Safety monitoring method and device for special crowds
By combining environmental detection and bioinformatics with artificial intelligence prediction models, the problem of the inability to proactively handle emergencies in existing technologies has been solved, and safety monitoring, prediction and early intervention for special populations have been achieved, thereby improving safety.
Patent Information
- Application Number
- CN202510787120.9
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-06-12
- Publication Date
- 2025-09-26
AI Technical Summary
Existing technologies can only provide early warnings after emergencies occur, but are unable to proactively take emergency measures, and are unable to predict and intervene in advance to avoid safety risks for special groups of people.
By collecting motion data through environmental detection components and biological information through wearable devices, we use artificial intelligence technology to establish a prediction model, conduct real-time risk prediction, and determine emergency response plans based on the prediction information.
It enables the prediction of crisis situations for special groups of people, improves safety assurance, and enables measures to be taken before risks occur, thus reducing the possibility of accidents.
Smart Images
Figure CN120708360A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the field of computer technology, and in particular to a security monitoring method and device for special groups of people. Background Art
[0002] Special groups, such as the elderly, children, and people with disabilities, often face higher safety risks in their lives. They may face unexpected situations such as falls, getting lost, or experiencing acute illness. Once these situations occur, emergency measures often need to be taken immediately to prevent serious consequences.
[0003] Currently, wearable devices such as smart bracelets can be used to monitor the behavior and status of specific groups of people. For example, smart bracelets can be used to locate individuals, preventing them from getting lost. They can also monitor falls, providing immediate warnings to family members. Physiological characteristics such as heart rate and blood oxygen levels can be monitored to identify acute illnesses.
[0004] However, existing technologies often only provide early warnings after an emergency occurs. In other words, they are typically warning methods. They are unable to proactively implement effective emergency response measures after an incident occurs, nor can they predict and prevent emergencies in advance. Summary of the Invention
[0005] The present invention provides a safety monitoring method and device for special groups of people, thereby achieving more comprehensive safety protection for special groups of people.
[0006] In a first aspect, the present invention provides a safety monitoring method for a special group of people, the method being applied to a monitoring system, comprising:
[0007] Use environmental detection components to collect target personnel's movement data within the target area;
[0008] Using a wearable device to collect biometric information of the target person;
[0009] establishing a prediction model based on artificial intelligence technology, utilizing the prediction model and performing real-time prediction based on the action data and the biological information to determine prediction information;
[0010] When the prediction information meets a preset risk condition, a first emergency response plan is determined according to the risk condition, and the first emergency response plan is executed.
[0011] Preferably, it also includes:
[0012] When the action data meets a preset risk condition, a second emergency response plan is determined according to the risk condition, and the second emergency response plan is executed.
[0013] Preferably, it also includes:
[0014] When the biological information meets a preset risk condition, a third emergency treatment plan is determined according to the risk condition, and the third emergency treatment plan is executed.
[0015] Preferably, the establishment of a prediction model based on artificial intelligence technology includes:
[0016] Determine the original model and collect historical action data and historical biological information;
[0017] The original model is trained with data based on the historical action data and the historical biological information to determine the prediction model.
[0018] Preferably, the using the prediction model and performing real-time prediction based on the action data and the biological information to determine the prediction information includes:
[0019] Determine the basic information of the target person, and determine the risk type corresponding to the target person based on the basic information;
[0020] The action data, the biological information, and the risk type are input into the prediction model so that the prediction model determines the prediction information.
[0021] Preferably, inputting the action data, the biological information, and the risk type into the prediction model so that the prediction model determines the prediction information includes:
[0022] determining a risk level of the action data and the biometric information relative to the risk type;
[0023] The risk level is used as the prediction information.
[0024] Preferably, executing the first emergency treatment plan includes:
[0025] Sending early warning information to the guardian of the target person, sending alarm information to related personnel, and / or taking control measures for the target area.
[0026] In a second aspect, the present invention provides a safety monitoring device for special groups of people, the device being placed in a monitoring system and comprising:
[0027] An environmental monitoring module is used to collect the target person's movement data in the target area using the environmental detection component;
[0028] A biometric detection module, configured to collect biometric information of the target person using a wearable device;
[0029] a prediction module, configured to establish a prediction model based on artificial intelligence technology, and utilize the prediction model to perform real-time prediction based on the action data and the biological information to determine prediction information;
[0030] The emergency processing module is used to determine a first emergency processing plan according to the risk condition when the prediction information meets the preset risk condition, and execute the first emergency processing plan.
[0031] In a third aspect, the present invention provides a readable medium comprising an execution instruction. When a processor of an electronic device executes the execution instruction, the electronic device executes any method described in the first aspect.
[0032] In a fourth aspect, the present invention provides an electronic device comprising a processor and a memory storing execution instructions, wherein when the processor executes the execution instructions stored in the memory, the processor executes any method described in the first aspect.
[0033] The present invention provides a security monitoring method and device for special groups of people. It uses environmental detection components to collect the action data of target personnel in the target area, and uses wearable devices to collect the biological information of the target personnel, so as to achieve comprehensive analysis and judgment of risk conditions; based on the prediction model, the action data and the biological information are predicted in real time to determine the prediction information; thus, the prejudgment of crisis conditions is achieved, and the safety of the target personnel is improved.
[0034] The further effects of the above-mentioned non-conventional preferred embodiment will be described below in conjunction with specific embodiments. BRIEF DESCRIPTION OF THE DRAWINGS
[0035] In order to more clearly illustrate the embodiments of the present invention or the existing technical solutions, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.
[0036] Figure 1 A flowchart of a safety monitoring method for special groups provided by one embodiment of the present invention;
[0037] Figure 2 A schematic flow chart of another safety monitoring method for special groups provided by one embodiment of the present invention;
[0038] Figure 3 A schematic structural diagram of a security monitoring device for special groups of people provided by one embodiment of the present invention;
[0039] Figure 4 A schematic structural diagram of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION
[0040] To make the objectives, technical solutions, and advantages of the present invention more clear, the technical solutions of the present invention will be clearly and completely described below in conjunction with specific embodiments and corresponding drawings. Obviously, the embodiments described are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.
[0041] Special groups, such as the elderly, children, and people with disabilities, often face higher safety risks in their lives. They may face unexpected situations such as falls, getting lost, or experiencing acute illness. Once these situations occur, emergency measures often need to be taken immediately to prevent serious consequences.
[0042] Currently, wearable devices such as smart bracelets can be used to monitor the behavior and status of specific groups of people. For example, smart bracelets can be used to locate individuals, preventing them from getting lost. They can also monitor falls, providing immediate warnings to family members. Physiological characteristics such as heart rate and blood oxygen levels can be monitored to identify acute illnesses.
[0043] However, existing technologies often only provide early warnings after an emergency occurs. In other words, they are typically warning methods. They are unable to proactively implement effective emergency response measures after an incident occurs, nor can they predict and prevent emergencies in advance.
[0044] Conventional monitoring solutions like these have limited practical applications. This is because family members of individuals with special needs often cannot accompany them for extended periods due to life-related reasons, leaving them alone for extended periods. This is why security monitoring is necessary. However, with existing security monitoring solutions, even if family members receive an immediate alert, they likely won't be able to quickly respond to the scene. Conversely, if individuals with special needs have family members or caregivers accompanying them for extended periods, the need for security monitoring is essentially nonexistent.
[0045] So in general, the existing security monitoring solutions are actually equivalent to having no effective measures at all, and the security value they can provide is very limited.
[0046] In view of this, the present invention provides a safety monitoring method for special groups of people. Figure 1FIG. 1 is a specific embodiment of the security monitoring method for special groups provided by the present invention. In this embodiment, the method is applied to a monitoring system and includes the following steps:
[0047] Step 101: Use the environment detection component to collect the target person's action data in the target area.
[0048] In this embodiment, the area monitored by the monitoring system is referred to as the target area. For example, a monitoring system can be deployed within a smart community. The area within that community is the target area. The target person is an individual from the aforementioned special population. Generally speaking, the target person's range of activity is relatively limited, and their usual area of activity often overlaps with the target area. Therefore, effective security monitoring of the target area can generally meet most needs.
[0049] The monitoring system's data sources are primarily divided into two parts. One part involves environmental monitoring of the target area to determine the status of the target individual. This involves using environmental monitoring components to collect the target individual's behavioral data. These components can include millimeter-wave radar arrays, infrared cameras, environmental sensors, and RGB cameras. The millimeter-wave radar array enables non-invasive vital sign monitoring, while infrared and RGB cameras use image recognition technology to identify the target individual's behavior. Environmental sensors provide general environmental monitoring. The data collected by these environmental monitoring components is called behavioral data.
[0050] For example, through motion data, it can be detected whether a child has left the target area or is about to leave the target area. It can also be detected whether the elderly or children have fallen, collided or been injured, etc. It can also be detected whether a target person is lost or trapped.
[0051] Step 102: Use a wearable device to collect the biometric information of the target person.
[0052] On the other hand, in this embodiment, wearable devices can also be used to collect the target person's biometric information. Wearable devices can be smart bracelets, smart watches, heart rate monitors, or other types of devices. They can collect various vital signs of the target person, such as heart rate, blood oxygen, respiratory rate, blood pressure, etc. Furthermore, wearable devices can also identify the target person's movements, such as determining whether they are walking or running, determining their specific speed, and whether they have fallen. This information is referred to as biometric information in this embodiment. Biometric information can be used to more specifically monitor the target person's health status and determine whether they have an acute illness or signs of illness.
[0053] The aforementioned motion data and biometric information can be used to comprehensively determine the target person's current condition, such as whether they have fallen, are within the target area, are lost, or are experiencing an acute illness. In some cases, motion data or biometric information alone can be used to independently perform such judgments, enabling safety monitoring and hazard identification.
[0054] Specifically, when the action data meets a preset risk condition, a second emergency response plan is determined based on the risk condition and executed. When the biometric information meets a preset risk condition, a third emergency response plan is determined based on the risk condition and executed.
[0055] Risk conditions are specific circumstances generally considered to pose a risk. These "specific circumstances" are also reflected to some extent in the data, such as behavioral data or biometrics. In other words, if similar data characteristics appear in behavioral data or biometrics, the risk condition can be considered met. For example, if behavioral data or biometrics show characteristics similar to a fall, the target person can be considered to have fallen. Alternatively, if behavioral data indicates that the target person has left the target area, they can be considered lost. If biometrics show elevated heart rate or blood pressure, it can be considered an acute illness. And so on.
[0056] When behavioral data or biometric information meets risk conditions, an emergency response plan can be further determined. When behavioral data triggers a risk condition, the corresponding emergency response plan is called the second emergency response plan. When biometric information triggers a risk condition, the corresponding emergency response plan is called the third emergency response plan. The specific contents of the emergency response plan may include notifying family members, medical staff, police, community workers, etc. The specific details will depend on the actual situation of the emergency.
[0057] Step 103: Establish a prediction model based on artificial intelligence technology, use the prediction model, and perform real-time prediction based on the action data and the biological information to determine prediction information.
[0058] It should also be noted that the above-mentioned direct assessment of the target person's current situation based on behavioral data and biometric information is a post-incident assessment method. Furthermore, this embodiment can also perform further predictions based on behavioral data and biometric information, thereby identifying risks in advance and taking preemptive measures to further enhance the safety of the target person.
[0059] In this embodiment, predictions are achieved through artificial intelligence technology. Specifically, a prediction model can be established based on artificial intelligence technology. The process of establishing the prediction model is as follows: determining an original model and collecting historical behavioral data and historical biometric information; training the original model based on the historical behavioral data and historical biometric information to determine the prediction model. This prediction model can predict risks based on behavioral data and biometric information.
[0060] During the prediction process, the basic information of the target person can be determined, and the risk type corresponding to the target person can be determined based on the basic information; the action data, the biological information and the risk type are input into the prediction model so that the prediction model determines the prediction information.
[0061] Basic information can include the target person's type, such as children, the elderly, or people with disabilities. Different types of people often face different risks. For children, the greatest risk is typically getting lost, followed by traumatic risks like falls. For the elderly, the greatest risks are typically acute illness and traumatic risks like falls. Of course, the types of risks faced by the elderly vary depending on the specific circumstances. Those with underlying medical conditions are often at greater risk of acute illness. Whether there's a risk of getting lost depends on the individual's mental state. Generally, elderly people with good mental health and intellectual development are not at high risk of falling, while the opposite is true. In short, basic information determines the target person's risk type.
[0062] The behavioral data, biometric information, and risk type are input into the prediction model, which then determines prediction information. The prediction information indicates the target individual's risk level relative to their risk type. Specifically, the risk level of the behavioral data and biometric information relative to the risk type is determined, and the risk level is used as the prediction information. Generally speaking, a higher risk level indicates a greater risk.
[0063] Step 104: When the prediction information meets a preset risk condition, a first emergency response plan is determined according to the risk condition, and the first emergency response plan is executed.
[0064] Similarly, the prediction information can also be judged based on the aforementioned risk conditions. When the prediction information meets the pre-set risk conditions, the first emergency response plan can be further determined. Since this is still in the prediction stage, meaning the dangerous situation has not actually occurred, the first emergency response plan can often be more diverse, focusing on the potential crisis.
[0065] For example, if a target person is judged to be at risk of getting lost in a target area, the target area can be blocked in advance to prevent them from leaving. At the same time, staff can be dispatched to guide them, helping them return to the correct range before they get lost.
[0066] In addition, in this embodiment, the logical structure of the monitoring system involved can refer to the following:
[0067] Perception layer: Deploy millimeter-wave radar arrays (non-invasive vital sign monitoring), infrared cameras (behavior recognition), and environmental sensors (temperature, humidity, and smoke). Wearable devices (such as badge-mounted fall detectors and wristband-mounted heart rate monitors) are also deployed. Terminal devices, such as smart terminals used by property management staff, receive tasks and provide status feedback.
[0068] Network layer: Data is transmitted to the edge computing node via LoRaWAN or 5G network for preliminary filtering and encryption.
[0069] Platform layer: Digital twin engines build high-precision 3D models based on Unity / Unreal Engine, integrating physics engines (such as NVIDIA PhysX) to simulate human motion and environmental interaction. Multimodal fusion algorithms employ deep learning frameworks (such as TensorFlow) to fuse spatiotemporal data (such as gait cycles and activity heat maps) to improve behavior recognition accuracy. Knowledge graphs construct profiles of specific populations (health records, social relationships) to support decision-making and reasoning.
[0070] Application layer: The property management and control layer provides a real-time monitoring screen, alarm handling workflows, and historical data backtracking. On the user side, safety reports and care logs are pushed to family members via WeChat mini-programs / apps, and remote video calls are supported.
[0071] It can be seen from the above technical solution that the beneficial effects of this embodiment are: using the environmental detection component to collect the action data of the target person in the target area, and using the wearable device to collect the biological information of the target person, so as to realize the comprehensive analysis and judgment of the risk situation; based on the prediction model, the action data and the biological information are predicted in real time to determine the prediction information; it realizes the prediction of the crisis situation and improves the safety protection of the target person.
[0072] Figure 1 What is shown is only a basic embodiment of the method of the present invention. By performing certain optimization and expansion on this basis, other preferred embodiments of the method can be obtained.
[0073] like Figure 2FIG. 1 is another specific embodiment of the security monitoring method for special populations of the present invention. This embodiment further describes the above embodiment. In this embodiment, the method includes the following steps:
[0074] Step 201: Use the environment detection component to collect the target person's action data in the target area.
[0075] In this embodiment, the area monitored by the monitoring system is referred to as the target area. For example, a monitoring system can be deployed within a smart community. The area within that community is the target area. The target person is an individual from the aforementioned special population. Generally speaking, the target person's range of activity is relatively limited, and their usual area of activity often overlaps with the target area. Therefore, effective security monitoring of the target area can generally meet most needs.
[0076] The monitoring system's data sources are primarily divided into two parts. One part involves environmental monitoring of the target area to determine the status of the target individual. This involves using environmental monitoring components to collect the target individual's behavioral data. These components can include millimeter-wave radar arrays, infrared cameras, environmental sensors, and RGB cameras. The millimeter-wave radar array enables non-invasive vital sign monitoring, while infrared and RGB cameras use image recognition technology to identify the target individual's behavior. Environmental sensors provide general environmental monitoring. The data collected by these environmental monitoring components is called behavioral data.
[0077] For example, through motion data, it can be detected whether a child has left the target area or is about to leave the target area. It can also be detected whether the elderly or children have fallen, collided or been injured, etc. It can also be detected whether a target person is lost or trapped.
[0078] Step 202: Use a wearable device to collect the biometric information of the target person.
[0079] On the other hand, in this embodiment, wearable devices can also be used to collect the target person's biometric information. Wearable devices can be smart bracelets, smart watches, heart rate monitors, or other types of devices. They can collect various vital signs of the target person, such as heart rate, blood oxygen, respiratory rate, blood pressure, etc. Furthermore, wearable devices can also identify the target person's movements, such as determining whether they are walking or running, determining their specific speed, and whether they have fallen. This information is referred to as biometric information in this embodiment. Biometric information can be used to more specifically monitor the target person's health status and determine whether they have an acute illness or signs of illness.
[0080] The aforementioned motion data and biometric information can be used to comprehensively determine the target person's current condition, such as whether they have fallen, are within the target area, are lost, or are experiencing an acute illness. In some cases, motion data or biometric information alone can be used to independently perform such judgments, enabling safety monitoring and hazard identification.
[0081] Specifically, when the action data meets a preset risk condition, a second emergency response plan is determined based on the risk condition and executed. When the biometric information meets a preset risk condition, a third emergency response plan is determined based on the risk condition and executed.
[0082] Risk conditions are specific circumstances generally considered to pose a risk. These "specific circumstances" are also reflected to some extent in the data, such as behavioral data or biometrics. In other words, if similar data characteristics appear in behavioral data or biometrics, the risk condition can be considered met. For example, if behavioral data or biometrics show characteristics similar to a fall, the target person can be considered to have fallen. Alternatively, if behavioral data indicates that the target person has left the target area, they can be considered lost. If biometrics show elevated heart rate or blood pressure, it can be considered an acute illness. And so on.
[0083] When behavioral data or biometric information meets risk conditions, an emergency response plan can be further determined. When behavioral data triggers a risk condition, the corresponding emergency response plan is called the second emergency response plan. When biometric information triggers a risk condition, the corresponding emergency response plan is called the third emergency response plan. The specific contents of the emergency response plan may include notifying family members, medical staff, police, community workers, etc. The specific details will depend on the actual situation of the emergency.
[0084] Step 203: Establish a prediction model based on artificial intelligence technology, use the prediction model, and perform real-time prediction based on the action data and the biological information to determine prediction information.
[0085] It should also be noted that the above-mentioned direct assessment of the target person's current situation based on behavioral data and biometric information is a post-incident assessment method. Furthermore, this embodiment can also perform further predictions based on behavioral data and biometric information, thereby identifying risks in advance and taking preemptive measures to further enhance the safety of the target person.
[0086] In this embodiment, predictions are achieved through artificial intelligence technology. Specifically, a prediction model can be established based on artificial intelligence technology. The process of establishing the prediction model is as follows: determining an original model and collecting historical behavioral data and historical biometric information; training the original model based on the historical behavioral data and historical biometric information to determine the prediction model. This prediction model can predict risks based on behavioral data and biometric information.
[0087] During the prediction process, the basic information of the target person can be determined, and the risk type corresponding to the target person can be determined based on the basic information; the action data, the biological information and the risk type are input into the prediction model so that the prediction model determines the prediction information.
[0088] Basic information can include the target person's type, such as children, the elderly, or people with disabilities. Different types of people often face different risks. For children, the greatest risk is typically getting lost, followed by traumatic risks like falls. For the elderly, the greatest risks are typically acute illness and traumatic risks like falls. Of course, the types of risks faced by the elderly vary depending on the specific circumstances. Those with underlying medical conditions are often at greater risk of acute illness. Whether there's a risk of getting lost depends on the individual's mental state. Generally, elderly people with good mental health and intellectual development are not at high risk of falling, while the opposite is true. In short, basic information determines the target person's risk type.
[0089] The behavioral data, biometric information, and risk type are input into the prediction model, which then determines prediction information. The prediction information indicates the target individual's risk level relative to their risk type. Specifically, the risk level of the behavioral data and biometric information relative to the risk type is determined, and the risk level is used as the prediction information. Generally speaking, a higher risk level indicates a greater risk.
[0090] Step 204: When the prediction information meets a preset risk condition, a first emergency response plan is determined according to the risk condition.
[0091] Step 205: Send a warning message to the guardian of the target person, send an alarm message to related personnel, and / or take control measures for the target area.
[0092] The aforementioned guardians usually refer to the target individual's family members, caregivers, and other personnel specifically responsible for caring for the target individual. Associated personnel include public service personnel such as police, doctors, and community workers.
[0093] In this embodiment, Figure 1Based on the embodiment shown, the process of determining and executing the first emergency response plan is further described. As previously known, different target personnel correspond to different risk types, so the judgment of risk level is different and the measures to be taken are also different.
[0094] For example, for an elderly person who is conscious but suffers from underlying medical conditions, the risks they face primarily include falls and acute illness. However, they are generally not considered to be at high risk of getting lost. Therefore, risk assessments may be biased. For falls and acute illness, the preferred response is to send a warning message to the target individual's guardian or an alert to connected personnel. Generally speaking, if the crisis has not yet occurred but is predicted to occur, the guardian (or family member) can be notified, enabling them to take swift action (e.g., rush to the scene). However, if the crisis has already occurred and the guardian is often far away, connected personnel (such as medical staff) can be notified to enable them to take immediate emergency measures, avoiding delays while waiting for family members.
[0095] Alternatively, for children, the primary risk is getting lost, and the secondary risk is falling. Generally, there is no need to worry about acute illnesses, and ordinary falls often do not cause serious injuries. Only more serious injuries require measures. If it can be found through behavioral data that children often wander around the edge of the target area, or near the entrances and exits, it means that they are very likely to leave the target area, which means that the risk of getting lost is increased. At this time, control measures can be taken for the target area, such as temporarily blocking the entrances and exits. Or related personnel, such as community workers, can be notified so that they can advise and stop the child at the first time. If it is found that the child has left the target area, the police can be notified (automatically alarm) to prevent the child from getting lost. At the same time, family members can also be notified so that they can effectively manage the child as soon as possible.
[0096] As can be seen, the development and implementation of the first emergency response plan depends on the specific circumstances. Targeted measures can be taken in different situations to select the most advantageous option immediately, rather than blindly waiting for the family to respond. This can greatly improve safety.
[0097] like Figure 3 The figure shows a specific embodiment of the security monitoring device for special groups of people described in the present invention. The device is placed in the monitoring system. The device described in this embodiment is used to perform Figures 1-2 The physical device of the method. Its technical solution is essentially consistent with the above embodiment, and the corresponding descriptions in the above embodiment are also applicable to this embodiment. The device in this embodiment includes:
[0098] The environment monitoring module 301 is used to collect the action data of the target person in the target area by using the environment detection component.
[0099] The biometric detection module 302 is configured to collect the biometric information of the target person using a wearable device.
[0100] The prediction module 303 is used to establish a prediction model based on artificial intelligence technology, and use the prediction model to perform real-time prediction based on the action data and the biological information to determine prediction information.
[0101] The emergency processing module 304 is configured to determine a first emergency processing plan according to a preset risk condition when the prediction information meets the risk condition, and execute the first emergency processing plan.
[0102] In addition Figure 3 Based on the embodiment shown, preferably, the present invention further includes:
[0103] Also includes:
[0104] The second emergency module 305 is configured to determine a second emergency treatment plan according to a preset risk condition when the action data meets the risk condition, and execute the second emergency treatment plan.
[0105] The third emergency module 306 is configured to determine a third emergency treatment plan according to a preset risk condition when the biometric information meets the risk condition, and execute the third emergency treatment plan.
[0106] The prediction module 303 includes:
[0107] The risk type unit 331 is used to determine the basic information of the target person and determine the risk type corresponding to the target person according to the basic information;
[0108] The prediction analysis unit 332 is configured to input the action data, the biological information, and the risk type into the prediction model so that the prediction model determines the prediction information.
[0109] Figure 4 : This is a schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. At the hardware level, the electronic device includes a processor, and optionally also includes an internal bus, a network interface, and a memory. Among them, the memory may include internal memory, such as high-speed random access memory (RAM), and may also include non-volatile memory (non-volatile memory), such as at least one disk storage. Of course, the electronic device may also include hardware required for other services.
[0110] The processor, network interface, and memory can be interconnected via an internal bus, which can be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect) bus, or an EISA (Extended Industry Standard Architecture) bus. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 4 Only one bidirectional arrow is used in the diagram, but this does not mean that there is only one bus or one type of bus.
[0111] Memory is used to store execution instructions. Specifically, execution instructions are computer programs that can be executed. Memory can include internal memory and non-volatile memory, and provides execution instructions and data to the processor.
[0112] In one possible implementation, a processor reads corresponding execution instructions from non-volatile memory into internal memory and then executes them. Alternatively, the processor can obtain corresponding execution instructions from other devices to logically form a security monitoring device for special populations. The processor executes the execution instructions stored in the memory to implement the security monitoring method for special populations provided in any embodiment of the present invention.
[0113] The present invention Figure 3 The method for security monitoring execution for special groups provided in the illustrated embodiment can be applied to a processor or implemented by a processor. The processor may be an integrated circuit chip with signal processing capabilities. During implementation, each step of the above method can be completed by an integrated logic circuit of hardware in the processor or instructions in the form of software. The above processor can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components. The various methods, steps and logic block diagrams disclosed in the embodiments of the present invention can be implemented or executed. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor, etc.
[0114] The steps of the method disclosed in conjunction with the embodiments of the present invention can be directly implemented and executed by a hardware decoding processor, or by a combination of hardware and software modules within the decoding processor. The software modules can be located in a storage medium well-known in the art, such as random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, etc. The storage medium is located in the memory, and the processor reads the information in the memory and, in conjunction with its hardware, completes the steps of the method described above.
[0115] The embodiment of the present invention further provides a readable medium, which stores an execution instruction. When the stored execution instruction is executed by the processor of the electronic device, the electronic device can execute the security monitoring method for special groups provided in any embodiment of the present invention, and is specifically used to execute the following Figure 1 or Figure 2 The method shown.
[0116] The electronic device described in each of the aforementioned embodiments may be a computer.
[0117] Those skilled in the art will appreciate that the embodiments of the present invention may be provided as methods or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or a combination of software and hardware.
[0118] The various embodiments of the present invention are described in a progressive manner. Similar portions between the various embodiments can be referred to in conjunction with each other. Each embodiment focuses on the differences between the other embodiments. In particular, the device embodiments are generally similar to the method embodiments, so their description is relatively simple. For relevant portions, refer to the description of the method embodiments.
[0119] It should also be noted that the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, commodity, or apparatus that includes a series of elements includes not only those elements but also other elements not explicitly listed, or includes elements inherent to such process, method, commodity, or apparatus. In the absence of further limitations, an element defined by the phrase "comprises a ..." does not exclude the presence of other identical elements in the process, method, commodity, or apparatus that includes the element.
[0120] The foregoing is merely an embodiment of the present invention and is not intended to limit the present invention. It will be apparent to those skilled in the art that various modifications and variations of the present invention are possible. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention are intended to be included within the scope of the claims of the present invention.
Claims
1. A safety monitoring method for special groups of people, characterized in that: The method is applied to a monitoring system and includes: Use environmental detection components to collect target personnel's movement data within the target area; Using a wearable device to collect biometric information of the target person; establishing a prediction model based on artificial intelligence technology, and utilizing the prediction model to perform real-time prediction based on the action data and the biological information to determine prediction information; When the prediction information meets a preset risk condition, a first emergency response plan is determined according to the risk condition, and the first emergency response plan is executed.
2. The method according to claim 1, characterized in that Also includes: When the action data meets a preset risk condition, a second emergency response plan is determined according to the risk condition, and the second emergency response plan is executed.
3. The method according to claim 1, characterized in that Also includes: When the biological information meets a preset risk condition, a third emergency treatment plan is determined according to the risk condition, and the third emergency treatment plan is executed.
4. The method according to claim 1, characterized in that The establishment of a prediction model based on artificial intelligence technology includes: Determine the original model and collect historical action data and historical biological information; The original model is trained with data based on the historical action data and the historical biological information to determine the prediction model.
5. The method according to claim 4, characterized in that: The using the prediction model and performing real-time prediction based on the action data and the biological information to determine the prediction information includes: Determine the basic information of the target person, and determine the risk type corresponding to the target person based on the basic information; The action data, the biological information, and the risk type are input into the prediction model so that the prediction model determines the prediction information.
6. The method according to claim 5, characterized in that Inputting the action data, the biological information, and the risk type into the prediction model so that the prediction model determines the prediction information includes: determining a risk level of the action data and the biometric information relative to the risk type; The risk level is used as the prediction information.
7. The method according to any one of claims 1 to 6, characterized in that: Executing the first emergency response plan includes: Sending early warning information to the guardian of the target person, sending alarm information to related personnel, and / or taking control measures for the target area.
8. A safety monitoring device for special groups of people, characterized by: The device is placed in a monitoring system, including: An environmental monitoring module is used to collect the target person's movement data in the target area using the environmental detection component; A biometric detection module, configured to collect biometric information of the target person using a wearable device; a prediction module, configured to establish a prediction model based on artificial intelligence technology, and utilize the prediction model to perform real-time prediction based on the action data and the biological information to determine prediction information; The emergency processing module is used to determine a first emergency processing plan according to the risk condition when the prediction information meets the preset risk condition, and execute the first emergency processing plan.
9. A computer-readable storage medium storing a computer program, wherein the computer program is used to execute the security monitoring method for special groups of people according to any one of claims 1 to 7.
10. An electronic device, comprising: processor; a memory for storing instructions executable by the processor; The processor is configured to read the executable instructions from the memory and execute the instructions to implement the safety monitoring method for special groups of people as described in any one of claims 1 to 7.