Outgoing personnel safety monitoring and early warning system and terminal based on remote communication
By integrating multi-dimensional data analysis of voice, physiological data, and location analysis modules, combined with logic modules and remote communication, the false alarm and missed alarm problems of existing security monitoring systems are solved, intelligent security early warning is realized, and users' security protection capabilities in complex environments are improved.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Utility models(China)
- Current Assignee / Owner
- Filing Date
- 2025-01-06
- Publication Date
- 2026-03-10
AI Technical Summary
Existing security monitoring systems are usually limited to single-dimensional data analysis, which is prone to false alarms or missed alarms. They lack automated and intelligent response mechanisms and are difficult to accurately identify the user's real danger situation.
It integrates a voice analysis module, a user analysis module, and a location analysis module. By recognizing keywords and semantics, user physiological data, and changes in geographical location through voice recognition, combined with a logic module, it achieves multi-dimensional data analysis and triggers alarms under multiple conditions. The remote communication module ensures the efficient transmission of alarm information.
It improves the identification accuracy and response timeliness of the security monitoring system, reduces the risk of false alarms, ensures timely alarms in real dangerous situations, and enhances users' security protection capabilities in complex environments.
Smart Images
Figure CN223986344U_ABST
Abstract
Description
Technical Field
[0001] This utility model relates to the field of personnel safety monitoring and early warning technology, specifically to a remote communication-based personnel safety monitoring and early warning system and terminal. Background Technology
[0002] With advancements in mobile communication and sensor technologies, smart wearable devices have rapidly gained popularity in the market. These devices, with their convenience and real-time monitoring capabilities, are gradually becoming essential tools in people's daily lives, especially in the fields of personal safety and health monitoring. However, existing security monitoring systems are often limited to single-dimensional data analysis, such as relying solely on location tracking or simple voice recognition to detect potential hazards. This singular approach is prone to false alarms or missed alarms in practical applications, making it difficult to accurately identify the true danger a user is in. Furthermore, many systems rely solely on manual user intervention to send alerts after identifying anomalies, lacking automated and intelligent response mechanisms. Therefore, developing a system capable of integrating multi-dimensional data analysis and achieving automatic early warning is particularly important.
[0003] The information disclosed in the background section is only intended to enhance the understanding of the background of this disclosure, and therefore may include information that does not constitute prior art known to those skilled in the art. Utility Model Content
[0004] The purpose of this invention is to provide a remote communication-based safety monitoring and early warning system and terminal for people traveling outside the area, in order to solve the problems mentioned in the background art.
[0005] To achieve the above objectives, this utility model provides the following technical solution:
[0006] A remote communication-based safety monitoring and early warning system for people going out includes:
[0007] The voice analysis module is electrically connected to the logic module. It is equipped with a semantic recognition algorithm and a keyword detection algorithm to collect and analyze the user's voice data. During the voice data analysis process, when the user is identified to be in an emergency or when a preset keyword appears, a high-level signal is sent to the logic module.
[0008] The user analysis module is used to detect the user's human body data and send a high-level signal to the logic module when the user's human body data exceeds a preset data threshold.
[0009] The location analysis module is used to detect the user's real-time location information and send a high-level signal to the logic module when the user's real-time location information exceeds a preset common range.
[0010] A logic module, electrically connected to the remote communication module, is used to drive the remote communication module when it simultaneously receives a high-level signal from any one of the voice analysis module, user analysis module, and location analysis module.
[0011] A remote communication module, which is used to remotely send danger signals when driven.
[0012] Preferably, the voice analysis module includes a voice acquisition unit, a first voice unit, a second voice unit, and a switching unit, wherein:
[0013] The voice acquisition unit is electrically connected to the first voice unit and the second voice unit, and is used to identify and acquire the user's voice data and send it to the first voice unit and the second voice unit respectively.
[0014] The first voice unit is electrically connected to the logic module and has a built-in semantic detection algorithm for performing feature analysis on the user's voice data and sending a high-level signal to the logic module when it determines that the user is in a dangerous situation.
[0015] The second voice unit is electrically connected to the switching unit. It has a built-in keyword detection algorithm for identifying keywords in the user's voice data and sending a high-level signal to the switching unit to turn it on when a keyword is identified.
[0016] The input terminal of the switching unit is electrically connected to a preset high-level signal source, and the output terminal is electrically connected to the output terminal of the first voice unit.
[0017] Preferably, the voice acquisition unit is a microphone array;
[0018] Both the first and second voice units are integrated chips. The semantic recognition algorithm pre-set in the first voice unit is a natural language processing algorithm and a machine learning algorithm, and the keyword detection algorithm pre-set in the second voice unit is a KS detection algorithm.
[0019] The switching unit is an NMOS transistor, whose gate is electrically connected to the output terminal of the second voice unit, and whose drain and source are electrically connected to a preset high-level signal source and the output terminal of the first voice unit, respectively.
[0020] Preferably, the user analysis module includes a human body detection unit and a human body analysis unit, wherein:
[0021] The human body detection unit is electrically connected to the human body analysis unit and is used to detect the user's human body data and send it to the human body analysis unit.
[0022] The human body analysis unit is electrically connected to the logic module and is used to send a high-level signal to the logic module when the user's human body data exceeds a preset data threshold.
[0023] Preferably, the human body detection unit includes a heart rate sensor and a skin conductance sensor, and the human body data includes heart rate data and skin conductance data.
[0024] Preferably, the location analysis module includes a GPS positioning unit and an area identification unit, wherein:
[0025] The GPS positioning unit is electrically connected to the area identification unit and is used to detect the user's real-time location information and send it to the area identification unit.
[0026] The area identification unit is electrically connected to the logic module and is an integrated chip. It has built-in map data and the user's commonly used map range, and is used to send a high-level signal to the logic module when the user's real-time location information exceeds the preset commonly used map range.
[0027] Preferably, the logic module includes two sets of AND gates and one set of OR gates, wherein the two inputs of one set of AND gates are electrically connected to the outputs of the voice analysis module and the user analysis module, respectively; the two inputs of the other set of AND gates are electrically connected to the outputs of the location analysis module and the user analysis module, respectively; the outputs of the two sets of AND gates are electrically connected to the two inputs of the OR gate, respectively; and the output of the OR gate is electrically connected to the remote communication module.
[0028] A remote communication-based safety monitoring and early warning terminal for outgoing personnel, wherein the terminal is a smart wearable wristband terminal, and the smart wearable wristband terminal integrates the aforementioned remote communication-based safety monitoring and early warning system for outgoing personnel.
[0029] Compared with the prior art, the beneficial effects of this utility model are:
[0030] This invention integrates voice analysis, user physiological data, and location monitoring to achieve an intelligent safety early warning system. The voice analysis module, by recognizing keywords, speech semantics, user physiological data, and changes in user geographical location, can not only quickly identify potential emergencies but also reduce the risk of false alarms. It can still provide warnings, especially when the user's speech semantics cannot properly identify their environment. This allows the system to trigger alarms under multiple conditions, reducing false alarms caused by single factors and ensuring timely alerts in real danger situations. The integrated remote communication module ensures efficient transmission of alarm information, thereby improving the timeliness and reliability of the response and significantly enhancing the user's safety protection capabilities in complex environments. Attached Figure Description
[0031] Figure 1This is a schematic diagram of the module structure of this utility model. Detailed Implementation
[0032] To make the objectives, technical solutions, and advantages of this utility model clearer, the present utility model will be further described in detail below with reference to specific embodiments.
[0033] It should be noted that, unless otherwise defined, the technical or scientific terms used in this utility model should have the ordinary meaning understood by one of ordinary skill in the art to which this utility model pertains. The terms "first," "second," and similar words used in this utility model do not indicate any order, quantity, or importance, but are merely used to distinguish different components. Terms such as "comprising" or "including" mean that the element or object preceding the word encompasses the elements or objects listed following the word and their equivalents, without excluding other elements or objects. Terms such as "connected" or "linked" are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. Terms such as "upper," "lower," "left," and "right" are used only to indicate relative positional relationships; when the absolute position of the described object changes, the relative positional relationship may also change accordingly.
[0034] Example:
[0035] Please see Figure 1 This utility model provides a technical solution:
[0036] A safety monitoring and early warning system for people traveling outside the area based on remote communication includes: a voice analysis module, a user analysis module, a location analysis module, a logic module, and a remote communication module.
[0037] The voice analysis module is electrically connected to the logic module. It has a preset semantic recognition algorithm and keyword detection algorithm, which are used to collect and analyze the user's voice data. During the voice data analysis process, when the user is identified to be in an emergency or when preset keywords appear, a high-level signal is sent to the logic module.
[0038] The voice analysis module includes a voice acquisition unit, a first voice unit, a second voice unit, and a switch unit, wherein:
[0039] The voice acquisition unit is electrically connected to the first voice unit and the second voice unit, and is a microphone array. The specific model can be the Knowles SPH0645LM4H-B series MEMS microphone, which has the characteristics of high signal-to-noise ratio and low power consumption, making it very suitable for portable devices. It can be used to identify and acquire the user's voice data and send it to the first voice unit and the second voice unit respectively.
[0040] The first voice unit is electrically connected to the logic module. It has a built-in semantic detection algorithm for analyzing the features of the user's voice data and sending a high-level signal to the logic module when it determines that the user is in a dangerous situation. The second voice unit is electrically connected to the switch unit. It has a built-in keyword detection algorithm for recognizing keywords in the user's voice data and sending a high-level signal to the switch unit to turn it on when a keyword is recognized.
[0041] Both the first and second voice units are integrated chips. Specifically, the first voice unit can use the Quacomm QCC5124 series audio processing chip, which has efficient audio processing capabilities and supports complex voice processing tasks. The second voice unit can use the Sensory TrulyHandsfree series audio processing chip, which has lower power consumption than the first voice unit and supports more keywords. The semantic recognition algorithm pre-built into the first voice unit is a natural language processing algorithm and a machine learning algorithm, while the keyword detection algorithm pre-built into the second voice unit is the KS detection algorithm.
[0042] Here, the basic working logic of the first and second voice units is as follows: both first convert the user's voice data into text. Then, the first voice unit uses natural language processing and machine learning algorithms for detailed analysis to identify whether the user is in a dangerous situation. Simultaneously, the second voice unit uses the KS detection algorithm to detect whether keywords appear in the user's voice data. Similarly, various smart home devices and voice assistants in existing technologies can use these algorithms to achieve semantic recognition and keyword recognition. These are mature existing technologies, so their specific working principles will not be elaborated upon here.
[0043] Understandably, when users are in dangerous situations, they sometimes need to use more common everyday language to calm down attackers. Therefore, relying solely on semantic recognition makes it difficult to distinguish this situation from ordinary circumstances, necessitating the use of specific keywords for triggering responses. Unlike the various keywords in semantic recognition algorithms, the keywords in the second speech unit do not involve specific scene recognition. Therefore, they can be set to be more everyday words. Specifically, keywords in various semantic recognition algorithms might include "robbery" or "help" to identify whether a user is in danger, but keywords in the second speech unit could be set to more everyday words like "keys" or "bicycle." This allows each speech recognition chip to be optimized for a specific task, thereby improving recognition accuracy and efficiency. For example, one chip can focus on recognizing specific emergency words and speech features, while another focuses on general keyword recognition. The two chips can run different recognition tasks simultaneously, reducing the burden on a single chip handling multiple tasks, thus improving the overall system's response speed. Furthermore, if one chip malfunctions, the other can continue performing its specific task, improving the system's fault tolerance and reliability. Furthermore, because each chip focuses on a specific recognition task, recognition accuracy can be improved and the false recognition rate reduced through debugging and training. For example, dangerous situation recognition can be more sensitive and accurate without worrying about misjudging general keywords.
[0044] The input terminal of the switching unit is electrically connected to a preset high-level signal source, and the output terminal is electrically connected to the output terminal of the first voice unit. Specifically, the switching unit is an NMOS transistor, whose gate is electrically connected to the output terminal of the second voice unit, and whose drain and source are electrically connected to the preset high-level signal source and the output terminal of the first voice unit, respectively. In other words, when the second voice unit detects a keyword, it will turn on the switching unit, causing it to output a high level to the logic module.
[0045] The user analysis module is used to detect the user's human body data and sends a high-level signal to the logic module when the user's human body data exceeds a preset data threshold. The user analysis module includes a human body detection unit and a human body analysis unit, wherein:
[0046] The human body detection unit is electrically connected to the human body analysis unit to detect the user's human body data and send it to the human body analysis unit. The human body analysis unit is electrically connected to the logic module to send a high-level signal to the logic module when the user's human body data exceeds a preset data threshold. The human body detection unit includes a heart rate sensor and a skin conductance sensor. Specifically, it can use the MAX30102 series integrated heart rate sensor and the GSR Senso series skin conductance sensor, respectively. The human body data includes heart rate data and skin conductance data.
[0047] The location analysis module is used to detect the user's real-time location information and send a high-level signal to the logic module when the user's real-time location information exceeds the preset common range.
[0048] The location analysis module includes a GPS positioning unit and an area identification unit, wherein:
[0049] The GPS positioning unit is electrically connected to the area identification unit, and is used to detect the user's real-time location information and send it to the area identification unit;
[0050] The area identification unit is electrically connected to the logic module and is an integrated chip. It has built-in map data and the user's commonly used map range. It is used to send a high-level signal to the logic module when the user's real-time location information exceeds the preset commonly used map range.
[0051] The logic module is electrically connected to the remote communication module and is used to drive the remote communication module when it receives a high level signal from any one of the voice analysis module, user analysis module, and location analysis module at the same time.
[0052] The logic module includes two sets of AND gates and one set of OR gates. The two inputs of one set of AND gates are electrically connected to the outputs of the voice analysis module and the user analysis module, respectively. The two inputs of the other set of AND gates are electrically connected to the outputs of the location analysis module and the user analysis module, respectively. The outputs of the two sets of AND gates are electrically connected to the two inputs of the OR gate, respectively. The output of the OR gate is electrically connected to the remote communication module.
[0053] Understandably, the logic module is configured so that the remote communication module will only issue a warning when both the voice analysis module and the user analysis module detect an anomaly simultaneously, or when both the location analysis module and the user analysis module detect an anomaly simultaneously. This is because when a user is in a dangerous situation, their heart rate and skin conductance data will inevitably be affected. Therefore, these two factors are used as general evaluation criteria. However, since these effects also occur after exercise under normal circumstances, to reduce false alarms, supplementary evaluations using the user's voice data and real-time location data are needed to identify different dangerous situations. In other words, when a user is in a state of heightened anxiety with an accelerated heart rate, and semantic recognition also identifies a dangerous situation; or when a user is in a state of heightened anxiety with an accelerated heart rate and the system recognizes that the user has uttered a keyword; or when a user is in a state of heightened anxiety with an accelerated heart rate and is outside the commonly used map area, the logic module will trigger the remote communication module to send a danger signal to issue a warning. When the user's heart rate remains relatively constant and they are relaxed, they are more likely to joke with friends, say things like "robbery" or "help" or use preset keywords, or go to other areas on their own. In this case, the logic module will not drive the remote communication module to send a danger signal for warning, thus reducing the risk of false alarms.
[0054] The remote communication module can use the Quectel EC21 communication module, which supports 4G LTE communication to ensure high-speed and stable data transmission. Backup contacts or alarm phone numbers can be set in advance to remotely send danger signals when activated.
[0055] This utility model also provides a remote communication-based safety monitoring and early warning terminal for people going out. The terminal is a smart wearable wristband terminal, which integrates the aforementioned remote communication-based safety monitoring and early warning system for people going out.
[0056] This invention integrates voice analysis, user physiological data, and location monitoring to achieve an intelligent safety early warning system. The voice analysis module, by recognizing keywords, speech semantics, user physiological data, and changes in user geographical location, can not only quickly identify potential emergencies but also reduce the risk of false alarms. It can still provide warnings, especially when the user's speech semantics cannot properly identify their environment. This allows the system to trigger alarms under multiple conditions, reducing false alarms caused by single factors and ensuring timely alerts in real danger situations. The integrated remote communication module ensures efficient transmission of alarm information, thereby improving the timeliness and reliability of the response and significantly enhancing the user's safety protection capabilities in complex environments.
[0057] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.
[0058] The embodiments described above are merely illustrative of several implementations of this utility model, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the utility model patent. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this utility model, and these all fall within the protection scope of this utility model. Therefore, the protection scope of this utility model patent should be determined by the appended claims.
Claims
1. A remote communication based safety monitoring and early warning system for field staff, characterized in that, include: The voice analysis module is electrically connected to the logic module. It is equipped with a semantic recognition algorithm and a keyword detection algorithm to collect and analyze the user's voice data. During the voice data analysis process, when the user is identified to be in an emergency or when a preset keyword appears, a high-level signal is sent to the logic module. The user analysis module is used to detect the user's human body data and send a high-level signal to the logic module when the user's human body data exceeds a preset data threshold. The location analysis module is used to detect the user's real-time location information and send a high-level signal to the logic module when the user's real-time location information exceeds a preset common range. A logic module, electrically connected to the remote communication module, is used to drive the remote communication module when it simultaneously receives a high-level signal from any one of the voice analysis module, user analysis module, and location analysis module. The logic module includes two sets of AND gates and one set of OR gates. The two inputs of one set of AND gates are electrically connected to the outputs of the voice analysis module and the user analysis module, respectively. The two inputs of the other set of AND gates are electrically connected to the outputs of the location analysis module and the user analysis module, respectively. The outputs of the two sets of AND gates are electrically connected to the two inputs of the OR gate, respectively. The output of the OR gate is electrically connected to the remote communication module. A remote communication module, which is used to remotely send danger signals when driven.
2. The remote communication based safety monitoring and early warning system for outbound personnel according to claim 1, characterized in that: The voice analysis module includes a voice acquisition unit, a first voice unit, a second voice unit, and a switching unit, wherein: The voice acquisition unit is electrically connected to the first voice unit and the second voice unit, and is used to identify and acquire the user's voice data and send it to the first voice unit and the second voice unit respectively. The first voice unit is electrically connected to the logic module and has a built-in semantic detection algorithm for performing feature analysis on the user's voice data and sending a high-level signal to the logic module when it determines that the user is in a dangerous situation. The second voice unit is electrically connected to the switching unit. It has a built-in keyword detection algorithm for identifying keywords in the user's voice data and sending a high-level signal to the switching unit to turn it on when a keyword is identified. The input terminal of the switching unit is electrically connected to a preset high-level signal source, and the output terminal is electrically connected to the output terminal of the first voice unit.
3. The remote communication-based safety monitoring and early warning system for outgoing personnel according to claim 2, characterized in that: The voice acquisition unit is a microphone array; Both the first and second voice units are integrated chips. The semantic recognition algorithm pre-set in the first voice unit is a natural language processing algorithm and a machine learning algorithm, and the keyword detection algorithm pre-set in the second voice unit is a KS detection algorithm. The switching unit is an NMOS transistor, whose gate is electrically connected to the output terminal of the second voice unit, and whose drain and source are electrically connected to a preset high-level signal source and the output terminal of the first voice unit, respectively.
4. The remote communication based safety monitoring and early warning system for outbound personnel according to claim 1, characterized in that: The user analysis module comprises a human body detection unit and a human body analysis unit, wherein: The human body detection unit is electrically connected to the human body analysis unit, and is configured to detect human body data of the user and send the human body data to the human body analysis unit; The human body analysis unit is electrically connected to the logic module, and is configured to send a high-level signal to the logic module when the human body data of the user exceeds a preset data threshold.
5. The remote communication based safety monitoring and early warning system for outbound personnel according to claim 4, characterized in that: The human body detection unit comprises a heart rate sensor and a skin electricity sensor, and the human body data comprises heart rate data and skin electricity data.
6. The remote communication based safety monitoring and early warning system for outbound personnel according to claim 1, characterized in that: The position analysis module comprises a GPS positioning unit and a region identification unit, wherein: The GPS positioning unit is electrically connected to the region identification unit, and is configured to detect real-time position information of the user and send the real-time position information to the region identification unit; The region identification unit is electrically connected to the logic module, and is an integrated chip, which is internally provided with map data and a commonly used map range of the user, and is configured to send a high-level signal to the logic module when the real-time position information of the user exceeds the preset commonly used map range.
7. A remote communication-based safety monitoring and early warning terminal for outbound personnel, characterized in that: The terminal is a smart wearable bracelet terminal, and the smart wearable bracelet terminal is integrated with the safety monitoring and early warning system for out-of-town personnel based on remote communication according to any one of claims 1-6.