CN114827977B - Beidou positioning mobile communication system based on neural network and vital signs - Google Patents

Beidou positioning mobile communication system based on neural network and vital signs Download PDF

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CN114827977B
CN114827977B CN202210757895.8A CN202210757895A CN114827977B CN 114827977 B CN114827977 B CN 114827977B CN 202210757895 A CN202210757895 A CN 202210757895A CN 114827977 B CN114827977 B CN 114827977B
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孙爱梅
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Abstract

The invention relates to the technical field of signal devices, in particular to a Beidou positioning mobile communication system based on a neural network and vital signs. The system is a signal device and is also an alarm which is based on mobile data communication service and can ensure personal safety; the system includes a memory and a processor that executes a computer program stored by the memory to implement the steps of: obtaining the abnormal degree of the vital signs and the geographical danger degree of the user according to the vital sign information and the positioning information of the user; and taking the maximum value of the abnormal degree and the geographical danger degree of the vital sign of the user as the communication emergency degree, obtaining the binary code of the communication emergency degree, and transmitting the communication emergency degree of the user. The invention is implemented based on a new generation mobile communication core network, realizes that the help-seeking information can be sent to the receiving center under the condition that the user does not have manual help-seeking, and ensures the personal safety of the user.

Description

Beidou positioning mobile communication system based on neural network and vital signs
Technical Field
The invention relates to the technical field of signal devices, in particular to a Beidou positioning mobile communication system based on a neural network and vital signs.
Background
The outdoor alarm device is an alarm for ensuring personal safety, and corresponding positioning information and help-seeking information can be sent only after an SOS help-seeking key is manually pressed.
The existing Beidou positioning communication terminal for displaying vital signs in CN112085919A comprises a Beidou card, a Beidou card slot, a central processing unit module, a Beidou positioning module, a Beidou communication module, a Beidou antenna, a power supply boosting module, a wireless communication module, a vital sign module, an SOS switch and a power supply switch; the Beidou positioning module is used for acquiring real-time positioning data of the device, the Beidou communication module is used for communicating with a command center, and the Beidou antenna is in two-way connection with the Beidou communication module and is used for receiving and sending wireless signals sent by the remote terminal.
The Beidou positioning communication terminal realizes real-time positioning of a wearer through a Beidou positioning module, realizes communication between the device and the terminal through the Beidou communication module, monitors vital signs of the wearer through a vital sign module, and sends distress information through an SOS key so as to realize emergency distress; however, in a severe environment, dangers (such as an avalanche, a sand storm, etc.) suddenly occur, and sometimes it is difficult for a user to press a help-seeking key, so that a help-seeking signal cannot be sent out when a danger occurs, and the optimal rescue time is missed. When the vital signs of the user and the geographic position of the user are critical, a more urgent and faster help-seeking signal needs to be sent to achieve the best rescue effect; however, how to send out the help-seeking information to the receiving center more quickly and efficiently without manual help-seeking to ensure the personal safety of the user is a problem to be solved at present.
Disclosure of Invention
In order to solve the problem of how to more quickly and efficiently send out help-seeking information to a receiving center without manual help seeking so as to ensure the personal safety of a user, the invention aims to provide a Beidou positioning mobile communication system based on a neural network and vital signs, and the adopted technical scheme is as follows:
the invention provides a Beidou positioning mobile communication system based on a neural network and vital signs, which comprises a memory and a processor, and is characterized in that the processor executes a computer program stored in the memory to realize the following steps:
collecting vital sign information and positioning information of a current user at the current moment, wherein the vital sign information comprises body temperature data and pulse data, and the positioning information comprises longitude, latitude and height;
calculating the abnormal degree of the vital sign corresponding to the current moment of the user according to the vital sign information of the current moment of the user;
inputting the positioning information of the user at the current moment into a trained target neural network to obtain the geographic danger degree corresponding to the current position of the user;
taking the maximum value of the abnormal degree of the vital sign and the geographic danger degree corresponding to the current moment of the user as the corresponding communication emergency degree; coding the communication emergency degree corresponding to the current moment of the user to obtain a corresponding binary code; and transmitting the binary code of the communication emergency degree corresponding to the current moment of the user.
Preferably, the calculating, according to the vital sign information of the user at the current time, the abnormal degree of the vital sign corresponding to the user at the current time includes:
calculating to obtain the corresponding body temperature abnormal degree according to the body temperature data of the user at the current moment;
calculating to obtain the corresponding pulse abnormal degree according to the pulse data of the user at the current moment;
and calculating the abnormal degree of the vital signs corresponding to the current moment of the user according to the abnormal degree of the body temperature and the abnormal degree of the pulse corresponding to the current moment of the user.
Preferably, the calculating the abnormal degree of the vital sign corresponding to the current time of the user according to the abnormal degree of the body temperature and the abnormal degree of the pulse corresponding to the current time of the user includes:
acquiring the body temperature abnormal degree and the pulse abnormal degree corresponding to each time of information transmission of a user in a target time period; the target time period is the time length from the last time of obtaining the response to the current time;
calculating to obtain the accumulated forced degree of the user at the current moment according to the body temperature abnormal degree and the pulse abnormal degree corresponding to the user during each information transmission in the target time period;
and multiplying the maximum value of the body temperature abnormal degree and the pulse abnormal degree corresponding to the current moment of the user by the sum of 1 plus the accumulated forced tangent degree to obtain the vital sign abnormal degree corresponding to the current moment of the user.
Preferably, the formula for calculating the accumulated forced cutting degree is as follows:
Figure 100002_DEST_PATH_IMAGE002
wherein the content of the first and second substances,
Figure 100002_DEST_PATH_IMAGE004
in order to accumulate the forced cutting degree,
Figure 100002_DEST_PATH_IMAGE006
the number of information transmissions in the target time period,
Figure 100002_DEST_PATH_IMAGE008
the corresponding body temperature abnormal degree when the nth information is transmitted in the target time period,
Figure 100002_DEST_PATH_IMAGE010
the degree of pulse abnormality corresponding to the nth information transmission in the target time period,
Figure 100002_DEST_PATH_IMAGE012
the max () is the maximum value of the corresponding abnormal state when the nth information is transmitted in the target time period;
if the maximum value of the pulse abnormal degree and the body temperature abnormal degree corresponding to the nth information transmission is smaller than the preset abnormal state threshold value, determining that the pulse abnormal degree and the body temperature abnormal degree are abnormal for the nth information transmission
Figure 761135DEST_PATH_IMAGE012
Is 0; if the maximum value of the pulse abnormal degree and the body temperature abnormal degree corresponding to the nth information transmission is more than or equal to the preset abnormal state threshold value, determining that the maximum value of the pulse abnormal degree and the body temperature abnormal degree is not less than the preset abnormal state threshold value
Figure 694587DEST_PATH_IMAGE012
Is 1.
Preferably, the communication emergency degree corresponding to the current moment of the user is coded to obtain a corresponding binary code; the method for transmitting the binary code of the communication emergency degree corresponding to the current moment of the user comprises the following steps:
calculating a self-adaptive judgment interval corresponding to the current moment of the user according to the communication emergency degree corresponding to the current moment of the user, wherein the self-adaptive judgment interval is a time interval for judging the communication emergency degree of the user;
calculating the sending frequency corresponding to the current moment of the user according to the self-adaptive judgment interval corresponding to the current moment of the user and the corresponding communication emergency degree, wherein the sending frequency is the frequency for sending information in the self-adaptive judgment interval;
according to the communication emergency degree and the code table corresponding to the current moment of the user, obtaining a binary code corresponding to the communication emergency degree corresponding to the current moment of the user;
transmitting the binary code of the communication emergency degree corresponding to the current moment of the user according to the sending frequency corresponding to the current moment of the user;
the process of obtaining the coding table comprises the following steps:
acquiring a plurality of different communication emergencies; calculating corresponding sending frequency according to each communication emergency degree;
calculating a coding rate corresponding to each communication emergency degree according to each communication emergency degree and the corresponding sending frequency;
according to the coding rate of each communication emergency degree, carrying out Huffman coding on each communication emergency degree to obtain binary codes of each communication emergency degree; and constructing a coding table according to the binary codes of the communication emergency degrees.
Preferably, the formula for calculating the transmission frequency is:
Figure 100002_DEST_PATH_IMAGE014
wherein the content of the first and second substances,
Figure 100002_DEST_PATH_IMAGE016
the sending frequency corresponding to the current time of the user,
Figure 100002_DEST_PATH_IMAGE018
for a standard number of transmissions of information within a standard interval time,
Figure 100002_DEST_PATH_IMAGE020
for the adaptive judgment interval corresponding to the current time of the user,
Figure 100002_DEST_PATH_IMAGE022
the communication emergency degree corresponding to the current moment of the user is obtained;
the calculation formula of the self-adaptive judgment interval is as follows:
Figure 100002_DEST_PATH_IMAGE024
wherein the content of the first and second substances,
Figure 100002_DEST_PATH_IMAGE026
is a standard interval time.
Preferably, the formula for calculating the coding rate corresponding to each communication emergency degree is as follows:
Figure 100002_DEST_PATH_IMAGE028
wherein the content of the first and second substances,
Figure 100002_DEST_PATH_IMAGE030
for the coding rate corresponding to the communication urgency i,
Figure 100002_DEST_PATH_IMAGE032
for each sequence of the urgency of the communication,
Figure 100002_DEST_PATH_IMAGE034
a sequence of transmission frequencies corresponding to the respective communication urgency levels,
Figure 100002_DEST_PATH_IMAGE036
in order to communicate the degree of urgency i,
Figure 100002_DEST_PATH_IMAGE038
for the transmission frequency corresponding to the communication urgency i,
Figure 100002_DEST_PATH_IMAGE040
is the minimum value of the number of the optical fibers,
Figure 100002_DEST_PATH_IMAGE042
is the maximum value.
Preferably, the step of inputting the positioning information of the user at the current moment into the trained target neural network to obtain the geographic risk degree corresponding to the current position of the user includes:
inputting the positioning information of the user at the current moment into a trained target neural network to obtain the geographic danger degree and the landform type corresponding to the current position of the user;
the loss function of the training target neural network comprises:
utilizing a mean square error loss function to monitor the training of the target neural network on the obtained geographic danger degree;
a cross entropy loss function is used for monitoring the training of a target neural network for classifying the landform types;
and monitoring the training of the target neural network by using the comprehensive loss function.
Preferably, the formula of the synthetic loss function is:
Figure 100002_DEST_PATH_IMAGE044
wherein the content of the first and second substances,
Figure 100002_DEST_PATH_IMAGE046
in order to synthesize the value of the loss function,
Figure 100002_DEST_PATH_IMAGE048
the geographical danger degree inferred by the target neural network according to the geographical information,
Figure DEST_PATH_IMAGE050
the target neural network deduces the mode risk degree corresponding to the landform type according to the geographic information,
Figure DEST_PATH_IMAGE052
deducing a judgment radius corresponding to the landform type for the target neural network according to the geographic information;
the mode risk degree corresponding to the landform type is a geographical risk degree which accounts for a large number of geographical risk degrees in each geographical risk degree corresponding to the corresponding landform type and is obtained through a large number of statistics; and the judgment radius corresponding to the landform type is half of the sum of the maximum value and the minimum value of each geographic danger degree corresponding to the corresponding landform type.
The invention has the following beneficial effects:
according to the method, firstly, the abnormal degree and the geographical danger degree of the vital sign corresponding to the current moment of the user are obtained according to the vital sign information and the positioning information of the current moment of the user, and then the communication emergency degree corresponding to the current moment of the user is obtained, and finally, before the communication emergency degree corresponding to the current moment of the user is transmitted, the communication emergency degree corresponding to the current moment of the user is coded, and the communication emergency degree corresponding to the current moment of the user is transmitted by utilizing binary coding. The Beidou positioning mobile communication system is a signal device and is also an alarm which is based on mobile data communication service and can ensure personal safety; the invention is implemented based on a new generation mobile communication core network, automatically judges the communication emergency degree of the user by monitoring the vital sign information and the positioning information of the user in real time, and further adaptively adjusts the signal sending frequency and the information acquisition interval, thereby realizing that the help-seeking information can be sent to a receiving center more quickly and efficiently under the condition that the user does not have manual help-seeking, and ensuring the personal safety of the user.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions and advantages of the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts.
Fig. 1 is a flow chart of a Beidou positioning mobile communication system based on a neural network and vital signs provided by the invention;
FIG. 2 is a schematic diagram of the back side of the internal structure of the Beidou positioning mobile communication system provided by the invention;
fig. 3 is a schematic front view of the internal structure of the beidou positioning mobile communication system provided by the invention.
Detailed Description
To further illustrate the technical means and effects of the present invention for achieving the predetermined purpose, the following detailed description, the structure, the features and the effects of the Beidou positioning mobile communication system based on the neural network and the vital signs according to the present invention are provided with the accompanying drawings and the preferred embodiments. Furthermore, the particular features, structures, or characteristics of the embodiments may be combined in any suitable manner.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.
The following specifically describes a specific scheme of the Beidou positioning mobile communication system based on the neural network and the vital signs, which is provided by the invention, with reference to the attached drawings.
The embodiment of the Beidou positioning mobile communication system based on the neural network and the vital signs comprises the following steps:
as shown in fig. 1, the Beidou positioning mobile communication system based on the neural network and the vital signs of the embodiment includes a memory and a processor, and the processor executes a computer program stored in the memory to implement the following steps:
step S1, collecting the vital sign information and the positioning information of the current user at the current moment, wherein the vital sign information comprises body temperature data and pulse data, and the positioning information comprises longitude, latitude and height.
According to the Beidou positioning mobile communication system in the embodiment, the state of the user can be judged according to the collected vital signs and the positioning information of the user, so that the information sending frequency and the code can be intelligently adjusted. After a user wears the system, the information can be sent even if the user does not manually ask for help, and the problem that field accidents suddenly happen and the user is not in time or difficult to manually ask for help is solved. When the user does sports and travels outdoors or outdoors, the system can monitor vital signs of the user in real time, and can communicate in time and send help-seeking information to ensure the life safety of the user when the user is in danger and needs to be rescued.
The Beidou positioning mobile communication system comprises an alarm main body and an external sensor, wherein the alarm main body comprises a data acquisition module, a judgment calculation module and a data sending module; the external sensor comprises a body temperature measuring sensor and a pulse measuring sensor. The present embodiment mainly reflects the vital sign condition of the human body based on the body temperature data and the pulse data of the human body. This embodiment big dipper location mobile communication system's concrete inner structure is shown in fig. 2 and fig. 3, and 1 is the big dipper card in the figure, and 2 is the big dipper draw-in groove, and 3 is central processing unit, and 4 is big dipper orientation module, and 5 is big dipper communication module, and 6 is the big dipper antenna, and 7 is wireless communication module, and 8 is vital sign module, and 9 is the power boost module, and 10 is the SOS switch, and 11 is switch, and 12 is body temperature measurement sensor, and 13 is pulse measurement sensor.
The embodiment utilizes the body temperature measuring sensor to acquire body temperature data of a user in real time, and utilizes the pulse measuring sensor to acquire pulse data of the user in real time; the body temperature measuring sensor is a patch type temperature measuring sensor, and the pulse measuring sensor is a bracelet type pulse measuring sensor.
According to the embodiment, firstly, a Beidou positioning mobile communication system is utilized to obtain vital sign information and positioning information of a current user at the current moment, wherein the vital sign information comprises body temperature data and pulse data, and the positioning information comprises longitude, latitude and height of the position of the user; in this embodiment, the positioning information of the user is obtained through a Beidou positioning module of a Beidou positioning mobile communication system.
And step S2, calculating the abnormal degree of the vital sign corresponding to the current time of the user according to the vital sign information of the current time of the user.
The present embodiment determines the current state of the user through two aspects, which are denoted as communication urgency in the present embodiment, so as to determine the frequency of subsequent information transmission and the corresponding code, where the two aspects are the vital sign of the user and the location positioning of the user respectively; the body state of the user can be judged according to the vital sign information of the user, the danger degree of the current position of the user is judged according to the positioning information, and for example, a city is safer than a mountain forest.
When the user state is normal, longer information codes are transmitted at a lower frequency; when the user state is abnormal, a shorter information code is transmitted at a higher frequency, so that the calling rescue can be carried out as best as possible when the communication state is not good, and the possibility of rescue is increased.
According to the embodiment, the abnormal degree of the vital sign of the user at the current moment is obtained according to the collected vital sign information of the user at the current moment, which specifically includes:
in this embodiment, the current vital sign information of the user is obtained according to step S1, and first, regarding the body temperature, the normal body temperature range of the human body is 36.3 to 37.2 ℃, the body temperature is low heat when the body temperature is 37.3 to 38 ℃, the body temperature is medium heat when the body temperature is 38.1 to 39 ℃, the body temperature is high heat when the body temperature is 39.1 to 41 ℃, and the body temperature is ultrahigh heat when the body temperature is above 41 ℃.
In this embodiment, the current body temperature abnormal degree of the user is calculated according to the body temperature data of the user at the current time, and the specific formula is as follows:
Figure DEST_PATH_IMAGE054
wherein the content of the first and second substances,
Figure DEST_PATH_IMAGE056
the degree of the abnormality of the body temperature,
Figure DEST_PATH_IMAGE058
is the data of the body temperature, and the body temperature data,
Figure DEST_PATH_IMAGE060
is a body temperature abnormal function, when the body temperature of the user is within 36.3-37.2 ℃, namely the body temperature of the user is normal, the corresponding body temperature is
Figure 288640DEST_PATH_IMAGE056
Is 0; when the body temperature of the user is not within 36.3-37.2 ℃, namely the body temperature is abnormal, the corresponding body temperature is determined
Figure 746166DEST_PATH_IMAGE056
Calculated by a temperature anomaly function. The body temperature abnormality function has the formula:
Figure DEST_PATH_IMAGE062
wherein the content of the first and second substances,
Figure DEST_PATH_IMAGE064
in the form of a circumferential ratio,
Figure DEST_PATH_IMAGE066
obtained by deformation of standard normal distribution, when the body temperature of the user is not in the normal range, the farther the body temperature deviates from the normal range, the longer the body temperature deviation is
Figure 324653DEST_PATH_IMAGE066
The smaller the value of (a) is,
Figure 475012DEST_PATH_IMAGE060
the larger the temperature, the larger the corresponding degree of abnormality of body temperature.
Then, for the pulse, the normal pulse of the human body is between 60 times/minute and 100 times/minute, when the pulse of the human body is more than or equal to 100 times/minute, the pulse of the human body is increased, and the reasons for the situation can be emotional excitement, tension, strenuous physical activity (such as running, climbing mountains, climbing stairs, carrying heavy objects and the like), hot climate, after meals, after drinking and the like; when the pulse rate of the human body is less than or equal to 60 times/minute, the pulse rate of the human body is slow, and the reasons for the situation can be intracranial pressure increase, obstructive jaundice, hypothyroidism and the like.
In this embodiment, the current pulse abnormality degree of the user is calculated according to the pulse data of the user at the current time, and the specific formula is as follows:
Figure DEST_PATH_IMAGE068
wherein the content of the first and second substances,
Figure DEST_PATH_IMAGE070
the degree of the abnormal pulse is the degree of the abnormal pulse,
Figure DEST_PATH_IMAGE072
the pulse data is the pulse data, and the pulse data is the pulse data,
Figure DEST_PATH_IMAGE074
when the pulse data of the user is between 60 times/min and 100 times/min, the corresponding pulse is abnormal
Figure 443842DEST_PATH_IMAGE070
Is 0; when the pulse of the user is not between 60 times/minute and 100 times/minute, namely the pulse is abnormal, the pulse may be too fast or too slow, and the corresponding
Figure 705060DEST_PATH_IMAGE070
Calculated by a pulse anomaly function. The formula of the pulse anomaly function is as follows:
Figure DEST_PATH_IMAGE076
wherein, the first and the second end of the pipe are connected with each other,
Figure DEST_PATH_IMAGE078
the pulse data is also obtained by the deformation of the standard normal distribution, when the pulse of the user is not in the normal range, the farther the pulse data deviates from the normal range, then
Figure 390250DEST_PATH_IMAGE078
The smaller the value of (a) is,
Figure 396996DEST_PATH_IMAGE074
the larger the pulse, the larger the corresponding degree of pulse abnormality.
In the embodiment, when the state information with a large abnormal degree (the abnormal degree includes the body temperature abnormal degree and the pulse abnormal degree) is transmitted, a quick response should be obtained, but due to reasons such as communication delay, it cannot be ensured that a corresponding response is obtained within a certain time. The information transmitted in each time in the target time period does not obtain corresponding response, and the more the number of times of non-response is, the greater the cumulative urgency is; in the calculation process, the farther away from the current time, the more attention should be paid to the information transmission, i.e., the greater the influence. The calculation formula for calculating the accumulated forced shear degree is as follows:
Figure DEST_PATH_IMAGE002A
wherein, the first and the second end of the pipe are connected with each other,
Figure 354587DEST_PATH_IMAGE004
in order to accumulate the forced cutting degree,
Figure 904649DEST_PATH_IMAGE006
the number of information transmissions in the target time period,
Figure 490351DEST_PATH_IMAGE008
the corresponding body temperature abnormal degree when the nth information is transmitted in the target time period,
Figure 733244DEST_PATH_IMAGE010
the degree of pulse abnormality corresponding to the nth information transmission in the target time period,
Figure 112886DEST_PATH_IMAGE012
max () is the maximum value for the corresponding abnormal state at the nth information transfer within the target period.
If the maximum value of the corresponding pulse abnormal degree and the body temperature abnormal degree during the nth information transmission is less thanA predetermined abnormal state threshold value, then
Figure 450326DEST_PATH_IMAGE012
Is 0, which indicates that the user is in a normal state and has no danger; if the maximum value of the pulse abnormal degree and the body temperature abnormal degree corresponding to the nth information transmission is more than or equal to the preset abnormal state threshold value, determining that the maximum value of the pulse abnormal degree and the body temperature abnormal degree is not less than the preset abnormal state threshold value
Figure 906846DEST_PATH_IMAGE012
Is 1. In this embodiment, the abnormal state threshold needs to be set according to actual needs.
In the embodiment, after the system receives the response of the control center or the manual key of the user, the accumulated urgency is recalculated; for example, if a response is obtained at the ith time, when the cumulative urgency of the n times is calculated, the target time period is the time length from the ith time to the nth time.
In this embodiment, the maximum value of the abnormal body temperature and the abnormal pulse degree corresponding to the current time of the user is multiplied by the sum of 1 plus the cumulative forcing degree to be used as the abnormal vital sign degree corresponding to the current time of the user, that is, the abnormal vital sign degree is obtained
Figure DEST_PATH_IMAGE080
。
In the embodiment, a large amount of vital sign data are collected based on a statistical mode, corresponding vital sign abnormal degree values are obtained through calculation, and then normalization processing is performed, so that the value range of the vital sign abnormal degree SY corresponding to the user is [0,1 ].
And step S3, inputting the positioning information of the user at the current moment into the trained target neural network to obtain the geographic danger degree corresponding to the current position of the user.
In this embodiment, the geographic risk level of the current location of the user is obtained according to the current location information of the user collected in step S1, which is specifically:
the embodiment infers the landform type and the geographic danger degree of the geographic position of the user by constructing the target neural network. The specific content of the target neural network in this embodiment is as follows:
in this embodiment, the target neural network adopts a structure of full connection FC, and the input is positioning information, i.e., longitude, latitude and altitude; the output is the landform classification corresponding to the positioning information, namely the geographic danger degree and the landform type (namely, cities, towns, villages, mountain forests, lakes, deserts, Gobi and the like).
In the embodiment, the data set for training the target neural network uniformly and randomly selects the positioning information in the corresponding area (such as China), and then the corresponding landform type and the corresponding geographic danger degree are given by people according to the position of each positioning information to be used as a label; for example, the geomorphologic category is a city, and the geographic risk level is 0; the landform category is desert, the geographic danger degree is 0.7, and the like. Different positioning information in the embodiment may correspond to the same landform type, and different positioning information in the same landform type may have different geographic risk degrees.
The loss function of the training target neural network adopts a mean square error loss function and a cross entropy loss function; the mean square error loss function is used for training for monitoring the geographic danger degree, and the cross entropy loss function is used for training for monitoring landform classification; in this embodiment, a synthetic loss function is further added to supervise the overall training of the target neural network by comparing the landform type and the geographic risk degree, so that the landform type inferred by the network corresponds to the geographic risk degree, and the training of the target neural network is more reliable, wherein the calculation formula of the synthetic loss function is as follows:
Figure DEST_PATH_IMAGE044A
wherein the content of the first and second substances,
Figure 461587DEST_PATH_IMAGE046
in order to synthesize the value of the loss function,
Figure 252825DEST_PATH_IMAGE048
inferring geographic risk levels for a target neural network from geographic information，
Figure 141759DEST_PATH_IMAGE050
The method comprises the steps of reasoning a mode risk degree corresponding to a landform type according to geographic information for a target neural network, wherein the mode risk degree is a geographic risk degree which accounts for more than one geographic risk degree in various geographic risk degrees corresponding to the landform type obtained through a large number of statistics,
Figure 702053DEST_PATH_IMAGE052
a judgment radius corresponding to the landform type deduced by the target neural network according to the geographic information can be regarded as a fault-tolerant threshold, and when the difference value between the geographic danger degree output by the target neural network and the mode danger degree corresponding to the output landform type is smaller than or equal to the judgment radius corresponding to the output landform type, the comprehensive loss function value is 0; if the output landform type is larger than the judgment radius corresponding to the output landform type, calculating
Figure DEST_PATH_IMAGE082
As a corresponding value of the integrated loss function.
In this embodiment, the process of obtaining the mode risk degree and the corresponding judgment radius corresponding to each landform type is as follows: obtaining all possible danger degrees of all landform types through a large number of statistics, and then selecting a mode in all possible danger degrees corresponding to all landform types as a mode danger degree corresponding to the landform type; then, according to the maximum value and the minimum value of each geographic danger degree corresponding to each landform type, calculating half of the sum of the maximum value and the minimum value to be used as a judgment radius corresponding to the corresponding landform type; for example, if the plurality of geographic risk degrees corresponding to the counted cities are 0.1, 0.2, 0.2, and 0.3, the mode risk degree corresponding to the city is 0.2, and the corresponding judgment radius is 0.2
Figure DEST_PATH_IMAGE084
。
Although the positioning information can also be combined with an electronic map to judge whether the position is abnormal, corresponding data information needs to be stored at the main body end of the alarm, the alarm is mainly convenient and fast to move, the size is limited, and the storage space and the computing capacity are also limited, so that the embodiment infers the corresponding landform type and the geographic danger degree corresponding to the current geographic information of the user by constructing a target neural network and utilizing the trained target neural network.
Step S4, taking the maximum value of the abnormal degree of the vital sign and the geographic danger degree corresponding to the current moment of the user as the corresponding communication emergency degree; coding the communication emergency degree corresponding to the current moment of the user to obtain a corresponding binary code; and transmitting the binary code of the communication emergency degree corresponding to the current moment of the user.
According to the steps S2 and S3, the abnormal degree and the geographic danger degree of the current corresponding vital sign of the user can be calculated; according to the embodiment, the communication emergency degree corresponding to the current moment of the user can be obtained by integrating the abnormal degree of the vital sign and the geographic danger degree corresponding to the current moment of the user, wherein the communication emergency degree is used for reflecting the current state of the user, and the larger the communication emergency degree is, the more abnormal the current state of the user is, the higher the frequency is required, and the corresponding communication emergency degree is transmitted more quickly so as to ensure timely rescue; the smaller the communication emergency degree is, the more normal the current state of the user is; according to the communication emergency degree of the user at the current moment obtained by calculation, the sending frequency and the self-adaptive judgment interval of the subsequent information are adjusted. Specifically, the method comprises the following steps:
first, in this embodiment, a communication emergency degree currently corresponding to a user is first obtained according to a vital sign abnormal degree and a geographic danger degree currently corresponding to the user, where the communication emergency degree currently corresponding to the user is a maximum value of the vital sign abnormal degree and the geographic danger degree currently corresponding to the user, that is, the communication emergency degree is the maximum value
Figure DEST_PATH_IMAGE086
In which
Figure 490012DEST_PATH_IMAGE022
For communication emergency corresponding to current moment of userTo the extent that,
Figure DEST_PATH_IMAGE088
the abnormal degree of the vital sign corresponding to the current moment of the user,
Figure 391716DEST_PATH_IMAGE048
and the geographic danger degree corresponding to the current moment of the user.
Second, the higher the communication urgency, the higher the frequency with which information should be transmitted. In this embodiment, there are two parts that are adjustable and controllable in the aspect of information transmission, which are respectively: self-adaptive judging interval and sending frequency; the self-adaptive judgment interval is an interval for judging the communication emergency degree of the user by the Beidou positioning mobile communication system, namely the period for acquiring the vital sign information and the positioning information of the user; the transmission frequency is a frequency at which information is uniformly transmitted within the adaptive decision interval. Specifically, the method comprises the following steps:
in this embodiment, a calculation formula for calculating the adaptive determination interval corresponding to the user at the current time is as follows:
Figure DEST_PATH_IMAGE024A
wherein the content of the first and second substances,
Figure 211904DEST_PATH_IMAGE026
for the standard interval time, the present embodiment will
Figure 377437DEST_PATH_IMAGE026
The setting time is 10 minutes, and the setting time can be specifically set according to actual requirements;
Figure 382302DEST_PATH_IMAGE020
and the self-adaptive judgment interval corresponding to the current moment is provided for the user. When the communication emergency degree is larger, the current state of the user is more abnormal, so the judgment interval is reduced, the system detects the user more frequently, and the attention degree of the user is increased.
The calculation formula for calculating the transmission frequency in the adaptive determination interval in this embodiment is as follows:
Figure DEST_PATH_IMAGE014A
wherein, the first and the second end of the pipe are connected with each other,
Figure 771302DEST_PATH_IMAGE016
the sending frequency corresponding to the current time of the user,
Figure 254236DEST_PATH_IMAGE018
for the standard number of information transmission in the standard interval time, the embodiment will
Figure 539855DEST_PATH_IMAGE018
The setting is 10, and the setting can be specifically carried out according to actual needs. When the communication urgency level is higher, the current state of the user is more abnormal, and the frequency of information transmission is increased in the adaptive judgment interval in order to not only reduce the judgment interval, but also ensure that the receiving center can receive information more quickly and efficiently.
In this embodiment, before information transmission is performed on the communication emergency degree corresponding to the current time of the user, the communication emergency degree needs to be encoded to obtain a binary code corresponding to the communication emergency degree; since the binary code is easier to transmit and the shorter the transmission speed, the more urgent the communication is, the shorter the binary code should be.
In this embodiment, the code table is searched according to the communication emergency degree corresponding to the current time of the user to obtain the binary code corresponding to the communication emergency degree corresponding to the current time of the user, and finally, the information of the binary code corresponding to the communication emergency degree corresponding to the current time of the user is transmitted according to the transmission frequency obtained by the calculation. In actual use, the system stores a corresponding coding table for binary coding the communication emergency degree.

Claims (7)

1. A Beidou positioning mobile communication system based on a neural network and vital signs comprises a memory and a processor, and is characterized in that the processor executes a computer program stored in the memory to realize the following steps:
acquiring vital sign information and positioning information of a current user at the current moment, wherein the vital sign information comprises body temperature data and pulse data, and the positioning information comprises longitude, latitude and height;
calculating the abnormal degree of the vital sign corresponding to the current moment of the user according to the vital sign information of the current moment of the user;
inputting the positioning information of the user at the current moment into a trained target neural network to obtain the geographic danger degree corresponding to the current position of the user;
taking the maximum value of the abnormal degree of the vital sign and the geographic danger degree corresponding to the current moment of the user as the corresponding communication emergency degree; coding the communication emergency degree corresponding to the current moment of the user to obtain a corresponding binary code; transmitting a binary code of a communication emergency degree corresponding to the current moment of a user;
coding the communication emergency degree corresponding to the current moment of the user to obtain a corresponding binary code; the method for transmitting the binary code of the communication emergency degree corresponding to the current moment of the user comprises the following steps:
calculating a self-adaptive judgment interval corresponding to the current moment of the user according to the communication emergency degree corresponding to the current moment of the user, wherein the self-adaptive judgment interval is a time interval for judging the communication emergency degree of the user;
calculating the sending frequency corresponding to the current moment of the user according to the self-adaptive judgment interval corresponding to the current moment of the user and the corresponding communication emergency degree, wherein the sending frequency is the frequency for sending information in the self-adaptive judgment interval;
according to the communication emergency degree and the code table corresponding to the current moment of the user, obtaining a binary code corresponding to the communication emergency degree corresponding to the current moment of the user;
transmitting the binary code of the communication emergency degree corresponding to the current moment of the user according to the sending frequency corresponding to the current moment of the user;
the process of obtaining the coding table comprises the following steps:
acquiring a plurality of different communication emergencies; calculating corresponding sending frequency according to each communication emergency degree;
calculating a coding rate corresponding to each communication emergency degree according to each communication emergency degree and the corresponding sending frequency;
according to the coding rate of each communication emergency degree, carrying out Huffman coding on each communication emergency degree to obtain binary codes of each communication emergency degree; constructing a coding table according to binary codes of all communication emergency degrees;
the formula for calculating the coding rate corresponding to each communication emergency degree is as follows:
Figure DEST_PATH_IMAGE002
wherein the content of the first and second substances,
Figure DEST_PATH_IMAGE004
for the coding rate corresponding to the communication urgency i,
Figure DEST_PATH_IMAGE006
for each sequence of communication urgency levels it is,
Figure DEST_PATH_IMAGE008
a sequence of transmission frequencies corresponding to the respective communication urgency levels,
Figure DEST_PATH_IMAGE010
in order to communicate the degree of urgency i,
Figure DEST_PATH_IMAGE012
min () is the minimum value and max () is the maximum value for the transmission frequency corresponding to the communication emergency degree i.
2. The Beidou positioning mobile communication system based on neural network and vital signs according to claim 1, wherein the calculating of the abnormal degree of the vital signs corresponding to the current time of the user according to the vital sign information of the current time of the user comprises:
calculating to obtain a corresponding body temperature abnormal degree according to body temperature data of the user at the current moment;
calculating to obtain the corresponding pulse abnormal degree according to the pulse data of the user at the current moment;
and calculating the abnormal degree of the vital signs corresponding to the current moment of the user according to the abnormal degree of the body temperature and the abnormal degree of the pulse corresponding to the current moment of the user.
3. The Beidou positioning mobile communication system based on neural network and vital signs as set forth in claim 2, wherein the calculating the abnormal degree of the vital signs corresponding to the current time of the user according to the abnormal degree of the body temperature and the abnormal degree of the pulse corresponding to the current time of the user comprises:
acquiring the body temperature abnormal degree and the pulse abnormal degree corresponding to each time of information transmission of a user in a target time period; the target time period is the time length from the last time of response to the current time;
calculating to obtain the accumulated forced degree of the user at the current moment according to the body temperature abnormal degree and the pulse abnormal degree corresponding to the user during each information transmission in the target time period;
and multiplying the maximum value of the body temperature abnormal degree and the pulse abnormal degree corresponding to the current moment of the user by the sum of 1 plus the accumulated forced degree to obtain the vital sign abnormal degree corresponding to the current moment of the user.
4. The Beidou positioning mobile communication system based on neural network and vital signs according to claim 3, characterized in that the formula for calculating the accumulated forcing degree is as follows:
Figure DEST_PATH_IMAGE014
wherein, the first and the second end of the pipe are connected with each other,
Figure DEST_PATH_IMAGE016
in order to accumulate the forced cutting degree,
Figure DEST_PATH_IMAGE018
the number of information transmissions in the target time period,
Figure DEST_PATH_IMAGE020
the corresponding body temperature abnormal degree when the nth information is transmitted in the target time period,
Figure DEST_PATH_IMAGE022
the degree of pulse abnormality corresponding to the nth information transmission in the target time period,
Figure DEST_PATH_IMAGE024
the max () is the maximum value of the corresponding abnormal state in the nth information transmission in the target time period;
if the maximum value of the corresponding pulse abnormal degree and the body temperature abnormal degree during the nth information transmission is smaller than the preset abnormal state threshold value, determining that the pulse abnormal degree and the body temperature abnormal degree are abnormal for the nth information transmission
Figure 279278DEST_PATH_IMAGE024
Is 0; if the maximum value of the pulse abnormal degree and the body temperature abnormal degree corresponding to the nth information transmission is more than or equal to the preset abnormal state threshold value, determining that the maximum value of the pulse abnormal degree and the body temperature abnormal degree is not less than the preset abnormal state threshold value
Figure 993156DEST_PATH_IMAGE024
Is 1.
5. The Beidou positioning mobile communication system based on neural network and vital signs according to claim 1, characterized in that the formula for calculating the transmission frequency is:
Figure DEST_PATH_IMAGE026
wherein, the first and the second end of the pipe are connected with each other,
Figure DEST_PATH_IMAGE028
the sending frequency corresponding to the current time of the user,
Figure DEST_PATH_IMAGE030
for a standard number of transmissions of information within a standard interval time,
Figure DEST_PATH_IMAGE032
for the adaptive judgment interval corresponding to the current time of the user,
Figure DEST_PATH_IMAGE034
the communication emergency degree corresponding to the current moment is set for the user;
the calculation formula of the self-adaptive judgment interval is as follows:
Figure DEST_PATH_IMAGE036
wherein the content of the first and second substances,
Figure DEST_PATH_IMAGE038
is a standard interval time.
6. The Beidou positioning mobile communication system based on neural network and vital signs according to claim 1, characterized in that positioning information of a user at the current moment is input into a trained target neural network to obtain a geographic risk degree corresponding to the current position of the user, comprising:
inputting the positioning information of the user at the current moment into a trained target neural network to obtain the geographic danger degree and the landform type corresponding to the current position of the user;
training a loss function of a target neural network, comprising:
utilizing a mean square error loss function to monitor the training of the target neural network on the obtained geographic danger degree;
a cross entropy loss function is used for supervising the training of classifying the landform types by a target neural network;
and monitoring the training of the target neural network by using the comprehensive loss function.
7. The Beidou positioning mobile communication system based on neural network and vital signs according to claim 6, characterized in that the formula of the comprehensive loss function is as follows:
Figure DEST_PATH_IMAGE040
wherein the content of the first and second substances,
Figure DEST_PATH_IMAGE042
in order to synthesize the value of the loss function,
Figure DEST_PATH_IMAGE044
the geographical danger degree inferred by the target neural network according to the geographical information,
Figure DEST_PATH_IMAGE046
the target neural network infers the mode risk degree corresponding to the landform type according to the geographic information,
Figure DEST_PATH_IMAGE048
deducing a judgment radius corresponding to the landform type for the target neural network according to the geographic information;
the mode risk degree corresponding to the landform type is a geographical risk degree which accounts for a large number of geographical risk degrees in each geographical risk degree corresponding to the corresponding landform type and is obtained through a large number of statistics; and the judgment radius corresponding to the landform type is half of the sum of the maximum value and the minimum value of each geographic danger degree corresponding to the corresponding landform type.
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