CN116665402A - Anti-lost system and method based on Internet of things - Google Patents
Anti-lost system and method based on Internet of things Download PDFInfo
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- G06F18/2411—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches based on the proximity to a decision surface, e.g. support vector machines
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Abstract
The invention discloses an anti-lost system and method based on the Internet of things, wherein the operation method of the system comprises the following steps: step one: deploying the sensor and configuring; step two: analyzing the data acquired by the sensor; step three: monitoring and alarming the behavior of the monitored person; step four: user management and data storage, the sensor module is used for deploying sensors and collecting data; the behavior analysis module is used for analyzing and judging the behavior of the user; the user management module is used for encrypting and storing user data; the behavior feature extraction module is used for analyzing the sensor data and extracting features through the algorithm and the model operation; the abnormal behavior detection module is used for detecting abnormal behaviors of monitored personnel through judgment and comparison of behavior characteristics and giving an alarm in time; the invention has the characteristics of early warning in time and accurate monitoring.
Description
Technical Field
The invention relates to the technical field of the Internet of things, in particular to an anti-lost system and method based on the Internet of things.
Background
Along with the continuous development and popularization of the internet of things technology, the internet of things is widely applied in various fields, wherein the anti-lost system is used as one of the internet of things technology applications, is widely applied to people needing special care such as old people, children and the like, and at present, various anti-lost systems are already appeared on the market, and mainly comprise the following: RFID anti-lost system: the anti-lost tag is placed on a monitored person by adopting a wireless radio frequency identification technology, and when the monitored person leaves a set range, the system can send out an alarm. However, this system has the disadvantage that it can only be monitored within a set range and that the tag can be easily removed or lost. And the GPS anti-lost system: the position of the monitored person is positioned by the GPS technology, and when the monitored person leaves the set range, the system can give an alarm. The disadvantage of such a system is that GPS positioning is not accurate enough and requires a lot of power. Therefore, it is necessary to design an anti-lost system and method based on the internet of things with high monitoring precision and high alarm accuracy.
Disclosure of Invention
The invention aims to provide an anti-lost system and method based on the Internet of things, which are used for solving the problems in the background technology.
In order to solve the technical problems, the invention provides the following technical scheme: an anti-lost system based on the Internet of things, and the operation method of the system comprises the following steps:
step one: deploying the sensor and configuring;
step two: analyzing the data acquired by the sensor;
step three: monitoring and alarming the behavior of the monitored person;
step four: user management and data storage.
According to the above technical solution, the step of deploying the sensor and configuring the sensor includes:
selecting a bracelet or pendant device with a low-power Bluetooth technology;
initializing and connecting the sensor devices;
developing corresponding software or firmware at the equipment end;
and the real-time monitoring of the user is realized.
According to the above technical solution, the step of analyzing the data collected by the sensor includes:
collecting the motion state data of the monitored personnel in real time;
uploading data to a cloud or terminal device;
analyzing and processing data acquired by the sensor;
and monitoring abnormal behaviors through an algorithm and a model.
According to the above technical solution, the steps of analyzing and processing the data collected by the sensor include:
the data are processed through the algorithm and the operation of the model, so that behavior characteristics and abnormal behavior judgment of the monitored personnel are obtained, the number of steps, the gesture and the position information can be analyzed through the data mining algorithm to know the motion rule and habit of the monitored personnel, on the basis, a behavior model is built, the abnormal behavior of the monitored personnel is found through the comparison and judgment of the model, specifically, the rule and the threshold of the abnormal behavior are set, and when the monitored personnel does not move for a long time, suddenly accelerates or changes the motion track and the like, the system gives out an alarm.
According to the above technical solution, the step of monitoring abnormal behavior through an algorithm and a model includes:
according to the embodiment of the invention, the behavior states of the monitored personnel are divided into walking, resting and running states according to the data acquired by the sensor, the states are used as training samples of the SVM algorithm, higher classification accuracy is obtained through repeated iterative training, then a logistic regression algorithm is adopted to predict the behavior characteristics of the monitored personnel, a logistic regression model is established according to the data characteristics acquired by the sensor, such as information of the number of steps, the gesture, the position and the like, model optimization and adjustment are carried out through a cross verification method, finally, an abnormal detection algorithm is adopted to judge whether abnormal behaviors of the monitored personnel occur, a large amount of normal behavior data are acquired, and a model of the normal behavior data is established, so that whether the abnormal behaviors of the monitored personnel occur is judged, if the behavior characteristics of the monitored personnel differ greatly from the normal behavior data, the system can send an alarm, the data is acquired, analyzed and processed in real time through various algorithms and the model, the judgment of the behavior characteristics and the behaviors of the monitored personnel can be realized, and the abnormal behavior can be accurately supported, and the running preventing system is realized.
According to the above technical solution, the step of monitoring and alarming the behavior of the monitored person includes:
different alarm modes and trigger conditions are set according to the demands of different crowds;
when the system gives an alarm, the position of the monitored person is positioned in real time;
specific abnormal behavior monitoring rules and early warning mechanisms are set.
According to the above technical solution, the step of setting a specific abnormal behavior monitoring rule and an early warning mechanism includes:
the system can monitor the behavior state of the monitored person more comprehensively by setting the rules and mechanisms and send out an alarm in time when the abnormal condition occurs so as to ensure the safety of the monitored person.
According to the technical scheme, the steps of user management and data storage comprise the following steps:
the user carries out user registration, login and password resetting operation through the terminal equipment or the cloud platform;
storing and managing the motion state data of the monitored personnel;
for the storage of sensitive information, an encryption storage mode is adopted.
According to the above technical solution, the step of storing the sensitive information by adopting an encrypted storage mode includes:
for the storage of sensitive information, an encryption storage mode is adopted to ensure the safety and privacy of data, the system encrypts personal information of a user and the motion state data of monitored personnel so as to avoid malicious attack and illegal acquisition, meanwhile, an authority management function is set, the access authority of the user can be controlled, illegal operation and data leakage are prevented, and a system administrator can manage and modify the authority of the user so as to ensure the safety and stability of the system.
According to the above technical solution, the system comprises:
the sensor module is used for deploying sensors and collecting data;
the behavior analysis module is used for analyzing and judging the user behavior;
and the user management module is used for carrying out encryption storage on the user data.
Compared with the prior art, the invention has the following beneficial effects: according to the invention, the anti-lost system based on the Internet of things is realized by the sensor module, the behavior analysis module and the user management module, firstly, the bracelet or pendant equipment with the low-power consumption Bluetooth technology is selected and integrated with the required sensor, the sensor is used for collecting user data, the data are uploaded to the cloud, the data collected by the sensor are analyzed and processed, the abnormal behavior monitoring is realized by using an algorithm and a model, different alarm modes and trigger conditions are set according to the requirements of different people, when the system gives an alarm, the position of a monitored person is positioned in real time, and finally, the sensitive data generated by the user in the system are encrypted and stored.
Drawings
The accompanying drawings are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate the invention and together with the embodiments of the invention, serve to explain the invention. In the drawings:
fig. 1 is a flowchart of an anti-lost method based on the internet of things according to a first embodiment of the present invention;
fig. 2 is a schematic diagram of module composition of an anti-lost system based on the internet of things according to a second embodiment of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Embodiment one: fig. 1 is a flowchart of an anti-lost method based on the internet of things, where the embodiment of the invention may apply an anti-lost scenario, and the method may be performed by an anti-lost system based on the internet of things, as shown in fig. 1, and the method specifically includes the following steps:
step one: deploying the sensor and configuring;
in the embodiment of the invention, the sensor is required to be deployed on the monitored person, and the sensor is correspondingly configured so as to acquire various information such as the position, the movement state, the heart rate and the like of the monitored person in real time, thereby realizing real-time monitoring and early warning of the monitored person;
by way of example, selecting devices such as a bracelet or a pendant with a low-power Bluetooth technology, so as to realize the functions of low-power and high-precision positioning and motion detection, and simultaneously have longer battery life and stable signal transmission capacity, initializing and connecting the sensor devices, binding the devices with the ID of a user in a code scanning or manual input mode, and the like, so as to realize the association of unique identification and data of the devices;
the method comprises the steps of developing corresponding software or firmware at the equipment end to realize the data acquisition and transmission function, testing and optimizing the software or firmware to ensure the stability and reliability, binding the anti-lost node equipment to the wrist, shoelace and other parts of a user, or directly placing the anti-lost node equipment in articles carried by the user, such as a knapsack, a handbag and the like, so as to realize the real-time monitoring of the user.
Step two: analyzing the data acquired by the sensor;
in the embodiment of the invention, the sensor is deployed, so that the motion state data of the monitored personnel, including the information of the step number, the gesture, the position and the like, can be collected in real time, and the data are uploaded to the cloud or terminal equipment and are analyzed and processed in real time;
the method comprises the steps of analyzing and processing data acquired by a sensor, firstly, uploading the acquired data to a cloud or terminal equipment for real-time analysis, then, processing the data through algorithm and model operation, so as to obtain behavior characteristics and abnormal behavior judgment of a monitored person, for example, analyzing information such as step numbers, postures and positions through a data mining algorithm to know the motion rule and habit of the monitored person, on the basis, establishing a behavior model, comparing and judging through the model, finding abnormal behaviors of the monitored person, specifically, setting rules and thresholds of the abnormal behaviors, and giving an alarm when the behaviors such as a long-time motionless, suddenly accelerated or transformed motion track of the monitored person appear;
the method comprises the steps of firstly classifying motion state data of monitored personnel by adopting a support vector machine algorithm in machine learning, establishing a logic regression model according to data characteristics such as step number, gesture, position and the like acquired by the sensors, establishing a logic regression model, carrying out model optimization and adjustment by a cross verification method, finally judging whether abnormal behaviors of the monitored personnel occur or not by adopting an abnormal detection algorithm, acquiring a large amount of normal behavior data, establishing a model of the normal behavior data, judging whether the abnormal behaviors of the monitored personnel occur or not by adopting the abnormal detection algorithm, and further judging whether the abnormal behaviors of the monitored personnel are different from the normal behaviors of the monitored personnel by adopting a plurality of training samples of the SVM algorithm through iterative training, obtaining higher classification accuracy, secondly predicting behavior characteristics of the monitored personnel by adopting a logic regression algorithm, carrying out model optimization and adjustment by adopting a cross verification method and the like, and carrying out accurate processing on the abnormal behaviors of the monitored personnel by adopting a plurality of the monitoring system, and realizing the accurate alarm and the alarm characteristics by adopting a plurality of monitoring system.
Step three: monitoring and alarming the behavior of the monitored person;
in the embodiment of the invention, according to the behavior characteristics and abnormal behavior judgment obtained in the second step, the system monitors and alarms the behaviors of the monitored personnel in real time;
for example, different alarm modes and trigger conditions are set according to the demands of different crowds, for example, an automatic sounding alarm mode is set according to the old people, and meanwhile, an alarm is set to be triggered under the condition that no activity occurs in a specific time so as to take rescue measures in time, and for the children, a watch vibration and push reminding alarm mode can be set, and meanwhile, conditions for triggering the alarm are set according to the traveling habit and daily behavior habit of the children in a specific area and in time;
the system can realize accurate positioning of the monitored person by integrating a GPS positioning technology and a Bluetooth signal positioning technology, and can send alarm information to preset contacts including families, caregivers and the like at the same time, so that the system can know the condition of the monitored person and take corresponding measures at the first time;
for example, in order to enhance the reliability and practicability of the system, specific abnormal behavior monitoring rules and early warning mechanisms are set, an alarm is set to be triggered when a monitored person does not move or leaves a specific area for a long time within a night or within a specific time period, or an alarm is set to be triggered when the monitored person frequently leaves the specific area, and by setting the rules and mechanisms, the system can monitor the behavior state of the monitored person more comprehensively and timely send out the alarm when an abnormal condition occurs, so that the safety of the monitored person is ensured.
Step four: user management and data storage.
In the embodiment of the invention, in order to better manage users and data, the invention adds the functions of user management and data storage;
firstly, a user can perform operations such as user registration, login and password resetting through a terminal device or a cloud platform so as to better manage users of the system, when registering, the users need to provide necessary personal information, account numbers and passwords are set, the information is stored in a system database, when logging in, the users need to input correct account numbers and passwords for verification, the system allows the users to enter the system after verification, so that system functions are used, when the users forget the passwords, the password resetting functions provided by the system can be used for password modification so as to ensure the safety of the account numbers;
the system processes and analyzes the data acquired from the sensor, and stores the result in a cloud or local database of the terminal equipment so as to facilitate the inquiry and management of a user, and when the system also supports the data backup and recovery function so as to ensure the safety and the integrity of the data;
for example, for the storage of sensitive information, an encryption storage mode is adopted to ensure the safety and privacy of data, the system encrypts personal information of a user and the motion state data of monitored personnel so as to avoid malicious attack and illegal acquisition, meanwhile, a permission management function is set, the access permission of the user can be controlled, illegal operation and data leakage are prevented, and a system administrator can manage and modify the permission of the user so as to ensure the safety and stability of the system.
Embodiment two: the second embodiment of the present invention provides an anti-lost system based on the internet of things, and fig. 2 is a schematic diagram of module composition of the anti-lost system based on the internet of things, as shown in fig. 2, where the system includes:
the sensor module is used for deploying sensors and collecting data;
the behavior analysis module is used for analyzing and judging the user behavior;
the user management module is used for encrypting and storing user data;
in some embodiments of the invention, the sensor module comprises:
the sensor deployment module is used for selecting a sensor type suitable for the system and deploying the sensor type on a monitored person;
the data acquisition module is used for uploading the data acquired by the sensor to the cloud or terminal equipment in real time through Bluetooth, wi-Fi and other technologies;
the data processing module is used for carrying out preprocessing such as denoising and filtering on the uploaded sensor data so as to reduce errors and improve data quality;
in some embodiments of the invention, the behavior analysis module comprises:
the data management module is used for storing and managing the uploaded sensor data;
the behavior feature extraction module is used for analyzing the sensor data and extracting features through the algorithm and the operation of the model;
the abnormal behavior detection module is used for detecting abnormal behaviors of the monitored personnel through judgment and comparison of behavior characteristics and giving an alarm in time;
in some embodiments of the invention, the user management module comprises:
the user registration and login module is used for providing user registration and login functions so that a user can use and manage the system;
and the data security management module is used for ensuring the security and privacy of the data by adopting an encryption storage mode.
It is noted that relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.
Finally, it should be noted that: the foregoing description is only a preferred embodiment of the present invention, and the present invention is not limited thereto, but it is to be understood that modifications and equivalents of some of the technical features described in the foregoing embodiments may be made by those skilled in the art, although the present invention has been described in detail with reference to the foregoing embodiments. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. An anti-lost method based on the Internet of things is characterized by comprising the following steps of: the method comprises the following steps:
step one: deploying the sensor and configuring;
step two: analyzing the data acquired by the sensor;
step three: monitoring and alarming the behavior of the monitored person;
step four: user management and data storage.
2. The anti-lost method based on the internet of things according to claim 1, wherein the anti-lost method comprises the following steps: the step of deploying the sensor and configuring includes:
selecting a bracelet or pendant device with a low-power Bluetooth technology;
initializing and connecting the sensor devices;
developing corresponding software or firmware at the equipment end;
and the real-time monitoring of the user is realized.
3. The anti-lost method based on the internet of things according to claim 1, wherein the anti-lost method comprises the following steps: the step of analyzing the data collected by the sensor comprises the following steps:
collecting the motion state data of the monitored personnel in real time;
uploading data to a cloud or terminal device;
analyzing and processing data acquired by the sensor;
and monitoring abnormal behaviors through an algorithm and a model.
4. The anti-lost method based on the internet of things according to claim 3, wherein the anti-lost method comprises the following steps: the step of analyzing and processing the data collected by the sensor comprises the following steps:
the data are processed through the algorithm and the operation of the model, so that behavior characteristics and abnormal behavior judgment of the monitored personnel are obtained, the number of steps, the gesture and the position information can be analyzed through the data mining algorithm to know the motion rule and habit of the monitored personnel, on the basis, a behavior model is built, the abnormal behavior of the monitored personnel is found through the comparison and judgment of the model, specifically, the rule and the threshold of the abnormal behavior are set, and when the monitored personnel is motionless, suddenly accelerates or changes the motion trail behavior for a long time, the system can give an alarm.
5. The anti-lost method based on the internet of things according to claim 3, wherein the anti-lost method comprises the following steps: the step of monitoring abnormal behaviors through an algorithm and a model comprises the following steps:
according to the embodiment of the invention, the behavior states of the monitored personnel are divided into walking, resting and running states according to the data acquired by the sensor, the states are used as training samples of the SVM algorithm, higher classification accuracy is obtained through repeated iterative training, a logistic regression algorithm is used for predicting behavior characteristics of the monitored personnel, a logistic regression model is established according to the data characteristics acquired by the sensor, such as the number of steps, the gesture and the position information, model optimization and adjustment are carried out through a cross verification method, finally, an abnormal detection algorithm is used for judging whether abnormal behaviors of the monitored personnel occur or not, a large number of normal behavior data are acquired, and a model of the normal behavior data is established, so that whether the abnormal behaviors of the monitored personnel occur or not is judged, if the behavior characteristics of the monitored personnel differ greatly from the normal behavior data, the system can send an alarm, and the data are acquired, analyzed and processed in real time through various algorithms and the model, so that the behavior characteristics and the abnormal behaviors of the monitored personnel can be accurately judged, and the abnormal behaviors of the monitored personnel can be realized, and the system is effectively supported.
6. The anti-lost method based on the internet of things according to claim 1, wherein the anti-lost method comprises the following steps: the step of monitoring and alarming the behavior of the monitored person comprises the following steps:
different alarm modes and trigger conditions are set according to the demands of different crowds;
when the system gives an alarm, the position of the monitored person is positioned in real time;
specific abnormal behavior monitoring rules and early warning mechanisms are set.
7. The anti-lost method based on the internet of things according to claim 6, wherein the anti-lost method comprises the following steps: the step of setting a specific abnormal behavior monitoring rule and an early warning mechanism comprises the following steps:
the system can monitor the behavior state of the monitored person more comprehensively by setting the rules and mechanisms and send out an alarm in time when the abnormal condition occurs so as to ensure the safety of the monitored person.
8. The anti-lost method based on the internet of things according to claim 1, wherein the anti-lost method comprises the following steps: the steps of user management and data storage include:
the user carries out user registration, login and password resetting operation through the terminal equipment or the cloud platform;
storing and managing the motion state data of the monitored personnel;
for the storage of sensitive information, an encryption storage mode is adopted.
9. The anti-lost method based on the internet of things according to claim 8, wherein the anti-lost method comprises the following steps: the step of storing the sensitive information in an encrypted storage mode comprises the following steps:
for the storage of sensitive information, an encryption storage mode is adopted to ensure the safety and privacy of data, the system encrypts personal information of a user and the motion state data of monitored personnel so as to avoid malicious attack and illegal acquisition, meanwhile, an authority management function is set, the access authority of the user can be controlled, illegal operation and data leakage are prevented, and a system administrator can manage and modify the authority of the user so as to ensure the safety and stability of the system.
10. Anti-lost system based on thing networking, its characterized in that: the system comprises:
the sensor module is used for deploying sensors and collecting data;
the behavior analysis module is used for analyzing and judging the user behavior;
and the user management module is used for carrying out encryption storage on the user data.
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Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105740621A (en) * | 2016-01-29 | 2016-07-06 | 江阴中科今朝科技有限公司 | Moving monitoring and intelligent aged nursing health cloud platform of human body behavior data |
| CN111723633A (en) * | 2019-12-09 | 2020-09-29 | 深圳市鸿逸达科技有限公司 | Personnel behavior pattern analysis method and system based on depth data |
| US20220271996A1 (en) * | 2021-02-25 | 2022-08-25 | Insight Direct Usa, Inc. | Iot deployment configuration template |
| KR20220121744A (en) * | 2021-02-25 | 2022-09-01 | 유용환 | IoT device monitoring method based on Big Data and Artificial intelligence and IoT device monitoring system performing the same |
| CN115690653A (en) * | 2022-10-28 | 2023-02-03 | 福寿康智慧(上海)医疗养老服务有限公司 | Monitoring and early warning for realizing abnormal nursing behaviors of nursing staff based on AI behavior recognition |
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2023
- 2023-04-27 CN CN202310467306.7A patent/CN116665402A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105740621A (en) * | 2016-01-29 | 2016-07-06 | 江阴中科今朝科技有限公司 | Moving monitoring and intelligent aged nursing health cloud platform of human body behavior data |
| CN111723633A (en) * | 2019-12-09 | 2020-09-29 | 深圳市鸿逸达科技有限公司 | Personnel behavior pattern analysis method and system based on depth data |
| US20220271996A1 (en) * | 2021-02-25 | 2022-08-25 | Insight Direct Usa, Inc. | Iot deployment configuration template |
| KR20220121744A (en) * | 2021-02-25 | 2022-09-01 | 유용환 | IoT device monitoring method based on Big Data and Artificial intelligence and IoT device monitoring system performing the same |
| CN115690653A (en) * | 2022-10-28 | 2023-02-03 | 福寿康智慧(上海)医疗养老服务有限公司 | Monitoring and early warning for realizing abnormal nursing behaviors of nursing staff based on AI behavior recognition |
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