CN110611880A - Household WiFi prediction method and device, electronic equipment and storage medium - Google Patents
Household WiFi prediction method and device, electronic equipment and storage medium Download PDFInfo
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- CN110611880A CN110611880A CN201910914627.0A CN201910914627A CN110611880A CN 110611880 A CN110611880 A CN 110611880A CN 201910914627 A CN201910914627 A CN 201910914627A CN 110611880 A CN110611880 A CN 110611880A
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04B—TRANSMISSION
- H04B17/00—Monitoring; Testing
- H04B17/30—Monitoring; Testing of propagation channels
- H04B17/309—Measuring or estimating channel quality parameters
- H04B17/318—Received signal strength
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W24/00—Supervisory, monitoring or testing arrangements
- H04W24/08—Testing, supervising or monitoring using real traffic
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
- H04W4/02—Services making use of location information
- H04W4/023—Services making use of location information using mutual or relative location information between multiple location based services [LBS] targets or of distance thresholds
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
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- 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
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- 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
- H04W64/006—Locating users or terminals or network equipment for network management purposes, e.g. mobility management with additional information processing, e.g. for direction or speed determination
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Abstract
The embodiment of the application discloses a family WiFi prediction method and device, electronic equipment and a storage medium. The method comprises the following steps: the method comprises the steps that WiFi data are collected at a plurality of set moments, a plurality of mac lists are generated, the mac lists comprise a plurality of WiFi signal sources, and the set moments are selected according to a fixed time period of the position state of equipment; analyzing each mac list to obtain signal intensity information corresponding to each WiFi signal source at each set moment; and calculating the prediction scores of all WiFi signal sources serving as the family WiFi to which the current mobile terminal equipment belongs based on the signal strength information, and determining the family WiFi to which the current mobile terminal equipment belongs according to the prediction scores. The matching relation between the mobile terminal device and the family WiFi does not need to be manually input, the automatic prediction of the family WiFi is realized, the more accurate matching between the mobile terminal device and the family WiFi is realized, and the use experience of a user is further optimized.
Description
Technical Field
The embodiment of the application relates to the technical field of indoor positioning, in particular to a family WiFi prediction method and device, electronic equipment and a storage medium.
Background
At present, with the development and optimization of mobile terminal equipment, the functions of the mobile terminal equipment are gradually improved. Generally, a mobile terminal device is integrated with a GPS module to implement a positioning function of the mobile terminal device. The current position of the mobile terminal equipment user can be obtained through the GPS, and then the corresponding application function is realized.
However, the existing GPS positioning method has relatively low positioning accuracy, and in an indoor environment, the GPS signal is relatively weak, so that the requirement of accurate positioning to a building, a floor, and the like in the indoor environment cannot be met. For this reason, in an indoor environment, WiFi positioning is generally adopted to determine the location information of the mobile terminal. The position information of the mobile terminal connected with the WiFi access point is determined by acquiring the position information of the WiFi access point in advance. In particular, in a home WiFi use scenario, the possibility of home indoor positioning is realized by using a WiFi positioning mode. The positioning information of the corresponding family member can be obtained by inquiring that the corresponding mobile terminal equipment is accessed to the family WiFi.
However, in the existing home WiFi positioning method, the matching relationship between the mobile terminal and the home WiFi needs to be determined in advance, and the matching relationship between the mobile terminal and the home WiFi needs to be entered manually, so that the operation is complicated, and the use experience is relatively poor.
Disclosure of Invention
The embodiment of the application provides a family WiFi prediction method, a family WiFi prediction device, electronic equipment and a storage medium, which can solve the problem that the matching process of the existing mobile terminal equipment and the family WiFi is complex, and realize accurate matching of the mobile terminal and the family WiFi.
In a first aspect, an embodiment of the present application provides a home WiFi prediction method, including:
the method comprises the steps that WiFi data are collected at a plurality of set moments, a plurality of mac lists are generated, the mac lists comprise a plurality of WiFi signal sources, and the set moments are selected according to a fixed time period of the position state of equipment;
analyzing each mac list to obtain signal intensity information corresponding to each WiFi signal source at each set moment;
and calculating the prediction scores of all WiFi signal sources serving as the family WiFi to which the current mobile terminal equipment belongs based on the signal strength information, and determining the family WiFi to which the current mobile terminal equipment belongs according to the prediction scores.
Further, the calculating, based on the signal strength information, a prediction score of each WiFi signal source as a home WiFi to which the current mobile terminal device belongs includes:
and calculating the prediction score of the household WiFi which the corresponding WiFi signal sources belong to as the current mobile terminal equipment by attenuation weighting based on the signal strength information corresponding to the set moments and the signal attenuation condition of the WiFi signal sources corresponding to the set moments.
Further, the determining, according to the prediction score, the home WiFi to which the current mobile terminal device belongs includes:
and calculating the prediction probability corresponding to the prediction score by adopting a variable normalization method, and taking the WiFi signal source with the highest prediction probability as the family WiFi to which the current mobile terminal equipment belongs.
Further, the prediction probability calculation formula is as follows:
wherein, macjThe corresponding prediction score for the jth WiFi signal source,is macjThe corresponding prediction probability.
Further, after determining the home WiFi to which the current mobile terminal device belongs according to the prediction score, the method further includes:
the method comprises the steps of collecting WiFi data of the current position of mobile terminal equipment, and judging whether the WiFi data contain the family WiFi or not;
and if the WiFi data contains the family WiFi, positioning the current position of the mobile terminal equipment in the corresponding family of the user.
Further, in analyzing each mac list to obtain signal strength information corresponding to each WiFi signal source at each set time, filtering each analyzed WiFi signal source, and deleting WiFi signal sources belonging to the mobile hotspot.
Further, in analyzing each Mac list to obtain signal strength information corresponding to each WiFi signal source at each set time, the Mac list is analyzed to obtain a Mac address, a WiFi name, and signal strength information corresponding to each WiFi signal source.
In a second aspect, an embodiment of the present application provides a home WiFi prediction apparatus, including:
the device comprises an acquisition module, a storage module and a processing module, wherein the acquisition module is used for acquiring WiFi data at a plurality of set moments to generate a plurality of mac lists, the mac lists comprise a plurality of WiFi signal sources, and the set moments are selected according to fixed time periods of the position state of the device;
the analysis module is used for analyzing each mac list to obtain signal intensity information corresponding to each WiFi signal source at each set moment;
and the prediction module is used for calculating the prediction scores of the WiFi signal sources serving as the family WiFi to which the current mobile terminal equipment belongs based on the signal strength information, and determining the family WiFi to which the current mobile terminal equipment belongs according to the prediction scores.
In a third aspect, an embodiment of the present application provides an electronic device, including:
a memory and one or more processors;
the memory for storing one or more programs;
when executed by the one or more processors, cause the one or more processors to implement a home WiFi prediction method as described in the first aspect.
In a fourth aspect, embodiments of the present application provide a storage medium containing computer-executable instructions for performing the home WiFi prediction method as described in the first aspect when executed by a computer processor.
In the embodiment of the application, WiFi data are collected at a plurality of set moments to generate a plurality of mac lists, the mac lists are analyzed, the predication scores of the WiFi signal sources serving as the family WiFi to which the current mobile terminal equipment belongs are calculated based on the analyzed signal intensity information of the WiFi signal sources, and the corresponding WiFi signal sources are determined as the family WiFi to which the current mobile terminal equipment belongs through the predication scores. By adopting the technical means, the matching relationship between the mobile terminal equipment and the family WiFi is not required to be manually input, the automatic prediction of the family WiFi is realized, the more accurate matching between the mobile terminal equipment and the family WiFi is realized, and the use experience of a user is further optimized.
In addition, according to the matching relationship between the mobile terminal device and the family WiFi, when the current position of the mobile terminal device detects WiFi data corresponding to the family WiFi, the current position of the mobile terminal device is determined to be in the family corresponding to the user, and therefore accurate indoor positioning of the position where the user is located is achieved. Meanwhile, indoor positioning can be carried out without accessing home WiFi, so that the positioning process is simplified, and the use experience of a user is further optimized.
Drawings
Fig. 1 is a flowchart of a home WiFi prediction method provided in an embodiment of the present application;
fig. 2 is a flowchart of another home WiFi prediction method provided in the second embodiment of the present application;
fig. 3 is a home WiFi label diagram in the second embodiment of the present application;
fig. 4 is a schematic structural diagram of a home WiFi predicting device provided in the third embodiment of the present application;
fig. 5 is a schematic structural diagram of an electronic device according to a fourth embodiment of the present application.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, specific embodiments of the present application will be described in detail with reference to the accompanying drawings. It is to be understood that the specific embodiments described herein are merely illustrative of the application and are not limiting of the application. It should be further noted that, for the convenience of description, only some but not all of the relevant portions of the present application are shown in the drawings. Before discussing exemplary embodiments in more detail, it should be noted that some exemplary embodiments are described as processes or methods depicted as flowcharts. Although a flowchart may describe the operations (or steps) as a sequential process, many of the operations can be performed in parallel, concurrently or simultaneously. In addition, the order of the operations may be re-arranged. The process may be terminated when its operations are completed, but may have additional steps not included in the figure. The processes may correspond to methods, functions, procedures, subroutines, and the like.
The method for predicting the family WiFi aims to establish accurate matching between the mobile terminal device and the family WiFi by predicting the family WiFi to which the mobile terminal device belongs so as to conveniently carry out WiFi indoor positioning through the matching relation between the mobile terminal device and the family WiFi and determine indoor positioning information of the mobile terminal device. According to the existing home WiFi positioning mode, when WiFi positioning is carried out, position information of home WiFi and the matching relation between related mobile terminal equipment and home WiFi are recorded in advance, so that when home user positioning is carried out subsequently, each mobile terminal equipment accessed to the home WiFi is inquired, the pre-established matching relation between the mobile terminal equipment and the home WiFi is compared, and if mobile terminal equipment corresponding to the matching relation exists, the current positioning information of the corresponding home user is considered to be in the home of the user. Because the existing family WiFi positioning mode needs to inquire the mobile terminal equipment with the matching relationship, the matching relationship between the mobile terminal equipment and the family WiFi can be manually input in advance, and the operation process is relatively complex. Based on this, the problem that the matching process of the existing mobile terminal device and the family WiFi is complex is solved through the family WiFi prediction method.
The first embodiment is as follows:
fig. 1 is a flowchart of a home WiFi prediction method provided in an embodiment of the present application, where the home WiFi prediction method provided in this embodiment may be executed by a home WiFi prediction device, and the home WiFi prediction device may be implemented in a software and/or hardware manner, and the home WiFi prediction device may be formed by two or more physical entities or may be formed by one physical entity. Generally, the home WiFi prediction device may be a mobile terminal device such as a cell phone, tablet or phone watch.
The following description will be given taking a home WiFi prediction apparatus as an example of an apparatus that performs a home WiFi prediction method. Referring to fig. 1, the home WiFi prediction method specifically includes:
s110, WiFi data are collected at a plurality of set moments, a plurality of mac lists are generated, the mac lists comprise a plurality of WiFi signal sources, and the set moments are selected according to fixed time intervals of the position state of the equipment.
When the family WiFi prediction is carried out, WiFi data needs to be collected through family WiFi prediction equipment. And the collected WiFi data is used as a data source for the subsequent calculation of the family WiFi prediction score. The WiFi module of the family WiFi prediction equipment is started, and a plurality of set moments are taken for acquiring WiFi data. When the set time is selected and the WiFi data is required to be collected at the time, the household WiFi prediction equipment is in a non-moving fixed position state, so that the situation that the detected WiFi data fluctuates due to the fact that the equipment is in a continuous moving state is avoided, and the influence of position change on the WiFi data is eliminated. Therefore, the moment when the device is generally in a fixed position state at night or during charging of the device is selected as the set moment, and the WiFi data collection is performed at the set moment. For example, two or less minutes in the morning are selected as the set time of the family WiFi prediction device, and the family WiFi prediction device collects WiFi data at two or less minutes in the morning. The WiFi data is collected at a set moment within a period of time (such as within a week). And obtaining WiFi data corresponding to each set moment in the period of time. And each WiFi data correspondingly generates a mac list, and the mac list comprises all the WiFi signal sources acquired at the corresponding set moment.
And S120, analyzing each mac list to obtain signal strength information corresponding to each WiFi signal source at each set time.
Specifically, the mac list generated at each set time needs to be analyzed to obtain the signal strength information of each WiFi signal source, so as to calculate the subsequent prediction score through the signal strength information.
In the mac list analysis, corresponding to 1, 2, the mac list at each time t is analyzed, and then a mac column generated based on the acquired WiFi data at time t is represented as:
macst＝{(mac0，rssi0，name0)，(mac1，rssi1，name1)，...，(macn，rssin，namen)}
wherein, mac0，mac1...macnDenoted as mac address, rssi, of each WiFi signal source0，rssi1，...rssinRepresented as signal strength information, name, of each WiFi signal source0，name1，...namenDenoted as the names of the respective WiFi signal sources. Further, m mac lists generated corresponding to the WiFi data at m time instants are expressed as:
user＝(t1:macs1，t2:macs2，t3:macs3，...，tm-1:macsm-1，tm:macsm)
wherein t is1，t2，...tmExpressed as respective set times, macs0，macs1...macsmShowing the mac list generated for each set time.
And then, analyzing the mac list to obtain the signal intensity information corresponding to each WiFi signal source. The analyzed signal strength information of each WiFi signal source at different set time is represented as:
mac＝[(t1，rssi1)，...，(tm-1，rssim-1)，(tm，rssim)]
wherein t is1，t2，...tmExpressed as respective set times, rssi1，rssim-1，...rssimAnd the signal strength information of the WiFi signal source corresponding to each set time is represented. If the WiFi signal source is not detected corresponding to a certain set time, the signal strength information is recorded as "0".
More specifically, when the mac list is analyzed, corresponding WiFi names and mac address information are also obtained through analysis, the WiFi names and the mac address information are used as identification information of WiFi signal sources, the WiFi signal sources are distinguished through the identification information, and the mac address information is used as index data to collect WiFi data.
In addition, when the mac list is analyzed, filtering is performed on each WiFi signal source obtained through analysis, and the WiFi signal sources belonging to the mobile hotspot are deleted. It can be understood that the WiFi signal source as the mobile hotspot does not belong to the family WiFi category, and when the mac list is parsed, this part of WiFi data may be deleted, so as to reduce the data amount of the subsequent prediction score calculation, and further simplify the calculation process.
S130, calculating the prediction scores of the WiFi signal sources serving as the family WiFi to which the current mobile terminal equipment belongs based on the signal strength information, and determining the family WiFi to which the current mobile terminal equipment belongs according to the prediction scores.
Specifically, based on the signal strength information of each WiFi signal source obtained by the analysis in step S120 at different setting times, the corresponding signal source is used as the prediction score of the home WiFi to which the current mobile terminal device belongs. It can be understood that, at a certain set time, the WiFi signal source with the strongest signal strength detected by the home WiFi prediction device (mobile terminal device) may have a relatively high probability of being the home WiFi signal source. However, considering the contingency of single data, the data obtained by only one detection cannot be directly used. Therefore, the embodiment of the application carries out the determination of family WiFi by collecting the signal strength information of each WiFi signal source at a plurality of set moments, so as to avoid the contingency of single data and ensure the accuracy of the prediction result.
When the prediction score is calculated, the prediction score of the family WiFi to which the current mobile terminal equipment belongs is calculated by attenuation weighting based on the signal strength information corresponding to each set moment and the signal attenuation condition of the WiFi signal source corresponding to each set moment. Compared with a mode of calculating the mean value of a plurality of data by summation, the method and the device for calculating the prediction score are more suitable for a use scene of calculation of the prediction score by adopting a mode of attenuation weighting calculation. Because different WiFi signals have different stability conditions, the signal strength of some WiFi signal sources may be strong or weak, and even the signal of the WiFi signal source may not be detected sometimes. And some WiFi signals are relatively stable and keep certain intensity continuously. Obviously, the probability of being a home WiFi would be relatively high for a continuously steady-strength WiFi signal source. Therefore, for a more stable WiFi signal source, a larger weight needs to be set. And setting relatively smaller weight for the WiFi signal source which is relatively unstable and has serious signal attenuation. I.e. determining the calculated weights of the prediction scores based on the attenuation of the signal.
Specifically, the calculation process of the prediction score of each WiFi signal source is as follows:
wherein, MACscoreDenotes a prediction score, beta denotes an initial weight value, tau denotes a time required for an initial amount to decay to 1/e, tiSet time corresponding to the ith WiFi signal source, rssiiTo correspond to tiSignal strength information of.
And performing attenuation weighted calculation on the WiFi signal sources correspondingly to obtain the prediction scores of the WiFi signal sources serving as family WiFi to which the current mobile terminal equipment belongs. It can be understood that, in the time period corresponding to each set time, the WiFi signal source with the higher prediction score is considered to have a higher possibility of being the home WiFi, so that it can be predicted which WiFi signal source is the home WiFi corresponding to the current mobile terminal device, and the matching relationship between the current mobile terminal device and the corresponding home WiFi is established.
Further, since the WiFi signal strengths acquired at different setting times may have a large difference, the magnitudes of the prediction scores calculated by different WiFi signal sources may have a large difference. And, since there is no score criterion, it cannot be determined from a predicted score whether the probability that the corresponding WiFi signal source is the home WiFi to which the current mobile terminal device belongs is high or low. Therefore, for convenience of comparison and quantity value normalization, the prediction probability corresponding to each prediction score is calculated by adopting a variable normalization method in the embodiment of the application, and the WiFi signal source with the highest prediction probability is taken as the family WiFi to which the current mobile terminal equipment belongs. Specifically, the prediction probability calculation formula is as follows:
wherein, macjThe corresponding prediction score for the jth WiFi signal source,is macjThe corresponding prediction probability.
After the prediction probability is calculated through normalization, the possibility that each WiFi signal source serves as the family WiFi to which the current mobile terminal device belongs can be obtained more visually through the prediction probability, the family WiFi can be predicted conveniently, and the use experience of a user is further optimized.
Optionally, the home WiFi predicting device performs WiFi data acquisition at a plurality of set times every set time period (one week or one month), generates a plurality of new mac lists by using the newly acquired WiFi data as a data source, calculates a prediction score of home WiFi to which each WiFi signal source belongs as the current mobile terminal device, and re-determines home WiFi matched with the current mobile terminal device. And the family WiFi is used as the basis for indoor positioning within a set time period in the future. By updating the matching relationship in stages, the problem that the indoor positioning effect is influenced due to the fact that the matching relationship is not updated synchronously when the family WiFi of the mobile terminal device is replaced is avoided.
The WiFi data are collected at a plurality of set moments to generate a plurality of mac lists, the mac lists are analyzed, the predication scores of the WiFi signal sources serving as the family WiFi to which the current mobile terminal device belongs are calculated based on the analyzed signal intensity information of the WiFi signal sources, and the corresponding WiFi signal sources are determined to serve as the family WiFi to which the current mobile terminal device belongs through the predication scores. By adopting the technical means, the matching relationship between the mobile terminal equipment and the family WiFi is not required to be manually input, the automatic prediction of the family WiFi is realized, the more accurate matching between the mobile terminal equipment and the family WiFi is realized, and the use experience of a user is further optimized.
Example two:
based on the foregoing embodiment, fig. 2 is a flowchart of another home WiFi prediction method provided in the second embodiment of the present application. Referring to fig. 2, the home WiFi prediction method provided in this embodiment specifically includes:
s210, WiFi data are collected at a plurality of set moments, a plurality of mac lists are generated, the mac lists comprise a plurality of WiFi signal sources, and the set moments are selected according to a fixed time period of the position state of the equipment;
s220, analyzing each mac list to obtain signal strength information corresponding to each WiFi signal source at each set moment;
s230, calculating the prediction scores of all WiFi signal sources serving as the family WiFi to which the current mobile terminal equipment belongs based on the signal strength information, and determining the family WiFi to which the current mobile terminal equipment belongs according to the prediction scores;
s240, collecting WiFi data of the current position of the mobile terminal equipment, and judging whether the WiFi data contains the family WiFi or not;
and S250, if the WiFi data contains the family WiFi, positioning the current position of the mobile terminal equipment in the corresponding user family.
According to the embodiment of the application, after the family WiFi to which the current mobile terminal device belongs is determined, indoor positioning of the user corresponding to the current mobile terminal device can be carried out according to the determined family WiFi. When indoor positioning is carried out, WiFi data of the current position are collected by starting the WiFi module of the user mobile terminal equipment. And inquiring whether the acquired WiFi data contains the family WiFi according to the matching relationship between the mobile terminal equipment and the family WiFi established in the step S230. Referring to fig. 3, a WiFi data list collected by the mobile terminal device is provided, and by inquiring whether the WiFi data list includes the family WiFi, if the WiFi data list includes the family WiFi, the current position of the mobile terminal device is located at the corresponding user family. It is understood that WiFi is a short-range wireless communication technology, and when a mobile terminal device can detect a certain WiFi signal source in response, it is determined that the mobile terminal device is in the vicinity of this WiFi signal source. If the WiFi data list does not contain the family WiFi, the current position of the mobile terminal device is not in the family of the corresponding user or the family WiFi is not started.
Further, when the WiFi data list includes home WiFi, according to the query result, the mobile terminal device marks the corresponding WiFi signal source of the WiFi data list as "home WiFi" for displaying, so that the mobile terminal device user knows that WiFi signal source is home WiFi.
It should be noted that, in the embodiment of the present application, when performing indoor positioning, access to the home WiFi is not required, and as long as the mobile terminal device detects the home WiFi, the current positioning information may be determined. Using children's phone wrist-watch as an example, when the keeper need learn current children's position information, look over whether children when at home, through head of a family's mobile terminal phone wrist-watch management application, open children's phone wrist-watch's wiFi function, phone wrist-watch passes through the wiFi signal source that wiFi detected current position, if detect family wiFi, then the position of location phone wrist-watch is at home. The phone watch returns the positioning information to the parent's mobile terminal at this moment, so that the accurate positioning of the child phone watch can be realized, and the parent can monitor the child better.
The WiFi data are collected at a plurality of set moments to generate a plurality of mac lists, the mac lists are analyzed, the predication scores of the WiFi signal sources serving as the family WiFi to which the current mobile terminal device belongs are calculated based on the analyzed signal intensity information of the WiFi signal sources, and the corresponding WiFi signal sources are determined to serve as the family WiFi to which the current mobile terminal device belongs through the predication scores. By adopting the technical means, the matching relationship between the mobile terminal equipment and the family WiFi is not required to be manually input, the automatic prediction of the family WiFi is realized, the more accurate matching between the mobile terminal equipment and the family WiFi is realized, and the use experience of a user is further optimized.
In addition, according to the matching relationship between the mobile terminal device and the family WiFi, when the current position of the mobile terminal device detects WiFi data corresponding to the family WiFi, the current position of the mobile terminal device is determined to be in the family corresponding to the user, and therefore accurate indoor positioning of the position where the user is located is achieved. Meanwhile, indoor positioning can be carried out without accessing home WiFi, so that the positioning process is simplified, and the use experience of a user is further optimized.
EXAMPLE III
Based on the foregoing embodiments, fig. 4 is a schematic structural diagram of a home WiFi predicting apparatus provided in a third embodiment of the present application. Referring to fig. 4, the home WiFi prediction apparatus provided in this embodiment specifically includes: an acquisition module 31, a parsing module 32 and a prediction module 33.
The acquisition module 31 is configured to acquire WiFi data at a plurality of set times, and generate a plurality of mac lists, where each mac list includes a plurality of WiFi signal sources, and the set times are selected according to a time period in which the device location state is fixed;
the analysis module 32 is configured to analyze each mac list to obtain signal strength information corresponding to each WiFi signal source at each set time;
the prediction module 33 is configured to calculate, based on the signal strength information, prediction scores of WiFi signal sources serving as home WiFi to which the current mobile terminal device belongs, and determine, according to the prediction scores, home WiFi to which the current mobile terminal device belongs.
The WiFi data are collected at a plurality of set moments to generate a plurality of mac lists, the mac lists are analyzed, the predication scores of the WiFi signal sources serving as the family WiFi to which the current mobile terminal device belongs are calculated based on the analyzed signal intensity information of the WiFi signal sources, and the corresponding WiFi signal sources are determined to serve as the family WiFi to which the current mobile terminal device belongs through the predication scores. By adopting the technical means, the matching relationship between the mobile terminal equipment and the family WiFi is not required to be manually input, the automatic prediction of the family WiFi is realized, the more accurate matching between the mobile terminal equipment and the family WiFi is realized, and the use experience of a user is further optimized.
Specifically, the prediction module 33 includes:
and the calculating unit is used for calculating the prediction scores of the household WiFi which the corresponding WiFi signal sources belong to as the current mobile terminal equipment by attenuation weighting based on the signal strength information corresponding to the set moments and the signal attenuation condition of the WiFi signal source corresponding to the set moments.
Specifically, the prediction module 33 further includes:
and the normalization unit is used for calculating the prediction probability corresponding to the prediction score by adopting a variable normalization method, and taking the WiFi signal source with the highest prediction probability as the family WiFi to which the current mobile terminal equipment belongs.
More specifically, the prediction probability calculation formula is:
wherein, macjThe corresponding prediction score for the jth WiFi signal source,is macjThe corresponding prediction probability.
Specifically, the system further comprises a positioning module, which is used for acquiring WiFi data of the current position of the mobile terminal equipment and judging whether the WiFi data contains the family WiFi or not; and if the WiFi data contains the family WiFi, positioning the current position of the mobile terminal equipment in the corresponding family of the user.
Specifically, the parsing module includes:
and the filtering unit is used for filtering each WiFi signal source obtained by analysis in the process of analyzing each mac list to obtain the signal intensity information corresponding to each WiFi signal source at each set moment, and deleting the WiFi signal sources belonging to the mobile hotspots.
Specifically, the analysis module analyzes the Mac list to obtain the Mac address, the WiFi name and the signal strength information corresponding to each WiFi signal source in the signal strength information corresponding to each set time of each WiFi signal source.
According to the embodiment of the application, when the current position of the mobile terminal device detects WiFi data corresponding to family WiFi according to the matching relation between the mobile terminal device and the family WiFi, the current position of the mobile terminal device is determined to be at the family corresponding to the user, and therefore accurate indoor positioning of the position where the user is located is achieved. Meanwhile, indoor positioning can be carried out without accessing home WiFi, so that the positioning process is simplified, and the use experience of a user is further optimized.
The family WiFi prediction device provided by the third embodiment of the application can be used for executing the family WiFi prediction method provided by the first embodiment and the second embodiment, and has corresponding functions and beneficial effects.
Example four:
an embodiment of the present application provides an electronic device, and with reference to fig. 5, the electronic device includes: a processor 41, a memory 42, a communication module 43, an input device 44, and an output device 45. The number of processors in the electronic device may be one or more, and the number of memories 42 in the electronic device may be one or more. The processor 41, the memory 42, the communication module 43, the input device 44 and the output device 45 of the electronic device may be connected by a bus or other means.
The memory 42 serves as a computer-readable storage medium, and can be used for storing software programs, computer-executable programs, and modules, such as program instructions/modules corresponding to the home WiFi prediction method (for example, an acquisition module, a parsing module, and a prediction module in a home WiFi prediction apparatus) according to any embodiment of the present application. The memory can mainly comprise a program storage area and a data storage area, wherein the program storage area can store an operating system and an application program required by at least one function; the storage data area may store data created according to use of the device, and the like. Further, the memory may include high speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other non-volatile solid state storage device. In some examples, the memory may further include memory located remotely from the processor, and these remote memories may be connected to the device over a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The communication module 43 is used for data transmission.
The processor 41 executes various functional applications and data processing of the device by running software programs, instructions and modules stored in the memory, that is, implements the home WiFi prediction method described above.
The input device 44 is operable to receive input numeric or character information and to generate key signal inputs relating to user settings and function controls of the apparatus. The output device 45 may include a display device such as a display screen.
The electronic device provided by the above can be used to execute the home WiFi prediction methods provided by the above embodiments one and two, and has corresponding functions and beneficial effects.
Example five:
embodiments of the present application also provide a storage medium containing computer-executable instructions, which when executed by a computer processor, are configured to perform a home WiFi prediction method, the home WiFi prediction method comprising: the method comprises the steps that WiFi data are collected at a plurality of set moments, a plurality of mac lists are generated, the mac lists comprise a plurality of WiFi signal sources, and the set moments are selected according to a fixed time period of the position state of equipment; analyzing each mac list to obtain signal intensity information corresponding to each WiFi signal source at each set moment; and calculating the prediction scores of all WiFi signal sources serving as the family WiFi to which the current mobile terminal equipment belongs based on the signal strength information, and determining the family WiFi to which the current mobile terminal equipment belongs according to the prediction scores.
Storage medium-any of various types of memory devices or storage devices. The term "storage medium" is intended to include: mounting media such as CD-ROM, floppy disk, or tape devices; computer system memory or random access memory such as DRAM, DDR RAM, SRAM, EDO RAM, Lanbas (Rambus) RAM, etc.; non-volatile memory such as flash memory, magnetic media (e.g., hard disk or optical storage); registers or other similar types of memory elements, etc. The storage medium may also include other types of memory or combinations thereof. In addition, the storage medium may be located in a first computer system in which the program is executed, or may be located in a different second computer system connected to the first computer system through a network (such as the internet). The second computer system may provide program instructions to the first computer for execution. The term "storage medium" may include two or more storage media residing in different locations, e.g., in different computer systems connected by a network. The storage medium may store program instructions (e.g., embodied as a computer program) that are executable by one or more processors.
Of course, the storage medium provided in the embodiments of the present application contains computer-executable instructions, and the computer-executable instructions are not limited to the home WiFi prediction method described above, and may also perform related operations in the home WiFi prediction method provided in any embodiments of the present application.
The home WiFi prediction apparatus, the storage medium, and the electronic device provided in the foregoing embodiments may execute the home WiFi prediction method provided in any embodiment of the present application, and reference may be made to the home WiFi prediction method provided in any embodiment of the present application without detailed technical details described in the foregoing embodiments.
The foregoing is considered as illustrative of the preferred embodiments of the invention and the technical principles employed. The present application is not limited to the particular embodiments described herein, but is capable of various obvious changes, rearrangements and substitutions as will now become apparent to those skilled in the art without departing from the scope of the invention. Therefore, although the present application has been described in more detail with reference to the above embodiments, the present application is not limited to the above embodiments, and may include other equivalent embodiments without departing from the spirit of the present application, and the scope of the present application is determined by the scope of the claims.
Claims (10)
1. A home WiFi prediction method, comprising:
the method comprises the steps that WiFi data are collected at a plurality of set moments, a plurality of mac lists are generated, the mac lists comprise a plurality of WiFi signal sources, and the set moments are selected according to a fixed time period of the position state of equipment;
analyzing each mac list to obtain signal intensity information corresponding to each WiFi signal source at each set moment;
and calculating the prediction scores of all WiFi signal sources serving as the family WiFi to which the current mobile terminal equipment belongs based on the signal strength information, and determining the family WiFi to which the current mobile terminal equipment belongs according to the prediction scores.
2. The home WiFi prediction method of claim 1, wherein the calculating the prediction score of each WiFi signal source as the home WiFi to which the current mobile terminal device belongs based on the signal strength information includes:
and calculating the prediction score of the household WiFi which the corresponding WiFi signal sources belong to as the current mobile terminal equipment by attenuation weighting based on the signal strength information corresponding to the set moments and the signal attenuation condition of the WiFi signal sources corresponding to the set moments.
3. The home WiFi prediction method of claim 2, wherein the determining the home WiFi to which the current mobile terminal device belongs according to the prediction score comprises:
and calculating the prediction probability corresponding to the prediction score by adopting a variable normalization method, and taking the WiFi signal source with the highest prediction probability as the family WiFi to which the current mobile terminal equipment belongs.
4. The home WiFi prediction method of claim 3, characterized by the prediction probability calculation formula is:
wherein, macjThe corresponding prediction score for the jth WiFi signal source,is macjThe corresponding prediction probability.
5. The home WiFi prediction method of claim 1, after determining the home WiFi to which the current mobile terminal device belongs according to the prediction score, further comprising:
the method comprises the steps of collecting WiFi data of the current position of mobile terminal equipment, and judging whether the WiFi data contain the family WiFi or not;
and if the WiFi data contains the family WiFi, positioning the current position of the mobile terminal equipment in the corresponding family of the user.
6. The home WiFi prediction method of claim 1, wherein in analyzing each mac list to obtain the signal strength information corresponding to each WiFi signal source at each set time, filtering each analyzed WiFi signal source to delete the WiFi signal sources belonging to the mobile hotspot.
7. The home WiFi prediction method of claim 1, wherein in the analyzing each Mac list to obtain the signal strength information corresponding to each WiFi signal source at each setting time, the Mac list is analyzed to obtain the Mac address, WiFi name and signal strength information corresponding to each WiFi signal source.
8. A home WiFi prediction apparatus, comprising:
the device comprises an acquisition module, a storage module and a processing module, wherein the acquisition module is used for acquiring WiFi data at a plurality of set moments to generate a plurality of mac lists, the mac lists comprise a plurality of WiFi signal sources, and the set moments are selected according to fixed time periods of the position state of the device;
the analysis module is used for analyzing each mac list to obtain signal intensity information corresponding to each WiFi signal source at each set moment;
and the prediction module is used for calculating the prediction scores of the WiFi signal sources serving as the family WiFi to which the current mobile terminal equipment belongs based on the signal strength information, and determining the family WiFi to which the current mobile terminal equipment belongs according to the prediction scores.
9. An electronic device, comprising:
a memory and one or more processors;
the memory for storing one or more programs;
when executed by the one or more processors, cause the one or more processors to implement the home WiFi prediction method of any one of claims 1-7.
10. A storage medium containing computer-executable instructions, which when executed by a computer processor, are configured to perform the home WiFi prediction method of any of claims 1-7.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112423224A (en) * | 2020-11-19 | 2021-02-26 | 杭州云深科技有限公司 | Residence type identification method based on wifi characteristic data, electronic device and medium |
Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103260130A (en) * | 2012-02-17 | 2013-08-21 | 三星电子株式会社 | Method and apparatus for positioning location of wireless access point |
| CN104053215A (en) * | 2014-06-11 | 2014-09-17 | 西安中兴新软件有限责任公司 | Method and device for selecting optimum position of access point |
| US20160135012A1 (en) * | 2012-05-03 | 2016-05-12 | Indooratlas Oy | Applying indoor magnetic fields for acquiring movement information |
| CN106535283A (en) * | 2016-11-25 | 2017-03-22 | 南京科技职业学院 | Indoor positioning method based on beacon clustering |
| US20170150338A1 (en) * | 2015-11-25 | 2017-05-25 | Senao Networks, Inc. | Method and device having pruning mechanism to identify wireless access points in multiple-ssid environment |
| CN107333235A (en) * | 2017-06-14 | 2017-11-07 | 珠海市魅族科技有限公司 | WiFi connections probability forecasting method, device, terminal and storage medium |
| CN108234686A (en) * | 2017-12-20 | 2018-06-29 | 中国联合网络通信集团有限公司 | A kind of method and apparatus of indoor and outdoor judgement |
| CN109041218A (en) * | 2018-09-25 | 2018-12-18 | 广东小天才科技有限公司 | A kind of method and Intelligent hardware for predicting user location |
| CN109168129A (en) * | 2018-08-08 | 2019-01-08 | 贵阳市城市轨道交通有限公司 | Positioning system and method based on WLAN WLAN |
| CN109672980A (en) * | 2018-12-25 | 2019-04-23 | 腾讯科技（深圳）有限公司 | Determine the method, apparatus and storage medium of the corresponding WLAN hot spot of point of interest |
-
2019
- 2019-09-26 CN CN201910914627.0A patent/CN110611880B/en active Active
Patent Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103260130A (en) * | 2012-02-17 | 2013-08-21 | 三星电子株式会社 | Method and apparatus for positioning location of wireless access point |
| US20160135012A1 (en) * | 2012-05-03 | 2016-05-12 | Indooratlas Oy | Applying indoor magnetic fields for acquiring movement information |
| CN104053215A (en) * | 2014-06-11 | 2014-09-17 | 西安中兴新软件有限责任公司 | Method and device for selecting optimum position of access point |
| US20170150338A1 (en) * | 2015-11-25 | 2017-05-25 | Senao Networks, Inc. | Method and device having pruning mechanism to identify wireless access points in multiple-ssid environment |
| CN106535283A (en) * | 2016-11-25 | 2017-03-22 | 南京科技职业学院 | Indoor positioning method based on beacon clustering |
| CN107333235A (en) * | 2017-06-14 | 2017-11-07 | 珠海市魅族科技有限公司 | WiFi connections probability forecasting method, device, terminal and storage medium |
| CN108234686A (en) * | 2017-12-20 | 2018-06-29 | 中国联合网络通信集团有限公司 | A kind of method and apparatus of indoor and outdoor judgement |
| CN109168129A (en) * | 2018-08-08 | 2019-01-08 | 贵阳市城市轨道交通有限公司 | Positioning system and method based on WLAN WLAN |
| CN109041218A (en) * | 2018-09-25 | 2018-12-18 | 广东小天才科技有限公司 | A kind of method and Intelligent hardware for predicting user location |
| CN109672980A (en) * | 2018-12-25 | 2019-04-23 | 腾讯科技（深圳）有限公司 | Determine the method, apparatus and storage medium of the corresponding WLAN hot spot of point of interest |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112423224A (en) * | 2020-11-19 | 2021-02-26 | 杭州云深科技有限公司 | Residence type identification method based on wifi characteristic data, electronic device and medium |
| CN112423224B (en) * | 2020-11-19 | 2022-07-01 | 杭州云深科技有限公司 | Residence type identification method based on wifi characteristic data, electronic device and medium |
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|---|---|
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