CN114296073A - Abnormity warning method and system based on millimeter wave radar and electronic device - Google Patents
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Abstract
The application relates to an abnormality warning method, system, electronic device and storage medium based on millimeter wave radar, wherein the method comprises the following steps: acquiring an observation event detected by a millimeter wave radar in a preset monitoring scene; acquiring a reporting event which is acquired by a preset local reporting device and is reported locally by any target; determining the danger level of a monitoring scene according to the characteristic information of the observation event and the reported event and the incidence relation between the observation event and the reported event; and selecting a warning device matched with the danger grade from a plurality of preset warning devices for warning treatment according to the danger grade of the monitored scene. Through the application, the problem of low accuracy rate of warning the abnormal event in the related technology is solved, and the technical effect of improving the accuracy rate of warning the abnormal event is realized.
Description
Technical Field
The present disclosure relates to the field of target detection technologies, and in particular, to an abnormality warning method and system based on a millimeter wave radar, an electronic device, and a storage medium.
Background
With the continuous development and application of smart cities, smart homes and smart buildings, how to ensure the safety of users is very important. Old people or patients are easy to fall down, have sudden diseases and other accidents due to various reasons, lose the help seeking ability after the accidents happen, and have serious consequences if the accidents are not found and treated in time.
Nursing institutions (nursing homes, hospitals and the like) periodically make rounds through nursing staff, nurses and the like to check abnormal conditions, however, the number and the energy of nursing staff are limited, and how to find the abnormal conditions at the first time under the condition of reducing the input of staff is a difficult problem to be faced by the nursing institutions, which is to provide help for old people or patients accurately and efficiently. On the other hand, the problem of home-based care for the aged is more difficult, the professional staff cannot make a ward visit regularly, and the professional staff can only depend on the care of relatives and friends, in recent years, the solitary old people often fall down or no people can rescue after sudden cardiovascular diseases, and even the solitary old people still cannot find the problem after a long time. Therefore, how to accurately and efficiently realize the abnormal warning of the indoor target is a major problem to be solved urgently.
Currently, there are many methods available in the related art for warning an indoor target of an abnormality, for example: a user can carry one-button type warning equipment, and when an accident occurs, a wire is pulled manually or a warning button is pressed down to trigger an acousto-optic signal of the warning equipment to warn; or by arranging the radar early warning all-in-one machine, acoustic and optical signals are triggered to warn when abnormal events occur in a radar monitoring area, and the like. However, in such technical solutions, the one-key warning device requires that the user has certain behavior ability, and cannot warn the user who has fallen down or loses the behavior ability due to other situations, and the radar early warning all-in-one machine has the problems of high false alarm rate, high missing report rate and the like in a home environment with a complex environment.
At present, no effective solution is provided for the problem of low accuracy of warning abnormal events in the related technology.
Disclosure of Invention
The embodiment of the application provides an abnormity warning method, a system, an electronic device and a storage medium based on a millimeter wave radar, so as to at least solve the problem of low accuracy rate of warning an abnormal event in the related technology.
In a first aspect, an embodiment of the present application provides an abnormality warning method based on a millimeter wave radar, where the method includes: acquiring an observation event detected by a millimeter wave radar in a preset monitoring scene; acquiring a reporting event which is acquired by a preset local reporting device and is reported locally by any target; determining the danger level of the monitoring scene according to the characteristic information of the observation event and the reported event and the incidence relation between the observation event and the reported event; and selecting the warning device matched with the danger grade from a plurality of preset warning devices to perform warning processing according to the danger grade of the monitoring scene.
In some embodiments, determining the risk level of the monitoring scenario according to the feature information of the observation event and the reported event and the association relationship between the observation event and the reported event includes: determining whether abnormal behaviors exist in the monitoring scene or not according to the characteristic information of the observation event, wherein the abnormal behaviors comprise target falling behaviors and target breathing abnormal behaviors; determining the danger level of the monitoring scene according to the characteristic information of the observation event under the condition that abnormal behaviors exist in the monitoring scene; determining the danger level of the monitoring scene according to the characteristic information of the reported events under the condition that the number of the reported events is greater than zero; and under the condition that abnormal behaviors exist in the monitoring scene and the number of the reported events is larger than zero, determining the danger level of the monitoring scene according to the characteristic information of the observation event and the reported events and the incidence relation between the observation event and the reported events.
In some embodiments, determining the risk level of the monitoring scenario according to the characteristic information of the observation event includes: determining the number of normal targets except for abnormal targets in the monitoring scene according to the characteristic information of the observation event, wherein the abnormal targets correspond to the abnormal behaviors; determining whether the abnormal behavior disappears or not according to the characteristic information of the observation event; and determining the danger level of the monitoring scene according to the number of the normal targets in the monitoring scene and the existence state of the abnormal behavior.
In some embodiments, determining the risk level of the monitoring scenario according to the characteristic information of the reported event includes: determining the occurrence frequency of the reporting event in a preset first time period according to the characteristic information of the reporting event; and determining the danger level of the monitoring scene according to the occurrence frequency of the reporting event in the first time period.
In some embodiments, determining the risk level of the monitoring scenario according to the feature information of the observation event and the reported event and the association relationship between the observation event and the reported event includes: determining the number of normal targets except for abnormal targets in the monitoring scene according to the characteristic information of the observation event, wherein the abnormal targets correspond to the abnormal behaviors; determining whether the abnormal behavior disappears or not according to the characteristic information of the observation event; determining the occurrence frequency of the reporting event in a preset first time period according to the characteristic information of the reporting event; determining occurrence time intervals of the abnormal behaviors and the reported events in a preset second time period according to the characteristic information of the observation events and the reported events; determining that an association relationship exists between the abnormal behavior and the reporting event under the condition that whether the occurrence time interval of the abnormal behavior and the reporting event in the second time period is smaller than a preset fifth threshold value or not; and determining the danger level of the monitoring scene according to the number of the normal targets in the monitoring scene, the existence state of the abnormal behavior, the occurrence frequency of the reporting event in the first time period and the incidence relation between the abnormal behavior and the reporting event.
In some of these embodiments, the hazard levels include a low hazard level, a medium hazard level, and a high hazard level; determining the risk level of the monitoring scene according to the number of the normal targets in the monitoring scene, the existence state of the abnormal behavior, the occurrence frequency of the reporting event in the first time period and the incidence relation between the abnormal behavior and the reporting event, wherein the determining the risk level of the monitoring scene comprises: determining a risk degree score of the monitoring scene according to the number of the normal targets in the monitoring scene, the existence state of the abnormal behavior, the occurrence frequency of the reporting event in the first time period and the incidence relation between the abnormal behavior and the reporting event; when the risk degree score of the monitoring scene is smaller than or equal to a preset first threshold value, determining that the monitoring scene is in a low risk level; or when the risk degree score of the monitoring scene is larger than the first threshold and is smaller than or equal to a preset second threshold, determining that the monitoring scene is a medium risk level; or when the risk degree score of the monitoring scene is larger than the second threshold value, determining that the monitoring scene is in a high risk level.
In some embodiments, determining the risk score of the monitoring scenario according to the number of the normal targets in the monitoring scenario, the existence status of the abnormal behavior, the occurrence frequency of the reporting event in the first time period, and the association relationship between the abnormal behavior and the reporting event includes: when abnormal behaviors exist in the monitoring scene and/or the number of reported events is larger than zero, determining the risk score of the monitoring scene as a preset first score; when the number of the normal targets is larger than a preset third threshold value, determining that the risk score is reduced by a preset second score; when the occurrence frequency of the reporting event in the first time period is greater than a preset fourth threshold, determining that the risk score is increased by a preset third score; when the abnormal behavior and the reported event have an incidence relation, determining the risk score as a preset fourth score; and when the abnormal behavior disappears, determining that the risk score is reduced by a preset fifth value.
In a second aspect, an embodiment of the present application provides an anomaly warning system based on a millimeter wave radar, where the system includes: the system comprises an event observation device, a local reporting device and a warning device; the event observation device comprises a millimeter wave radar and a processor, the processor is in communication connection with the millimeter wave radar, the local reporting device and the warning device, and the millimeter wave radar is used for detecting observation events occurring in a preset monitoring scene; the local reporting device is used for collecting a reporting event of any target reported locally and manually; the processor is configured to execute the millimeter wave radar-based anomaly warning method according to the first aspect; the warning device comprises a local warning device, a remote warning device and a designated warning device, and the warning device is controlled by the processor to execute corresponding warning actions.
In a third aspect, an embodiment of the present application further provides an electronic apparatus, which includes a memory and a processor, where the memory stores a computer program, and the processor is configured to execute the computer program to perform the abnormality warning method based on millimeter wave radar according to the first aspect.
In a fourth aspect, an embodiment of the present application further provides a storage medium, where a computer program is stored in the storage medium, where the computer program, when executed by a processor, implements the abnormality warning method based on the millimeter wave radar according to the first aspect.
Compared with the related art, the anomaly warning method, the anomaly warning system, the electronic device and the storage medium based on the millimeter wave radar provided by the embodiment of the application detect the obtained observation event in the preset monitoring scene by the millimeter wave radar; acquiring a reporting event which is acquired by a preset local reporting device and is reported locally by any target; determining the danger level of a monitoring scene according to the characteristic information of the observation event and the reported event and the incidence relation between the observation event and the reported event; and selecting a warning device matched with the danger grade from a plurality of preset warning devices for warning treatment according to the danger grade of the monitored scene. The problem of low accuracy rate of warning the abnormal event in the correlation technique is solved, and the technical effect of improving the accuracy rate of warning the abnormal event is achieved.
The details of one or more embodiments of the application are set forth in the accompanying drawings and the description below to provide a more thorough understanding of the application.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the application and together with the description serve to explain the application and not to limit the application. In the drawings:
fig. 1 is a block diagram of an abnormality warning system based on a millimeter wave radar according to an embodiment of the present application;
FIG. 2 is a flow chart of an anomaly warning method based on millimeter wave radar according to an embodiment of the present application;
fig. 3 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application will be described and illustrated below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application. All other embodiments obtained by a person of ordinary skill in the art based on the embodiments provided in the present application without any inventive step are within the scope of protection of the present application. Moreover, it should be appreciated that in the development of any such actual implementation, as in any engineering or design project, numerous implementation-specific decisions must be made to achieve the developers' specific goals, such as compliance with system-related and business-related constraints, which may vary from one implementation to another.
Reference in the specification to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the specification. The appearances of the phrase in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. Those of ordinary skill in the art will explicitly and implicitly appreciate that the embodiments described herein may be combined with other embodiments without conflict.
Unless defined otherwise, technical or scientific terms referred to herein shall have the ordinary meaning as understood by those of ordinary skill in the art to which this application belongs. Reference to "a," "an," "the," and similar words throughout this application are not to be construed as limiting in number, and may refer to the singular or the plural. The present application is directed to the use of the terms "including," "comprising," "having," and any variations thereof, which are intended to cover non-exclusive inclusions; for example, a process, method, system, article, or apparatus that comprises a list of steps or modules (elements) is not limited to the listed steps or elements, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus. Reference to "connected," "coupled," and the like in this application is not intended to be limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. Reference herein to "a plurality" means greater than or equal to two. "and/or" describes an association relationship of associated objects, meaning that three relationships may exist, for example, "A and/or B" may mean: a exists alone, A and B exist simultaneously, and B exists alone. Reference herein to the terms "first," "second," "third," and the like, are merely to distinguish similar objects and do not denote a particular ordering for the objects.
This embodiment provides an abnormality warning system based on millimeter wave radar, and fig. 1 is a block diagram of a structure of an abnormality warning system based on millimeter wave radar according to an embodiment of the present application, and as shown in fig. 1, the system includes: the system comprises an event observation device 10, a local reporting device 11 and a warning device 12; the event observation device 10 comprises a millimeter wave radar 101 and a processor 102, the processor 102 is in communication connection with the millimeter wave radar 101, the local reporting device 11 and the warning device 12, and the millimeter wave radar 101 is used for detecting observation events occurring in a preset monitoring scene; the local reporting device 11 is configured to collect a reporting event that any target is manually reported locally; the processor 102 is used for executing an abnormity warning method based on the millimeter wave radar 101; the warning device 12 comprises a local warning device 121, a remote warning device 122 and a designated warning device 123, and the warning device 12 is controlled by the processor 102 to execute corresponding warning actions.
In this embodiment, the millimeter wave radar 101 is used to monitor various observation events in a monitoring scene in real time, for example: a target entry event, a target exit event, a target fall event, a target body posture height change event, a target breathing abnormality event, a target bed-in event, a target bed-out event, and the like.
In this embodiment, the local reporting device 11 may adopt a key-type reporting device and/or a pull-cord type reporting device, and after any target finds that an abnormal condition occurs, it may select to press a key or pull-cord, and when the key-type reporting device or the pull-cord type reporting device is triggered, a reporting event is generated, and the reporting event may be sent to the processor 102 through the local wireless network module in a message form.
In this embodiment, the local warning device 121 may include an acousto-optic device and a ringing device, the processor 102 may send a control instruction to the local warning device 121 through the local wireless network module, and the local warning device 121 may be controlled by the control instruction to send an acousto-optic signal or a ringing warning; the remote warning device 122 may include application software installed on the mobile terminal, and the processor 102 may send first warning information to the remote warning device 122 through the plmn module, where the first warning information may include corresponding abnormal event information, and the corresponding warning operation is completed by the corresponding application software; the designated warning device 123 may include a communication device corresponding to the designated communication address, and the processor 102 may send a second warning message to the designated warning device 123 through the pstn module, where the second warning message may be a warning record including corresponding abnormal event information, and when the corresponding communication device answers, the warning record is played.
In the above embodiment, the millimeter waves include electromagnetic waves with a millimeter wavelength, the millimeter wave radar 101 may be a radar system that transmits electromagnetic waves with a wavelength greater than 0.1mm and less than 0.2mm and a frequency between 20GHz and 300GHz, and currently, the common millimeter wave radar 101 mainly uses a transmission frequency band such as 24GHz, 60GHz, or 77 GHz.
In the above embodiment, a fall detection algorithm, a breathing detection algorithm, a target detection algorithm, and the like may be built in the processor 102, the event observation device 10 can effectively identify various human body gestures in a single-target scene, and the millimeter wave radar 101 has good detection accuracy and sensitivity, and can effectively eliminate interference of daily actions and static environment interferents on event observation.
In this embodiment, the fall detection algorithm can effectively identify various fall attitudes in a single-target scene, wherein the dependence parameters of the fall detection algorithm on the fall state determination of the target mainly include: the dynamic number of people, namely the falling detection algorithm can detect falling behaviors in a single-target scene; when the height of the target from the ground is smaller than a height threshold value preset by an algorithm, triggering falling judgment; the falling duration is triggered when the target falling duration is larger than a falling duration threshold preset by an algorithm; recovering the standing time, and maintaining falling judgment when the standing recovery duration after the target falls to the ground is less than the standing time threshold preset by the algorithm; and the confidence coefficient triggers fall judgment when the confidence coefficient of the target suspected fall is higher than a confidence coefficient threshold preset by the algorithm.
In this embodiment, the target detection algorithm may detect the number of targets in the monitored scene through the clustering algorithm, and provide an auxiliary judgment for whether to trigger an alarm operation after an abnormal event is sent, for example, when an abnormal event occurs in a single target scene (for example, a single target has a fall event in the single target scene), if it is detected that the number of targets in the monitored scene is greater than 1 person through the target detection algorithm in real time, a local alarm and a remote alarm are not triggered.
In this embodiment, the respiration detection algorithm may be implemented by a frequency modulated continuous wave radar, and may automatically locate a target position and detect respiration information of the target, where the respiration information includes a respiration frequency, a minute-average respiration interval, a minute-average respiration time, and the like, and may warn about abnormal situations such as sudden respiration stop, sudden respiration, and the like, and the respiration detection algorithm employs an optimized kalman filtering algorithm to track and locate the target, filter and smooth phase information of the target, and acquire the respiration information of the target.
In the above embodiment, the fall detection algorithm, the breath detection algorithm, and the target detection algorithm support the algorithm parameter custom configuration, and the user can adjust the parameters by himself or herself as needed to meet the personalized requirements, for example, the default value of the fall duration threshold in the fall detection algorithm is 2 seconds, the adjustable range is 1 second to 10 seconds, the default value of the standing duration threshold is 1 second, the adjustable range is 0.5 second to 5 seconds, the default value of the confidence level threshold is 0.6, and 0.4 or 0.8 can be selected as the confidence level threshold.
In the above embodiment, the processor 102 and the local warning device 121 may be communicatively connected through a 433M communication protocol, the processor 102 sends a control instruction to the local warning device 121, and the local warning device 121 receives the control instruction; correspondingly, the processor 102 and the local reporting device 11 may also be communicatively connected via a 433M communication protocol, and the local reporting device 11 sends a reporting event to the processor 102, and the processor 102 receives the reporting event.
In this embodiment, the processor 102 needs to be paired with the local reporting device 11 first, and then paired with the local warning device 121.
In some embodiments, after the local reporting device 11 reports a reporting event, the processor 102 may issue a control instruction to the local warning device 121 every 5 seconds, and the local warning device 121 may be controlled by the control instruction to perform ring warning, and may issue up to 12 control instructions, that is, a ring warning lasting for 60 seconds at most, where the ring duration and the ring tone may be both controlled by programming.
In the above embodiment, the processor 102 sends the second warning information to the designated warning device 123 through the public telephone network module may be implemented by using an internet of things IVR (Interactive Voice Response, abbreviated as IVR) technology, and sends a Voice call to a designated communication address through an operator network by calling an API (Application Programming Interface, abbreviated as API) of the Voice call, after the call is received, a section of designated audio is played, the user may reply through the key information of the communication device as necessary according to the audio guidance, the Voice platform returns the key information to the Application service system through a message receipt, and the Application service system may take a Response measure through a Response classification policy. The initiating end of the voice call in the scene is jointly initiated by the observation event provided by the event observation device 10 and the exception classification handling rule preset in the system, so that manual intervention is not needed, the system is stable and reliable, and the overall consistency of the system is high.
The embodiment provides an abnormality warning method based on a millimeter wave radar, and fig. 2 is a flowchart of the abnormality warning method based on the millimeter wave radar according to the embodiment of the present application, and as shown in fig. 2, the method includes:
step S201, acquiring an observation event detected by the millimeter wave radar in a preset monitoring scene.
In the present embodiment, the millimeter wave radar is capable of measuring and outputting the following parameters: radial distance, radial velocity, target RCS (Radar Cross Section, RCS for short), signal-to-noise ratio, etc., and for a multi-channel Radar system, a direction angle or a pitch angle of a detection point can also be provided. Based on the measurement results, the monitoring of emergency events such as falling detection, abnormal activities, abnormal respiration and the like of personnel can be realized by applying technologies and algorithm schemes such as space geometric relationship, human body posture characteristic classification, machine learning and the like.
Step S202, a preset reporting event which is reported locally by any target collected by a local reporting device is obtained.
In this embodiment, the manual warning operation realized based on the local reporting device and the automatic warning operation realized based on the millimeter wave radar are combined to form a unified abnormal warning system, so that the automatic warning is taken as a main body, the manual warning is assisted and ensured, and the abnormal warning operation under the monitoring scene with higher automation degree, higher reliability, more convenient operation and better detection accuracy is realized.
In the above embodiment, the power of the local reporting device may be provided by the self-generating device, and the self-generating device is matched with a low-power local area network technology, so that the local area network communication capability of the local reporting device can be ensured indefinitely, the service requirement of emergency warning with high reliability requirement is met, and the phenomena of abnormal missing report and the like caused by the exhaustion of the battery power of the local reporting device are avoided.
Step S203, determining the danger level of the monitoring scene according to the characteristic information of the observation event and the reported event and the incidence relation between the observation event and the reported event.
In this embodiment, the local reporting device includes a key-type reporting device and/or a pull-cord type reporting device, and the reporting event includes a key-type reporting event and a pull-cord type reporting event.
In this embodiment, the processor may determine whether an abnormal behavior exists in the monitoring scene according to the feature information of the observation event according to a preset abnormal classification rule, determine whether an association relationship exists between the abnormal behavior and the reported event according to the occurrence time interval between the abnormal behavior and the reported event, and determine the danger level of the monitoring scene according to the feature information of the observation event and the reported event and the association relationship between the abnormal behavior and the reported event.
And step S204, selecting a warning device matched with the danger level from the preset multiple warning devices for warning processing according to the danger level of the monitoring scene.
In this embodiment, an exception classification handling rule may be further preset in the processor, so as to ensure that the warning information can be correctly issued according to the exception classification handling rule in an emergency state, and an automatic warning processing policy is given, where the automatic warning processing policy may include: the method comprises the steps of warning through local ringing, warning through application software installed on a mobile terminal, warning through linkage community or medical institution, an endowment service organization and emergency information push of a health service unit, and warning through automatic voice calling of an appointed emergency contact person.
Through the steps S201 to S204, an observation event detected by the millimeter wave radar in a preset monitoring scene is obtained; acquiring a reporting event which is acquired by a preset local reporting device and is reported locally by any target; determining the danger level of a monitoring scene according to the characteristic information of the observation event and the reported event and the incidence relation between the observation event and the reported event; and selecting a warning device matched with the danger grade from a plurality of preset warning devices for warning treatment according to the danger grade of the monitored scene. According to the method and the device, manual warning operation realized based on a local reporting device and automatic warning operation realized based on a millimeter wave radar are combined to form a unified abnormal warning system, automatic warning is taken as a main body, manual warning is taken as assistance and guarantee, and abnormal warning operation under a monitoring scene with higher automation degree, higher reliability, more convenience in operation and better detection accuracy is realized; and the danger level of the monitoring scene is determined based on various information, and the corresponding warning scheme is determined based on the danger level of the monitoring scene, so that warning processing can be performed according to the corresponding warning scheme under various conditions, the problem of low warning accuracy of abnormal events in the related technology is solved, and the technical effect of improving the warning accuracy of the abnormal events is realized.
In some embodiments, determining the risk level of the monitoring scene according to the feature information of the observation event and the reported event and the incidence relation between the observation event and the reported event is implemented by the following steps:
step 1, determining whether abnormal behaviors exist in a monitoring scene or not according to characteristic information of an observation event, wherein the abnormal behaviors comprise a target falling behavior and a target breathing abnormal behavior.
And 2, determining the danger level of the monitoring scene according to the characteristic information of the observation event under the condition that abnormal behaviors exist in the monitoring scene.
And 3, determining the danger level of the monitoring scene according to the characteristic information of the reported events under the condition that the number of the reported events is greater than zero.
And 4, under the condition that abnormal behaviors exist in the monitoring scene and the number of the reported events is larger than zero, determining the danger level of the monitoring scene according to the characteristic information of the observation event and the reported events and the incidence relation between the observation event and the reported events.
In this embodiment, a falling detection algorithm, a breathing detection algorithm, a target detection algorithm, and the like may be built in the processor, characteristic analysis is performed on observation events including a target entering event, a target leaving event, a target falling event, a target posture height change event, a target breathing abnormality event, a target bed-in event, and a target bed-out event by the processor, and whether a target falling behavior and a target breathing abnormality behavior occur in a monitoring scene is determined by using the falling detection algorithm, the breathing detection algorithm, the target detection algorithm, and the like built in the processor.
In this embodiment, the danger level of the monitoring scene may be determined according to a preset exception classification rule under three conditions that only the millimeter wave radar detects that an exception behavior exists in the monitoring scene, only the local reporting device collects an event that is greater than or equal to one reporting event, and the millimeter wave radar detects that an exception behavior exists in the monitoring scene and the local reporting device collects an event that is greater than or equal to one reporting event.
For example, when the millimeter wave radar detects that there is an abnormal behavior in the monitoring scene but the local reporting device does not send a reporting event, determining the risk level of the monitoring scene according to the feature information of the observation event may include the following steps:
step 1, determining the number of normal targets except for abnormal targets in a monitoring scene according to the characteristic information of the observation event, wherein the abnormal targets correspond to abnormal behaviors.
And 2, determining whether the abnormal behavior disappears or not according to the characteristic information of the observation event.
And 3, determining the danger level of the monitoring scene according to the number of the normal targets in the monitoring scene and the existence state of the abnormal behaviors.
When the millimeter wave radar does not detect that abnormal behaviors exist in the monitoring scene but the number of reporting events sent by the local reporting device is more than zero, determining the danger level of the monitoring scene according to the characteristic information of the reporting events comprises the following steps:
step 1, determining the occurrence frequency of the reporting event in a preset first time period according to the characteristic information of the reporting event.
And step 2, determining the danger level of the monitoring scene according to the occurrence frequency of the reported event in the first time period.
Similarly, when the millimeter wave radar detects that an abnormal behavior exists in the monitoring scene but the number of reporting events sent by the local reporting device is greater than zero, the millimeter wave radar needs to combine the feature information of the observation event and the reporting event, judge whether an association relationship exists between the abnormal behavior existing in the observation event and the reporting event, and determine the danger level of the monitoring scene according to the association relationship between the abnormal behavior and the reporting event, which specifically includes the following steps:
step 1, determining the number of normal targets except for abnormal targets in a monitoring scene according to the characteristic information of the observation event, wherein the abnormal targets correspond to abnormal behaviors.
And 2, determining whether the abnormal behavior disappears or not according to the characteristic information of the observation event.
And step 3, determining the occurrence frequency of the reporting event in a preset first time period according to the characteristic information of the reporting event.
And 4, determining the occurrence time interval of the abnormal behavior and the reported event in a preset second time period according to the characteristic information of the observation event and the reported event.
And 5, determining that an association relation exists between the abnormal behavior and the reporting event under the condition that whether the occurrence time interval of the abnormal behavior and the reporting event in the second time period is smaller than a preset fifth threshold value.
And 6, determining the danger level of the monitoring scene according to the number of normal targets in the monitoring scene, the existence state of the abnormal behavior, the occurrence frequency of the reporting event in the first time period and the incidence relation between the abnormal behavior and the reporting event.
In this embodiment, when the occurrence time interval of the abnormal behavior and the reporting event in the second time period is smaller than a preset fifth threshold, for example, the abnormal behavior and the reporting event both occur at least once in 3 minutes, it is determined that the abnormal behavior and the reporting event have an association relationship, and the monitoring scenario is a high-risk level at this time, where the second time period and the fifth threshold may be configured according to actual needs of users, so as to meet actual needs in different application scenarios.
In the above embodiment, the manual warning operation realized based on the local reporting device and the automatic warning operation realized based on the millimeter wave radar are combined to form a unified abnormal warning system, so that the automatic warning is taken as a main body, the manual warning is assisted and ensured, and the abnormal warning operation under the monitoring scene with higher automation degree, higher reliability, more convenient operation and better detection accuracy is realized.
In the present embodiment, the risk levels include a low risk level, a medium risk level, and a high risk level; determining the risk level of the monitoring scene according to the number of normal targets in the monitoring scene, the existence state of the abnormal behavior, the occurrence frequency of the reporting event in the first time period and the incidence relation between the abnormal behavior and the reporting event comprises the following steps: determining a risk degree score of a monitoring scene according to the number of normal targets in the monitoring scene, the existence state of abnormal behaviors, the occurrence frequency of a reporting event in a first time period and the incidence relation between the abnormal behaviors and the reporting event; when the risk degree score of the monitoring scene is smaller than or equal to a preset first threshold value, determining that the monitoring scene is in a low risk level; or when the risk degree score of the monitoring scene is larger than a first threshold value and is smaller than or equal to a preset second threshold value, determining that the monitoring scene is a medium risk level; or when the risk degree score of the monitored scene is larger than a second threshold value, determining that the monitored scene is in a high risk level.
In this embodiment, the lower limit of the risk score may be set to 0 point, and the upper limit may be set to 100 points, where the first threshold may be set to 50 points, and the second threshold may be set to 70 points, that is, when the risk score of the monitored scene is between 0 and 50 points, the monitored scene is determined to be a low risk level; when the risk degree score of the monitoring scene is between 51 and 70 points, determining that the monitoring scene is a medium risk level; and when the risk degree score of the monitoring scene is between 71 and 100, determining that the monitoring scene is in a high risk level.
In this embodiment, determining the risk score of the monitoring scene according to the number of normal targets in the monitoring scene, the existence state of the abnormal behavior, the occurrence frequency of the reporting event in the first time period, and the incidence relation between the abnormal behavior and the reporting event is implemented by the following steps:
step 1, when abnormal behaviors exist in a monitoring scene and/or the number of reported events is larger than zero, determining the risk score of the monitoring scene as a preset first score.
And 2, when the number of the normal targets is larger than a preset third threshold value, determining that the risk score is reduced by a preset second score.
And step 3, when the occurrence frequency of the reporting event in the first time period is greater than a preset fourth threshold value, determining that the risk score is increased by a preset third score value.
And 4, when the abnormal behavior and the reported event have an incidence relation, determining the risk score as a preset fourth score.
And 5, when the abnormal behavior disappears, determining that the risk score is reduced by a preset fifth value.
In this embodiment, when the millimeter wave radar detects that an abnormal behavior (for example, a target falling behavior, a target breathing abnormal behavior, etc.) occurs in a monitoring scene, it may be determined that the risk score of the monitoring scene is a preset first score, where the first score is 60 minutes, and similarly, when the local reporting device is triggered once (for example, when the key-press type reporting device is pressed once or the pull-wire type reporting device is pulled once), it may be determined that the risk score of the monitoring scene is 60 minutes.
In the above embodiment, a target detection algorithm is preset in the processor, so that the number of normal targets in the monitoring scene except for the abnormal target may be detected by the millimeter wave radar, and if after the abnormal behavior and/or the reporting event occurs, other normal targets exist in the monitoring scene except for the abnormal target (the fallen target or the breathing abnormal target), that is, the number of normal targets is greater than or equal to 1, the risk score is reduced by a preset second score, where the second score may be 20 minutes.
In the foregoing embodiment, for example, the local reporting device is a key-type reporting device, and if the occurrence frequency of the reporting event in the first time period is greater than a preset fourth threshold, that is, when a second or more key operations occur after a key is pressed, it is determined that the risk score is increased by a preset third score, and each key operation in the first time period may increase the risk score by 10 minutes, where the first time period may be 3 minutes.
In the above embodiment, when the occurrence time interval of the abnormal behavior and the reporting event in the second time period is smaller than a preset fifth threshold, for example, the abnormal behavior and the reporting event both occur at least once in 3 minutes, it is determined that the abnormal behavior and the reporting event have an association relationship, and at the same time, it is determined that the monitored scenario is a high-risk level, and the risk score of the monitored scenario is a preset fourth score, where the fourth score may be 100.
In the above embodiment, a fall detection algorithm and a breath detection algorithm are also preset in the processor, and when the millimeter wave radar detects that the abnormal target recovers from a fall state to a standing state (for example, when the abnormal target rises to a default value of 1.4 meters or more) and reaches a certain time length, or detects that the breathing condition of the abnormal target recovers from an abnormality to a normal state and reaches a certain time length, it may be determined that the abnormal behavior disappears; or the reporting event of the local reporting device is manually reset, the reporting event is determined to disappear at this time, and when the abnormal behavior and/or the reporting event disappears, the risk score can be determined to be reduced by a preset fifth value, wherein the fifth value is 50 points.
In the above embodiment, the first score, the second score, the third score, the fourth score, the fifth score, and the corresponding relationship between the risk score and the risk level may all be configured by user-defined according to the user needs, so as to meet the personalized needs of the user.
In some of these embodiments, the alerting device comprises a local alerting device, a remote alerting device, and a designated alerting device; according to the danger level of a monitored scene, selecting a warning device matched with the danger level from a plurality of preset warning devices to perform warning processing, and realizing the following steps:
and step 1, when the monitored scene is at a low danger level, performing local warning or not triggering warning by using a local warning device.
And 2, when the monitored scene is in the medium-risk level, performing local warning by using a local warning device, and sending first warning information to a preset mobile terminal by using a remote warning device.
And 3, when the monitoring scene is in a high-risk level, performing local warning by using a local warning device, sending first warning information to a preset mobile terminal by using a remote warning device, and sending second warning information to a preset communication address by using a specified warning device.
In this embodiment, the method further includes: when the danger level of the monitoring scene is increased from a first danger level to a second danger level, the warning device matched with the second danger level is selected to perform warning processing in an overlapping mode on the basis that the warning device matched with the first danger level is selected to perform warning processing, and determines that the first warning information and/or the second warning information includes danger level elevation information of the monitored scene, for example, when the danger level of the monitored scene is elevated from a middle danger level to a high danger level, on the basis of utilizing the local warning device to make local warning and utilizing the remote warning device to send first warning information to the preset mobile terminal, the appointed warning device can be utilized to send second warning information to the preset communication address, meanwhile, the first warning information and/or the second warning information comprise danger level rise information of the monitoring scene.
In this embodiment, sub-rules may be set for the local warning device, the remote warning device and the designated warning device, for example, when the monitored scene is a low risk level, the local warning device is used to perform local warning or no warning is triggered; when the monitored scene is in a medium danger level, performing local warning by using a local warning device, and sending first warning information to a preset mobile terminal by using a remote warning device; when the monitoring scene is in a high-risk level, a local warning device is used for carrying out local warning, a remote warning device is used for sending first warning information to a preset mobile terminal, and an appointed warning device is used for sending second warning information to a preset communication address.
In other embodiments, when the monitored scene is at a medium-risk level, the designated warning device may also be used to send second warning information to the preset communication address, which is different from the situation that the monitored scene is at a high-risk level in that when the monitored scene is at a medium-risk level, message bodies of the first warning information and the second warning information are medium-risk warnings; when the monitoring scene is high-risk level, the message subject of the first warning information and the second warning information is high-risk warning, the warning accuracy can be improved when emergency is faced, and the danger level information of the monitoring scene can be provided for off-site alarm receiving personnel, so that the alarm receiving personnel can better handle the abnormal events.
The present embodiment further provides an electronic apparatus, and fig. 3 is a schematic diagram of a hardware structure of the electronic apparatus according to an embodiment of the present application, and as shown in fig. 3, the electronic apparatus includes a memory 304 and a processor 302, where the memory 304 stores a computer program, and the processor 302 is configured to execute the computer program to perform the steps in any of the method embodiments.
Specifically, the processor 302 may include a Central Processing Unit (CPU), or A Specific Integrated Circuit (ASIC), or may be configured to implement one or more Integrated circuits of the embodiments of the present Application.
The processor 302 may read and execute the computer program instructions stored in the memory 304 to implement any of the above-described embodiments of the millimeter wave radar-based anomaly alert method.
Optionally, the electronic apparatus may further include a transmission device 306 and an input/output device 308, where the transmission device 306 is connected to the processor 302, and the input/output device 308 is connected to the processor 302.
Alternatively, in this embodiment, the processor 302 may be configured to execute the following steps by a computer program:
and S1, acquiring an observation event detected by the millimeter wave radar in a preset monitoring scene.
And S2, acquiring a reporting event which is acquired by a preset local reporting device and reported locally by any target.
And S3, determining the danger level of the monitoring scene according to the characteristic information of the observation event and the reported event and the incidence relation between the observation event and the reported event.
And S4, selecting the warning device matched with the danger level from the preset multiple warning devices to perform warning processing according to the danger level of the monitoring scene.
It should be noted that, for specific examples in this embodiment, reference may be made to examples described in the foregoing embodiments and optional implementations, and details of this embodiment are not described herein again.
In addition, in combination with the anomaly warning method based on the millimeter wave radar in the above embodiment, the embodiment of the present application may provide a storage medium to implement. The storage medium having stored thereon a computer program; when executed by a processor, the computer program implements any one of the above-described millimeter wave radar-based abnormality warning methods.
It should be understood by those skilled in the art that various features of the above embodiments can be combined arbitrarily, and for the sake of brevity, all possible combinations of the features in the above embodiments are not described, but should be considered as within the scope of the present disclosure as long as there is no contradiction between the combinations of the features.
The above examples are merely illustrative of several embodiments of the present application, and the description is more specific and detailed, but not to be construed as limiting the scope of the present application. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the concept of the present application, which falls within the scope of protection of the present application. Therefore, the protection scope of the present application shall be subject to the appended claims.
Claims (10)
1. An abnormality warning method based on a millimeter wave radar, characterized by comprising:
acquiring an observation event detected by a millimeter wave radar in a preset monitoring scene;
acquiring a reporting event which is acquired by a preset local reporting device and is reported locally by any target;
determining the danger level of the monitoring scene according to the characteristic information of the observation event and the reported event and the incidence relation between the observation event and the reported event;
and selecting the warning device matched with the danger grade from a plurality of preset warning devices to perform warning processing according to the danger grade of the monitoring scene.
2. The millimeter wave radar-based anomaly warning method according to claim 1, wherein determining the risk level of the monitoring scene according to the feature information of the observation event and the reported event and the association relationship between the observation event and the reported event comprises:
determining whether abnormal behaviors exist in the monitoring scene or not according to the characteristic information of the observation event, wherein the abnormal behaviors comprise target falling behaviors and target breathing abnormal behaviors;
determining the danger level of the monitoring scene according to the characteristic information of the observation event under the condition that abnormal behaviors exist in the monitoring scene;
determining the danger level of the monitoring scene according to the characteristic information of the reported events under the condition that the number of the reported events is greater than zero;
and under the condition that abnormal behaviors exist in the monitoring scene and the number of the reported events is larger than zero, determining the danger level of the monitoring scene according to the characteristic information of the observation event and the reported events and the incidence relation between the observation event and the reported events.
3. The millimeter wave radar-based anomaly warning method according to claim 2, wherein determining the danger level of the monitoring scene according to the characteristic information of the observation event comprises:
determining the number of normal targets except for abnormal targets in the monitoring scene according to the characteristic information of the observation event, wherein the abnormal targets correspond to the abnormal behaviors;
determining whether the abnormal behavior disappears or not according to the characteristic information of the observation event;
and determining the danger level of the monitoring scene according to the number of the normal targets in the monitoring scene and the existence state of the abnormal behavior.
4. The millimeter wave radar-based anomaly warning method according to claim 2, wherein determining the risk level of the monitoring scene according to the characteristic information of the reported event comprises:
determining the occurrence frequency of the reporting event in a preset first time period according to the characteristic information of the reporting event;
and determining the danger level of the monitoring scene according to the occurrence frequency of the reporting event in the first time period.
5. The millimeter wave radar-based anomaly warning method according to claim 2, wherein determining the risk level of the monitoring scene according to the feature information of the observation event and the reported event and the association relationship between the observation event and the reported event comprises:
determining the number of normal targets except for abnormal targets in the monitoring scene according to the characteristic information of the observation event, wherein the abnormal targets correspond to the abnormal behaviors;
determining whether the abnormal behavior disappears or not according to the characteristic information of the observation event;
determining the occurrence frequency of the reporting event in a preset first time period according to the characteristic information of the reporting event;
determining occurrence time intervals of the abnormal behaviors and the reported events in a preset second time period according to the characteristic information of the observation events and the reported events;
determining that an association relationship exists between the abnormal behavior and the reporting event under the condition that whether the occurrence time interval of the abnormal behavior and the reporting event in the second time period is smaller than a preset fifth threshold value or not;
and determining the danger level of the monitoring scene according to the number of the normal targets in the monitoring scene, the existence state of the abnormal behavior, the occurrence frequency of the reporting event in the first time period and the incidence relation between the abnormal behavior and the reporting event.
6. The millimeter wave radar-based abnormality warning method according to claim 5, wherein the danger levels include a low danger level, a medium danger level, and a high danger level; determining the risk level of the monitoring scene according to the number of the normal targets in the monitoring scene, the existence state of the abnormal behavior, the occurrence frequency of the reporting event in the first time period and the incidence relation between the abnormal behavior and the reporting event, wherein the determining the risk level of the monitoring scene comprises:
determining a risk degree score of the monitoring scene according to the number of the normal targets in the monitoring scene, the existence state of the abnormal behavior, the occurrence frequency of the reporting event in the first time period and the incidence relation between the abnormal behavior and the reporting event;
when the risk degree score of the monitoring scene is smaller than or equal to a preset first threshold value, determining that the monitoring scene is in a low risk level;
or when the risk degree score of the monitoring scene is larger than the first threshold and is smaller than or equal to a preset second threshold, determining that the monitoring scene is a medium risk level;
or when the risk degree score of the monitoring scene is larger than the second threshold value, determining that the monitoring scene is in a high risk level.
7. The millimeter wave radar-based anomaly warning method according to claim 6, wherein according to the number of the normal targets in the monitoring scene, the existence state of the abnormal behavior, the occurrence frequency of the reporting event in the first time period and the correlation between the abnormal behavior and the reporting event, determining the risk score of the monitoring scene comprises:
when abnormal behaviors exist in the monitoring scene and/or the number of reported events is larger than zero, determining the risk score of the monitoring scene as a preset first score;
when the number of the normal targets is larger than a preset third threshold value, determining that the risk score is reduced by a preset second score;
when the occurrence frequency of the reporting event in the first time period is greater than a preset fourth threshold, determining that the risk score is increased by a preset third score;
when the abnormal behavior and the reported event have an incidence relation, determining the risk score as a preset fourth score;
and when the abnormal behavior disappears, determining that the risk score is reduced by a preset fifth value.
8. An abnormality warning system based on a millimeter wave radar, characterized in that the system comprises: the system comprises an event observation device, a local reporting device and a warning device; wherein,
the event observation device comprises a millimeter wave radar and a processor, and the processor is in communication connection with the millimeter wave radar, the local reporting device and the warning device;
the millimeter wave radar is used for detecting observation events occurring in a preset monitoring scene;
the local reporting device is used for collecting a reporting event of any target reported locally and manually;
the processor is used for executing the millimeter wave radar-based abnormality warning method according to any one of claims 1 to 7;
the warning device comprises a local warning device, a remote warning device and a designated warning device, and the warning device is controlled by the processor to execute corresponding warning actions.
9. An electronic device comprising a memory and a processor, wherein the memory stores a computer program, and the processor is configured to execute the computer program to perform the millimeter wave radar-based abnormality warning method according to any one of claims 1 to 7.
10. A storage medium having a computer program stored therein, wherein the computer program when executed by a processor implements the millimeter wave radar-based abnormality warning method according to any one of claims 1 to 7.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN115174767A (en) * | 2022-05-27 | 2022-10-11 | 青岛海尔科技有限公司 | Video recording method, edge device, monitoring system and storage medium |
| WO2024021669A1 (en) * | 2022-07-26 | 2024-02-01 | 青岛海尔空调器有限总公司 | Method and apparatus for air conditioner control, air conditioner, and storage medium |
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN115174767A (en) * | 2022-05-27 | 2022-10-11 | 青岛海尔科技有限公司 | Video recording method, edge device, monitoring system and storage medium |
| CN115174767B (en) * | 2022-05-27 | 2024-03-26 | 青岛海尔科技有限公司 | Video recording method, edge equipment, monitoring system and storage medium |
| WO2024021669A1 (en) * | 2022-07-26 | 2024-02-01 | 青岛海尔空调器有限总公司 | Method and apparatus for air conditioner control, air conditioner, and storage medium |
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