Device positioning method, device and storage medium

Abstract

The invention relates to the field of equipment positioning, and discloses an equipment positioning method, equipment and a storage medium. The method comprises the following steps: acquiring N pieces of WiFi information accessed by target equipment; based on a preset browser plug-in, reading operation system information of the target equipment; performing geometric solution processing on the N position coordinates and the N detection distances to obtain a first positioning coordinate; performing feature comparison processing on the signal intensity values of the N pieces of WiFi information to obtain a second positioning coordinate; performing geographic position prediction on the operation system information and the N pieces of WiFi information to obtain a third positioning coordinate; performing fusion calculation on the first positioning coordinate, the second positioning coordinate and the third positioning coordinate to obtain equipment positioning data; and performing visualization processing on the equipment positioning data to generate a map positioning interface. In the embodiment of the invention, through multi-source data fusion and dynamic adjustment of algorithm parameters, high stability and high precision of a positioning result are realized, and a positioning error caused by failure of a single data source is avoided.

Description

Technical Field

[0001] The present invention relates to the field of device positioning, and in particular to a device positioning method, device and storage medium. Background Art

[0002] Computer positioning technology aims to solve the problem of device location, that is, to determine the spatial position of the device in a certain coordinate system. It is generally divided into two categories: outdoor positioning technology and indoor positioning technology. Different application scenarios have different corresponding needs, and the positioning technologies used are also different.

[0003] Outdoor positioning technology mainly relies on satellite signals. GPS performs positioning by receiving longitude and latitude coordinate signals provided by satellites. It consists of a space part, a ground control part, and a user device part. The demand for indoor positioning technology is growing with the acceleration of urbanization. Because people spend most of their time indoors, indoor positioning has also become a research direction in the field of positioning. Indoor positioning can be achieved through IP address positioning, Wi-Fi signals, and other methods. Although the method of positioning by IP address is convenient, its accuracy is low, especially when using VPN, proxy server or dynamic IP, which may lead to inaccurate positioning information. IP address cannot provide accurate information about the specific location indoors, and can only locate the approximate city or region. Wi-Fi signal positioning has high accuracy when the device is connected to the network, but it is limited by factors such as network signal strength and changes in the surrounding environment, resulting in poor reliability and stability.

[0004] Therefore, in view of the current technical problems of insufficient accuracy and poor stability of indoor positioning, new technologies are needed to solve the current problems. Summary of the invention

[0005] The main purpose of the present invention is to solve the technical problems of insufficient accuracy and poor stability of current indoor positioning.

[0006] A first aspect of the present invention provides a method for positioning a device, the method comprising:

[0007] Obtain information about N WiFi networks connected to the target device, where N is a positive integer not less than 3;

[0008] Read the target device’s operating system information based on the preset browser plug-in;

[0009] According to a preset WiFi coordinate set, N location coordinates corresponding to the N WiFi information are matched, and detection signals are sent to the N location coordinates to obtain N detection distances corresponding to the N location coordinates;

[0010] Based on a preset geometric positioning algorithm, geometrically solve the N position coordinates and the N detection distances to obtain a first positioning coordinate;

[0011] According to a preset information fingerprint library, feature comparison processing is performed on the signal strength values ​​of the N WiFi information to obtain a second positioning coordinate;

[0012] According to a preset machine learning algorithm, the operating system information and the N pieces of WiFi information are predicted to obtain a third positioning coordinate;

[0013] Calculating the deviation rates between the first positioning coordinates, the second positioning coordinates, and the third positioning coordinates to obtain a first deviation rate, a second deviation rate, and a third deviation rate;

[0014] According to the first deviation rate, the second deviation rate, and the third deviation rate, the first positioning coordinate, the second positioning coordinate, and the third positioning coordinate are fused and calculated to obtain device positioning data;

[0015] The device positioning data is visualized to generate a map positioning interface so that users can view the positioning information of the device.

[0016] Optionally, in a first implementation of the first aspect of the present invention, performing geometric solution processing on the N position coordinates and the N detection distances based on a preset geometric positioning algorithm to obtain the first positioning coordinates includes:

[0017] Setting unknown parameters of the first positioning coordinates;

[0018] Substitute the unknown parameters, the N position coordinates, and the N detection distances into the solution equation to calculate the coordinate parameters of the first positioning coordinates.

[0019] Optionally, in a second implementation of the first aspect of the present invention, the solving equation includes:

[0020] (X-X1) 2 +(Y-Y1) 2 =d1;

[0021] (X-X2) 2 +(Y-Y2) 2 =d2;

[0022] (X-X3) 2 +(Y-Y3) 2 =d3;

[0023] Among them, d1 is the first detection distance, d2 is the second detection distance, d3 is the third detection distance, (X1, Y1) is the first position coordinate corresponding to the first detection distance, (X2, Y2) is the second position coordinate corresponding to the second detection distance, and (X3, Y3) is the third position coordinate corresponding to the third detection distance.

[0024] Optionally, in a third implementation of the first aspect of the present invention, performing feature comparison processing on the signal strength values ​​of the N WiFi information according to the preset information fingerprint library to obtain the second positioning coordinates includes:

[0025] Reading a standard value group in a preset information fingerprint library, wherein the standard value group includes: mapping coordinates;

[0026] According to a preset feature comparison formula, a multi-dimensional deviation value between the signal strength values ​​of the N WiFi information and a standard value group is calculated to obtain a feature deviation value;

[0027] When the characteristic deviation value is less than the preset characteristic threshold, the mapping coordinates corresponding to the characteristic deviation value are confirmed as the second positioning coordinates.

[0028] Optionally, in a fourth implementation of the first aspect of the present invention, the feature comparison formula includes:

[0029] D = (RSSI1-RSSI1′) 2 +(RSSI2-RSSI2′) 2 +....+(RSSI N -RSSI N ′) 2 ,

[0030] Among them, RSSI1, RSSI2, ..., RSSI N is the signal strength value of N WiFi information, RSSI1′, RSSI2′, ..., RSSI N ′ is the standard value group, and D is the characteristic deviation value.

[0031] Optionally, in a fifth implementation of the first aspect of the present invention, calculating the deviation rate between the first positioning coordinates, the second positioning coordinates, and the third positioning coordinates to obtain the first deviation rate, the second deviation rate, and the third deviation includes:

[0032] Calculate the mean deviation of the horizontal and vertical coordinates between the first positioning coordinates and the second positioning coordinates to obtain a first deviation rate;

[0033] Calculate the average of the horizontal and vertical coordinate deviations between the second positioning coordinate and the third positioning coordinate to obtain a second deviation rate;

[0034] The average of the horizontal and vertical coordinate deviations between the third positioning coordinates and the first positioning coordinates is calculated to obtain a third deviation rate.

[0035] Optionally, in a sixth implementation manner of the first aspect of the present invention, performing a fusion calculation on the first positioning coordinates, the second positioning coordinates, and the third positioning coordinates according to the first deviation rate, the second deviation rate, and the third deviation rate to obtain the device positioning data includes:

[0036] Compare the first deviation rate, the second deviation rate, and the third deviation rate with a preset deviation rate threshold to obtain a comparison result;

[0037] Determine whether the number of deviation rates for which the comparison result is less than the deviation rate threshold is not less than 2;

[0038] When the number of deviation rates is not less than 2, the horizontal and vertical mean values ​​of the positioning coordinates corresponding to the deviation rates less than the deviation rate threshold are calculated to obtain the device positioning data.

[0039] Optionally, in a seventh implementation manner of the first aspect of the present invention, after determining whether the number of deviation rates whose comparison results are less than the deviation rate threshold is not less than 2, the method further includes:

[0040] When the number of deviation rates is less than 2, determining whether the number of deviation rates whose comparison result is less than the deviation rate threshold is 1;

[0041] When the number of deviation rates is 1, the positioning coordinates corresponding to the deviation rate less than the deviation rate threshold are determined as the device positioning data;

[0042] When the deviation rate is not 1, the N WiFi information to which the target device is connected is obtained again.

[0043] A second aspect of the present invention provides a device for positioning a device, comprising: a memory and at least one processor, wherein instructions are stored in the memory, and the memory and the at least one processor are interconnected via a line; the at least one processor calls the instructions in the memory so that the device positioning device executes the above-mentioned device positioning method.

[0044] A third aspect of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores instructions, which, when executed on a computer, enable the computer to execute the above-mentioned device positioning method.

[0045] In an embodiment of the present invention, the positioning accuracy of the computer position is improved by integrating multi-source data such as the signal strength of WiFi information, the positioning coordinates of the signal fingerprint library, and the operating system information of the browser plug-in. Compared with the traditional method that relies solely on IP addresses or Wi-Fi signals, this solution can provide more accurate location information, especially in complex network environments. Multi-source data fusion and dynamic adjustment of algorithm parameters achieve the stability of positioning results. In the case of network fluctuations or weak signals, reliable location information can be provided, avoiding positioning errors caused by the failure of a single data source, and solving the technical problems of insufficient accuracy and poor stability of current indoor positioning. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 A schematic diagram of an embodiment of a positioning method for a device in an embodiment of the present invention;

[0047] Figure 2 It is a schematic diagram of an embodiment of step 105 of the method for positioning a device in an embodiment of the present invention;

[0048] Figure 3 It is a schematic diagram of an embodiment of step 107 of the method for positioning a device in an embodiment of the present invention;

[0049] Figure 4 It is a schematic diagram of an embodiment of step 108 of the method for positioning a device in an embodiment of the present invention;

[0050] Figure 5 The figure is a schematic diagram of an embodiment of a positioning device of a device in an embodiment of the present invention. DETAILED DESCRIPTION

[0051] The embodiments of the present invention provide a device positioning method, a device and a storage medium.

[0052] The embodiments disclosed in the present invention will be described in more detail below with reference to the accompanying drawings. Although certain embodiments disclosed in the present invention are shown in the accompanying drawings, it should be understood that the present invention can be implemented in various forms and should not be construed as being limited to the embodiments described herein, which are instead provided for a more thorough and complete understanding of the present invention. It should be understood that the drawings and embodiments disclosed in the present invention are only for exemplary purposes and are not intended to limit the scope of protection disclosed in the present invention.

[0053] In the description of the embodiments disclosed in the present invention, the term "including" and similar terms should be understood as open inclusion, that is, "including but not limited to". The term "based on" should be understood as "based at least in part on". The term "one embodiment" or "the embodiment" should be understood as "at least one embodiment". The terms "first", "second", etc. may refer to different or the same objects. Other explicit and implicit definitions may also be included below.

[0054] For ease of understanding, the specific process of the embodiment of the present invention is described below. Figure 1 , an embodiment of a positioning method of a device in an embodiment of the present invention includes:

[0055] 101. Obtain information of N WiFi networks to which the target device is connected, where N is a positive integer not less than 3;

[0056] In this embodiment, the target device may have multiple WiFi information receivers at a location indoors, and the number of WiFi information may be greater than 3. The N WiFi information accessed by the target device computer may be directly obtained. Each WiFi information may contain specific information such as signal strength, communication speed, WiFi name, and MAC address of the WiFi device.

[0057] 102. Read the operating system information of the target device based on the preset browser plug-in;

[0058] In this embodiment, the unique features of the browser plug-in (such as user-agent, browser version, screen resolution, etc.) are used to perform device identification and environmental analysis, and read operating system information, which includes data such as operating system version and time zone setting.

[0059] 103. According to a preset WiFi coordinate set, N location coordinates corresponding to the N WiFi information are matched, and detection signals are sent to the N location coordinates to obtain N detection distances corresponding to the N location coordinates;

[0060] In this embodiment, the coordinate data of the WiFi information is pre-set according to the WiFi name + device MAC address and is set in the WiFi coordinate set. Matching is performed according to the combination of WiFi name + device MAC address, and the corresponding N location coordinates in the N WiFi information are found from the WiFi coordinate set.

[0061] Then, the detection signal is sent to the devices corresponding to the N position coordinates, and the N detection distances corresponding to the detection signal to the N position coordinates are calculated.

[0062] 104. Based on a preset geometric positioning algorithm, perform geometric solution processing on the N position coordinates and the N detection distances to obtain a first positioning coordinate;

[0063] In this embodiment, N position coordinates and N detection distances corresponding to the N position coordinates are used to perform geometric calculation to solve the coordinate distance. This processing process belongs to a multi-variable measurement method.

[0064] Taking a pentagon as an example, let the five vertices of the pentagon be A(x1, y1), B(x2, y2), C(x3, y3), D(x4, y4), E(x5, y5), set the coordinates P(x, y) to be solved geometrically, and the distance between point P and A, B, C, D, and E is d A ,d B ,d C ,d D ,d E .

[0065] Calculate according to the following equation

[0066] (x-x1) 2 +(y-y1) 2 =d A ;

[0067] (x-x2) 2 +(y-y2) 2 =d B ;

[0068] (x-x3) 2 +(y-y3) 2 =d C ;

[0069] (x-x4) 2 +(y-y4) 2 =d D ;

[0070] (x-x5) 2 +(y-y5) 2 =d E ;

[0071] The above formula is a solution to five equations containing two unknowns. The equation has no analytical solution. The equation is directly solved using Newton's iteration method and the least squares method to obtain the data of the first positioning coordinate P (x, y).

[0072] Specifically, step 104 includes the following specific implementation methods:

[0073] 1041. Setting unknown parameters of the first positioning coordinates;

[0074] 1042. Substitute the unknown parameters, the N position coordinates, and the N detection distances into a solution equation to calculate the coordinate parameters of the first positioning coordinates.

[0075] In steps 1041-1042, the unknown parameters of the first positioning coordinates P(x, y) are set, and the first positioning coordinates P(x, y) are substituted into the set solution equation, and the coordinate parameters of the first positioning coordinates are solved through Newton iteration method, least squares method, numerical optimization algorithm, etc.

[0076] When the data coordinates are triangular, the simplest solution equation is as follows:

[0077] (X-X1) 2 +(Y-Y1) 2 =d1;

[0078] (X-X2) 2 +(Y-Y2) 2 =d2;

[0079] (X-X3) 2 +(Y-Y3) 2 =d3;

[0080] Among them, d1 is the first detection distance, d2 is the second detection distance, d3 is the third detection distance, (X1, Y1) is the first position coordinate corresponding to the first detection distance, (X2, Y2) is the second position coordinate corresponding to the second detection distance, and (X3, Y3) is the third position coordinate corresponding to the third detection distance.

[0081] 105. Perform feature comparison processing on the signal strength values ​​of the N WiFi information according to a preset information fingerprint library to obtain a second positioning coordinate;

[0082] In this embodiment, a fingerprint database is built in advance, in which wireless signal characteristics (e.g., Wi-Fi signal strength, network latency, etc.) at different locations are recorded. When the device needs to be located, the wireless signal data collected in real time is compared with the records in the fingerprint database to find the best matching record, thereby inferring the geographical location of the device.

[0083] For details, please refer to Figure 2 , Figure 2 This is an embodiment of step 105 of the method for positioning a device in an embodiment of the present invention, and step 105 includes the following specific implementation methods:

[0084] 1051. Read a standard value group in a preset information fingerprint library, wherein the standard value group includes: mapping coordinates;

[0085] 1052. Calculate the multi-dimensional deviation values ​​between the signal strength values ​​of the N WiFi information and the standard value group according to a preset feature comparison formula to obtain a feature deviation value;

[0086] 1053. When the feature deviation value is less than a preset feature threshold, the mapping coordinates corresponding to the feature deviation value are confirmed as second positioning coordinates.

[0087] In steps 1051-1053, a standard value group in the information fingerprint library is read. The standard value group includes corresponding mapping coordinates. The standard value group records multiple WiFi signal strength values ​​RSSI2′~RSSI N '. Using the comparison formula, the standard value group RSSI2'~RSSI N 'Signal strength and the signal strength values ​​of the N WiFi information obtained RSSI2~RSSI N Compare and obtain the characteristic deviation value.

[0088] It is determined whether the feature deviation value is less than a preset feature threshold. If the feature deviation value is less than the preset feature threshold, it means that the standard value group matches the N WiFi information, and the mapping coordinates corresponding to the feature deviation value are confirmed as the second positioning coordinates.

[0089] Furthermore, the feature comparison formula includes:

[0090] D = (RSSI1-RSSI1′) 2 +(RSSI2-RSSI2′) 2 +....+(RSSI N -RSSI N ′) 2 ,

[0091] Among them, RSSI1, RSSI2, ..., RSSI N is the signal strength value of N WiFi information, RSSI1′, RSSI2′, ..., RSSI N ′ is the standard value group, and D is the characteristic deviation value.

[0092] Match the WiFi signal strength RSSI of each WiFi name + MAC address in turn 1～N RSSI with standard value 1～N ’ is subtracted and squared to obtain the characteristic deviation value.

[0093] 106. Predicting the geographic location of the operating system information and the N pieces of WiFi information according to a preset machine learning algorithm to obtain a third positioning coordinate;

[0094] In this embodiment, a large amount of historical positioning data is collected as a training data set. Each piece of data includes features such as the Wi-Fi signal strength of the device, operating system information, and the corresponding known geographical location. Feature vector: [f1, f2, ..., fn][f_1, f_2, ..., f_n][f1, f2, ..., fn], for example: [RSSI 1, RSSI2, IP1, UserAgent1, OS1][RSSI_1, RSSI_2, IP_1, UserAgent_1, OS_1][RSSI1, RSSI2, IP1, UserAgent1, OS1] is input into a machine learning model (such as: decision tree, support vector machine, K nearest neighbor algorithm, neural network, etc.), so that the model learns the relationship between the feature vector and the location, forming a mapping: Location = f(Features)\text{Location} = f(\text{Features})Location = f(Features).

[0095] Based on the above mode, N WiFi information and operating system information are input into the model as features to obtain a predicted geographic location, and the predicted geographic location data is determined as the third positioning coordinate.

[0096] 107. Calculate the deviation rates among the first positioning coordinates, the second positioning coordinates, and the third positioning coordinates to obtain a first deviation rate, a second deviation rate, and a third deviation rate;

[0097] In this embodiment, it is assumed that the first positioning coordinate P(x p ,y p ), the second positioning coordinate M(x M ,y M ), the third positioning coordinate N(x N ,y N ), first find the mean coordinates of the three positioning coordinates T((x p +x M +x N ) / 3,(y p +y M +y N ) / 3). Calculate the first positioning coordinate P(x p ,y p ) and the mean coordinate T((x p +x M +x N ) / 3,(y p +y M +y N ) / 3) is used as the first deviation rate, and the second deviation rate and the third deviation rate are calculated similarly.

[0098] For details, please refer to Figure 3 , Figure 3 This is an embodiment of step 107 of the method for positioning a device in an embodiment of the present invention, and step 107 includes the following specific implementation methods:

[0099] 1071. Calculate the mean deviations of the horizontal and vertical coordinates between the first positioning coordinates and the second positioning coordinates to obtain a first deviation rate.

[0100] 1072. Calculate the mean deviations of the horizontal and vertical coordinates between the second positioning coordinate and the third positioning coordinate to obtain a second deviation rate.

[0101] 1073. Calculate the average of the horizontal and vertical coordinate deviations between the third positioning coordinates and the first positioning coordinates to obtain a third deviation rate.

[0102] In this embodiment, the first positioning coordinate P(x p ,y p ) and the second positioning coordinates M(x M ,y M ), that is, to calculate (x p / x M +y p / y M ) / 2, and we get the first deviation rate. Of course, we can also use [(x p -x M ) 2 +(y p -y M ) 2 ] / 2 is calculated as the first deviation rate.

[0103] Similarly, calculate the second positioning coordinates M(x M ,y M ) and the third positioning coordinate N(x N ,y N ), that is, to calculate (x M / x N +y M / y N ) / 2, and we get the first deviation rate. Of course, we can also use [(x M -x N ) 2 +(y M -y N ) 2 ] / 2 is calculated as the second deviation rate.

[0104] Calculate the third positioning coordinate N(x N ,y N ) and the first positioning coordinate P(x p,y p ), that is, to calculate (x p / x N +y p / y N ) / 2, and we get the first deviation rate. Of course, we can also use [(x N -x M ) 2 +(y p -y N ) 2 ] / 2 is calculated as the third deviation rate.

[0105] 108. Perform a fusion calculation on the first positioning coordinate, the second positioning coordinate, and the third positioning coordinate according to the first deviation rate, the second deviation rate, and the third deviation rate to obtain device positioning data;

[0106] In this embodiment, the positioning coordinates corresponding to the largest deviation rate of the first deviation rate, the second deviation rate, and the third deviation rate are eliminated, and then the mean values ​​of the horizontal and vertical coordinates of the remaining two positioning coordinates are calculated to obtain the device positioning data.

[0107] For details, please refer to Figure 4 , Figure 4 This is an embodiment of step 108 of the device positioning method in an embodiment of the present invention, and step 108 includes the following specific implementation methods:

[0108] 1081. Compare the first deviation rate, the second deviation rate, and the third deviation rate with a preset deviation rate threshold to obtain a comparison result;

[0109] 1082. Determine whether the number of deviation rates whose comparison results are less than the deviation rate threshold is not less than 2;

[0110] 1083. When the number of deviation rates is not less than 2, the horizontal and vertical mean values ​​of the positioning coordinates corresponding to the deviation rates less than the deviation rate threshold are calculated to obtain the device positioning data.

[0111] In steps 1081-1083, the relationship between the first deviation rate and the deviation rate threshold, the relationship between the second deviation rate and the deviation rate threshold, and the relationship between the third deviation rate and the deviation rate threshold are analyzed in turn to obtain the results of the three deviation rate comparisons, and the number of deviation rates less than the deviation rate threshold is counted. When the number of deviation rates is not less than 2, the horizontal and vertical mean values ​​of the positioning coordinates corresponding to the deviation rates less than the deviation rate threshold are calculated to obtain the fused device positioning data.

[0112] Furthermore, after step 1082, the following specific implementations are also included:

[0113] 1084. When the number of deviation rates is less than 2, determining whether the number of deviation rates whose comparison result is less than the deviation rate threshold is 1;

[0114] 1085. When the number of deviation rates is 1, the positioning coordinates corresponding to the deviation rates less than the deviation rate threshold are determined as the device positioning data;

[0115] 1086. When the deviation rate is not 1, reacquire the N WiFi information to which the target device is connected.

[0116] In steps 1084-1086, if the number of deviation rates is less than 2, then it is analyzed and determined whether the number of deviation rates less than the deviation rate threshold in the comparison result is equal to 1. If it is equal to 1, it is considered that there are no positioning coordinates that can be fused, and the positioning coordinates corresponding to the deviation rate less than the deviation rate threshold are directly determined as the device positioning data. For example, if the first deviation rate is less than the deviation rate threshold, the first positioning coordinates are determined as the device positioning data. If the second deviation rate is less than the deviation rate threshold, the second positioning coordinates are determined as the device positioning data. If the third deviation rate is less than the deviation rate threshold, the third positioning coordinates are determined as the device positioning data. If the number of deviation rates is not equal to 1, it means that there is no deviation rate less than the deviation rate threshold, and it is necessary to re-acquire the N WiFi information connected to the target device, return to step 101 and recalculate to overcome the problem of inaccurate positioning in the positioning data.

[0117] 109. Visualize the device positioning data and generate a map positioning interface so that the user can view the positioning information of the device.

[0118] In this embodiment, the device positioning data after coordinate fusion is visualized on the GIS map and displayed to the user in the form of a map positioning interface through the front-end UI module of the plug-in, and provides detailed location information, including latitude and longitude, city, region, etc. The positioning results can also be output in a specific format (such as JSON, XML, etc.) for other applications or services to call.

[0119] After the map location interface is generated, personalized content recommendations and advertising push can be provided based on the user's location. For internal enterprise applications, access rights can be controlled based on the computer location to improve security.

[0120] In an embodiment of the present invention, the positioning accuracy of the computer position is improved by integrating multi-source data such as the signal strength of WiFi information, the positioning coordinates of the signal fingerprint library, and the operating system information of the browser plug-in. Compared with the traditional method that relies solely on IP addresses or Wi-Fi signals, this solution can provide more accurate location information, especially in complex network environments. Multi-source data fusion and dynamic adjustment of algorithm parameters achieve the stability of positioning results. In the case of network fluctuations or weak signals, reliable location information can be provided, avoiding positioning errors caused by the failure of a single data source, and solving the technical problems of insufficient accuracy and poor stability of current indoor positioning.

[0121] Figure 5 1 is a schematic diagram of the structure of a positioning device of a device provided in an embodiment of the present invention. The positioning device 500 of the device may have relatively large differences due to different configurations or performances, and may include one or more processors (central processing units, CPU) 510 (for example, one or more processors) and a memory 520, and one or more storage media 530 (for example, one or more mass storage devices) storing application programs 533 or data 532. Among them, the memory 520 and the storage medium 530 can be temporary storage or permanent storage. The program stored in the storage medium 530 may include one or more modules (not shown in the figure), and each module may include a series of instruction operations in the positioning device 500 of the device. Furthermore, the processor 510 can be configured to communicate with the storage medium 530 to execute a series of instruction operations in the storage medium 530 on the positioning device 500 of the device.

[0122] The device-based positioning device 500 may also include one or more power supplies 540, one or more wired or wireless network interfaces 550, one or more input and output interfaces 560, and / or one or more operating systems 531, such as Windows Serve, Mac OS X, Unix, Linux, FreeBSD, etc. It will be appreciated by those skilled in the art that Figure 5 The positioning device structure of the device shown does not constitute a limitation on the device-based positioning device, and may include more or less components than shown in the figure, or combine certain components, or arrange the components differently.

[0123] The present invention also provides a computer-readable storage medium, which may be a non-volatile computer-readable storage medium or a volatile computer-readable storage medium. Instructions are stored in the computer-readable storage medium. When the instructions are executed on a computer, the computer executes the steps of the device positioning method.

[0124] In the context of the present disclosure, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or equipment, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0125] In addition, although each operation is described in a specific order, this should be understood as requiring such operation to be performed in the specific order shown or in a sequential order, or requiring that all illustrated operations should be performed to obtain desired results. Under certain circumstances, multitasking and parallel processing may be advantageous. Similarly, although some specific implementation details are included in the above discussion, these should not be interpreted as limiting the scope of the present disclosure. Some features described in the context of a separate embodiment can also be implemented in a single implementation in combination. On the contrary, the various features described in the context of a single implementation can also be implemented in multiple implementations individually or in any suitable sub-combination mode.

[0126] Although the subject matter has been described in language specific to structural features and / or methodological logical actions, it should be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or actions described above. On the contrary, the specific features and actions described above are merely example forms of implementing the claims.

Claims

1. A method for positioning a device, characterized in that: Includes steps: Obtain information about N WiFi networks connected to the target device, where N is a positive integer not less than 3; Read the target device’s operating system information based on the preset browser plug-in; According to a preset WiFi coordinate set, N location coordinates corresponding to the N WiFi information are matched, and detection signals are sent to the N location coordinates to obtain N detection distances corresponding to the N location coordinates; Based on a preset geometric positioning algorithm, geometrically solve the N position coordinates and the N detection distances to obtain a first positioning coordinate; According to a preset information fingerprint library, feature comparison processing is performed on the signal strength values ​​of the N WiFi information to obtain a second positioning coordinate; According to a preset machine learning algorithm, the operating system information and the N pieces of WiFi information are predicted to obtain a third positioning coordinate; Calculating the deviation rates between the first positioning coordinates, the second positioning coordinates, and the third positioning coordinates to obtain a first deviation rate, a second deviation rate, and a third deviation rate; According to the first deviation rate, the second deviation rate, and the third deviation rate, the first positioning coordinate, the second positioning coordinate, and the third positioning coordinate are fused and calculated to obtain device positioning data; The device positioning data is visualized to generate a map positioning interface so that users can view the positioning information of the device.

2. The device positioning method according to claim 1, characterized in that: The step of performing geometric calculation on the N position coordinates and the N detection distances based on a preset geometric positioning algorithm to obtain the first positioning coordinates includes: Setting unknown parameters of the first positioning coordinates; Substitute the unknown parameters, the N position coordinates, and the N detection distances into the solution equation to calculate the coordinate parameters of the first positioning coordinates.

3. The device positioning method according to claim 2, characterized in that: The solving equations include: (X-X1) 2 +(Y-Y1) 2 =d1; (X-X2) 2 +(Y-Y2) 2 =d2; <h2 style=";text-align:left;direction:ltr">(X-X3)<h2 style=";text-align:left;direction:ltr"> 2 <h2 style=";text-align:left;direction:ltr"> +(Y-Y3)<h2 style=";text-align:left;direction:ltr"> 2 <h2 style=";text-align:left;direction:ltr"> (d3) Among them, d1 is the first detection distance, d2 is the second detection distance, d3 is the third detection distance, (X1, Y1) is the first position coordinate corresponding to the first detection distance, (X2, Y2) is the second position coordinate corresponding to the second detection distance, and (X3, Y3) is the third position coordinate corresponding to the third detection distance.

4. The device positioning method according to claim 1, characterized in that: The performing feature comparison processing on the signal strength values ​​of the N WiFi information according to the preset information fingerprint library to obtain the second positioning coordinates includes: Reading a standard value group in a preset information fingerprint library, wherein the standard value group includes: mapping coordinates; According to a preset feature comparison formula, a multi-dimensional deviation value between the signal strength values ​​of the N WiFi information and a standard value group is calculated to obtain a feature deviation value; When the characteristic deviation value is less than the preset characteristic threshold, the mapping coordinates corresponding to the characteristic deviation value are confirmed as the second positioning coordinates.

5. The device positioning method according to claim 4, characterized in that: The feature comparison formula includes: D=(RSSI1-RSSI1′) 2 +(RSSI2-RSSI2′) 2 +....+(RSSI N -RSSI N ′) 2 , Among them, RSSI1, RSSI2, ..., RSSI N is the signal strength value of N WiFi information, RSSI1′, RSSI2′, ..., RSSI N ′ is the standard value group, and D is the characteristic deviation value.

6. The device positioning method according to claim 1, characterized in that: The calculating the deviation rate between the first positioning coordinate, the second positioning coordinate, and the third positioning coordinate to obtain the first deviation rate, the second deviation rate, and the third deviation includes: Calculate the mean deviation of the horizontal and vertical coordinates between the first positioning coordinates and the second positioning coordinates to obtain a first deviation rate; Calculate the average of the horizontal and vertical coordinate deviations between the second positioning coordinate and the third positioning coordinate to obtain a second deviation rate; The average of the horizontal and vertical coordinate deviations between the third positioning coordinates and the first positioning coordinates is calculated to obtain a third deviation rate.

7. The device positioning method according to claim 1, characterized in that: The fusing calculation of the first positioning coordinate, the second positioning coordinate, and the third positioning coordinate according to the first deviation rate, the second deviation rate, and the third deviation rate to obtain the device positioning data includes: Compare the first deviation rate, the second deviation rate, and the third deviation rate with a preset deviation rate threshold to obtain a comparison result; Determine whether the number of deviation rates for which the comparison result is less than the deviation rate threshold is not less than 2; When the number of deviation rates is not less than 2, the horizontal and vertical mean values ​​of the positioning coordinates corresponding to the deviation rates less than the deviation rate threshold are calculated to obtain the device positioning data.

8. The device positioning method according to claim 7, characterized in that: After determining whether the number of deviation rates whose comparison results are less than the deviation rate threshold is not less than 2, the method further includes: When the number of deviation rates is less than 2, determining whether the number of deviation rates whose comparison result is less than the deviation rate threshold is 1; When the number of deviation rates is 1, the positioning coordinates corresponding to the deviation rate less than the deviation rate threshold are determined as the device positioning data; When the deviation rate is not 1, the N WiFi information to which the target device is connected is obtained again.

9. A positioning device for a device, characterized in that: The positioning device of the device comprises: a memory and at least one processor, the memory stores instructions, and the memory and the at least one processor are interconnected via a line; The at least one processor calls the instructions in the memory to enable the positioning device of the device to execute the positioning method of the device according to any one of claims 1 to 8.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the positioning method of the device according to any one of claims 1 to 8 is implemented.

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