consumption habit data, and the consumption habit data includes consumption location, consumption amount and consumption type; the above historical behavior data also includes social data, and the social data includes common contact data and contact time data; by constructing a behavioral pattern portrait through various historical behavior data of the correction object, analyzing and describing the behavioral pattern of the individual, so as to form a representative feature portrait, and then managing data such as the activity area, activity route and social scope of the correction object.

[0023] Step S300: Obtain the real-time travel data of the correction object based on the information platform, so as to determine the location information of the correction object based on the real-time travel data.

[0024] Preferably, in this embodiment, the step of determining the location information of the correction object based on the real-time travel data specifically includes: Step S310: Determine the first location information of the correction object based on the first positioning data obtained by the mobile device.

[0025] Step S320: Determine the second location information of the correction object based on the second positioning data obtained by the means of transportation.

[0026] Step S330: Check the personal information of the correction object based on the image data obtained by the camera in the monitoring area corresponding to the first location information and / or the second location information, so as to determine the location information of the correction object. When the community correction object uses a mobile phone, its location information is obtained through mobile phone base station positioning; when it takes public transportation, its precise location is determined according to the vehicle positioning data corresponding to the ticket information; when it is active in the area covered by the monitoring camera, its identity is further confirmed through image recognition technology to obtain the location information.

[0027] Step S400: Continuously monitor and obtain the matching degree between the location information and the behavioral pattern portrait. If the matching degree is lower than the preset threshold, send a warning prompt based on the information platform. A special monitoring module is set up in the information platform. This module automatically obtains the real-time location information of the correction object at a certain time interval (for example, every 15 minutes) and calculates the matching degree with the pre-constructed behavioral pattern portrait. The calculation of the matching degree involves data analysis in multiple dimensions, including not only the deviation degree of the location information from the daily travel route and activity area, but also considering the time factor, that is, whether it appears at a specific location during a time period when it usually does not appear.

[0028] Meanwhile, the monitoring module will also dynamically adjust the matching degree in combination with external factors such as traffic conditions and weather conditions. For example, in case of bad weather, the corrected object may change the travel mode or route. At this time, the monitoring module will appropriately relax the judgment criteria for the matching degree, but still keep it within a reasonable range to avoid false alarms caused by uncontrollable factors. Once the calculated matching degree is lower than the preset threshold, the monitoring module will immediately trigger the early warning mechanism and send detailed early warning prompt information to the personnel responsible for supervision through the information platform.

[0029] Preferably, in this embodiment, the step of continuously monitoring and obtaining the matching degree of the position information and the behavior pattern portrait specifically includes: Step S410, obtaining the predicted position data corresponding to the behavior pattern portrait at the current time based on the daily travel data. Extract the daily travel data from the historical behavior data of the corrected object stored in the information platform. After long-term accumulation and analysis, these data form a travel pattern with certain rules. Through in-depth mining of these data and using time series analysis and machine learning algorithms, predict the position range where the corrected object is most likely to appear at the current time point according to its past behavior habits. For example, if the corrected object usually appears near the work place from 9:00 to 10:00 am from Monday to Friday in the past few months, then at 9:30 am on the current Monday, the system will set the area near the work place as the predicted position data.

[0030] Step S420, comparing the predicted position data with the position information to obtain the matching degree of the position information and the behavior pattern portrait. Using geographic information system (GIS) technology, accurately compare the area represented by the predicted position data with the actually obtained position information of the corrected object. Calculate parameters such as the distance deviation, direction deviation between the two, and whether the position is outside the reasonable activity range, and then through the pre-set weight assignment and mathematical model, convert these parameters into a specific matching degree value. For example, if the actual position of the corrected object is more than a certain number of kilometers away from the center of the predicted position area and the direction is significantly inconsistent with the normal travel direction, the matching degree will be correspondingly reduced.

[0031] The above shared data also includes real-time consumption data. Preferably, in this embodiment, the step of continuously monitoring and obtaining the matching degree of the position information and the behavior pattern portrait specifically includes: Step S430: Obtain the estimated consumption data corresponding to the behavior pattern portrait at the current consumption location based on the consumption habit data. Analyze the consumption habit data of the correction object, including the places where they often consume, the range of consumption amounts, and the types of consumption (such as dining, shopping, entertainment, etc.). When the real-time location information of the correction object is obtained, determine whether this location is one of their common consumption locations. If so, according to the statistical rules of historical consumption data, estimate the estimated consumption data such as the consumption amount and consumption type that may be generated at the current consumption location. For example, if the correction object often consumes at a certain coffee shop, with an average consumption amount of between 30 - 50 yuan each time and a preference for latte coffee, then when he appears near the coffee shop, the system will generate estimated consumption data of a latte coffee with a consumption amount of about 40 yuan.

[0032] Step S440: Compare the estimated consumption data with the real-time consumption data to obtain the matching degree of the location information and the behavior pattern portrait. Conduct data docking with the payment platform or the merchant's transaction system to obtain the actual consumption data of the correction object in real time, including information such as consumption amount, consumption time, and consumption items. Compare these real-time consumption data in detail with the estimated consumption data generated in Step S430. If there are significant differences between the actual consumption data and the estimated consumption data, such as the consumption amount exceeding 50% of the normal range, or the consumption type being completely inconsistent with past habits (such as a correction object who never buys luxury goods suddenly making a large consumption at a high-end jewelry store), it will have a negative impact on the matching degree of the location information and the behavior pattern portrait, resulting in a decrease in the matching degree.

[0033] The above shared data also includes communication data, and the method further includes: Step S510: Obtain the communication data and determine whether the contacts corresponding to the communication data are contacts in the common contact data. The information platform establishes a legal data sharing mechanism with the communication operator to obtain the communication data of the correction object in real time. Extract the contact information from these communication data and compare it with the common contact data of the correction object pre-stored in the information platform. Through technical means such as precise number matching, name recognition, and social relationship graph analysis, determine whether the communication object is a person they often contacted in the past.

[0034] Step S520: If the contact person is an abnormal contact person, a warning prompt is sent based on the information platform. If it is found during the comparison process in Step S510 that the communication object is not in the list of common contact persons, the system will further conduct a risk assessment on this abnormal contact person. This includes querying the background information of this contact person and analyzing factors such as the frequency, duration, and content keywords of the communication. If the comprehensive assessment result shows that there are potential risks for this abnormal contact person, then the information platform will immediately send a warning prompt to the supervisors, detailing the relevant information of the abnormal contact person and the communication situation with the correction object, so that the supervisors can take corresponding measures in a timely manner for investigation and intervention.

[0035] The portrait of the behavior pattern of community correction objects constructed based on big data analysis can provide a strong basis for formulating personalized correction plans. Supervisors can deeply understand the characteristics, needs, and potential risks of each object, so as to formulate more targeted education and transformation plans and supervision measures. For example, for objects with complex social relationships and bad social tendencies, social guidance and supervision can be strengthened; for objects with employment difficulties, more accurate employment assistance and training recommendations can be provided in combination with their skills and employment intentions. This personalized correction method helps to improve the enthusiasm and success rate of community correction objects in transformation.

[0036] In summary, in the above embodiments of the present invention, the activity warning method for correction objects summarizes various shared data through a standardized data protocol to establish an information platform, realizing the comprehensive and real-time information grasp of community correction objects, solving the problems of information lag and inaccuracy caused by relying on manual records in the past. Supervisors can quickly obtain the accurate location, behavior dynamics, and various associated information of the objects, so that they can plan supervision work more efficiently, allocate supervision resources reasonably, conduct investigation and verification and take corresponding measures in the shortest time, greatly improving the timeliness and accuracy of supervision, and effectively reducing supervision loopholes caused by information errors or delays; through the analysis and generation of real-time location and behavior pattern portraits, the control of the activity range and behavior rules of community correction objects is greatly enhanced. Whether they exceed the specified area or show abnormal behavior patterns, they can be detected and warned in a timely manner, improving the effect of correction work.

[0037] Embodiment 2 The second embodiment of the present application also provides a correction object activity warning system, which is used to implement the above embodiments and preferred implementation manners, and those that have been described will not be repeated. As used below, terms such as "module", "unit", "sub-unit", etc. can be a combination of software and / or hardware that can achieve a predetermined function. Although the system described in the following embodiments is preferably implemented in software, implementation in hardware, or a combination of software and hardware is also possible and contemplated.

[0038] As Figure 2 shown, the system includes: an information platform module 100, a behavior analysis module 200, a location module 300, and a warning module 400.

[0039] The information platform module 100 is used to obtain shared data based on a standardized data protocol, and establish an information platform based on the shared data. The shared data includes real-time travel data and historical behavior data corresponding to the correction object. The behavior analysis module 200 is used to analyze the historical behavior data of the correction object, determine the activity area and activity route of the correction object, and construct a behavior pattern portrait of the correction object. The location module 300 is used to obtain the real-time travel data of the correction object based on the information platform, and determine the location information of the correction object based on the real-time travel data. The warning module 400 is used to continuously monitor and obtain the matching degree between the location information and the behavior pattern portrait. If the matching degree is lower than a preset threshold, a warning prompt is sent based on the information platform.

[0040] Preferably, in this embodiment, the real-time travel data includes first positioning data obtained based on a mobile device and second positioning data obtained based on a means of transportation. The location module 300 is specifically used for: The steps of determining the location information of the correction object based on the real-time travel data specifically include: Based on the first positioning data obtained based on the mobile device, determine the first location information of the correction object. Based on the second positioning data obtained based on the means of transportation, determine the second location information of the correction object. Based on the image data obtained by the camera in the monitoring area corresponding to the first location information and / or the second location information, verify the personal information of the correction object to determine the location information of the correction object.

[0041] Preferably, in this embodiment, the historical behavior data includes daily travel data, and the daily travel data includes travel time and travel destination. The warning module 400 is specifically used for: The steps of continuously monitoring and obtaining the matching degree between the location information and the behavior pattern portrait specifically include: Based on the daily travel data, obtain the predicted location data corresponding to the behavior pattern portrait at the current time. Compare the predicted location data with the location information to obtain the matching degree between the location information and the behavior pattern portrait.

[0042] Preferably, in this embodiment, the shared data further includes communication data, the historical behavior data further includes social data, and the social data includes common contact data and contact time data; The system further includes: A communication module, configured to obtain the communication data and determine whether the contact corresponding to the communication data is a contact in the common contact data; If the contact is an abnormal contact, a warning prompt is sent based on the information platform.

[0043] It should be noted that each of the above modules can be a functional module or a program module, and can be implemented either by software or by hardware. For the modules implemented by hardware, each of the modules can be located in the same processor; or each of the modules can also be located in different processors in any combination.

[0044] Embodiment Three The third embodiment of the present application provides a computer. Understandably, the principles mentioned in the correction object activity warning system in this embodiment correspond to the correction object activity warning method in the first embodiment of the present application. For the related principles not described, refer to the first embodiment for corresponding reference, and details will not be elaborated here.

[0045] The computer may include a processor 81 and a memory 82 storing computer program instructions.

[0046] Specifically, the above processor 81 may include a central processing unit (CPU), or an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of the present application.

[0047] Among them, the memory 82 may include a mass storage for data or commands. By way of example and not limitation, the memory 82 may include a hard disk drive (HDD), a floppy disk drive, a solid state drive (SSD), a flash memory, an optical disc, a magneto-optical disc, a magnetic tape, or a universal serial bus (USB) drive, or a combination of two or more of these. Where appropriate, the memory 82 may include removable or non-removable (or fixed) media. Where appropriate, the memory 82 may be internal or external to the data processing device. In a particular embodiment, the memory 82 is non-volatile memory. In a particular embodiment, the memory 82 includes a read-only memory (ROM) and a random access memory (RAM). Where appropriate, the ROM may be a mask-programmed ROM, a programmable ROM (PROM), an erasable PROM (EPROM), an electrically erasable PROM (EEPROM), an electrically alterable ROM (EAROM), or a flash memory, or a combination of two or more of these. Where appropriate, the RAM may be a static random access memory (SRAM) or a dynamic random access memory (DRAM), where the DRAM may be a fast page mode dynamic random access memory (FPMDRAM), an extended date out dynamic random access memory (EDODRAM), a synchronous dynamic random access memory (SDRAM), etc.

[0048] The memory 82 can be used to store or cache various data files required for processing and / or communication, as well as possible computer program commands executed by the processor 81.

[0049] The processor 81 reads and executes the computer program commands stored in the memory 82 to implement any one of the correction object activity warning methods in the above embodiments.

[0050] In some of these embodiments, the computer may further include a communication interface 83 and a bus 80. Among them, as Figure 3 shown, the processor 81, the memory 82, and the communication interface 83 are connected through the bus 80 and complete communication with each other.

[0051] The communication interface 83 is used to implement communication between the various modules, devices, units, and / or devices in the embodiments of the present application. The communication interface 83 can also implement data communication with other components, such as external devices, image / data acquisition devices, databases, external storage, and image / data processing workstations, etc.

[0052] Bus 80 includes hardware, software, or both, and couples components of a computer to each other. Bus 80 includes, but is not limited to, at least one of the following: Data Bus, Address Bus, Control Bus, Expansion Bus, Local Bus. By way of example and not limitation, Bus 80 may include an Accelerated Graphics Port (AGP) or other graphics bus, an Extended Industry Standard Architecture (EISA) bus, a Front Side Bus (FSB), a Hyper Transport (HT) interconnect, an Industry Standard Architecture (ISA) bus, an InfiniBand interconnect, a Low Pin Count (LPC) bus, a memory bus, a Micro Channel Architecture (MCA) bus, a Peripheral Component Interconnect (PCI) bus, a PCI-Express (PCI-X) bus, a Serial Advanced Technology Attachment (SATA) bus, a Video Electronics Standards Association Local Bus (VLB) bus, or other suitable bus or a combination of two or more of these. In suitable cases, Bus 80 may include one or more buses. Although the embodiments of the present application describe and illustrate specific buses, the present application contemplates any suitable bus or interconnect.

[0053] Embodiment Four Combined with the correction object activity warning method in the above embodiments, the fourth embodiment of the present application provides a readable storage medium. A computer program command is stored on the readable storage medium; when the computer program command is executed by a processor, any one of the correction object activity warning methods in the above embodiments is implemented.

[0054] The technical features of the above-described embodiments can be combined arbitrarily. For the sake of brevity of description, not all possible combinations of the technical features in the embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered to be within the scope described in this specification.

[0055] The above-described embodiments merely represent several implementation manners of the present application. The description thereof is relatively specific and detailed, but it should not be construed as a limitation on the scope of the invention patent. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present application, several modifications and improvements can still be made, and these all fall within the protection scope of the present application. Therefore, the protection scope of the patent of the present application shall be subject to the appended claims.

Claims

1. A correction object activity early warning method, characterized in that: The following steps are involved: Acquiring shared data based on a standardized data protocol to establish an information platform based on the shared data, wherein the shared data includes real-time travel data and historical behavior data corresponding to the correction object; Analyze the historical behavior data of the correction object to determine the activity area and activity route of the correction object to construct a behavior pattern portrait of the correction object; Acquiring real-time travel data of the correction object based on the information platform to determine location information of the correction object based on the real-time travel data; Continuously monitor and obtain the matching degree between the location information and the behavior pattern portrait, and if the matching degree is lower than a preset threshold, send an early warning prompt based on the information platform.

2. The correction object activity early warning method according to claim 1, characterized in that: The real-time travel data includes first positioning data acquired based on a mobile device and second positioning data acquired based on a transportation tool; The step of determining the location information of the correction object based on the real-time travel data specifically includes: Determining first position information of the correction object based on first positioning data acquired by the mobile device; Determining second position information of the correction object based on second positioning data acquired by the vehicle; Based on the image data acquired by the camera of the monitoring area corresponding to the first location information and / or the second location information, the personal information of the correction object is checked to determine the location information of the correction object.

3. The correction object activity early warning method according to claim 2, characterized in that: The historical behavior data includes daily travel data, and the daily travel data includes travel time and travel destination; The steps of continuously monitoring and obtaining the matching degree of the location information and the behavior pattern portrait specifically include: Acquire estimated location data corresponding to the behavior pattern portrait at the current time based on the daily travel data; The estimated location data is compared with the location information to obtain a matching degree between the location information and the behavior pattern portrait.

4. The correction object activity early warning method according to claim 3 is characterized in that: The shared data also includes real-time consumption data, and the historical behavior data also includes consumption habit data, which includes consumption location, consumption amount and consumption type; The steps of continuously monitoring and obtaining the matching degree of the location information and the behavior pattern portrait specifically include: Based on the consumption habit data, obtain the estimated consumption data corresponding to the behavior pattern portrait at the current consumption location; The estimated consumption data is compared with the real-time consumption data to obtain the matching degree of the location information and the behavior pattern portrait.

5. The correction object activity early warning method according to claim 3, characterized in that: The shared data also includes communication data, and the historical behavior data also includes social data, and the social data includes frequently used contact data and contact time data; The method further comprises: Acquire the communication data, and determine whether the contact corresponding to the communication data is a contact in the common contact data; If the contact is an abnormal contact, an early warning prompt is sent based on the information platform.

6. A correction object activity warning system, characterized in that: include: An information platform module, used for acquiring shared data based on a standardized data protocol to establish an information platform based on the shared data, wherein the shared data includes real-time travel data and historical behavior data corresponding to the correction object; A behavior analysis module, used to analyze the historical behavior data of the correction object, determine the activity area and activity route of the correction object, and construct a behavior pattern portrait of the correction object; A location module, used for acquiring the real-time travel data of the correction object based on the information platform, so as to determine the location information of the correction object based on the real-time travel data; The early warning module is used to continuously monitor and obtain the matching degree between the location information and the behavior pattern portrait. If the matching degree is lower than a preset threshold, an early warning prompt is sent based on the information platform.

7. The correction subject activity warning system according to claim 6, characterized in that: The real-time travel data includes first positioning data acquired based on a mobile device and second positioning data acquired based on a transportation tool; The location module is specifically used for: The step of determining the location information of the correction object based on the real-time travel data specifically includes: Determining first position information of the correction object based on first positioning data acquired by the mobile device; Determining second position information of the correction object based on second positioning data acquired by the vehicle; Based on the image data acquired by the camera of the monitoring area corresponding to the first location information and / or the second location information, the personal information of the correction object is checked to determine the location information of the correction object.

8. The correction subject activity warning system according to claim 7, characterized in that: The historical behavior data includes daily travel data, and the daily travel data includes travel time and travel destination; The early warning module is specifically used for: The steps of continuously monitoring and obtaining the matching degree of the location information and the behavior pattern portrait specifically include: Acquire estimated location data corresponding to the behavior pattern portrait at the current time based on the daily travel data; The estimated location data is compared with the location information to obtain a matching degree between the location information and the behavior pattern portrait.

9. A computer comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the correction object activity warning method according to any one of claims 1 to 5 is implemented.

10. A storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the correction object activity warning method described in any one of claims 1 to 5 is implemented.

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