CN115935003A - Information collection method and system based on big data - Google Patents
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Abstract
The invention is suitable for the field of computers, and provides an information collection method and an information collection system based on big data, wherein the method comprises the following steps: acquiring image data in a target area in real time; identifying the image data through a preset screening condition, wherein the preset screening condition is obtained by extracting and simulating behavior characteristics under big data, and the behavior characteristics comprise characteristic states of a target object approaching a preset target sub-region and/or the target object; when the image data are identified to meet the preset screening conditions, judging that a characteristic event containing a target object occurs; reporting first characteristic information indicating the occurrence of a characteristic event, wherein the first characteristic information comprises a first characteristic picture indicating the occurrence of the characteristic event, and the first characteristic picture meets a preset screening condition, and the method has the advantages that: the characteristic events are collected and captured in time to carry out on-site and remote feedback, so that the characteristic events can be responded and solved in time.
Description
Technical Field
The invention belongs to the field of computers, and particularly relates to an information collection method and system based on big data.
Background
Big Data (Big Data): the method refers to massive, high-growth rate and diversified information assets which can not be captured, managed and processed by a conventional software tool within a certain time range and have stronger decision edge, insight discovery capability and flow optimization capability only by a new processing mode.
Big data is closely related to information collection, opens an era of mass production, sharing and data application, brings huge changes to technology and business, and the penetration speed of the big data in the core field is witnessed, however, investigation shows that the unused information proportion is as high as 99.4%, and the big data is due to the fact that high-value information cannot be acquired.
In summary, in the background of big data era, how to collect useful information from big data is one of the key factors for big data development, and in the prior art, for some abnormal events in specified locations, the abnormal events are often realized by setting a monitoring mode, but this mode has the disadvantages that: the acquired video needs to be patrolled manually, and the problem that abnormal events are captured not timely enough exists.
Disclosure of Invention
An embodiment of the present invention provides an information collection method and system based on big data, and aims to solve the problems in the background art.
The embodiment of the invention is realized in such a way that, on one hand, an information collection method based on big data comprises the following steps:
acquiring image data in a target area in real time;
identifying the image data through a preset screening condition, wherein the preset screening condition is obtained by extracting and simulating behavior characteristics under big data, and the behavior characteristics comprise characteristic states of a target object approaching a preset target sub-region and/or the target object;
when the image data are identified to meet the preset screening conditions, judging that a characteristic event containing a target object occurs;
reporting first feature information indicating the occurrence of a feature event, wherein the first feature information comprises a first feature picture indicating the occurrence of the feature event, and the first feature picture meets a preset screening condition;
and issuing a first reminding instruction containing the characteristic event to a reminding terminal in a target area so as to indicate the reminding terminal to remind based on the characteristic event.
As a further solution of the present invention, after the first reminding instruction is issued to the reminding terminal in the target area, the method further includes:
continuously acquiring picture information containing a target object, and reporting the picture information;
and if the end of the characteristic event is continuously detected, reporting second characteristic information indicating the end of the characteristic event, wherein the second characteristic information comprises a second characteristic picture indicating the end of the characteristic event, and the second characteristic picture does not accord with a preset screening condition.
As a further aspect of the present invention, the receiving terminals corresponding to the second characteristic information and the first characteristic information respectively display different receiving prompts when reporting.
As still further aspect of the present invention, the method further comprises:
extracting behavior characteristics associated with the first sub-area based on historical image data, wherein the behavior characteristics comprise characteristic states of the target object close to the first sub-area and/or the target object;
normalizing the behavior characteristics to generate normalized behavior characteristics;
establishing a general model of the target area, and marking feature points corresponding to all preset target sub-areas in the general model;
and mapping the standard behavior characteristics to a universal model according to the distribution of the characteristic points to generate a screening model, and acquiring preset screening conditions based on the screening model.
As a further aspect of the present invention, the method further comprises:
within a preset time length, if a feedback instruction returned by the reminding terminal based on the first reminding instruction is not received, judging that the state of the reminding terminal is abnormal, wherein the abnormal state comprises that the reminding condition is not met;
and positioning the position of the reminding terminal according to the last returned signal of the reminding terminal, wherein the reminding terminal comprises wearable equipment.
As a further aspect of the present invention, the method further comprises:
when the reminding terminal is judged not to have the reminding condition, a movement detection instruction is issued in a preset target area,
when a response instruction of at least one moving part to the movement detection instruction is received, analyzing the position of the moving part and the equipment identification code according to the response instruction;
sending a movement control instruction to the corresponding moving part according to the position of the moving part and the equipment identification code;
and indicating the corresponding moving part to move according to the specified route, and issuing early warning prompt information in the moving process.
As a further aspect of the present invention, the method further comprises:
generating a quasi-target sub-region with a preset size according to the position of the reminding terminal, wherein the quasi-target sub-region comprises the position of the reminding terminal;
and generating a designated route for representing the moving member in the target area according to the quasi-target subarea and the position of the moving member.
As a further aspect of the present invention, the method further comprises: and after the corresponding moving member is indicated to reach the quasi-target subarea, circularly moving in the quasi-target subarea and issuing early warning prompt information.
As a further aspect of the present invention, in another aspect, a big data-based information collecting system includes:
the image acquisition module is used for: acquiring image data in a target area in real time;
an identification module to: identifying the image data through a preset screening condition, wherein the preset screening condition is obtained by extracting and simulating behavior characteristics under big data, and the behavior characteristics comprise characteristic states of a target object approaching a preset target sub-region and/or the target object;
a condition determining module to: when the image data are identified to meet the preset screening conditions, judging that a characteristic event containing a target object occurs;
an information acquisition module for: reporting first feature information indicating the occurrence of a feature event, wherein the first feature information comprises a first feature picture indicating the occurrence of the feature event, and the first feature picture meets a preset screening condition;
a reminder module for: and issuing a first reminding instruction containing the characteristic event to a reminding terminal in a target area so as to indicate the reminding terminal to remind based on the characteristic event.
According to the information collection method and system based on the big data, the image data are identified through the preset screening condition, the preset screening condition is obtained by extracting and simulating the behavior characteristics under the big data, the behavior characteristics comprise the characteristic state of a target object close to a preset target sub-region and/or the target object, the first characteristic information indicating the occurrence of the characteristic event is reported, then the first reminding instruction containing the characteristic event is issued to the reminding terminal in the target region, the reminding terminal is indicated to remind based on the characteristic event, the collection of the related information of the characteristic event can be facilitated, the remote decision can be made, the reminding terminal can be indicated to remind based on the characteristic event, the site and remote feedback can be carried out through the characteristic event collected and captured in time, and the timely coping and solving of the characteristic event are facilitated.
Drawings
Fig. 1 is a main flow diagram of a big data based information gathering method.
FIG. 2 is a flow chart of a big data based information gathering method relating to extracting behavioral features associated with a first sub-region.
Fig. 3 is a flow chart related to issuing a movement detection command within a preset target area in a big data based information collection method.
FIG. 4 is a flow chart of a big data based information gathering method for generating a designated route for characterizing a moving member within a target area.
Fig. 5 is a main structural diagram of a big data based information collection system.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
Specific implementations of the present invention are described in detail below with reference to specific embodiments.
The invention provides an information collection method and system based on big data, which solves the technical problem in the background technology.
As shown in fig. 1, a main flow chart of a big data based information collecting method according to an embodiment of the present invention includes:
step S10: acquiring image data in a target area in real time;
step S11: identifying the image data through a preset screening condition, wherein the preset screening condition is obtained by extracting and simulating behavior characteristics under big data, and the behavior characteristics comprise the characteristic state of a target object approaching to a preset target sub-area and/or the target object; the target object comprises main living organisms or objects with the capability of artificially controlling activities, such as people or unmanned planes, and the characteristic state of the target object represents whether the state of the target object needs to be noticed or not;
step S12: when the image data are identified to meet the preset screening conditions, judging that a characteristic event containing a target object occurs; the image data meets the preset screening condition, namely the state of the target object needs to be noticed or the behavior of the target object needs to be noticed; the behaviors or states generally refer to abnormal behaviors or states, for example, a child may approach an area prone to falling, approach an area prone to electric shock, and the like, or the child falls over and is not detected by an old person, and for example, some behaviors such as approaching a non-selling area, doing damage, and the like of a consumer or falling down in a shopping mall and the like exist in the shopping mall;
step S13: reporting first feature information indicating the occurrence of a feature event, wherein the first feature information comprises a first feature picture indicating the occurrence of the feature event, and the first feature picture meets a preset screening condition; reporting first characteristic information indicating the occurrence of the characteristic event, so that effective information can be collected in time to make a remote decision;
step S14: and issuing a first reminding instruction containing the characteristic event to a reminding terminal in a target area so as to indicate the reminding terminal to remind based on the characteristic event. The reminding terminal is generally controlled by related personnel in a target area so as to process the characteristic events on site in time; or the system is not controlled by people and only reminds the target object related to the characteristic event;
when the method is applied, the image data is identified through a preset screening condition, wherein the preset screening condition is obtained by extracting and simulating behavior characteristics under big data, the behavior characteristics comprise characteristic states of a target object close to a preset target sub-area and/or the target object, first characteristic information indicating characteristic events is reported, a first reminding instruction containing the characteristic events is sent to a reminding terminal in the target area to indicate that the reminding terminal reminds based on the characteristic events, so that the method not only can facilitate the collection of relevant information of the characteristic events and make remote decisions, but also can instruct the reminding terminal to remind based on the characteristic events, and the on-site and remote feedback is performed through the characteristic events which are collected and captured in time, so that the characteristic events can be responded and solved in time.
As a preferred embodiment of the present invention, after the first reminding instruction is issued to a reminding terminal in a target area, the method further includes:
step S20: continuously acquiring picture information containing a target object, and reporting the picture information;
step S21: and if the end of the characteristic event is continuously detected, reporting second characteristic information indicating the end of the characteristic event, wherein the second characteristic information comprises a second characteristic picture indicating the end of the characteristic event, and the second characteristic picture does not accord with a preset screening condition.
It can be understood that, in this embodiment, the situation that the feature event is resolved after being discovered and prompted is summarized, that is, the situation that the feature event is ended is reported in time, and the remote end can make a decision in time by reporting that the feature event is ended in time.
As a preferred embodiment of the present invention, the receiving terminals corresponding to the second characteristic information and the first characteristic information respectively display different receiving prompts when reporting.
It is understood that, mainly when the receiving terminal receives the second characteristic information and the first characteristic information, different receiving prompts are displayed to draw different attention, which indicates that the second characteristic information is different from the first characteristic information, and specifically, the receiving prompts can be realized by enhanced voice and/or color prompts or by changing the states of the two.
As shown in fig. 2, as a preferred embodiment of the present invention, the method further includes:
step S30: extracting behavior characteristics associated with the first sub-region based on historical image data, wherein the behavior characteristics comprise characteristic states of the target object close to the first sub-region and/or the target object;
step S31: normalizing the behavior characteristics to generate normalized behavior characteristics; the feature normalization here includes generalizing the behavior features to conform to the feature transformation within the target region; specifically, when applied to practice, the method includes standardizing the action and state of the target object; for example, the target object is close to the dangerous area, and the target object is in a special state, such as falling down;
step S32: establishing a general model of the target area, and marking feature points corresponding to all preset target sub-areas in the general model; the marking of the characteristic points comprises marking the characteristic positions of preset target sub-areas, such as the sub-areas with obvious distinguishing marks;
step S33: and mapping the standard behavior characteristics to a universal model according to the distribution of the characteristic points to generate a screening model, and acquiring preset screening conditions based on the screening model. The method comprises the following steps of converting possible behavior characteristics in historical data into a general model where a specific target area is located; therefore, when the image data are identified, whether the image data meet the preset screening condition or not can be rapidly identified; for example, in a case where a child in a home area is under the supervision of a single person, the single person may be an old person, and in an inattentive case, the old person may touch a dangerous area, such as a region close to a falling area, a region close to a touch area, or the like, or the child falls down without being detected by the old person, and only a pet close to the falling area may exist in the history data, but obviously, the dangerous areas are also potentially dangerous for the child; for another example, some consumers in a shopping mall are close to non-selling areas, do damage and the like or fall down in the shopping mall, and the staff are difficult to find; therefore, the characteristic points corresponding to all the preset target sub-regions in the universal model are marked, and the behavior characteristics can be generalized;
in application, the behavior features associated with the first sub-region are extracted based on the historical image data, the behavior features contained in a large number of feature events can be extracted, and the behavior features are normalized, so that the behavior features can conform to a general model of a target region, that is, the relation between a relevant region and the target region in the historical image data is established through the normalized behavior features, so that the normalized behavior features can be mapped into the general model according to the distribution of the feature points, and the behavior features are mainly used for representing behaviors conforming to the feature events.
As a preferred embodiment of the present invention, the method further comprises:
step S40: within a preset time length, if a feedback instruction returned by the reminding terminal based on the first reminding instruction is not received, judging that the state of the reminding terminal is abnormal, wherein the abnormal state comprises that the reminding condition is not met;
step S41: and positioning the position of the reminding terminal according to the last returned signal of the reminding terminal, wherein the reminding terminal comprises wearable equipment. Under a general condition, the reminding terminal returns a return signal at a certain interval, and the return signal contains the real-time position of the reminding terminal.
In this embodiment, when applying, within a preset duration, if a feedback instruction returned by the reminding terminal based on the first reminding instruction is not received, it is determined that the state of the reminding terminal is abnormal, and these abnormalities include: and when the reminding function is closed, the terminal cannot feed back based on the first reminding instruction.
As shown in fig. 3, as a preferred embodiment of the present invention, the method further includes:
step S50: when the reminding terminal is judged not to have the reminding condition, a mobile detection instruction is issued in a preset target area; the condition without reminding comprises reminding the terminal of a fault or a closed reminding function (such as a vibration function, a voice reminding function and the like); the mobile detection instruction can be issued through a wireless network, can also be issued through a wired network, and can also be issued through Bluetooth communication connection (without a network) so as to issue a related instruction to the moving part, try to establish connection between the mobile part and the issuing equipment and further realize issuing of the mobile control instruction;
step S51: when a response instruction of at least one moving part to the movement detection instruction is received, analyzing the position of the moving part and the equipment identification code according to the response instruction; the equipment identification code is analyzed mainly for matching with a receiving object of the mobile control instruction and a releasing mode; the position is analyzed to move according to the designated route from the position;
step S52: sending a movement control instruction to the corresponding moving part according to the position of the moving part and the equipment identification code;
step S53: and indicating the corresponding moving part to move according to the specified route, and issuing early warning prompt information in the moving process. The early warning prompt information comprises voice and/or color warning prompts to prompt the occurrence of the characteristic event, and the stopping of the early warning prompt information can be stopped after the characteristic event is finished or can be manually closed.
It can be understood that the moving part is not limited herein, and the moving part may be a toy car, a sweeping robot, etc. having some functions, and some functions include a self-walking function, a general alarm prompting function, a positioning function, etc. under control, and by instructing the corresponding moving part to move according to a specified route and issuing an early warning prompting message in the moving process, a 'capture tracking type' prompt can be performed on the corresponding user of the prompting terminal to remind the characteristic event.
As shown in fig. 4, as a preferred embodiment of the present invention, the method further includes:
step S60: generating a quasi-target sub-region with a preset size according to the position of the reminding terminal, wherein the quasi-target sub-region comprises the position of the reminding terminal; the preset size can be set according to the size of the target area, for example, the size of an area occupying 1/10 of the target area, and the shape of the preset size can be determined based on the actual position of the reminding terminal, so that the position of the reminding terminal can be included as a reference, and the preset size should conform to the actual arrangement scene in the area as much as possible, so as to ensure the moving smoothness of the moving member.
Step S61: and generating a designated route for representing the moving element in the target area according to the quasi-target subarea and the position of the moving element.
It can be understood that, because the position of the reminding terminal is obtained based on the last returned signal positioning, the moving situation of the user of the reminding terminal is considered, and therefore, the quasi-target sub-region with the preset size is generated based on the position of the reminding terminal, the deviation caused by the movement can be considered, and the pertinence and the accuracy of the follow-up reminding are improved.
As a preferred embodiment of the present invention, the method further comprises:
step S70: and after the corresponding moving member is indicated to reach the quasi-target subarea, circularly moving in the quasi-target subarea and issuing early warning prompt information.
It should be understood that the movement of the moving member is generally unidirectional, i.e. non-cyclic, before reaching the quasi-target sub-area, and after reaching the quasi-target sub-area, the possibility of reminding the user of the terminal in the sub-area is very high, so that the user of the terminal needs to be reminded to alert the user.
As another preferred embodiment of the present invention, as shown in fig. 5, in another aspect, a big data based information collecting system includes:
an image acquisition module 100, configured to: acquiring image data in a target area in real time;
an identification module 200 for: identifying the image data through a preset screening condition, wherein the preset screening condition is obtained by extracting and simulating behavior characteristics under big data, and the behavior characteristics comprise the characteristic state of a target object approaching to a preset target sub-area and/or the target object;
a condition determining module 300 configured to: when the image data are identified to meet the preset screening conditions, judging that a characteristic event containing a target object occurs;
an information collection module 400 configured to: reporting first feature information indicating the occurrence of a feature event, wherein the first feature information comprises a first feature picture indicating the occurrence of the feature event, and the first feature picture meets a preset screening condition;
a reminder module 500 for: and issuing a first reminding instruction containing the characteristic event to a reminding terminal in a target area so as to indicate the reminding terminal to remind based on the characteristic event.
The embodiment of the invention provides an information collection method based on big data, and provides an information collection system based on big data, wherein the image data is identified through a preset screening condition, the preset screening condition is obtained by extracting and simulating behavior characteristics under the big data, the behavior characteristics comprise characteristic states of a target object close to a preset target sub-area and/or the target object, first characteristic information indicating the occurrence of the characteristic events is reported, and then a first reminding instruction containing the characteristic events is issued to a reminding terminal in the target area to indicate the reminding terminal to remind based on the characteristic events, so that the collection of the relevant information of the characteristic events can be facilitated, a remote decision can be made, the reminding terminal can be indicated to remind based on the characteristic events, and the handling and solution of the characteristic events can be facilitated through on-site and remote feedback of the characteristic events collected and captured in time; furthermore, the corresponding moving part is indicated to move according to the designated route, and early warning prompt information is issued in the moving process, so that the corresponding user of the reminding terminal can be reminded in a capture tracking mode to remind the characteristic event.
In order to load the above method and system to operate smoothly, the system may include more or less components than those described above, or combine some components, or different components, besides the various modules described above, for example, input/output devices, network access devices, buses, processors, memories, and the like.
The processor may be a Central Processing Unit (CPU), other general purpose processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components, etc. The general purpose processor may be a microprocessor or the processor may be any conventional processor or the like which is the control center of the system and which is connected to the various parts using various interfaces and lines.
The memory may be used to store computer and system programs and/or modules, and the processor may perform the various functions described above by operating or executing the computer programs and/or modules stored in the memory and invoking data stored in the memory. The memory may mainly include a program storage area and a data storage area, where the program storage area may store an operating system, an application program required by at least one function (such as an information collection template presentation function, a product information distribution function, and the like), and the like. The storage data area may store data created according to the use of the berth-state display system (e.g., product information acquisition templates corresponding to different product types, product information that needs to be issued by different product providers, etc.), and the like. In addition, the memory may include high-speed random access memory, and may also include non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) card, a flash memory card (FlashCard), at least one magnetic disk storage device, a flash memory device, or other volatile solid state storage device.
It should be understood that, although the steps in the flowcharts of the embodiments of the present invention are shown in sequence as indicated by the arrows, the steps are not necessarily executed in sequence as indicated by the arrows. The steps are not performed in the exact order shown and described, and may be performed in other orders, unless explicitly stated otherwise. Moreover, at least a portion of the steps in various embodiments may include multiple sub-steps or multiple stages that are not necessarily performed at the same time, but may be performed at different times, and the order of performance of the sub-steps or stages is not necessarily sequential, but may be performed in turn or alternately with other steps or at least a portion of the sub-steps or stages of other steps.
All possible combinations of the technical features of the above embodiments may not be described for the sake of brevity, but should be considered as within the scope of the present disclosure as long as there is no contradiction between the combinations of the technical features.
The above-mentioned embodiments only express several embodiments of the present invention, and the description thereof is more specific and detailed, but not construed as limiting the scope of the present invention. It should be noted that various changes and modifications can be made by those skilled in the art without departing from the spirit of the invention, and these changes and modifications are all within the scope of the invention. Therefore, the protection scope of the present patent should be subject to the appended claims.
The above description is intended to be illustrative of the preferred embodiment of the present invention and should not be taken as limiting the invention, but rather, the intention is to cover all modifications, equivalents, and alternatives falling within the spirit and scope of the invention.
Claims (9)
1. A big data-based information collection method, the method comprising:
acquiring image data in a target area in real time;
identifying the image data through a preset screening condition, wherein the preset screening condition is obtained by extracting and simulating behavior characteristics under big data, and the behavior characteristics comprise the characteristic state of a target object approaching to a preset target sub-area and/or the target object;
when the image data are identified to meet the preset screening conditions, judging that a characteristic event containing a target object occurs;
reporting first feature information indicating the occurrence of a feature event, wherein the first feature information comprises a first feature picture indicating the occurrence of the feature event, and the first feature picture meets a preset screening condition;
and issuing a first reminding instruction containing the characteristic event to a reminding terminal in a target area so as to indicate the reminding terminal to remind based on the characteristic event.
2. The big data-based information collection method according to claim 1, wherein after the first reminding instruction is issued to a reminding terminal in a target area, the method further comprises:
continuously acquiring picture information containing a target object, and reporting the picture information;
and if the end of the characteristic event is continuously detected, reporting second characteristic information indicating the end of the characteristic event, wherein the second characteristic information comprises a second characteristic picture indicating the end of the characteristic event, and the second characteristic picture does not accord with a preset screening condition.
3. The big-data-based information collection method according to claim 2, wherein the corresponding receiving terminals respectively display different receiving prompts when the second characteristic information and the first characteristic information are reported.
4. The big-data based information gathering method as recited in claim 1, wherein the method further comprises:
extracting behavior characteristics associated with the first sub-region based on historical image data, wherein the behavior characteristics comprise characteristic states of the target object close to the first sub-region and/or the target object;
normalizing the behavior characteristics to generate normalized behavior characteristics;
establishing a general model of the target area, and marking feature points corresponding to all preset target sub-areas in the general model;
and mapping the standard behavior characteristics to a universal model according to the distribution of the characteristic points to generate a screening model, and acquiring preset screening conditions based on the screening model.
5. A big data based information gathering method as recited in any one of claims 1-4, wherein the method further comprises:
within a preset time length, if a feedback instruction returned by the reminding terminal based on the first reminding instruction is not received, judging that the state of the reminding terminal is abnormal, wherein the abnormal state comprises that the reminding condition is not met;
and positioning the position of the reminding terminal according to the last returned signal of the reminding terminal, wherein the reminding terminal comprises wearable equipment.
6. The big-data based information gathering method as recited in claim 5, wherein the method further comprises:
when the reminding terminal is judged not to have the reminding condition, a movement detection instruction is issued in a preset target area,
when a response instruction of at least one moving part to the movement detection instruction is received, analyzing the position of the moving part and the equipment identification code according to the response instruction;
sending a movement control instruction to the corresponding moving part according to the position of the moving part and the equipment identification code;
and indicating the corresponding moving part to move according to the specified route, and issuing early warning prompt information in the moving process.
7. The big-data based information gathering method as recited in claim 6, wherein the method further comprises:
generating a quasi-target sub-region with a preset size according to the position of the reminding terminal, wherein the quasi-target sub-region comprises the position of the reminding terminal;
and generating a designated route for representing the moving member in the target area according to the quasi-target subarea and the position of the moving member.
8. The big data-based information gathering method as recited in claim 7, wherein the method further comprises: and after the corresponding moving member is indicated to reach the quasi-target subarea, circularly moving in the quasi-target subarea and issuing early warning prompt information.
9. A big data based information collection system, the system comprising:
the image acquisition module is used for: acquiring image data in a target area in real time;
an identification module to: identifying the image data through a preset screening condition, wherein the preset screening condition is obtained by extracting and simulating behavior characteristics under big data, and the behavior characteristics comprise characteristic states of a target object approaching a preset target sub-region and/or the target object;
a condition determining module to: when the image data are identified to meet the preset screening conditions, judging that a characteristic event containing a target object occurs;
an information acquisition module to: reporting first feature information indicating the occurrence of a feature event, wherein the first feature information comprises a first feature picture indicating the occurrence of the feature event, and the first feature picture meets a preset screening condition;
a reminder module for: and issuing a first reminding instruction containing the characteristic event to a reminding terminal in a target area so as to indicate the reminding terminal to remind based on the characteristic event.
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| CN116127401A (en) * | 2023-04-20 | 2023-05-16 | 西南石油大学 | Data authority management and control method and system |
| CN117274918A (en) * | 2023-11-23 | 2023-12-22 | 深圳市易图资讯股份有限公司 | Big data metering regional analysis system and method |
Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2017029779A1 (en) * | 2015-08-17 | 2017-02-23 | パナソニックＩｐマネジメント株式会社 | Security system, person image display method, and report creation method |
| CN111144291A (en) * | 2019-12-25 | 2020-05-12 | 中铁信(北京)网络技术研究院有限公司 | Method and device for distinguishing personnel invasion in video monitoring area based on target detection |
| CN111163420A (en) * | 2020-02-18 | 2020-05-15 | 浙江省建工集团有限责任公司 | Intelligent factory area positioning and identifying system |
| CN111613013A (en) * | 2020-06-03 | 2020-09-01 | 克拉玛依市格恩赛电子科技有限公司 | Security positioning analysis early warning system, storage medium and method |
| CN111614935A (en) * | 2020-04-30 | 2020-09-01 | 深圳市椰壳信息科技有限公司 | Intelligent monitoring method and device, terminal equipment and readable storage medium |
| CN111815916A (en) * | 2020-08-06 | 2020-10-23 | 汪先锋 | Monitoring and early warning method and system for target area and mobile terminal |
| CN112578724A (en) * | 2020-12-15 | 2021-03-30 | 国家电网有限公司 | Power transmission line monitoring system |
| CN113596397A (en) * | 2021-07-27 | 2021-11-02 | 未鲲(上海)科技服务有限公司 | Picture processing method, device, equipment and storage medium |
| CN114627612A (en) * | 2022-03-10 | 2022-06-14 | 阿里云计算有限公司 | Early warning processing method, device and system |
-
2022
- 2022-11-22 CN CN202211463627.1A patent/CN115935003B/en active Active
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2017029779A1 (en) * | 2015-08-17 | 2017-02-23 | パナソニックＩｐマネジメント株式会社 | Security system, person image display method, and report creation method |
| CN111144291A (en) * | 2019-12-25 | 2020-05-12 | 中铁信(北京)网络技术研究院有限公司 | Method and device for distinguishing personnel invasion in video monitoring area based on target detection |
| CN111163420A (en) * | 2020-02-18 | 2020-05-15 | 浙江省建工集团有限责任公司 | Intelligent factory area positioning and identifying system |
| CN111614935A (en) * | 2020-04-30 | 2020-09-01 | 深圳市椰壳信息科技有限公司 | Intelligent monitoring method and device, terminal equipment and readable storage medium |
| CN111613013A (en) * | 2020-06-03 | 2020-09-01 | 克拉玛依市格恩赛电子科技有限公司 | Security positioning analysis early warning system, storage medium and method |
| CN111815916A (en) * | 2020-08-06 | 2020-10-23 | 汪先锋 | Monitoring and early warning method and system for target area and mobile terminal |
| CN112578724A (en) * | 2020-12-15 | 2021-03-30 | 国家电网有限公司 | Power transmission line monitoring system |
| CN113596397A (en) * | 2021-07-27 | 2021-11-02 | 未鲲(上海)科技服务有限公司 | Picture processing method, device, equipment and storage medium |
| CN114627612A (en) * | 2022-03-10 | 2022-06-14 | 阿里云计算有限公司 | Early warning processing method, device and system |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116127401A (en) * | 2023-04-20 | 2023-05-16 | 西南石油大学 | Data authority management and control method and system |
| CN116127401B (en) * | 2023-04-20 | 2023-06-16 | 西南石油大学 | Data authority management and control method and system |
| CN117274918A (en) * | 2023-11-23 | 2023-12-22 | 深圳市易图资讯股份有限公司 | Big data metering regional analysis system and method |
| CN117274918B (en) * | 2023-11-23 | 2024-03-22 | 深圳市易图资讯股份有限公司 | Big data metering regional analysis system and method |
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