CN115515223A - Method, device and network equipment for processing fingerprint information - Google Patents

Abstract

The invention provides a fingerprint information processing method, device and network equipment, and relates to the technical field of the Internet of Things. The method includes: obtaining a target fingerprint hot spot set according to at least one fingerprint hot spot; acquiring quality information of each fingerprint hot spot according to feature information of each fingerprint hot spot in the target fingerprint hot spot set; The feature information and the quality information corresponding to the hot spots construct a Wi-Fi WiFi fingerprint database, and the WiFi fingerprint database is used for WiFi fingerprint positioning. The solution of the present invention solves the problem that the lack of a unified fingerprint information processing method in the prior art leads to insufficient fingerprint positioning accuracy.

Description

Method, device and network equipment for processing fingerprint information

technical field

The invention relates to the technical field of the Internet of Things, in particular to a fingerprint information processing method, device and network equipment.

Background technique

In the prior art, when smoothing the fingerprints of a certain collection point, it is usually achieved by calculating the average value, and the fingerprint information processed by different devices, different time periods or different models needs to be properly weighted. In order to increase the robustness of the fingerprint library, if there is a need to update, the average weighting is usually carried out.

However, for the collection of fingerprint information in the prior art, it is necessary to distinguish between different devices, different time periods or different models, but lacks a unified fingerprint information processing method, and does not fully explore and collect statistical characteristics of fingerprint information, but only Calculating the average value directly leads to insufficient fingerprint positioning accuracy.

Contents of the invention

The object of the present invention is to provide a fingerprint information processing method, device and network equipment, which solves the problem of insufficient fingerprint positioning accuracy due to the lack of a unified fingerprint information processing method in the prior art.

In order to achieve the above object, an embodiment of the present invention provides a method for processing fingerprint information, including:

Obtaining a target fingerprint hotspot set according to at least one fingerprint hotspot;

Acquiring quality information of each fingerprint hotspot according to the feature information of each fingerprint hotspot in the target fingerprint hotspot set;

A WiFi (Wireless Fidelity, Wireless Fidelity) fingerprint database is constructed according to the characteristic information and the quality information corresponding to each fingerprint hotspot, and the WiFi fingerprint database is used for WiFi fingerprint positioning.

Optionally, said obtaining a target fingerprint hotspot set according to at least one fingerprint hotspot includes:

Performing feature extraction on at least one fingerprint hotspot to obtain feature information corresponding to each said fingerprint hotspot;

According to the feature information, a screening operation is performed on the at least one fingerprint hotspot to obtain a target fingerprint hotspot set.

Optionally, the feature information includes at least one of the following:

hot spot mean;

Probability of hot spots;

Regional hotspot identification information;

Standard deviation of hotspots.

Optionally, the screening operations include:

When the first fingerprint hotspot in the at least one fingerprint hotspot satisfies a first preset condition, delete the first fingerprint hotspot from the at least one fingerprint hotspot;

Wherein, the first preset condition includes at least one of the following:

The hot spot mean value of the first fingerprint hot spot is less than or equal to the preset mean value;

A hotspot occurrence probability of the first fingerprint hotspot is less than or equal to a preset probability.

Optionally, the acquiring the quality information of each fingerprint hotspot according to the feature information of each fingerprint hotspot in the target fingerprint hotspot set includes:

According to the feature information, obtain the power coefficient corresponding to each feature information of each fingerprint hotspot;

According to the efficacy coefficient and the weight coefficient corresponding to the feature information, the quality information of the fingerprint hotspot is obtained.

Optionally, the processing method also includes:

Setting weight coefficients for each of the feature information according to the importance ranking of different feature information;

Wherein, the sum of weight coefficients corresponding to each feature information is 1.

Optionally, before obtaining the target fingerprint hotspot set according to at least one fingerprint hotspot, the processing method further includes:

Selecting a hotspot within a preset radius range with the first hotspot as the center as the at least one fingerprint hotspot; wherein, the first hotspot is a fixed hotspot with the largest RSSI (Received Signal Strength Indication) in the target area; The hotspots include fixed hotspots and/or hotspots containing location information in the environment.

Optionally, the WiFi fingerprint database includes numbers corresponding to each fingerprint hotspot, MAC address (MediaAccess Control Address, MAC address), recording time, signal strength average, quality information, recording duration, longitude and latitude at least one.

Optionally, the processing method also includes:

Perform an update operation on the WiFi fingerprint database;

Wherein, in the case where the environment where the fingerprint hotspot is located has not changed, the update operation includes at least one of the following:

Calculate the average value of the signal strength average value according to the duration ratio;

Calculate the average value of the quality information according to the duration ratio;

update said recorded duration;

The recorded time is updated.

To achieve the above object, an embodiment of the present invention provides a network device, including a processor and a transceiver, wherein the processor is used for:

Obtaining a target fingerprint hotspot set according to at least one fingerprint hotspot;

Acquiring quality information of each fingerprint hotspot according to the feature information of each fingerprint hotspot in the target fingerprint hotspot set;

According to the feature information and the quality information corresponding to each of the fingerprint hotspots, a Wi-Fi WiFi fingerprint database is constructed, and the WiFi fingerprint database is used for WiFi fingerprint positioning.

Optionally, when the processor obtains the target fingerprint hotspot set according to at least one fingerprint hotspot, it is specifically configured to:

Performing feature extraction on at least one fingerprint hotspot to obtain feature information corresponding to each said fingerprint hotspot;

According to the feature information, a screening operation is performed on the at least one fingerprint hotspot to obtain a target fingerprint hotspot set.

Optionally, the feature information includes at least one of the following:

hot spot mean;

Probability of hot spots;

Regional hotspot identification information;

Standard deviation of hotspots.

Optionally, the screening operations include:

When the first fingerprint hotspot in the at least one fingerprint hotspot satisfies a first preset condition, delete the first fingerprint hotspot from the at least one fingerprint hotspot;

Wherein, the first preset condition includes at least one of the following:

The hot spot mean value of the first fingerprint hot spot is less than or equal to the preset mean value;

A hotspot occurrence probability of the first fingerprint hotspot is less than or equal to a preset probability.

Optionally, when the processor obtains the quality information of each of the fingerprint hotspots according to the feature information of each of the fingerprint hotspots in the target fingerprint hotspot set, it is specifically configured to:

According to the feature information, obtain the power coefficient corresponding to each feature information of each fingerprint hotspot;

According to the efficacy coefficient and the weight coefficient corresponding to the feature information, the quality information of the fingerprint hotspot is obtained.

Optionally, the processor is also used for:

Setting weight coefficients for each of the feature information according to the importance ranking of different feature information;

Wherein, the sum of weight coefficients corresponding to each feature information is 1.

Optionally, before obtaining the target fingerprint hotspot set according to at least one fingerprint hotspot, the processor is further configured to:

Selecting a hotspot within a preset radius with the first hotspot as the center as the at least one fingerprint hotspot; wherein, the first hotspot is a fixed hotspot whose received signal strength indicates the maximum RSSI in the target area; the hotspot includes a fixed hotspot and/or hotspots in the environment that contain location information.

Optionally, the WiFi fingerprint database includes at least one of serial number, MAC address, recording time, average signal strength, quality information, recording duration, longitude and latitude corresponding to each fingerprint hotspot.

Optionally, the processor is also used for:

Perform an update operation on the WiFi fingerprint database;

Wherein, in the case where the environment where the fingerprint hotspot is located has not changed, the update operation includes at least one of the following:

Calculate the average value of the signal strength average value according to the duration ratio;

Calculate the average value of the quality information according to the duration ratio;

update said recorded duration;

The recorded time is updated.

In order to achieve the above purpose, an embodiment of the present invention provides a device for processing fingerprint information, including:

A first processing module, configured to obtain a target fingerprint hotspot set according to at least one fingerprint hotspot;

A second processing module, configured to acquire quality information of each fingerprint hotspot according to feature information of each fingerprint hotspot in the target fingerprint hotspot set;

The third processing module is configured to construct a Wi-Fi WiFi fingerprint database according to the feature information and the quality information corresponding to each fingerprint hotspot, and the WiFi fingerprint database is used for WiFi fingerprint positioning.

Optionally, the first processing module includes:

A feature extraction unit, configured to perform feature extraction on at least one fingerprint hotspot, and obtain feature information corresponding to each said fingerprint hotspot;

The data screening unit is configured to perform a screening operation on the at least one fingerprint hotspot according to the feature information to obtain a target fingerprint hotspot set.

Optionally, the feature information includes at least one of the following:

hot spot mean;

Probability of hot spots;

Regional hotspot identification information;

Standard deviation of hotspots.

Optionally, the data screening unit includes:

A data screening subunit, configured to delete the first fingerprint hotspot from the at least one fingerprint hotspot when the first fingerprint hotspot in the at least one fingerprint hotspot satisfies a first preset condition;

Wherein, the first preset condition includes at least one of the following:

The hot spot mean value of the first fingerprint hot spot is less than or equal to the preset mean value;

A hotspot occurrence probability of the first fingerprint hotspot is less than or equal to a preset probability.

Optionally, the second processing module includes:

A first processing unit, configured to obtain an efficacy coefficient corresponding to each of the feature information of each of the fingerprint hotspots according to the feature information;

The second processing unit is configured to obtain the quality information of the fingerprint hotspot according to the efficacy coefficient and the weight coefficient corresponding to the feature information.

Optionally, the processing device also includes:

A weight setting module, configured to set a weight coefficient for each feature information according to the importance ranking of different feature information;

Wherein, the sum of weight coefficients corresponding to each feature information is 1.

Optionally, the processing device also includes:

A hotspot determination module, configured to select a hotspot within a preset radius with the first hotspot as the center as the at least one fingerprint hotspot; wherein, the first hotspot is a fixed hotspot with the largest RSSI received in the target area; The hotspots include fixed hotspots and/or hotspots containing location information in the environment.

Optionally, the WiFi fingerprint database includes at least one of serial number, MAC address, recording time, average signal strength, quality information, recording duration, longitude and latitude corresponding to each fingerprint hotspot.

Optionally, the processing device also includes:

A database update module, configured to update the WiFi fingerprint database;

Wherein, in the case where the environment where the fingerprint hotspot is located has not changed, the update operation includes at least one of the following:

Calculate the average value of the signal strength average value according to the duration ratio;

Calculate the average value of the quality information according to the duration ratio;

update said recorded duration;

The recorded time is updated.

To achieve the above object, an embodiment of the present invention provides a network device, including a transceiver, a processor, a memory, and a program or instruction stored in the memory and operable on the processor; the processor executes The above-mentioned processing method is realized when the program or instruction is executed.

To achieve the above object, an embodiment of the present invention provides a readable storage medium, on which a program or instruction is stored, and when the program or instruction is executed by a processor, the steps in the processing method as described above are implemented.

The beneficial effects of above-mentioned technical scheme of the present invention are as follows:

In the method of the embodiment of the present invention, the WiFi fingerprint database is obtained by processing the fingerprint hotspots, without establishing various models for the environment, and has better versatility.

Description of drawings

Fig. 1 is the flowchart of the processing method of the embodiment of the present invention;

FIG. 2 is a structural diagram of a network device according to an embodiment of the present invention;

3 is a structural diagram of a processing device according to another embodiment of the present invention;

Fig. 4 is a structural diagram of a network device according to another embodiment of the present invention.

detailed description

In order to make the technical problems, technical solutions and advantages to be solved by the present invention clearer, the following will describe in detail with reference to the drawings and specific embodiments.

It should be understood that reference throughout this specification to "one embodiment" or "an embodiment" means that a particular feature, structure, or characteristic related to the embodiment is included in at least one embodiment of the present invention. Thus, appearances of "in one embodiment" or "in an embodiment" in various places throughout the specification are not necessarily referring to the same embodiment. Furthermore, the particular features, structures or characteristics may be combined in any suitable manner in one or more embodiments.

In various embodiments of the present invention, it should be understood that the sequence numbers of the following processes do not mean the order of execution, and the order of execution of each process should be determined by its functions and internal logic, rather than implementing the present invention. The implementation of the examples constitutes no limitation.

Additionally, the terms "system" and "network" are often used interchangeably herein.

In the embodiments provided in the present application, it should be understood that "B corresponding to A" means that B is associated with A, and B can be determined according to A. However, it should also be understood that determining B according to A does not mean determining B only according to A, and B may also be determined according to A and/or other information.

A description of factors affecting the accuracy of a Received Signal Strength Indication (RSSI for short).

The existing WiFi-based fingerprint database construction mainly adopts the method of collecting and uploading the signal strength of other hotspots (APs) at the collection point, and RSSI, which is the most important observation information, is generally considered to include:

Interference with electronic devices at the same frequency, Bluetooth devices, wireless cameras, ZigBee devices and WiFi work at the same frequency, and mutual interference will cause deviations in the measured radio wave parameters (signal strength, etc.);

Influenced by multi-path propagation effects, wireless signals in an indoor environment are easily blocked by various indoor objects, resulting in diffraction, reflection, diffraction and other phenomena, resulting in delays in signal propagation, and changes in signal frequency, amplitude or phase , causing multipath effects;

The interference of the human body, when the radiation frequency resonates with the natural frequency of the organism, the absorption is the strongest, that is, resonance absorption. In particular, when the collection personnel collect with their backs to the APs, the WiFi signal passes through the human body and is received by the mobile terminal, resulting in signal strength Weakness, thus affecting the positioning results;

The complexity and variability of the positioning environment, the indoor environment changes due to people walking back and forth or the constant changes of objects, in addition, changes in the number and location of deployed APs, as well as changes in the indoor pattern, will also affect the positioning environment. The results have a big impact.

That is, the environment and the human body constitute important factors affecting the accuracy of the RSSI.

As shown in Figure 1, a method for processing fingerprint information in an embodiment of the present invention includes:

Step 101, according to at least one fingerprint hotspot, obtain a target fingerprint hotspot set.

Here, the fingerprint hot spot can be a fixed AP (Wireless Access Point, wireless access point) of WiFi, wherein the fingerprint hot spot can also be understood as fingerprint data (or fingerprint information), and the fingerprint data can include fixed hot spots and A hotspot containing location information in the environment where the target area is located.

Optionally, this step 101 includes:

(1) Performing feature extraction on at least one fingerprint hot spot to obtain feature information corresponding to each said fingerprint hot spot.

In this step, WiFi fingerprint information (ie, fingerprint hotspots) can be classified by performing feature extraction on at least one fingerprint hotspot, that is, obtaining feature data (ie, feature information). In addition, the feature information can also be sorted, for example, sorted according to the importance of the feature information.

Among them, the key feature information may include: hotspot mean value (i.e. hotspot rssi mean value, for example represented by rssi-mean), hotspot appearance probability (i.e. hotspot rssi appearance probability, for example represented by rssi-appear), regional hotspot identification information (for example represented by rssi-reg) and the standard deviation of hotspots (that is, the standard deviation of hotspot rssi, for example represented by rssi-std).

(2) Perform a screening operation on the at least one fingerprint hotspot according to the feature information to obtain a target fingerprint hotspot set.

In this step, by setting a threshold on the feature information, fingerprint hotspots whose feature information exceeds the threshold can be screened out to form a target fingerprint hotspot set. For example, the threshold of the hot spot mean value (rssi-mean) is set to -80dBm, if the hot spot mean value (rssi-mean) corresponding to a certain fingerprint hot spot in the at least one fingerprint hot spot is less than or equal to -80dBm, then the fingerprint hot spot delete.

Step 102, according to the feature information of each fingerprint hotspot in the target fingerprint hotspot set, the quality information of each fingerprint hotspot is acquired.

It should be noted that after step 101 determines the target fingerprint hot spot set, the quality information of each fingerprint hot spot can be determined according to the quality information of each fingerprint hot spot in the target fingerprint hot spot set, and the quality information can be It is a comprehensive scoring information for fingerprint hotspots.

Step 103: Construct a Wi-Fi WiFi fingerprint database according to the feature information and the quality information corresponding to each fingerprint hotspot, and the WiFi fingerprint database is used for WiFi fingerprint positioning.

In this embodiment, a fingerprint information database (ie, WiFi fingerprint database) can be constructed according to the acquired fingerprint data (ie, fingerprint hot spots). In this way, there is no need to process data brought by various devices, which is simpler and more versatile.

Optionally, the feature information includes at least one of the following:

hot spot mean;

Probability of hot spots;

Regional hotspot identification information;

Standard deviation of hotspots.

Wherein, the regional hotspot identification information is used to indicate whether it is a regional hotspot identification.

Optionally, the screening operations include:

When the first fingerprint hotspot in the at least one fingerprint hotspot satisfies a first preset condition, delete the first fingerprint hotspot from the at least one fingerprint hotspot;

Wherein, the first preset condition includes at least one of the following:

The hot spot mean value of the first fingerprint hot spot is less than or equal to the preset mean value;

A hotspot occurrence probability of the first fingerprint hotspot is less than or equal to a preset probability.

For example, you can set thresholds for hotspot mean (rssi-mean) and hotspot appearance probability (rssi-appear), as shown in the following table:

project threshold rssi-mean -80dBm rssi-appear 50%

According to the above table, the preset mean value (i.e. the threshold of the hotspot mean value) can be set to -80dBm, the preset probability (i.e. the threshold of the occurrence probability of the hotspot) can be set to 50%, and the hotspot data below the above threshold (i.e. Fingerprint hotspots) are removed, that is, fingerprint hotspots with hotspot mean (rssi-mean) less than or equal to -80dBm are deleted, and fingerprint hotspots with hotspot occurrence probability (rssi-appear) less than or equal to 50% are deleted.

As an optional embodiment of the present invention, after the above screening operation, the obtained hotspot data are shown in the following table:

Mac Lat Lon rssi-mean rssi-appear rssi-reg Rssi-std APs1 Lat1 Lon1 -45.42 94.4% 1 1.36 Aps2 Lat2 Lon2 -48.16 100% 1 3.29 … APs LatN LonN -79.93 51.2% 0 1.62

Wherein, Mac represents the MAC address of the fingerprint hotspot, Lat represents the latitude of the fingerprint hotspot, and Lon represents the longitude of the fingerprint hotspot.

Optionally, the acquiring the quality information of each fingerprint hotspot according to the feature information of each fingerprint hotspot in the target fingerprint hotspot set includes:

According to the feature information, obtain the power coefficient corresponding to each feature information of each fingerprint hotspot;

According to the efficacy coefficient and the weight coefficient corresponding to the feature information, the quality information of the fingerprint hotspot is obtained.

For example, the quality information can be obtained by summing the product of the power coefficient and the weight coefficient corresponding to each feature information, specifically, it can be obtained according to the following formula:

Among them, APs _score represents the quality information; f _i represents the efficacy coefficient; w_coef _i represents the weight coefficient.

As an optional embodiment of the present invention, according to the feature information of each fingerprint hotspot in the target fingerprint hotspot set, the quality information of each fingerprint hotspot is obtained, that is, for each fingerprint hotspot in the target fingerprint hotspot set The specific process of scoring the above fingerprint hotspots is as follows:

First, the hotspot data in the target fingerprint hotspot set is shown in the following table:

rssi-mean rssi-appear rssi-reg rssi-std upper limit -45.42 100% 1 1.36 lower limit -79.93 51.2% 0 4.75 APs1 -45.42 94.4% 1 1.36 Aps2 -48.16 100% 1 2.29 … APs -79.93 51.2% 0 1.62

Among them, upper limit represents the maximum value of a feature information in the target fingerprint hotspot set; lower limit represents the minimum value of a certain feature information in the target fingerprint hotspot set.

The efficacy coefficient can be calculated using the dimensionless first formula, where the first public expression is:

Among them, f _i represents the efficiency coefficient; x _i represents a certain feature information; x _{lower_limit} represents the minimum value corresponding to the feature information; x _{upper_limit} represents the maximum value corresponding to the feature information.

For example, the power coefficient corresponding to the hotspot mean (rssi-mean) of fingerprint hotspot APs2 is:

Among them, f _{APs2_rssimean} represents the efficacy coefficient corresponding to the hotspot mean of fingerprint hotspot APs2.

Similarly, according to the first formula, the corresponding power coefficients are calculated for each index (ie feature information) in each fingerprint data respectively.

It should be pointed out that for parameters such as the standard deviation (rssi-std) of hotspots, the numerical value represents the lower limit, the inverse number needs to be taken during calculation, that is:

Among them, f _{APs2_rssistd} represents the power coefficient corresponding to the standard deviation of the hot spot of the fingerprint hot spot APs2.

Then, for the fingerprint hotspot Aps2, the power coefficients of each category (that is, the power coefficients corresponding to each feature information) are shown in the following table:

f_{APs2_rssimean} 0.9061 f_{APs2_appear} 1 f_{APs2_reg} 1 f_{APs2_std} 0.4310

Then, increase the weight coefficient to get the quality information of the fingerprint hotspot (ie AP) (ie the total score of the fingerprint hotspot):

Optionally, the processing method also includes:

Setting weight coefficients for each of the feature information according to the importance ranking of different feature information;

Wherein, the sum of weight coefficients corresponding to each feature information is 1.

It should be pointed out that in the embodiment of the present invention, considering that the above four key feature information have different importance, different weight coefficients (w_coef) can be assigned to them respectively, wherein the sum of each weight coefficient is 1, for example, each index The distribution of weight coefficients is shown in the table below:

rssi-mean rssi-appear rssi-reg rssi-std w_coef 0.3 0.4 0.2 0.1

Finally, the weight coefficient is multiplied by the corresponding efficacy coefficient to obtain a total AP score (that is, quality information) with an interval between [0,1]. Wherein, the total score is calculated by using the second formula, and the second formula is:

Among them, APs _score represents the quality information; f _i represents the efficacy coefficient; w_coef _i represents the weight coefficient.

Specifically, for the fingerprint hotspot Aps2, the data shown in the following table can be calculated according to the above process:

Among them, total represents quality information.

In this embodiment, the fingerprint hotspots can be scored, and weight coefficients can be added to obtain the total score of the fingerprint hotspots.

Optionally, before obtaining the target fingerprint hotspot set according to at least one fingerprint hotspot, the processing method further includes:

Selecting a hotspot within a preset radius with the first hotspot as the center as the at least one fingerprint hotspot; wherein, the first hotspot is a fixed hotspot whose received signal strength indicates the maximum RSSI in the target area; the hotspot includes a fixed hotspot and/or hotspots in the environment that contain location information.

It should be pointed out that a fingerprint collection point can be understood as a collection point where fingerprint data can be collected in a certain area. Usually, the collection time at a certain fingerprint collection point is 100 seconds or more. Among them, the fingerprint data can include fixed regional hotspots constructed and hotspots containing location information in the environment, but there are certain jump points in the fingerprint data in the environment, and such hotspots need to be selected.

Specifically, since the data containing locations in the environment is less, and fixed hotspots are relatively concentrated, the number of location jump points will be much smaller than the number of hotspots in the core area. Based on this fact, in the embodiment of the present invention, the centroid can be selected as the hotspot with the largest signal strength RSSI among the fixed hotspots in the area, and with this hotspot as the center, a hotspot with a radius smaller than a preset radius (for example, 1km) can be selected as the fingerprint hotspot, and the rest be eliminated.

Optionally, the WiFi fingerprint database includes a number corresponding to each fingerprint hotspot (for example, represented by Num), MAC address (for example, represented by MAC), recording time (for example, represented by time), average signal strength, quality information At least one of (for example, represented by APs _score ), recording duration (for example, represented by Times), longitude and latitude.

As an optional embodiment of the present invention, the specific information in the WiFi fingerprint database is shown in the following table:

Optionally, the processing method also includes:

Perform an update operation on the WiFi fingerprint database;

Wherein, when the environment has not changed, the update operation includes at least one of the following:

Calculate the average value of the signal strength average value according to the duration ratio;

Calculate the average value of the quality information according to the duration ratio;

update said recorded duration;

The recorded time is updated.

In this embodiment, the update mechanism of the WiFi fingerprint database can be determined by the change of the environment, wherein the change of the environment includes: the change of the fixed hot spot in the area (such as the change of the position of the fixed hot spot), the change of the main pattern of the area, the change of the large furniture in the area Rearrange etc.

In the case that the environment has not changed: For the signal strength mean (rssi-mean) and the total score (that is, the comprehensive score APs _score ), calculate the average value of the new fingerprint information and the old fingerprint information according to the time length ratio; (Times) are accumulated; the recording time (time) is changed to a new time; other parameters remain unchanged.

When the environment changes: re-enter the WiFi fingerprint database according to the new fingerprint information, and overwrite the original information.

In this embodiment, there is no need to discriminately process the data brought by various devices, and an update mechanism for the WiFi fingerprint database is established for the normalized weights of data in different time periods, which is more convenient.

The processing method of the embodiment of the present invention obtains the fingerprint database by processing the fingerprint hotspots, without the need to establish multiple models for the environment, and has better versatility; by normalizing the weights of data in different periods, there is no need to distinguish between different processing methods. The update mechanism of the fingerprint database is simpler for the data brought by the device.

As shown in FIG. 2, a network device 200 according to an embodiment of the present invention includes a processor 210 and a transceiver 220, wherein the processor 210 is used for:

Obtaining a target fingerprint hotspot set according to at least one fingerprint hotspot;

Acquiring quality information of each fingerprint hotspot according to the feature information of each fingerprint hotspot in the target fingerprint hotspot set;

According to the feature information and the quality information corresponding to each of the fingerprint hotspots, a Wi-Fi WiFi fingerprint database is constructed, and the WiFi fingerprint database is used for WiFi fingerprint positioning.

Optionally, when the processor 210 obtains the target fingerprint hotspot set according to at least one fingerprint hotspot, it is specifically configured to:

Performing feature extraction on at least one fingerprint hotspot to obtain feature information corresponding to each said fingerprint hotspot;

According to the feature information, a screening operation is performed on the at least one fingerprint hotspot to obtain a target fingerprint hotspot set.

Optionally, the feature information includes at least one of the following:

hot spot mean;

Probability of hot spots;

Regional hotspot identification information;

Standard deviation of hotspots.

Optionally, the screening operations include:

When the first fingerprint hotspot in the at least one fingerprint hotspot satisfies a first preset condition, delete the first fingerprint hotspot from the at least one fingerprint hotspot;

Wherein, the first preset condition includes at least one of the following:

The hot spot mean value of the first fingerprint hot spot is less than or equal to the preset mean value;

A hotspot occurrence probability of the first fingerprint hotspot is less than or equal to a preset probability.

Optionally, when the processor 210 obtains the quality information of each fingerprint hotspot according to the feature information of each fingerprint hotspot in the target fingerprint hotspot set, it is specifically configured to:

According to the feature information, obtain the power coefficient corresponding to each feature information of each fingerprint hotspot;

According to the efficacy coefficient and the weight coefficient corresponding to the feature information, the quality information of the fingerprint hotspot is obtained.

Optionally, the processor 210 is further configured to:

Setting weight coefficients for each of the feature information according to the importance ranking of different feature information;

Wherein, the sum of weight coefficients corresponding to each feature information is 1.

Optionally, before obtaining the target fingerprint hotspot set according to at least one fingerprint hotspot, the processor 210 is further configured to:

Selecting a hotspot within a preset radius with the first hotspot as the center as the at least one fingerprint hotspot; wherein, the first hotspot is a fixed hotspot whose received signal strength indicates the maximum RSSI in the target area; the hotspot includes a fixed hotspot and/or hotspots in the environment that contain location information.

Optionally, the WiFi fingerprint database includes at least one of serial number, MAC address, recording time, average signal strength, quality information, recording duration, longitude and latitude corresponding to each fingerprint hotspot.

Optionally, the processor 210 is further configured to:

Perform an update operation on the WiFi fingerprint database;

Wherein, in the case where the environment where the fingerprint hotspot is located has not changed, the update operation includes at least one of the following:

Calculate the average value of the signal strength average value according to the duration ratio;

Calculate the average value of the quality information according to the duration ratio;

update said recorded duration;

The recorded time is updated.

The network device of this embodiment obtains the fingerprint database by processing the fingerprint hotspots, without the need to build multiple models for the environment, and has better versatility; by normalizing the weights of data in different periods, it does not need to distinguish between different types of devices The update mechanism of the fingerprint database is more convenient.

As shown in Figure 3, a device for processing fingerprint information according to an embodiment of the present invention includes:

The first processing module 301 is configured to obtain a target fingerprint hotspot set according to at least one fingerprint hotspot;

The second processing module 302 is configured to acquire the quality information of each fingerprint hotspot according to the feature information of each fingerprint hotspot in the target fingerprint hotspot set;

The third processing module 303 is configured to construct a wireless fidelity WiFi fingerprint database according to the feature information and the quality information corresponding to each fingerprint hotspot, and the WiFi fingerprint database is used for WiFi fingerprint positioning.

Optionally, the first processing module 301 includes:

A feature extraction unit, configured to perform feature extraction on at least one fingerprint hotspot, and obtain feature information corresponding to each said fingerprint hotspot;

The data screening unit is configured to perform a screening operation on the at least one fingerprint hotspot according to the feature information to obtain a target fingerprint hotspot set.

Optionally, the feature information includes at least one of the following:

hot spot mean;

Probability of hot spots;

Regional hotspot identification information;

Standard deviation of hotspots.

Optionally, the data screening unit includes:

A data screening subunit, configured to delete the first fingerprint hotspot from the at least one fingerprint hotspot when the first fingerprint hotspot in the at least one fingerprint hotspot satisfies a first preset condition;

Wherein, the first preset condition includes at least one of the following:

The hot spot mean value of the first fingerprint hot spot is less than or equal to the preset mean value;

A hotspot occurrence probability of the first fingerprint hotspot is less than or equal to a preset probability.

Optionally, the second processing module 302 includes:

A first processing unit, configured to obtain an efficacy coefficient corresponding to each of the feature information of each of the fingerprint hotspots according to the feature information;

The second processing unit is configured to obtain the quality information of the fingerprint hotspot according to the efficacy coefficient and the weight coefficient corresponding to the feature information.

Optionally, the processing device also includes:

A weight setting module, configured to set a weight coefficient for each feature information according to the importance ranking of different feature information;

Wherein, the sum of weight coefficients corresponding to each feature information is 1.

Optionally, the processing device also includes:

A hotspot determination module, configured to select a hotspot within a preset radius with the first hotspot as the center as the at least one fingerprint hotspot; wherein, the first hotspot is a fixed hotspot with the largest RSSI received in the target area; The hotspots include fixed hotspots and/or hotspots containing location information in the environment.

Optionally, the WiFi fingerprint database includes at least one of serial number, MAC address, recording time, average signal strength, quality information, recording duration, longitude and latitude corresponding to each fingerprint hotspot.

Optionally, the processing device also includes:

A database update module, configured to update the WiFi fingerprint database;

Wherein, in the case where the environment where the fingerprint hotspot is located has not changed, the update operation includes at least one of the following:

Calculate the average value of the signal strength average value according to the duration ratio;

Calculate the average value of the quality information according to the duration ratio;

update said recorded duration;

The recorded time is updated.

The processing device of the embodiment of the present invention obtains the fingerprint database by processing the fingerprint hotspots, without the need to establish multiple models for the environment, and has better versatility; by normalizing the weights of data in different time periods, there is no need to distinguish between different processing methods. The update mechanism of the fingerprint database is simpler for the data brought by the device.

A network device according to another embodiment of the present invention, as shown in FIG. 4 , includes a transceiver 410, a processor 400, a memory 420, and programs or instructions stored in the memory 420 and operable on the processor 400; When the processor 400 executes the program or instruction, the above-mentioned applied processing method is realized.

The transceiver 410 is used for receiving and sending data under the control of the processor 400 .

Wherein, in FIG. 4 , the bus architecture may include any number of interconnected buses and bridges, specifically one or more processors represented by the processor 400 and various circuits of the memory represented by the memory 420 are linked together. The bus architecture can also link together various other circuits such as peripherals, voltage regulators, and power management circuits, etc., which are well known in the art and therefore will not be further described herein. The bus interface provides the interface. Transceiver 410 may be a plurality of elements, including a transmitter and a receiver, providing a means for communicating with various other devices over a transmission medium. The processor 400 is responsible for managing the bus architecture and general processing, and the memory 420 can store data used by the processor 400 when performing operations.

A readable storage medium according to an embodiment of the present invention stores a program or an instruction thereon, and when the program or instruction is executed by a processor, the steps in the above-mentioned processing method can be realized and the same technical effect can be achieved. To avoid repetition, I won't go into details here. Wherein, the computer-readable storage medium is, for example, a read-only memory (Read-Only Memory, ROM for short), a random access memory (Random Access Memory, RAM for short), a magnetic disk or an optical disk, and the like.

It should be further noted that the terminals described in this manual include but are not limited to smartphones, tablet computers, etc., and many of the described functional components are called modules, in order to more particularly emphasize the independence of their implementation.

In the embodiments of the present invention, the modules may be implemented by software so as to be executed by various types of processors. An identified module of executable code may, by way of example, comprise one or more physical or logical blocks of computer instructions which may, for example, be structured as an object, procedure, or function. Notwithstanding, the executable code of an identified module need not be physically located together, but may include distinct instructions stored in different bits which, when logically combined, constitute the module and implement the specified Purpose.

Indeed, a module of executable code may be a single instruction, or many instructions, and may even be distributed over several different code segments, among different programs and across multiple memory devices. Likewise, operational data may be identified within modules, and may be implemented in any suitable form and organized within any suitable type of data structure. The operational data may be collected as a single data set, or may be distributed in different locations (including on different storage devices), and may exist, at least in part, only as electronic signals on a system or network.

When the module can be realized by software, considering the level of the existing hardware technology, the module that can be realized by software, regardless of the cost, those skilled in the art can build the corresponding hardware circuit to realize the corresponding function. The hardware circuit includes conventional very large scale integration (VLSI) circuits or gate arrays as well as existing semiconductors such as logic chips, transistors, or other discrete components. A module may also be implemented in programmable hardware devices such as field programmable gate arrays, programmable array logic, programmable logic devices, and the like.

The exemplary embodiments described above are described with reference to these drawings. Many different forms and embodiments are possible without departing from the spirit and teachings of the present invention. Therefore, the present invention should not be construed as the exemplary embodiments set forth herein. limits. Rather, these exemplary embodiments are provided so that this disclosure will be thorough and complete, and will convey the scope of the invention to those skilled in the art. In the drawings, component sizes and relative sizes may be exaggerated for clarity. The terminology used herein is for the purpose of describing certain exemplary embodiments only and is not intended to be limiting. As used herein, the singular forms "a", "an" and "the" are intended to include these plural forms unless the context clearly dictates otherwise. It will be further understood that the terms "comprises" and/or "comprises", when used in this specification, indicate the presence of stated features, integers, steps, operations, components and/or components, but do not exclude one or more other The presence or addition of features, integers, steps, operations, components, components and/or groups thereof. Unless otherwise indicated, when stated a range of values includes the upper and lower limits of that range and any subranges therebetween.

The above description is a preferred embodiment of the present invention, it should be pointed out that for those of ordinary skill in the art, without departing from the principle of the present invention, some improvements and modifications can also be made, and these improvements and modifications can also be made. It should be regarded as the protection scope of the present invention.

Claims (13)

1. A method for processing fingerprint information, comprising:

obtaining a target fingerprint hot spot set according to at least one fingerprint hot spot;

acquiring quality information of each fingerprint hotspot according to the characteristic information of each fingerprint hotspot in the target fingerprint hotspot set;

and constructing a wireless fidelity WiFi fingerprint database according to the characteristic information and the quality information corresponding to each fingerprint hotspot, wherein the WiFi fingerprint database is used for WiFi fingerprint positioning.

2. The processing method according to claim 1, wherein the obtaining a target fingerprint hotspot set according to at least one fingerprint hotspot comprises:

performing feature extraction on at least one fingerprint hotspot to obtain feature information corresponding to each fingerprint hotspot;

and screening the at least one fingerprint hot spot according to the characteristic information to obtain a target fingerprint hot spot set.

3. The processing method of claim 2, wherein the feature information comprises at least one of:

hot spot mean value;

probability of occurrence of hot spots;

area hot spot identification information;

standard deviation of hot spots.

4. The process of claim 3, wherein the screening operation comprises:

deleting a first fingerprint hotspot of the at least one fingerprint hotspot from the at least one fingerprint hotspot if the first fingerprint hotspot meets a first preset condition;

wherein the first preset condition comprises at least one of:

the hot spot mean value of the first fingerprint hot spot is smaller than or equal to a preset mean value;

the probability of the first fingerprint hotspot occurring is less than or equal to a preset probability.

5. The processing method according to claim 1, wherein the obtaining quality information of each fingerprint hotspot according to the feature information of each fingerprint hotspot in the target fingerprint hotspot set comprises:

according to the characteristic information, obtaining an efficacy coefficient corresponding to each characteristic information of each fingerprint hotspot;

and obtaining the quality information of the fingerprint hot spot according to the efficacy coefficient and the weight coefficient corresponding to the characteristic information.

6. The processing method of claim 1, further comprising:

setting a weight coefficient for each piece of feature information according to the importance ranking of different pieces of feature information;

wherein the sum of the weight coefficients corresponding to the feature information is 1.

7. The processing method according to claim 1, wherein before obtaining the target fingerprint hotspot set based on at least one fingerprint hotspot, the processing method further comprises:

selecting a hotspot in a preset radius range by taking the first hotspot as a circle center as the at least one fingerprint hotspot; the first hot spot is a fixed hot spot with the maximum RSSI (signal strength indicator) received in a target area; the hot spots comprise fixed hot spots and/or hot spots containing position information in the environment.

8. The processing method of claim 1, wherein the WiFi fingerprint database comprises at least one of a number, a MAC address, a recording time, a signal strength mean, quality information, a recording duration, a longitude, and a latitude corresponding to each of the fingerprint hotspots.

9. The processing method according to claim 8, further comprising:

updating the WiFi fingerprint database;

wherein, in the case that the environment of the fingerprint hotspot is not changed, the updating operation includes at least one of:

according to the time length proportion, the average value of the signal intensity is obtained;

according to the time length proportion, the quality information is averaged;

updating the recording duration;

and updating the recording time.

10. A fingerprint information processing device is characterized by comprising

The first processing module is used for obtaining a target fingerprint hot spot set according to at least one fingerprint hot spot;

the second processing module is used for acquiring the quality information of each fingerprint hotspot according to the characteristic information of each fingerprint hotspot in the target fingerprint hotspot set;

and the third processing module is used for constructing a wireless fidelity WiFi fingerprint database according to the characteristic information and the quality information corresponding to each fingerprint hotspot, and the WiFi fingerprint database is used for WiFi fingerprint positioning.

11. A network device, comprising: a transceiver and a processor; the processor is configured to:

obtaining a target fingerprint hot spot set according to at least one fingerprint hot spot;

acquiring quality information of each fingerprint hotspot according to the characteristic information of each fingerprint hotspot in the target fingerprint hotspot set;

and constructing a wireless fidelity WiFi fingerprint database according to the characteristic information and the quality information corresponding to each fingerprint hotspot, wherein the WiFi fingerprint database is used for WiFi fingerprint positioning.

12. A network device, comprising: a transceiver, a processor, a memory, and a program or instructions stored on the memory and executable on the processor; wherein the processor, when executing the program or instructions, implements the processing method of any of claims 1 to 9.

13. A readable storage medium on which a program or instructions are stored, which, when executed by a processor, implement the steps in the processing method of any one of claims 1 to 9.

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