CN112635077A - Close contact judgment method and device, electronic equipment and medium - Google Patents
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Abstract
The embodiment of the invention discloses a close contact judgment method, a close contact judgment device, electronic equipment and a medium. The close contact judgment method includes: determining other acquisition values of other sensors in other devices located near the local device based on the close-range contact technology; taking the other acquisition values, the local acquisition value and the signal strength of the close-range contact technology as characteristic data in a training sample; wherein the training samples are associated with the local device and the other devices; determining label data in the training sample; the training sample is used for training the close contact judgment model, so that the accuracy of the close contact judgment model for judging whether the close contact between people is achieved is improved.
Description
Technical Field
The invention relates to the technical field of artificial intelligence, in particular to a close contact judgment method, a close contact judgment device, electronic equipment and a medium.
Background
Intimate contact refers to the distance between one person and the other person's relative physical location over a time frame that is less than the distance in normal contact. There is a higher probability of close contact between people co-living, studying or working than strangers. The information of a person's close contact can well reflect the person's relationship network. The method effectively obtains the close contact information of one person, and has important significance for epidemic prevention and traceability of infectious diseases and interpersonal relationship network research.
At present, most of methods for judging whether people are in close contact with each other are based on a single Bluetooth technology, and due to the defect that signals of the Bluetooth technology are unstable, the methods are often inaccurate in judging whether people are in close contact with each other.
Disclosure of Invention
The embodiment of the invention provides a close contact judgment method, a close contact judgment device, electronic equipment and a medium, and aims to improve the accuracy of judging whether people are in close contact or not.
In a first aspect, an embodiment of the present invention provides an intimate contact determination method, including:
determining other acquisition values of other sensors in other devices located near the local device based on the close-range contact technology;
determining a local acquisition value of a local sensor in local equipment;
taking the other acquisition values, the local acquisition value and the signal strength of the close-range contact technology as characteristic data in a training sample; wherein the training samples are associated with the local device and the other devices;
determining label data in the training sample; wherein the training samples are used for training an intimate contact judgment model.
In a second aspect, an embodiment of the present invention further provides an intimate contact determination apparatus, where the apparatus includes:
the other acquisition value determining module is used for determining other acquisition values of other sensors in other equipment nearby the local equipment based on the close-range contact technology;
the local acquisition value determining module is used for determining a local acquisition value of a local sensor in local equipment;
the characteristic data determining module is used for taking the other acquisition values, the local acquisition value and the signal intensity of the close-range contact technology as characteristic data in a training sample; wherein the training samples are associated with the local device and the other devices;
the sample label determining module is used for determining label data in the training sample; wherein the training samples are used for training an intimate contact judgment model.
In a third aspect, an embodiment of the present invention further provides an electronic device, where the electronic device includes:
one or more processors;
a memory for storing one or more programs;
when executed by the one or more processors, cause the one or more processors to implement a method of affinity determination according to any one of the embodiments of the present invention.
In a fourth aspect, the present invention further provides a non-transitory computer-readable storage medium storing computer instructions for causing a computer to execute a method for close contact determination according to any one of the embodiments.
The technical scheme provided by the embodiment of the invention determines other acquisition values of other sensors in other equipment near the local equipment based on the close-range contact technology; determining a local acquisition value of a local sensor in local equipment; taking the other acquisition values, the local acquisition value and the signal strength of the close-range contact technology as characteristic data in a training sample; used for training the close contact judgment model. According to the embodiment of the invention, the acquisition value of the sensor in the equipment is comprehensively considered, and richer characteristic information is extracted from the acquisition value of the sensor, so that the accuracy and the reliability of the relative position information acquired based on the close contact technology are improved, and the accuracy of close contact judgment is further improved.
Drawings
FIG. 1 is a flow chart of a method for determining close contact according to a first embodiment of the present invention;
FIG. 2 is a flowchart of a method for determining close contact according to a second embodiment of the present invention;
fig. 3 is a schematic structural diagram of an intimate contact determination apparatus according to a third embodiment of the present invention;
fig. 4 is a schematic structural diagram of an electronic device to which a close contact determination method is applied in an embodiment of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the invention and are not limiting of the invention. It should be further noted that, for the convenience of description, only some of the structures related to the present invention are shown in the drawings, not all of the structures.
Example one
Fig. 1 is a flowchart of a close contact determination method in an embodiment of the present invention, which is applicable to determining whether there is close contact between people. The method may be performed by an intimate contact determination apparatus, which may be implemented in software and/or hardware and may be configured in an electronic device. As shown in fig. 1, the method specifically includes:
and S110, determining other acquisition values of other sensors in other devices near the local device based on the close-range contact technology.
The short-range contact technology is short-range wireless communication technology in which both communication transceivers transmit information via radio waves and the transmission distance is limited to a short range. The proximity technology may be bluetooth technology and/or WiFi (Wireless Fidelity) technology, etc. In the case where the local device has simultaneous segment access to the same WiFi as other devices, other devices located near the local device may be determined based on WiFi technology. In addition, bluetooth is almost a standard for mobile communication devices such as cell phones, and almost every cell phone is equipped with a bluetooth module. Only the bluetooth in the device needs to be turned on, and other devices located near the local device can also be determined based on the bluetooth technology in the case where the communication conditions of the bluetooth technology are satisfied.
The proximity of the local device refers to a communicable range of the near field technology of the local device, and the other devices refer to devices located in an effective communication range of the near field technology with the local device as a center, that is, the other devices are located in a communicable range of the near field technology of the local device. Alternatively, the local device and the other devices may be portable mobile communication devices such as cell phones and/or tablet computers.
In the case where the proximity technology is the bluetooth technology, when the bluetooth of the local device scans the other device, the local device also determines the acquisition values of the other sensors disposed in the other device. The other sensor refers to a sensor configured in another device. Wherein the number of other devices scanned by the local device is at least one. Optionally, the determined other acquisition values of the other sensors in the other devices are stored in the cloud server.
In an optional embodiment, identification information of other devices near the local device is acquired based on a close-range contact technology; and acquiring other acquisition values of other sensors in other equipment from a server according to the identification information.
The identification information refers to unique information that can identify the device, and optionally, the identification information is a Media Access Control (MAC) Address. The server stores the device identification and other acquisition values of other sensors in the device identified by the device identification. Optionally, the acquired identification information of the other devices is stored in a local database or a cloud. According to the embodiment of the invention, the acquisition value of the sensor in the equipment is acquired from the server according to the equipment identifier, and the acquisition process of the acquisition value of the sensor of the equipment is separated from the acquisition process of the equipment identifier, so that the data management efficiency is improved, and the data acquisition operation is simplified.
In an alternative embodiment, the sensor comprises: at least one of a gravity sensor, an acceleration sensor, a gyro sensor, a light sensor, and a distance sensor.
The gravity sensor and the acceleration sensor can measure the acceleration of the equipment caused by gravity, the gyroscope sensor can accurately determine the orientation of the moving equipment, and the moving state of the equipment can be obtained by integrating the acquisition values of the three sensors. The light sensor can acquire the brightness of the environment where the equipment is located. The distance sensor may capture the distance between the user's head and the device. Whether the user uses the equipment or not and the external environment of the equipment can be judged according to the collected values of the light sensor and the distance sensor.
Optionally, the on-state data of the screen of the other device and the ambient noise level data of the other device are acquired while acquiring the acquisition value of the sensor in the other device. Generally, when the screen of the device is opened, the device is indicated to be in a state of being used by a user with a high probability, at the moment, the signal channel and the signal antenna of the close-range contact technology are in an unobstructed state, and the network state of the close-range contact technology is good. Conversely, when the screen of the device is closed, there is a high probability that the device is now unused by the user. Optionally, the device status and the user behavior status may be determined by matching the on-state data of the screen of the other device with other data collected by other sensors of the other device. Illustratively, combining the collected values of the gravity sensor, the acceleration sensor, and the gyro sensor, the movement state of the device can be obtained, and if the screen of the device is closed at this time, it can be inferred that the device is not used and the device is moving. And judging the brightness of the environment where the current equipment is located by combining the acquisition value of the light sensor, and judging whether the current equipment is held by a user or placed in a space such as a backpack or a pocket, so as to presume the states of a signal channel and a signal antenna of the near distance contact technology and the network state of the current near distance contact technology. If the states of the signal channel and the signal antenna of the current close-range contact technology and the network state of the current close-range contact technology are not good, other acquisition values of other sensors in other equipment are acquired, it is indicated that the relative positions of the local equipment and the other equipment are very close, and because the equipment is carried by a user, the possibility that the user using the local equipment is in close contact with the user using the other equipment is very high.
Optionally, the ambient noise level data of other devices may be specifically acquired by a microphone of the device. The ambient noise level data of other devices may not only indicate whether the user using the device is in a noisy environment, but also determine whether the user using the device is chatting with other people by obtaining the device movement state in combination with the gravity sensor, the acceleration sensor, and the gyro sensor, thereby determining the possibility that the user using the device is in close contact with other people.
And S120, determining a local acquisition value of a local sensor in the local equipment.
The local acquisition value refers to a sensor acquisition value configured in the local device.
S130, taking the other acquisition values, the local acquisition value and the signal intensity of the close-range contact technology as feature data in a training sample; wherein the training samples are associated with the local device and the other devices.
The local device and the other devices located in the vicinity of the local device form a device pair, and the acquisition values of the local sensor arranged in the local device, the acquisition values of the other sensors arranged in the other devices and the signal strength of the proximity contact technology together form characteristic data. Illustratively, in the case where the close proximity technology is bluetooth technology, the bluetooth scan of the local device to three other devices located near the local device is other device 1, other device 2, and other device 3, respectively. The local device and the other device 1 may form a first device pair, the local device and the other device 2 may form a second device pair, the local device and the other device 3 may form a third device pair, and the collected values of the sensors in the local device and the other device in each device pair together form 1 piece of feature data in the training sample. The collected values of the local sensor in the local device, the collected values of the sensors in all other devices scanned by the local device at one time and the signal strength of the close-range contact technology together form 1 group of characteristic data.
Illustratively, the proximity technology is bluetooth technology, and the signal strength of the proximity technology is the strength of a bluetooth signal. Except the Bluetooth signal intensity data, other acquisition values and the local acquisition value are also used as the characteristic data of the training sample, so that the problem of low accuracy of close contact judgment caused by only using single Bluetooth signal intensity as the characteristic data can be effectively avoided.
In an alternative embodiment, the current scan time at which the local device is currently scanning to any other device is determined; and taking the time interval between the current scanning time and the last scanning time when the local device last scans the other devices as the characteristic data in the training sample.
When the local device scans any other device nearby the local device based on the close-range contact technology, the identification information of the other device scanned by the local device and the time of scanning the other device are stored in a local database or a cloud terminal. The method comprises the steps of responding to the fact that a local device scans other devices, obtaining identification information of the other devices, obtaining previous scanning time of the local device from a local database or a cloud end to the same other devices according to the identification information, and using a time interval between the previous scanning time and the current time, an acquisition value of a local sensor in the local device, an acquisition value of other sensors in the other devices and signal intensity of a close-range contact technology as feature data of a training sample. The shorter the time interval between the last scan time when the local device last scanned the other device, the more frequent the contact between the local device and the other device is indicated, thereby indicating the greater the likelihood that the user using the local device will make close contact with others. The time interval between the last scanning time of the other device is used as the characteristic data of the training sample, so that the accuracy of the close contact judgment can be improved.
S140, determining label data in the training sample; wherein the training samples are used for training an intimate contact judgment model.
After the feature data of the training sample is obtained, a label needs to be added to the feature data. Specifically, corresponding tag data is added to feature data corresponding to each pair of devices formed by other devices scanned by the local device based on the proximity contact technology. The tag data are probability values used for representing that a user using the local device is in close contact with other people, the value can be 0-1, and the tag data can be obtained by user labeling. And taking the training samples and the label data of the samples as the input of the close contact judgment model to train the close contact judgment model. The close contact judgment model is used for outputting a deep learning algorithm model of the close contact probability between the local device and other devices. Exemplary, the intimate contact judgment model is a DNN (Deep Neural Networks) model.
Optionally, the output close contact probability of the close contact judgment model is input to the close contact judgment model as the feature data in the training sample. As a device is scanned by more devices, the exposure decision model will tend to give the device a higher probability of exposure.
In an optional embodiment, a target acquisition value of a target sensor in a target device near a device to be tested is determined based on a close-range contact technology; determining a to-be-detected acquisition value of a to-be-detected sensor in to-be-detected equipment; and inputting the target acquisition value and the acquisition value to be detected into the close contact judgment model to obtain the probability of close contact between the equipment to be detected and the target equipment.
The device to be tested is the device which needs to judge whether the device to be tested is in close contact with other devices. The target device is a device located in an effective communication range of the proximity contact technology with the device to be tested as a center.
Specifically, the trained close contact judgment model can be deployed in a background server of the close contact judgment application software, the application software can effectively invoke the bluetooth configured in the equipment registered by the software, periodically scan the target equipment nearby the equipment to be tested through the bluetooth to acquire the identification information of the target equipment, and read the sensor acquisition value configured in the equipment according to the identification information of the target equipment. And then inputting the read sensor acquisition value into the close contact judgment model, and outputting the probability of close contact between the equipment to be tested and the target equipment by the close contact judgment model.
The technical scheme provided by the embodiment of the invention determines other acquisition values of other sensors in other equipment near the local equipment based on the close-range contact technology; determining a local acquisition value of a local sensor in local equipment; taking the other acquisition values, the local acquisition value and the signal strength of the close-range contact technology as characteristic data in a training sample; used for training the close contact judgment model. According to the embodiment of the invention, the acquisition value of the sensor in the equipment is comprehensively considered, and richer characteristic information is extracted from the acquisition value of the sensor, so that the accuracy and the reliability of the relative position information acquired based on the close contact technology are improved, and the accuracy of close contact judgment is further improved.
Example two
Fig. 2 is a flowchart of an intimate contact determination method in the second embodiment of the present invention, and this embodiment is further optimized based on the above embodiments. Specifically, the determining the label data in the training sample includes: acquiring the marked content of a local user at regular time; wherein the annotation content comprises at least one of the following: whether the local user is in close contact with others, and the state data of the local user behavior and the local device position state data under the condition that the local user is in close contact with others; and determining label data in the training sample according to the labeling content.
As shown in fig. 2, the method includes:
and S210, determining other acquisition values of other sensors in other equipment nearby the local equipment based on the close-range contact technology.
And S220, determining a local acquisition value of a local sensor in the local equipment.
S230, taking the other acquisition values, the local acquisition value and the signal intensity of the close-range contact technology as feature data in a training sample; wherein the training samples are associated with the local device and the other devices.
S240, regularly acquiring the labeled content of the local user; wherein the annotation content comprises at least one of the following: whether the local user is in close contact with others, and the state data of the local user behavior and the local device position state data under the condition that the local user is in close contact with others.
Whether the local user has close contact with other people or not is given by the local user according to personal judgment on the close contact. The local user behavior state includes at least one of: stay at a location, walk on a road, and in a vehicle. The local device location status includes at least one of: hand held, in a handbag or pocket, and on a desk. The behavior state of the local user and the position state of the local device influence the states of a signal channel and a signal antenna of the close-range contact technology, so that the network state of the close-range contact technology is influenced, and the accuracy of judging whether the local user and other people have close contact is influenced.
In an optional embodiment, generating a callout questionnaire according to local user behavior and a network state of a local proximity contact communication technology; displaying the annotated questionnaire through the local equipment at regular time; and generating the labeling content of the local user according to the filling information of the local user on the labeling questionnaire.
Wherein, the annotated questionnaire is a questionnaire for collecting user annotations. Optionally, the annotated questionnaire comprises: at least one of whether the local user has close contact with others, status data of the local user behavior in the case that the local user has close contact with others, and the local device location status data.
The network state of the local proximity communication technology is influenced by the state of the signal path and the signal antenna of the proximity communication technology, which in turn is influenced by the location state of the local device. Taking bluetooth in a mobile phone as an example, the bluetooth signal channel and the signal antenna of the mobile phone placed in the user briefcase are different from those of the mobile phone held by the user, and the network state of the bluetooth of the mobile phone held by the user is stronger than that of the bluetooth of the mobile phone placed in the user briefcase. The user's behavior directly determines the likelihood that the user is in close contact with others.
Optionally, by periodically presenting the tagged questionnaire to the device registered in the application software by the close contact determination application software, the tagged questionnaire may be illustratively pushed to the local device every 15 minutes, where the local device is a device registered in the close contact determination application software. The local user fills in the annotated questionnaire, the filling information of the local user on the annotated questionnaire is sent to the close contact judgment application software through the local equipment within the specified time, and the application software generates the annotated content of the local user according to the filling information of the local user on the annotated questionnaire. The embodiment of the invention can timely acquire the behavior state of the user, the position state of the equipment and the condition whether the user is in close contact with other people or not by regularly sending the annotated questionnaire to the user, thereby enabling the acquired label data to be more accurate and further improving the accuracy of close contact judgment.
And S250, determining label data in the training sample according to the labeling content, wherein the training sample is used for training a close contact judgment model.
The annotation content is associated with information filled into the annotation questionnaire by the user, illustratively, whether the local user has close contact with other people or not is judged, the annotation content is 0 or 1, and 0 means that the local user does not have close contact with other people; 1 indicates that there is close contact with another person. And (3) regularly receiving the labeled content of the local user within the set duration to generate a numerical value between 0 and 1 according to a set algorithm, and taking the numerical value as the label data of the training sample. For example, if the set time is 1 hour, and 15 minutes is used as the timing time, 4 times of annotation contents are obtained within 1 hour. Meanwhile, if the local device takes 1 minute as a period, other devices located near the local device are scanned based on the close-range contact technology. The local device will perform 15 scans in 15 minutes. The 4 annotations were scaled up to 15 scans per annotation, and 60 0 or 1 values in one hour were taken as the first values. That is, one time of user annotation corresponds to 15 sets of feature data, and for example, assuming that 4 times of annotation are 0, 1, 0, and 1, respectively, the first numerical value corresponding to the 15 sets of feature data obtained in the first 15 minutes within one hour will be 15 0 s; in the second 15 minutes within one hour, the first values corresponding to the other 15 sets of feature data obtained will be 15 1, so that the first values corresponding to the 60 sets of feature data obtained within one hour can be determined, and then 60 first values corresponding to the 60 sets of feature data are smoothed in an EMA (Exponential Moving Average) manner, so that the 60 first values are converted into 60 second values between 0 and 1, which serve as tag data of the 60 sets of feature data obtained within one hour. For example, if the first value corresponding to the 15 sets of feature data obtained in the second 15 minutes within one hour is 15 1, the 15 1 s are processed by the EMA algorithm to obtain 15 second values, the 15 second values are 0.125, 0.234, 0.33, 0.414, 0.487, 0.551, 0.607, 0.656, 0.699, 0.737, 0.77, 0.799, 0.824, 0.846, and 0.865, respectively, and the second value is the tag data of the 15 sets of feature data obtained in the second 15 minutes within one hour. The second value is an exponential moving average value obtained after the first value is processed by the EMA algorithm, the number of the second value is the same as that of the first value, and the second value is label data of each group of feature data.
Optionally, a weight of a second numerical value is generated according to the state data of the local user behavior and the state data of the local device location under the condition that the local user is in close contact with others, the numerical value after weighted calculation is used as final label data, and the judgment of the close contact probability by the user subjectivity and objective user behavior state data and device location state data are integrated, so that the obtained label data are more accurate.
The local device may scan to multiple other devices 1 time, forming multiple device pairs. The local device scans all the device pairs formed once into 1 set of feature data, and each device pair is used as 1 piece of feature data forming the set of feature data. The 1 set of feature data may include at least 1 piece of feature data, and the tag data of each set of feature data is identical to the tag data of the respective pieces of feature data constituting the set of feature data.
According to the embodiment of the invention, the marking content of the local user is obtained at regular time, the label data in the training sample is determined according to the marking content, and the training sample is utilized to train the close contact judgment model. The embodiment of the invention determines the label data of the training sample according to the labeling content of the local user acquired at regular time, so that the label data of the training sample is more accurate, and the accuracy of close contact judgment is improved.
EXAMPLE III
Fig. 3 is a schematic structural diagram of an intimate contact determination device in a third embodiment of the present invention, which is applicable to determining whether intimate contact exists between people. The apparatus may be implemented by software and/or hardware, and may be configured in an electronic device.
As shown in fig. 3, the apparatus may include: other acquisition value determination module 310, local acquisition value determination module 320, feature data determination module 330, and sample label determination module 340.
A further acquisition value determination module 310, configured to determine further acquisition values of further sensors in further devices located in the vicinity of the local device based on the proximity technology;
a local acquisition value determining module 320, configured to determine a local acquisition value of a local sensor in a local device;
a feature data determination module 330, configured to use the other acquisition values, the local acquisition value, and the signal strength of the close-range contact technology as feature data in a training sample; wherein the training samples are associated with the local device and the other devices;
a sample label determination module 340, configured to determine label data in the training sample; wherein the training samples are used for training an intimate contact judgment model.
The technical scheme provided by the embodiment of the invention determines other acquisition values of other sensors in other equipment near the local equipment based on the close-range contact technology; determining a local acquisition value of a local sensor in local equipment; taking the other acquisition values, the local acquisition value and the signal strength of the close-range contact technology as characteristic data in a training sample; used for training the close contact judgment model. According to the embodiment of the invention, the acquisition value of the sensor in the equipment is comprehensively considered, and richer characteristic information is extracted from the acquisition value of the sensor, so that the accuracy and the reliability of the relative position information acquired based on the close contact technology are improved, and the accuracy of close contact judgment is further improved.
Optionally, the apparatus further comprises: the current scanning time determining module is used for determining the current scanning time of the local equipment for scanning to any other equipment; the feature data determining module 330 is further configured to use a time interval between the current scanning time and a last scanning time when the local device last scans the other device as the feature data in the training sample.
Optionally, the sample label determining module 340 includes: the annotation content acquisition submodule is used for acquiring the annotation content of the local user at regular time; wherein the annotation content comprises at least one of the following: whether the local user is in close contact with others, and the state data of the local user behavior and the local device position state data under the condition that the local user is in close contact with others; and the label data determining submodule is used for determining the label data in the training sample according to the labeling content.
Optionally, the annotation content obtaining sub-module includes: the annotated questionnaire generating unit is used for generating an annotated questionnaire according to local user behaviors and a network state of a local close-range contact communication technology; the annotated questionnaire display unit is used for displaying the annotated questionnaire through the local equipment at regular time; and the annotation content generating unit is used for generating the annotation content of the local user according to the filling information of the local user on the annotated questionnaire.
Optionally, the sensor includes: at least one of a gravity sensor, an acceleration sensor, a gyro sensor, a light sensor, and a distance sensor.
Optionally, the other acquisition value determining module 310 includes: the identification information acquisition submodule is used for acquiring identification information of other equipment nearby the local equipment based on a close-range contact technology; and the other acquisition value acquisition submodule is used for acquiring other acquisition values of other sensors in other equipment from the server according to the identification information.
Optionally, the apparatus further comprises: the target acquisition value determining module is used for determining a target acquisition value of a target sensor in target equipment near the equipment to be tested based on a close-range contact technology; the device comprises a to-be-detected acquisition value determining module, a to-be-detected acquisition value determining module and a to-be-detected acquisition value determining module, wherein the to-be-detected acquisition value determining module is used for determining to-be-detected acquisition values of to-be-detected sensors in equipment to be; and the close contact probability determination module is used for inputting the target acquisition value and the acquisition value to be detected into the close contact judgment model to obtain the probability of close contact between the equipment to be detected and the target equipment.
The close contact judgment device provided by the embodiment of the invention can execute the close contact judgment method provided by any embodiment of the invention, and has the corresponding functional module and beneficial effect of executing the close contact judgment method.
Example four
The invention also provides an electronic device and a readable storage medium according to the embodiment of the invention.
Fig. 4 is a schematic structural diagram of an electronic device implementing the close contact determination method according to the embodiment of the present invention. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be examples only, and are not meant to limit implementations of the present application that are described and/or claimed herein.
As shown in fig. 4, the electronic apparatus includes: one or more processors 410, memory 420, and interfaces for connecting the various components, including a high-speed interface and a low-speed interface. The various components are interconnected using different buses and may be mounted on a common motherboard or in other manners as desired. The processor 410 may process instructions for execution within the electronic device, including instructions stored in or on a memory to display graphical information of a GUI on an external input/output apparatus (such as a display device coupled to an interface). In other embodiments, multiple processors 410 and/or multiple buses may be used, along with multiple memories and multiple memories, as desired. Also, multiple electronic devices may be connected, with each device providing portions of the necessary operations (e.g., as an array of devices, a set of blades, or a multi-processor system). One processor 410 is illustrated in fig. 4.
The memory 420 serves as a non-transitory computer readable storage medium, and may be used to store non-transitory software programs, non-transitory computer executable programs, and modules, such as program instructions/modules corresponding to a big data based close contact determination method in the embodiment of the present invention (for example, the embodiment shown in fig. 3 includes the other acquisition value determination module 310, the local acquisition value determination module 320, the feature data determination module 330, and the sample tag determination module 340). The processor 410 executes various functional applications and data processing of the electronic device by executing non-transitory software programs, instructions and modules stored in the memory 420, namely, implements one of the above-described method embodiments.
The memory 420 may include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function; the stored data area may store data created by use of the electronic device that realizes one kind of close contact judgment, and the like. Further, the memory 420 may include high speed random access memory, and may also include non-transitory memory, such as at least one magnetic disk storage device, flash memory device, or other non-transitory solid state storage device. In some embodiments, memory 420 may optionally include memory located remotely from processor 410, which may be connected via a network to an electronic device that performs a method of making a touchdown determination. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The electronic device that executes an intimate contact determination method may further include: an input device 430 and an output device 440. The processor 410, the memory 420, the input device 430, and the output device 440 may be connected by a bus or other means, such as the bus connection in fig. 4.
The input device 430 may receive input numeric or character information and generate key signal inputs related to user settings and function control of an electronic apparatus that performs a close contact determination method, such as input devices such as a touch screen, a keypad, a mouse, a track pad, a touch pad, a pointing stick, one or more mouse buttons, a track ball, and a joystick. The output device 440 may include a display device, an auxiliary lighting device (e.g., an LED), a haptic feedback device (e.g., a vibration motor), and the like. The display device may include, but is not limited to, a Liquid Crystal Display (LCD), a Light Emitting Diode (LED) display, and a plasma display. In some implementations, the display device can be a touch screen.
Various implementations of the systems and techniques described here can be realized in digital electronic circuitry, integrated circuitry, application specific ASICs (application specific integrated circuits), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs that are executable and/or interpretable on a programmable system including at least one programmable processor, which may be special or general purpose, receiving data and instructions from, and transmitting data and instructions to, a storage system, at least one input device, and at least one output device.
These computer programs (also known as programs, software applications, or code) include machine instructions for a programmable processor, and may be implemented using high-level procedural and/or object-oriented programming languages, and/or assembly/machine languages. As used herein, the terms "machine-readable medium" and "computer-readable medium" refer to any computer program product, apparatus, and/or device (e.g., magnetic discs, optical disks, memory, Programmable Logic Devices (PLDs)) used to provide machine instructions and/or data to a programmable processor, including a machine-readable medium that receives machine instructions as a machine-readable signal. The term "machine-readable signal" refers to any signal used to provide machine instructions and/or data to a programmable processor.
To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) by which a user can provide input to the computer. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic, speech, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a back-end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back-end, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), Wide Area Networks (WANs), the internet, and blockchain networks.
The computer system may include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other.
It should be understood that various forms of the flows shown above may be used, with steps reordered, added, or deleted. For example, the steps described in the present application may be executed in parallel, sequentially, or in different orders, and the present invention is not limited thereto as long as the desired results of the technical solutions disclosed in the present application can be achieved.
The above-described embodiments should not be construed as limiting the scope of the present application. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions may be made in accordance with design requirements and other factors. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present application shall be included in the protection scope of the present application.
It is to be noted that the foregoing is only illustrative of the preferred embodiments of the present invention and the technical principles employed. It will be understood by those skilled in the art that the present invention is not limited to the particular embodiments described herein, but is capable of various obvious changes, rearrangements and substitutions as will now become apparent to those skilled in the art without departing from the scope of the invention. Therefore, although the present invention has been described in greater detail by the above embodiments, the present invention is not limited to the above embodiments, and may include other equivalent embodiments without departing from the spirit of the present invention, and the scope of the present invention is determined by the scope of the appended claims.
Claims (10)
1. An intimate contact determination method, comprising:
determining other acquisition values of other sensors in other devices located near the local device based on the close-range contact technology;
determining a local acquisition value of a local sensor in local equipment;
taking the other acquisition values, the local acquisition value and the signal strength of the close-range contact technology as characteristic data in a training sample; wherein the training samples are associated with the local device and the other devices;
determining label data in the training sample; wherein the training samples are used for training an intimate contact judgment model.
2. The method of claim 1, further comprising:
determining a current scan time at which the local device is currently scanning to any other device;
and taking the time interval between the current scanning time and the last scanning time when the local device last scans the other devices as the characteristic data in the training sample.
3. The method of claim 1, wherein determining the label data in the training sample comprises:
acquiring the marked content of a local user at regular time; wherein the annotation content comprises at least one of the following: whether the local user is in close contact with others, and the state data of the local user behavior and the local device position state data under the condition that the local user is in close contact with others;
and determining label data in the training sample according to the labeling content.
4. The method of claim 3, wherein the periodically obtaining the annotation content of the local user comprises:
generating a tagging questionnaire according to local user behaviors and a network state of a local close-range contact communication technology;
displaying the annotated questionnaire through the local equipment at regular time;
and generating the labeling content of the local user according to the filling information of the local user on the labeling questionnaire.
5. The method of claim 1, wherein the sensor comprises: at least one of a gravity sensor, an acceleration sensor, a gyro sensor, a light sensor, and a distance sensor.
6. The method of claim 1, wherein determining other acquisition values of other sensors in other devices located near the local device based on the close-proximity technology comprises:
acquiring identification information of other equipment nearby the local equipment based on a close-range contact technology;
and acquiring other acquisition values of other sensors in other equipment from a server according to the identification information.
7. The method of claim 1, further comprising:
determining a target acquisition value of a target sensor in target equipment near the equipment to be detected based on a close-range contact technology;
determining a to-be-detected acquisition value of a to-be-detected sensor in to-be-detected equipment;
and inputting the target acquisition value and the acquisition value to be detected into the close contact judgment model to obtain the probability of close contact between the equipment to be detected and the target equipment.
8. An intimate contact determination apparatus, comprising:
the other acquisition value determining module is used for determining other acquisition values of other sensors in other equipment nearby the local equipment based on the close-range contact technology;
the local acquisition value determining module is used for determining a local acquisition value of a local sensor in local equipment;
the characteristic data determining module is used for taking the other acquisition values, the local acquisition value and the signal intensity of the close-range contact technology as characteristic data in a training sample; wherein the training samples are associated with the local device and the other devices;
the sample label determining module is used for determining label data in the training sample; wherein the training samples are used for training an intimate contact judgment model.
9. An electronic device, characterized in that the device comprises:
one or more processors;
a memory for storing one or more programs;
when executed by the one or more processors, cause the one or more processors to implement a method of intimate contact determination as claimed in any of claims 1 to 7.
10. A non-transitory computer readable storage medium storing computer instructions for causing a computer to execute the method of any one of claims 1 to 7.
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| CN202011615582.6A CN112635077A (en) | 2020-12-30 | 2020-12-30 | Close contact judgment method and device, electronic equipment and medium |
| PCT/CN2021/116604 WO2022142442A1 (en) | 2020-12-30 | 2021-09-06 | Intimate contact determining method and apparatus, electronic device, and medium |
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| CN202011615582.6A CN112635077A (en) | 2020-12-30 | 2020-12-30 | Close contact judgment method and device, electronic equipment and medium |
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