CN115190586B - Mobile terminal assisted intelligent household equipment position sensing method - Google Patents
Mobile terminal assisted intelligent household equipment position sensing method Download PDFInfo
- Publication number
- CN115190586B CN115190586B CN202211094202.8A CN202211094202A CN115190586B CN 115190586 B CN115190586 B CN 115190586B CN 202211094202 A CN202211094202 A CN 202211094202A CN 115190586 B CN115190586 B CN 115190586B
- Authority
- CN
- China
- Prior art keywords
- sequence
- equipment
- rss
- intelligent
- room
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000000034 method Methods 0.000 title claims abstract description 31
- 238000004891 communication Methods 0.000 claims abstract description 12
- 238000001914 filtration Methods 0.000 claims description 13
- 230000003068 static effect Effects 0.000 claims description 12
- 238000004364 calculation method Methods 0.000 claims description 11
- 238000005070 sampling Methods 0.000 claims description 6
- 238000012544 monitoring process Methods 0.000 claims description 5
- 238000000605 extraction Methods 0.000 claims description 4
- 230000001174 ascending effect Effects 0.000 claims description 3
- 238000000059 patterning Methods 0.000 claims description 3
- 230000000630 rising effect Effects 0.000 claims description 3
- 238000012545 processing Methods 0.000 claims description 2
- 230000002452 interceptive effect Effects 0.000 abstract 1
- 238000010586 diagram Methods 0.000 description 4
- 230000004807 localization Effects 0.000 description 2
- 238000003491 array Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000001514 detection method Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 238000002474 experimental method Methods 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 238000011056 performance test Methods 0.000 description 1
- 238000011160 research Methods 0.000 description 1
Images
Classifications
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W64/00—Locating users or terminals or network equipment for network management purposes, e.g. mobility management
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
- H04W4/30—Services specially adapted for particular environments, situations or purposes
- H04W4/33—Services specially adapted for particular environments, situations or purposes for indoor environments, e.g. buildings
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W84/00—Network topologies
- H04W84/02—Hierarchically pre-organised networks, e.g. paging networks, cellular networks, WLAN [Wireless Local Area Network] or WLL [Wireless Local Loop]
- H04W84/10—Small scale networks; Flat hierarchical networks
- H04W84/12—WLAN [Wireless Local Area Networks]
Landscapes
- Engineering & Computer Science (AREA)
- Computer Networks & Wireless Communication (AREA)
- Signal Processing (AREA)
- Mobile Radio Communication Systems (AREA)
Abstract
The invention discloses a mobile terminal assisted intelligent household equipment position sensing method which can automatically sense the change of the position of intelligent household equipment and update the position. The method and the system can sense the position change of the intelligent equipment in a Wi-Fi sniffing mode under the condition of not interfering the normal communication of the intelligent equipment. The invention provides a coarse-grained location sensing scheme of intelligent home equipment assisted by a mobile terminal, which comprises the steps of firstly extracting room and equipment information from an intelligent home system, sniffing Wi-Fi communication messages of all equipment through a fixed terminal, and calculating the related attributes of an initial location; periodically continuing sniffing the Wi-Fi message through the fixed terminal, and judging whether the intelligent equipment moves; then sniffing Wi-Fi messages in the moving process through the mobile terminal, and matching the devices which move with the devices which do not move to obtain new positions of the devices; and finally, updating the position of the equipment in the intelligent home system.
Description
Technical Field
The invention belongs to the technical field of Wireless networks (Wireless networks) and Indoor Positioning (Indoor Positioning), and particularly relates to a mobile terminal assisted intelligent home equipment position sensing method.
Background
With the increasing popularization of the application of the internet of things, various internet of things devices are connected to the internet and serve various fields of intelligent energy, intelligent families, intelligent manufacturing and the like. Due to the appearance of smart homes, people continuously pursue convenience of family life. The position sensing of the intelligent household equipment refers to the fact that the position of the equipment can be automatically sensed and the place where the equipment is located after the position is changed. The position of the intelligent equipment in the intelligent home system is often manually input into the intelligent home system by people, and if the position moves, the change of the position cannot be automatically sensed. Once the number of intelligent devices is increased, the complexity of manual operation is increased. And the indoor positioning technology can monitor the positions of personnel, objects and the like in an indoor environment and can provide technical support for the personnel, the objects and the like. Currently, the application scenarios considered by indoor positioning research are mainly divided into two types: one is MBL and the other is DBL. The former needs to additionally deploy a plurality of signal nodes and monitoring points in the environment, and the latter needs the equipment to have stronger computing capability and controllability for active cooperation. However, the smart home devices in the current home environment belong to weak H-IoT devices, and therefore a method for automatically monitoring and sensing the position of the smart home devices in the current home environment is needed to facilitate the operation.
Disclosure of Invention
The purpose of the invention is as follows: aiming at the problems of weak H-IoT equipment scenes and the lack of an automatic intelligent household equipment position sensing scheme, the invention provides a mobile terminal-assisted intelligent household equipment coarse-grained position sensing method.
The technical scheme is as follows: the invention provides a mobile terminal assisted intelligent household equipment position sensing method. The solution is divided into three parts, namely initial information calculation of the intelligent device, device movement judgment based on a fixed terminal and device position calculation based on mobile terminal assistance, and the method specifically comprises the following steps:
(1) Calculating initial information of the intelligent equipment: calculating an RSS mean value, a standard deviation and a maximum timestamp difference value of an upper message and a lower message of the intelligent equipment message at the fixed terminal by using a Wi-Fi sniffing mode through the fixed terminal, and taking the RSS mean value and the standard deviation as the correlation attributes of the initial position of the intelligent equipment;
(2) And (3) equipment movement judgment based on the fixed terminal: sniffing the latest communication message through a fixed terminal, calculating position-related attributes, comparing the position-related attributes with the initial information of the intelligent equipment obtained in the step (1), and dividing all the equipment into two sets of movement and non-movement;
(3) Device position calculation based on mobile terminal assistance: and sniffing the message by the mobile terminal in the moving process, matching the moved equipment with the equipment which is not moved, and further obtaining the new room.
Further, the step (1) specifically includes:
(11) Information extraction: acquiring room and equipment information from an intelligent home system, using the room as a position tag for subsequent position sensing, extracting a room name, a room ID and equipment in the room from the room information, and extracting an equipment name, a room where the equipment is located and an equipment ID from the equipment information;
(12) Initial data acquisition and processing: monitoring a communication message of intelligent equipment in a static state in a Wi-Fi sniffing mode through a fixed terminal to obtain a signal strength RSS sequence, and then filtering outliers in the signal strength RSS sequence by using a 3sigma model;
(13) Equipment information initialization: and calculating the RSS mean value, the standard deviation and the maximum time stamp interpolation of the adjacent messages of the intelligent equipment in the current static state according to the filtered RSS sequence as the initial position correlation attributes.
Further, the step (12) of filtering outliers in the RSS sequence of signal strength by using a 3sigma model, specifically, for the RSS sequence
Calculating the mean value
And standard deviation of
Will satisfy the following conditions
Filtering:
wherein,
indicating the first in an RSS sequencelThe RSS value of the bit is set to,Lindicating the RSS sequence length.
Further, the step (2) specifically includes:
(21) Traversing the message sequence by a reverse time sequence sliding window, comparing the maximum timestamp difference of the upper message and the lower message, and considering that the movement occurs if the maximum timestamp difference is more than 1.25 times of the original maximum timestamp difference;
(22) And traversing the message sequence by a reverse time sequence sliding window, comparing the RSS mean value in the window, and considering that the message moves if the difference between the RSS mean value and the original mean value exceeds 1.25 times of the original standard deviation.
Further, the step (3) specifically includes:
(31) Patterning sequence: extracting an RSS sequence from the sniffed message, filtering outliers according to the method in the step (12), comparing the difference values of the RSS mean values of the messages in the front window and the rear window of the sliding window by taking the standard deviation in the original static state as a threshold value, judging and identifying the rising, unchanged and falling states through a PLR algorithm, and expressing the rising, unchanged and falling states by {1, 0-1 }, thereby obtaining a mode sequence;
(32) Calculating a new position: and (3) calculating the mode distance between each moving device and each non-moving device according to the two sets of moving and non-moving obtained in the step (2), if a similar mode sequence does not exist, considering that the target device enters a room without other devices, otherwise, finding the device with the minimum regular distance by using a DTW algorithm in the similar mode sequence devices, and further judging that the target device and the device are in the same room.
Further, the modeling sequence in step (31) specifically includes:
the method comprises the steps that a mobile terminal fixes device communication messages within sampling time in the moving process, an original RSS sequence is obtained, then, outliers are filtered from the original sequence, and then the original sequence is subjected to outlier filteringAfter the standard deviation of the original stationary phase
As a threshold, the states are judged and identified by comparing the difference of the RSS mean values of the messages in the front window and the back window of the sliding window, and the three states are total: ascending, unchanged and descending, correspondingly expressed as {1,0, -1}, and combining adjacent identical patterns to obtain [1,0, -1, \ 8230]Thus spacing the pattern sequences from each other.
Further, for different time spans of each pattern in the pattern sequence, in order to compare patterns of two sequences in the same time period, the two pattern sequences need to be modeled, specifically:
suppose two sequencesS1AndS2：
whereinnThe status of the mode is indicated,
，tindicating the end time of the mode or modes,t 1N =t 2M ；
hypothesized sequenceS1Number of elements in (1)N=3, sequenceS2Number of elements in (1)M=2，t 11 <t 21 <t 12 <t 13 When modeled, the values will become:
calculating the mode distance after obtaining two mode sequences with equal length, wherein the formula is as follows:
it is clear that,
the distance of the pattern is represented by,
representing a sequenceS1And sequenceS2The distance of the mode between them,Krepresenting patterned sequencesS1AndS2length, since each pattern spans a different length of time, and the longer the time, the more information it contains in the whole sequence, the weighting is needed, and the formula is as follows:
whereint wk Is as followskThe weighted time coefficients of the individual modes,t k is as followskThe time span of the individual modes is,t N the length of the total time is the length of the total time,
closer to 0 indicates more similar patterns, and closer to 2 indicates less similar patterns.
Further, the fixed sampling time is set to 10 minutes.
Further, the new position is calculated in step (32), specifically as follows:
computing a mobile device from a sequence of patternsmThe mode distances from all reference devices, assuming the mode distances are:
whereineA reference device is shown which is a reference device,
，Ea set of reference devices is represented as,vindicates the total number of reference devices,
；
due to the fact thatDThe closer to 0 indicates that the patterns are more similar, and the closer to 2 indicates that the patterns are less similar, so that a determination that the pattern distance is less than 1 is made as a similar pattern sequence device:
whereinVRepresenting a set of other devices having a similar pattern sequence to the device,MAC f a MAC address representing the above-mentioned device,Frepresenting the number of similar sequence devices with a pattern distance less than 1;
if it is usedVIf the target device enters a room without reference device, manual position setting is needed; otherwise, according to the setVThe reference device in (1) calculates the sequence with the maximum sequence similarity by using a dynamic time warping algorithm, and further obtains the new position of the target device.
Further, the calculating of the sequence similarity by using the dynamic time warping algorithm means that warping path distance is used to represent the sequence similarity, and the smaller the distance, the higher the similarity.
Assume two RSS sequences ofR1AndR2comprises the following steps:
whereinPIs the length of the light-emitting diode (R1),Qis the length of R2;
regular pathWComprises the following steps:
it is composed of
At the same time
Must be monotonically increasing;
calculating the regular path distance according to the dynamic programming
The formula is as follows:
by the method, regular path distances between the target equipment RSS sequence and all unmovable intelligent equipment RSS sequences are calculated
：
FAnd (3) the number of similar sequence devices with the mode distance smaller than 1 is represented, the unmoved intelligent device with the minimum distance is found, and then the room where the intelligent device is located is obtained:
wherein,
representing target devicestargetThe room in which the device is located is,room s presentation intelligence devicesThe room in which the device is located is,sis composed ofPdThe intelligent device with the minimum regular distance in the middle and the target device are updated finallytargetIs the room.
Has the beneficial effects that: compared with the prior art, the invention has the remarkable advantages that:
1. at present, the position of the intelligent household equipment is often manually input into the intelligent household system by people, and if the position moves, the change of the position cannot be automatically sensed. The invention automatically senses and updates the room where the equipment is located by sniffing the normal Wi-Fi communication message of the equipment through the mobile terminal.
2. The invention utilizes Wi-Fi signals in the existing home environment, does not need to additionally arrange other signal nodes, does not need active cooperation of intelligent home equipment, and realizes automatic position sensing only through a small number of environment terminals.
Drawings
Fig. 1 is a schematic diagram of a device movement determination method based on a fixed terminal according to the present invention.
Fig. 2 is a schematic diagram of the mobile terminal assisted device location calculation method of the present invention.
Fig. 3 is a schematic diagram of a mobile terminal assisted location-aware experimental scenario in accordance with the present invention.
Fig. 4 is a statistical diagram of the accuracy of the mobile terminal assisted device location based calculation method of the present invention.
Detailed Description
The invention designs and realizes a mobile terminal assisted intelligent household equipment position sensing method. The scheme is divided into three parts, namely an intelligent device initial information calculation method, a device movement judgment method based on a fixed terminal and a device position calculation method based on mobile terminal assistance, and specifically comprises the following steps:
1. intelligent device initial information calculation
The intelligent equipment initial information calculation comprises three steps of information extraction, data filtering and equipment information initialization.
Information extraction: and acquiring information of rooms, equipment and the like from the intelligent home system, and using the rooms as position tags for subsequent position sensing. The specific content is information such as name, room ID, and device in the room information, and information such as device name, room, device ID, and MAC address in the device information.
And (3) data filtering: the fixed terminal performs Wi-Fi sniffing on the intelligent equipment in a static state according to the MAC address of the equipment, and the Wi-Fi sniffing is performed on the intelligent equipment in a static state according to the captured MAC addressAnd extracting RSS data from the message sequence, and filtering outliers in the RSS sequence by adopting a 3sigma model so as to calculate the position correlation attribute subsequently. For RSS sequences
Calculating the mean value
And standard deviation of
Will satisfy the following conditions
Filtering out:
equipment information initialization: in order to be able to detect the device movement and the computing device location subsequently, the device location related attributes need to be designed. And calculating the mean value, the standard deviation and the maximum difference value of the upper and lower message timestamps according to the filtered RSS sequence to be used as the position correlation attribute.
2. Equipment movement judgment method based on fixed terminal
The latest communication message is sniffed by the fixed terminal and compared with the original initial information of the intelligent equipment, and the equipment list is obtainedTDivided into sets of devices subject to movementMovAnd set of devices without mobilityE。
The invention uses a fixed terminal to sniff the device communication messages for a period of time (10 minutes is sampling interval time), traverses the message sequence through a reverse time sequence sliding window, calculates the RSS mean value and standard deviation in the window, traverses the message sequence sniffed by the fixed terminal through the reverse time sequence sliding window by the upper and lower messages, and calculates the RSS mean value in the window
Standard deviation of
And the maximum difference value of the timestamp of the upper and lower messages
Initial information of the original static state
In comparison, a move is considered to occur when one of the following occurs:
(1) Greater than 1.25 times the original maximum timestamp difference.
(2) The mean within the window differs from the original mean by more than 1.25 times the original standard deviation.
And after the movement is judged, finding out a static message sequence after the movement to a new position through the RSS standard deviation in the window, and updating the position related attribute before the movement by using the sequence.
3. Equipment position calculation method based on mobile terminal assistance
The multi-position sampling of the RSS sequence of the equipment is realized by utilizing the mobility of the mobile terminal, and then the new position of the target equipment is determined. For a set of mobile devicesMovEach of the devices inmIn a set of devices where no movement takes placeEDevice for matching most similar message sequenceeAnd with an apparatuseWhere to update a devicemThe location information of (1). Including two steps of patterning the sequence and calculating a new position.
(1) Patterned sequences
The method comprises the steps that a mobile terminal sniffs a device communication message for 10 minutes in the moving process, an original RSS sequence is obtained, then an outlier is filtered from the original sequence, and then the standard deviation of an original static stage is used
As a threshold value, 1The states are judged and identified by comparing the difference value of the RSS mean values of the messages in the front window and the back window of the sliding window, and the three states are total: ascending, unchanged and descending, correspondingly expressed as {1,0, -1}, and combining adjacent identical patterns to obtain [1,0, -1, \ 8230]Thus spacing the pattern sequences from each other.
Since the time span of each pattern in the pattern sequence is different, in order to compare the patterns of two sequences in the same time period, the two pattern sequences need to be patterned, and the like. Suppose two sequencesS1AndS2comprises the following steps:
whereinnThe status of the mode is indicated,
，tindicating the end time of the pattern and,t 1N =t 2M 。
suppose thatN=3，M=2，t 11 <t 21 <t 12 <t 13 When modeled, the values will become:
and calculating the mode distance after obtaining two mode arrays with equal length, wherein the formula is as follows:
it is clear that,
since each pattern spans a different length of time, and the longer the time, the more information it contains for the whole sequence, the weighting is needed, and the formula is as follows:
whereint k Is as followskThe time span of the individual modes is,t N is the total time length.
Closer to 0 indicates more similar patterns, and closer to 2 indicates less similar patterns.
(2) Calculating new position
Computing a mobile device from a sequence of patternsmMode distance from all reference devices. Assume that the mode distance is:
whereinkA reference device is shown which is a reference device,
。
due to the fact thatDA closer to 0 indicates that the patterns are more similar, and a closer to 2 indicates that the patterns are less similar, so a determination that the pattern distance is less than 1 is made as a similar pattern sequence device:
whereinFRepresenting the number of similar sequence devices with a pattern distance less than 1.
If it is notV= 8709if the target device enters a room without reference device, manual position setting is required; otherwise, according to the setVThe reference device in (1) calculates the sequence with the maximum sequence similarity by using a dynamic time warping algorithm, and then obtains the new position of the target device. The invention uses the regular path distance to express the sequence similarity, and the smaller the distance, the higher the similarity.
Suppose two RSS sequencesIs listed asR1AndR2comprises the following steps:
whereinPIs the length of the light-emitting diode (R1),Qis the length of R2.
The regular path is:
wherein
At the same time
Must be monotonically increasing.
And calculating the regular path distance according to the dynamic programming, wherein the formula is as follows:
by the method, the regular path distance between the target equipment RSS sequence and all the unmovable intelligent equipment RSS sequences is calculated:
finding the unmoved intelligent device with the minimum distance, and further obtaining the room where the unmoved intelligent device is located:
wherein s isPdThe intelligent device with the minimum regular distance in the middle and the target device are updated finallymIs the room.
In order to evaluate the performance of the present invention, the present invention uses commercial equipment to perform performance tests in a real environment, and the experimental scenario is a high-rise residence with an area of 147 square meters, as shown in fig. 3. And respectively moving the equipment among the four positions, moving the mobile terminal under the four tracks after moving every time, calculating the new position of the equipment, and judging whether the equipment is a real room according to the result. Each group of experiments is performed for 10 times, 480 groups of data are collected totally, the four types are divided into four types according to the position of the target equipment after moving, 30 groups of data are collected in each type of track, the experimental result is shown in fig. 4, and if the mobile terminal always moves on only one straight track, a certain misjudgment probability exists in the equipment which is symmetrical to the track. When the moving tracks of the mobile terminal are intersected, the detection accuracy reaches 100%.
The english abbreviation MBL, as used in the present invention, is interpreted as the Monitor based indoor localization (MBL) of a monitoring point (reference point); a DBL that interprets Device based indoor localization (DBL); H-IoT, which is interpreted as the Home-Internet of Things (H-IoT); RSS, interpreted as Received Signal Strength (RSS); MAC, media Access Control (MAC); PLR, piece-wise Linear Representation (PLR); DTW, dynamic Time Warping (DTW).
Claims (3)
1. A mobile terminal assisted intelligent household equipment position sensing method is characterized by comprising the following steps:
(1) Calculating initial information of the intelligent equipment: calculating an RSS mean value, a standard deviation and a maximum timestamp difference value of an upper message and a lower message of the intelligent equipment message at the fixed terminal by using a Wi-Fi sniffing mode through the fixed terminal, and taking the RSS mean value and the standard deviation as the correlation attributes of the initial position of the intelligent equipment;
(2) And (3) equipment movement judgment based on the fixed terminal: sniffing the latest communication message through a fixed terminal, calculating position-related attributes, comparing the position-related attributes with the initial information of the intelligent equipment obtained in the step (1), and dividing all the equipment into two sets of movement and non-movement;
(3) Device position calculation based on mobile terminal assistance: the method comprises the steps that a mobile terminal sniffs a message in the moving process, and a device which moves is matched with a device which does not move, so that a new room is obtained;
the step (1) specifically comprises:
(11) Information extraction: acquiring room and equipment information from an intelligent home system, using the room as a position tag for subsequent position sensing, extracting a room name, a room ID and equipment in the room from the room information, and extracting an equipment name, a room where the equipment is located and an equipment ID from the equipment information;
(12) Initial data acquisition and processing: monitoring a communication message of intelligent equipment in a static state in a Wi-Fi sniffing mode through a fixed terminal to obtain a signal strength RSS sequence, and then filtering outliers in the signal strength RSS sequence by using a 3sigma model;
(13) Equipment information initialization: according to the filtered RSS sequence, calculating an RSS mean value, a standard deviation and an adjacent message maximum timestamp interpolation of the intelligent equipment in the current static state as initial position correlation attributes;
and (12) filtering outliers in the signal strength RSS sequence by using a 3sigma model, specifically, filtering the RSS sequence
Calculating the mean value
And standard deviation of
Will satisfy the following conditions
Filtering:
wherein,
indicating the first in an RSS sequencelThe RSS value of the bit is set,Lrepresenting the length of an RSS sequence;
the step (3) specifically comprises:
(31) Patterning sequence: extracting an RSS sequence from sniffed messages, filtering outliers according to the method in the step (12), taking the standard deviation in the original static state as a threshold value, comparing the difference value of the RSS mean values of the messages in the front window and the back window of a sliding window, judging and identifying the rising, invariable and falling states through a PLR algorithm, and expressing the states by {1, 0-1 } to obtain a mode sequence;
(32) Calculating a new position: calculating the mode distance between each moving device and each non-moving device according to the two sets of moving and non-moving obtained in the step (2), if a similar mode sequence does not exist, considering that the target device enters a room without other devices, otherwise, finding the device with the minimum regular distance by using a DTW algorithm in the similar mode sequence devices, and further judging that the target device and the device are in the same room;
the modeling sequence of step (31) is specifically:
the method comprises the steps that a mobile terminal fixes device communication messages within sampling time in the moving process, an original RSS sequence is obtained, then outliers are filtered from the original sequence, and then the standard deviation of an original static stage is used
As a threshold, the states are judged and identified by comparing the difference of the RSS mean values of the messages in the front window and the back window of the sliding window, and the three states are total: ascending, unchanged and descending, correspondingly expressed as {1,0, -1}, and combining adjacent identical patterns to obtain [1,0, -1, \ 8230]Thus spacing the pattern sequences from each other;
for different time lengths spanned by each mode in the mode sequence, in order to compare the modes of the two sequences in the same time period, the two mode sequences need to be modeled, and the like, specifically:
suppose two sequencesS1AndS2：
whereinnIndicating the mode status, QUOTE
，tIndicating the end time of the pattern and,t 1N = t 2M ；
hypothesis sequenceS1Number of elements in (1)N=3, sequenceS2Number of elements in (1)M=2，t 11 <t 21 <t 12 <t 13 When modeled, the values will become:
calculating the mode distance after obtaining two mode sequences with equal length, wherein the formula is as follows:
it is clear that,
the distance of the pattern is represented by,
representing a sequenceS1And sequenceS2The distance of the mode between them,Krepresenting patterned sequencesS1AndS2length, since each pattern spans different time lengths, and the longer the time, the more information it contains in the whole sequence, the weighting is needed, and the formula is as follows:
whereint wk Is as followskThe weighted time coefficients of the individual modes,t k is a firstkThe time span of the individual modes is,t N as a result of the total length of time,
closer to 0 indicates more similar patterns, closer to 2 indicates less similar patterns;
calculating a new position in the step (32), specifically as follows:
computing a mobile device from a sequence of patternsmThe mode distances from all reference devices, assuming the mode distances are:
whereineA reference device is shown which is a reference device,
，Ea set of reference devices is represented as,vindicates the total number of reference devices,
；
due to the fact thatDA closer to 0 indicates that the patterns are more similar, and a closer to 2 indicates that the patterns are less similar, so a determination that the pattern distance is less than 1 is made as a similar pattern sequence device:
whereinVRepresenting a set of other devices having a similar pattern sequence as the device,MAC f a MAC address representing the above-mentioned device,Frepresenting the number of similar sequence devices with a pattern distance less than 1;
if it is notVIf the target device enters a room without reference device, manual position setting is needed; otherwise, according to the setVThe reference equipment calculates the sequence with the maximum sequence similarity by using a dynamic time warping algorithm, and further obtains the new position of the target equipment;
the sequence similarity is calculated by using a dynamic time warping algorithm, namely, the warping path distance is used for representing the sequence similarity, and the similarity is higher when the distance is smaller;
assume two RSS sequences ofR1AndR2comprises the following steps:
whereinPIs the length of the light-emitting diode (R1),Qis the length of R2;
regular pathWComprises the following steps:
it is composed of
At the same time
Must be monotonically increasing;
calculating regular path distance according to dynamic programming
The formula is as follows:
by the method, regular path distances between the RSS sequences of the target equipment and all the RSS sequences of the unmovable intelligent equipment are calculated
：
FAnd (3) the number of similar sequence devices with the mode distance smaller than 1 is represented, the unmoved intelligent device with the minimum distance is found, and then the room where the intelligent device is located is obtained:
wherein,
representing target devicestargetThe room in which the device is located is,room s presentation intelligence devicesThe room in which the device is located is,sis composed ofPdThe intelligent device with the minimum regular distance in the middle and the target device are updated finallytargetIs the room.
2. The mobile terminal assisted intelligent household equipment position sensing method according to claim 1, characterized in that: the step (2) specifically comprises:
(21) Traversing the message sequence by a reverse time sequence sliding window, comparing the maximum timestamp difference of the upper message and the lower message, and considering that the movement occurs if the maximum timestamp difference is more than 1.25 times of the original maximum timestamp difference;
(22) And traversing the message sequence by a reverse time sequence sliding window, comparing the RSS mean value in the window, and considering that the message moves if the difference between the RSS mean value and the original mean value exceeds 1.25 times of the original standard deviation.
3. The mobile terminal assisted intelligent household equipment position sensing method according to claim 1, characterized in that: the fixed sampling time is set to 10 minutes.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211094202.8A CN115190586B (en) | 2022-09-08 | 2022-09-08 | Mobile terminal assisted intelligent household equipment position sensing method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211094202.8A CN115190586B (en) | 2022-09-08 | 2022-09-08 | Mobile terminal assisted intelligent household equipment position sensing method |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN115190586A CN115190586A (en) | 2022-10-14 |
| CN115190586B true CN115190586B (en) | 2022-12-09 |
Family
ID=83522492
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202211094202.8A Active CN115190586B (en) | 2022-09-08 | 2022-09-08 | Mobile terminal assisted intelligent household equipment position sensing method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115190586B (en) |
Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105376854A (en) * | 2013-12-12 | 2016-03-02 | 马维尔国际贸易有限公司 | Method and device for tracking position of mobile wireless communication device in real time |
| EP3297345A2 (en) * | 2016-09-18 | 2018-03-21 | Beijing Xiaomi Mobile Software Co., Ltd. | Wireless local area network based positioning method and device |
| WO2019125037A1 (en) * | 2017-12-21 | 2019-06-27 | 삼성전자 주식회사 | Method and apparatus for antenna beam tracking in wireless cellular communication system |
| CN111935820A (en) * | 2020-09-24 | 2020-11-13 | 腾讯科技（深圳）有限公司 | Positioning implementation method based on wireless network and related equipment |
| CN111988844A (en) * | 2020-08-31 | 2020-11-24 | 广东技术师范大学 | Indoor room-level positioning method based on block chain and mobile crowd sensing |
| KR20210105151A (en) * | 2020-02-18 | 2021-08-26 | 영진전문대학 산학협력단 | Smart home security |
| CN113916231A (en) * | 2021-09-28 | 2022-01-11 | 上海浦东发展银行股份有限公司 | Mobile equipment positioning navigation method and device, mobile equipment and storage medium |
| CN114264220A (en) * | 2021-12-23 | 2022-04-01 | 湖南大学 | Method for accurately sensing and detecting relative displacement of mobile equipment |
| CN114449444A (en) * | 2022-01-27 | 2022-05-06 | 东南大学 | Cross-intelligent portable equipment association method based on WiFi-BLE signal passive sniffing |
| CN114580452A (en) * | 2021-12-27 | 2022-06-03 | 江苏集萃未来城市应用技术研究所有限公司 | Olfactory algorithm-based method for remotely sensing RFID electronic interface |
Family Cites Families (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US9208676B2 (en) * | 2013-03-14 | 2015-12-08 | Google Inc. | Devices, methods, and associated information processing for security in a smart-sensored home |
| CN106052696B (en) * | 2016-08-17 | 2017-05-03 | 西安理工大学 | Museum real-time route guidance method based on mobile terminals |
| CN111947644B (en) * | 2020-08-10 | 2022-04-12 | 北京洛必德科技有限公司 | Outdoor mobile robot positioning method and system and electronic equipment thereof |
-
2022
- 2022-09-08 CN CN202211094202.8A patent/CN115190586B/en active Active
Patent Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105376854A (en) * | 2013-12-12 | 2016-03-02 | 马维尔国际贸易有限公司 | Method and device for tracking position of mobile wireless communication device in real time |
| EP3297345A2 (en) * | 2016-09-18 | 2018-03-21 | Beijing Xiaomi Mobile Software Co., Ltd. | Wireless local area network based positioning method and device |
| WO2019125037A1 (en) * | 2017-12-21 | 2019-06-27 | 삼성전자 주식회사 | Method and apparatus for antenna beam tracking in wireless cellular communication system |
| KR20210105151A (en) * | 2020-02-18 | 2021-08-26 | 영진전문대학 산학협력단 | Smart home security |
| CN111988844A (en) * | 2020-08-31 | 2020-11-24 | 广东技术师范大学 | Indoor room-level positioning method based on block chain and mobile crowd sensing |
| CN111935820A (en) * | 2020-09-24 | 2020-11-13 | 腾讯科技（深圳）有限公司 | Positioning implementation method based on wireless network and related equipment |
| CN113916231A (en) * | 2021-09-28 | 2022-01-11 | 上海浦东发展银行股份有限公司 | Mobile equipment positioning navigation method and device, mobile equipment and storage medium |
| CN114264220A (en) * | 2021-12-23 | 2022-04-01 | 湖南大学 | Method for accurately sensing and detecting relative displacement of mobile equipment |
| CN114580452A (en) * | 2021-12-27 | 2022-06-03 | 江苏集萃未来城市应用技术研究所有限公司 | Olfactory algorithm-based method for remotely sensing RFID electronic interface |
| CN114449444A (en) * | 2022-01-27 | 2022-05-06 | 东南大学 | Cross-intelligent portable equipment association method based on WiFi-BLE signal passive sniffing |
Non-Patent Citations (3)
| Title |
|---|
| A zigbee-based localization algorithm for indoor environments;Hu X et al;《Journal of computer networks and communications》;20111231;全文 * |
| 基于定位技术的智能家居监测系统的设计与实现;虎冠林;《中国优秀硕士学位论文全文数据库》;20201231;全文 * |
| 面向智能家居的人体定位系统研究;李振宇;《中国优秀硕士学位论文全文数据库》;20181231;全文 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN115190586A (en) | 2022-10-14 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Piciarelli et al. | On-line trajectory clustering for anomalous events detection | |
| Lu et al. | Robust occupancy inference with commodity WiFi | |
| CN111860318A (en) | Construction site pedestrian loitering detection method, device, equipment and storage medium | |
| CN105745687A (en) | Context aware moving object detection | |
| Afzalan et al. | Self-configuring event detection in electricity monitoring for human-building interaction | |
| Pauwels et al. | Sensor networks for ambient intelligence | |
| CN111739053A (en) | Online multi-pedestrian detection tracking method under complex scene | |
| CN105578414A (en) | Terminal and positioning method and apparatus for same | |
| Martella et al. | Leveraging proximity sensing to mine the behavior of museum visitors | |
| CN111708021A (en) | Personnel tracking and identifying algorithm based on millimeter wave radar | |
| CN107861098B (en) | Intelligent court positioning system and method | |
| CN111784729A (en) | Object tracking method and device, electronic equipment and storage medium | |
| CN105573134A (en) | Intelligent household equipment control method and intelligent household system | |
| EP3457635A1 (en) | Method and device for identifying a user in an environment | |
| CN115190586B (en) | Mobile terminal assisted intelligent household equipment position sensing method | |
| Yuan et al. | Research on indoor position fingerprint location based on machine learning combined particle filter | |
| JP2023527526A (en) | Positioning method, device, mobile terminal, storage medium | |
| CN118051800B (en) | Intelligent processing method for in-mine monitoring data based on cloud edge cooperation | |
| Zhu et al. | Fusion of wireless signal and computer vision for identification and tracking | |
| CN105488965A (en) | Alarm method and device | |
| CN114845387B (en) | Method and device for monitoring access, computer equipment and storage medium | |
| Gonzalez et al. | Mining hierarchical relations in building management variables | |
| CN112887485B (en) | Contact tracking method based on electronic trace | |
| CN209930345U (en) | Electronic device | |
| CN113866716A (en) | RSSI (received Signal Strength indicator) -based weighted neighbor positioning method |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |