CN111650554A - Positioning method and device, electronic equipment and storage medium - Google Patents

Positioning method and device, electronic equipment and storage medium Download PDF

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Publication number
CN111650554A
CN111650554A CN202010479175.0A CN202010479175A CN111650554A CN 111650554 A CN111650554 A CN 111650554A CN 202010479175 A CN202010479175 A CN 202010479175A CN 111650554 A CN111650554 A CN 111650554A
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China
Prior art keywords
floor
xgboost model
data
trained
positioning
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Chinese (zh)
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杨逸杰
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Zhejiang Shangtang Technology Development Co Ltd
Zhejiang Sensetime Technology Development Co Ltd
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Zhejiang Shangtang Technology Development Co Ltd
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Priority to CN202010479175.0A priority Critical patent/CN111650554A/en
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S5/00—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations
    • G01S5/02—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations using radio waves
    • G01S5/0252—Radio frequency fingerprinting
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S5/00—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations
    • G01S5/02—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations using radio waves
    • G01S5/0284—Relative positioning
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S5/00—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations
    • G01S5/02—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations using radio waves
    • G01S5/0294—Trajectory determination or predictive filtering, e.g. target tracking or Kalman filtering
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
    • H04W4/02—Services making use of location information
    • H04W4/021—Services related to particular areas, e.g. point of interest [POI] services, venue services or geofences
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
    • H04W4/30—Services specially adapted for particular environments, situations or purposes
    • H04W4/33—Services specially adapted for particular environments, situations or purposes for indoor environments, e.g. buildings
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W64/00—Locating users or terminals or network equipment for network management purposes, e.g. mobility management

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  • Engineering & Computer Science (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Signal Processing (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Radar, Positioning & Navigation (AREA)
  • Remote Sensing (AREA)
  • Position Fixing By Use Of Radio Waves (AREA)

Abstract

The disclosure relates to a positioning method and apparatus, an electronic device and a storage medium, wherein the method comprises: acquiring wireless signals received by a terminal at the current position, wherein the wireless signals comprise strength information of a plurality of wireless access points; and inputting the wireless signal received by the terminal at the current position into a pre-trained floor prediction model to obtain a floor label of the current position. The embodiment of the disclosure can realize high-precision floor positioning under the condition of low density of wireless access points.

Description

Positioning method and device, electronic equipment and storage medium
Technical Field
The present disclosure relates to the field of computer vision technologies, and in particular, to a positioning method and apparatus, an electronic device, and a storage medium.
Background
In a scenario where indoor positioning is achieved using computer vision, the problem of similar features on different floors is often encountered. In this case, it is not possible to distinguish which floor the scene belongs to with a simple visual sense. In the related art, the floor is located by using a wireless signal to solve the problem. In the related art, wireless Signal positioning needs to arrange enough wireless Access Points (APs) in a venue, and a fingerprint library is constructed according to Received Signal Strength (RSS) and Channel State Information (CSI).
Disclosure of Invention
The disclosure provides a positioning method and device, an electronic device and a storage medium.
According to an aspect of the present disclosure, there is provided a positioning method including:
acquiring wireless signals received by a terminal at the current position, wherein the wireless signals comprise strength information of a plurality of wireless access points;
and inputting the wireless signal received by the terminal at the current position into a pre-trained floor prediction model to obtain a floor label of the current position.
In one possible implementation, the floor prediction model includes an Xgboost model, and before the step of inputting the wireless signal received by the terminal at the current location into a pre-trained floor prediction model, the method further includes:
training an Xgboost model;
the step of training the Xgboost model comprises:
collecting positioning marking data, wherein the positioning marking data comprise wireless signals received by a terminal at a plurality of collecting positions and floor labels corresponding to the collecting positions;
acquiring training data from the positioning labeling data;
and training the Xgboost model to be trained by adopting the training data to obtain the trained Xgboost model.
By training the Xgboost model as the floor prediction model, the characteristics of small training data volume and high classification precision of the Xgboost model can be effectively utilized, and the floor positioning precision under the condition of low density of wireless access points is further improved.
In a possible implementation manner, the training of the Xgboost model to be trained by using the training data to obtain the trained Xgboost model includes:
training an Xgboost model to be trained by adopting the training data to obtain an Xgboost model in an intermediate state;
acquiring test data from the positioning marking data;
testing the Xgboost model of the intermediate state by adopting the test data to obtain the positioning accuracy of the Xgboost model of the intermediate state;
and under the condition that the positioning accuracy of the Xgboost model of the intermediate state is greater than a specified threshold value, determining the Xgboost model of the intermediate state as the trained Xgboost model.
By determining the Xgboost model in the intermediate state as the trained Xgboost model under the condition that the positioning accuracy of the Xgboost model in the intermediate state is higher, the accuracy of the floor prediction model can be guaranteed.
In a possible implementation manner, the training of the Xgboost model to be trained by using the training data to obtain the trained Xgboost model further includes:
determining the quantity of training data needing to be supplemented under the condition that the positioning accuracy of the Xgboost model of the intermediate state is smaller than or equal to the specified threshold;
acquiring supplementary training data according to the quantity, wherein the acquisition position of the supplementary training data is different from the acquisition position of the positioning marking data;
and training the Xgboost model of the intermediate state by adopting the supplemented training data to obtain the trained Xgboost model.
And under the condition that the positioning accuracy of the Xgboost model in the intermediate state is lower, the Xgboost model in the intermediate state is secondarily trained, so that the floor positioning accuracy of the floor prediction model can be improved.
In one possible implementation, the collecting the positioning annotation data includes:
determining the acquisition position of the positioning marking data;
and aiming at each acquisition position, acquiring a wireless signal received by the terminal at the acquisition position and a floor mark of a floor where the acquisition position is located, and determining the wireless signal received by the terminal at the acquisition position and the floor mark corresponding to the floor mark as the labeled positioning data of the acquisition position.
By respectively acquiring the wireless signals and the floor labels for each acquisition position, the marking positioning data of different acquisition positions can be obtained, and the Xgboost model training is facilitated.
In a possible implementation manner, the acquiring the wireless signal received by the terminal at the collecting position and the floor identifier of the floor where the collecting position is located includes:
displaying a floor mark corresponding to each floor label and a position mark of each acquisition position on the terminal;
and under the condition that the acquisition control is triggered, acquiring a wireless signal, and respectively determining the acquired wireless signal and a floor label corresponding to the currently triggered floor identifier as the wireless signal received by the terminal at the acquisition position corresponding to the currently triggered position identifier and the floor label of the floor where the acquisition position is located.
The acquisition of wireless signals and floor labels is carried out by triggering the acquisition control, so that the acquisition of positioning and labeling data can be simply and conveniently realized, convenience is provided for the acquisition operation of an acquirer, and the acquisition efficiency is improved.
According to an aspect of the present disclosure, there is provided a positioning apparatus including:
the system comprises a signal acquisition module, a signal processing module and a signal processing module, wherein the signal acquisition module is used for acquiring wireless signals received by a terminal at the current position, and the wireless signals comprise the strength information of a plurality of wireless access points;
and the signal input module is used for inputting the wireless signals received by the terminal at the current position into a pre-trained floor prediction model to obtain the floor label of the current position.
In one possible implementation, the apparatus further includes:
the model training module is used for training the Xgboost model;
the model training module is specifically configured to:
collecting positioning marking data, wherein the positioning marking data comprise wireless signals received by a terminal at a plurality of collecting positions and floor labels corresponding to the collecting positions;
acquiring training data from the positioning labeling data;
and training the Xgboost model to be trained by adopting the training data to obtain the trained Xgboost model.
In a possible implementation manner, the training of the Xgboost model to be trained by using the training data to obtain the trained Xgboost model includes:
training an Xgboost model to be trained by adopting the training data to obtain an Xgboost model in an intermediate state;
acquiring test data from the positioning marking data;
testing the Xgboost model of the intermediate state by adopting the test data to obtain the positioning accuracy of the Xgboost model of the intermediate state;
and under the condition that the positioning accuracy of the Xgboost model of the intermediate state is greater than a specified threshold value, determining the Xgboost model of the intermediate state as the trained Xgboost model.
In a possible implementation manner, the training of the Xgboost model to be trained by using the training data to obtain the trained Xgboost model further includes:
determining the quantity of training data needing to be supplemented under the condition that the positioning accuracy of the Xgboost model of the intermediate state is smaller than or equal to the specified threshold;
acquiring supplementary training data according to the quantity, wherein the acquisition position of the supplementary training data is different from the acquisition position of the positioning marking data;
and training the Xgboost model of the intermediate state by adopting the supplemented training data to obtain the trained Xgboost model.
In one possible implementation, the collecting the positioning annotation data includes:
determining the acquisition position of the positioning marking data;
and aiming at each acquisition position, acquiring a wireless signal received by the terminal at the acquisition position and a floor mark of a floor where the acquisition position is located, and determining the wireless signal received by the terminal at the acquisition position and the floor mark corresponding to the floor mark as the labeled positioning data of the acquisition position.
In a possible implementation manner, the acquiring the wireless signal received by the terminal at the collecting position and the floor identifier of the floor where the collecting position is located includes:
displaying a floor mark corresponding to each floor label and a position mark of each acquisition position on the terminal;
and under the condition that the acquisition control is triggered, acquiring a wireless signal, and respectively determining the acquired wireless signal and a floor label corresponding to the currently triggered floor identifier as the wireless signal received by the terminal at the acquisition position corresponding to the currently triggered position identifier and the floor label of the floor where the acquisition position is located.
According to an aspect of the present disclosure, there is provided an electronic device including: a processor; a memory for storing processor-executable instructions; wherein the processor is configured to invoke the memory-stored instructions to perform the above-described method.
According to an aspect of the present disclosure, there is provided a computer readable storage medium having stored thereon computer program instructions which, when executed by a processor, implement the above-described method.
In the embodiment of the disclosure, the strength information of the plurality of wireless access points is used as the classification feature, and the floor label prediction is performed by inputting the floor pre-layer model, so that the high-precision floor positioning under the condition of low density of the wireless access points is realized.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure. Other features and aspects of the present disclosure will become apparent from the following detailed description of exemplary embodiments, which proceeds with reference to the accompanying drawings.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present disclosure and, together with the description, serve to explain the principles of the disclosure.
Fig. 1 shows a flow chart of a positioning method according to an embodiment of the present disclosure;
FIG. 2 illustrates one example of an acquisition page according to an embodiment of the present disclosure;
FIG. 3 illustrates yet another example of an acquisition page according to an embodiment of the present disclosure;
FIG. 4 illustrates yet another example of an acquisition page according to an embodiment of the present disclosure;
FIG. 5 shows a block diagram of a positioning device according to an embodiment of the present disclosure;
FIG. 6 illustrates a block diagram of an electronic device 800 in accordance with an embodiment of the disclosure;
fig. 7 illustrates a block diagram of an electronic device 1900 in accordance with an embodiment of the disclosure.
Detailed Description
Various exemplary embodiments, features and aspects of the present disclosure will be described in detail below with reference to the accompanying drawings. In the drawings, like reference numbers can indicate functionally identical or similar elements. While the various aspects of the embodiments are presented in drawings, the drawings are not necessarily drawn to scale unless specifically indicated.
The word "exemplary" is used exclusively herein to mean "serving as an example, embodiment, or illustration. Any embodiment described herein as "exemplary" is not necessarily to be construed as preferred or advantageous over other embodiments.
The term "and/or" herein is merely an association describing an associated object, meaning that three relationships may exist, e.g., a and/or B, may mean: a exists alone, A and B exist simultaneously, and B exists alone. In addition, the term "at least one" herein means any one of a plurality or any combination of at least two of a plurality, for example, including at least one of A, B, C, and may mean including any one or more elements selected from the group consisting of A, B and C.
Furthermore, in the following detailed description, numerous specific details are set forth in order to provide a better understanding of the present disclosure. It will be understood by those skilled in the art that the present disclosure may be practiced without some of these specific details. In some instances, methods, means, elements and circuits that are well known to those skilled in the art have not been described in detail so as not to obscure the present disclosure.
Fig. 1 shows a flow chart of a positioning method according to an embodiment of the present disclosure. As shown in fig. 1, the method may include:
step S11, acquiring a wireless signal received by the terminal at the current location, where the wireless signal includes strength information of a plurality of wireless access points.
And step S12, inputting the wireless signal received by the terminal at the current position into a pre-trained floor prediction model to obtain the floor label of the current position.
In the embodiment of the disclosure, the strength information of the plurality of wireless access points is used as the classification feature, and the floor label prediction is performed by inputting the floor pre-layer model, so that the high-precision floor positioning under the condition of low density of the wireless access points is realized.
In a possible implementation manner, the positioning method may be performed by an electronic device such as a terminal or a server, the terminal device may be a User Equipment (UE), a mobile device, a User terminal, a cellular phone, a cordless phone, a Personal Digital Assistant (PDA), a handheld device, a computing device, an in-vehicle device, a wearable device, or the like, and the method may be implemented by a processor calling a computer readable instruction stored in a memory. Alternatively, the method may be performed by a server.
In step S11, the wireless signal may be used to characterize the strength of the wireless access point signal. Since the terminal can simultaneously receive signals sent by a plurality of wireless access points, the wireless signals received by the terminal at the current position can include strength information of the plurality of wireless access points. The Strength information of the wireless access point may be Received Signal Strength (RSS).
For example, the terminal may receive signals from the wireless access point 1, the wireless access point 2, and the wireless access point 3 at the current location, and the wireless signals received by the terminal at the current location may include the wireless signal 1 corresponding to the wireless access point 1, the wireless signal 2 corresponding to the wireless access point 2, and the wireless signal 3 corresponding to the wireless access point 3 in the united kingdom.
The wireless signal received by the terminal at the current position can be collected by the terminal. In one example, the terminal may initiate a floor location procedure upon detecting that a user triggered a floor location control in the terminal or a navigation application in the terminal, at which point the terminal may collect a wireless signal received at a current location.
In the case where the floor prediction model in step S12 is placed in the terminal, the terminal may directly input the radio signal received at the current position to the floor prediction model, and may obtain the floor label of the current position. In the case where the floor prediction model in step S12 is located in the server, the terminal may first transmit the wireless signal received at the current location to the server, and then the server may input the wireless signal received at the current location to the floor prediction model to obtain the floor tag of the current location. The server may then return to the terminal the floor label output by the floor prediction model.
In step S12, the floor prediction model trained in advance may represent a model for performing floor label prediction. After the wireless signal received by the terminal at the current position is input into a pre-trained floor prediction model, the floor prediction model can output a floor label of the current position. The floor labels can be used for representing floors, and the floor labels correspond to the floors one to one. In one example, floor label "1" may represent first floor, floor label "2" may represent second floor, and floor label "-1" may represent minus first floor. The embodiments of the present disclosure do not limit the form of floor representation.
In one possible implementation, the floor prediction model may include an Xgboost model. The Xgboost model has the characteristics of small training data volume and high classification precision. It is to be understood that the floor prediction model may also include other classification models, and the disclosure is not limited thereto. The following describes a training procedure of the floor prediction model, taking the floor prediction model as an Xgboost model as an example.
In one possible implementation, before step S12, the method may further include: and training the Xgboost model.
Wherein the step of training the Xgboost model may comprise: collecting positioning marking data; acquiring training data from the positioning labeling data; and training the Xgboost model to be trained by adopting the training data to obtain the trained Xgboost model, namely the pre-trained floor prediction model.
In the embodiment of the present disclosure, the positioning labeling data may include a wireless signal labeled with a positioning result, that is, the positioning labeling data may include a wireless signal received by the terminal at a plurality of collecting positions and a floor tag corresponding to each collecting position.
In one example, collecting the positioning annotation data can include: determining the collection position for collecting the positioning marking data; and aiming at each acquisition position, acquiring a wireless signal received by the terminal at the acquisition position and a floor mark of a floor where the acquisition position is located, and determining the wireless signal received by the terminal at the acquisition position and the floor mark corresponding to the floor mark as the labeled positioning data of the acquisition position.
FIG. 2 illustrates one example of an acquisition page according to an embodiment of the disclosure. The collection page is a display page of the application used for collecting the positioning marking data in the terminal. The collection page is used for collecting positioning marking data. As shown in fig. 2, the collection page shows a plan view of a floor and the collection location on the floor. After the acquisition personnel place the terminal in a certain acquisition position in the floor, the terminal can be controlled to acquire the wireless signal received at the acquisition position and the floor identification of the floor where the acquisition position is located. And then, the terminal can determine the acquired wireless signal and the acquired floor label corresponding to the floor identification as the labeled positioning data of the acquisition position. For example, after the terminal acquires the wireless signal "the wireless signal of the first point" and the floor identifier "first floor" from the acquisition position "first point", the terminal may determine the floor tag (for example, "1") corresponding to the wireless signal "of the first point" and "first floor" as the annotation positioning data of the "first point".
In one example, acquiring the wireless signal received by the terminal at the acquisition position and the floor identification of the floor where the acquisition position is located may include: displaying a floor mark corresponding to each floor label and a position mark of each acquisition position on the terminal; and under the condition that the acquisition control is triggered, acquiring a wireless signal, and respectively determining the acquired wireless signal and a floor label corresponding to the currently triggered floor identifier as the wireless signal received by the terminal at the acquisition position corresponding to the currently triggered position identifier and the floor label of the floor where the acquisition position is located.
The collection page can display the floor identification corresponding to each floor label (corresponding to "first floor," "second floor," "third floor," and "fourth floor" shown in fig. 2), the location identification of each collection location (corresponding to "·" in the collection page shown in fig. 2), and a collection control (corresponding to "collection" shown in fig. 2). Under the condition that the acquisition control is triggered, the terminal can acquire a wireless signal and respectively determine the acquired wireless signal and a floor label corresponding to the currently triggered floor identifier as the wireless signal received by the terminal at the acquisition position corresponding to the currently triggered position identifier and the floor label of the floor where the acquisition position is located, so that the marking and positioning data of the acquisition position corresponding to the currently triggered position identifier are obtained.
After the acquisition control is triggered at each acquisition position in sequence, the terminal can acquire the marking and positioning data of each acquisition position.
FIG. 3 illustrates yet another example of an acquisition page according to an embodiment of the present disclosure. After the acquisition person triggers the acquisition control, an acquisition page may display an acquisition prompt, such as "acquiring wireless signals" shown in fig. 3. It should be noted that, because the wireless signal scanning speeds of different terminals are not consistent, the acquiring personnel needs to wait for 2-10 seconds after triggering the acquisition control each time, so that the terminal can acquire the labeling positioning data of the corresponding position.
FIG. 4 illustrates yet another example of an acquisition page according to an embodiment of the present disclosure. After the collection of the mark positioning data of each collection position is completed, the collection page can be used for carrying out frame popping prompt so that collection personnel can start to collect next time. For example, the acquisition page may display the "wireless signal collection" shown in fig. 4: save is complete, for a total of 7 MAC addresses ".
In one example, the terminal can store the positioning annotation data in txt format.
After the collection of the positioning annotation data is completed, the terminal can send the positioning annotation data to the server. The server may obtain training data from the positioning annotation data. Then, the server can train the Xgboost model to be trained by adopting the training data to obtain the trained Xgboost model.
The server may then read the positioning annotation data (including the wireless signal and the floor tag) of the training set using the pandas library of Python, and train the positioning annotation data of the training set using the xgbclasifier (i.e., the Xgboost model to be trained) of the Xgboost library of Python, to obtain a trained xgbclasifier (i.e., the trained Xgboost model).
In a possible implementation manner, the training the Xgboost model to be trained by using the training data to obtain the trained Xgboost model may include:
training an Xgboost model to be trained by adopting the training data to obtain an Xgboost model in an intermediate state; acquiring test data from the positioning marking data; testing the Xgboost model of the intermediate state by adopting the test data to obtain the positioning accuracy of the Xgboost model of the intermediate state; and under the condition that the positioning accuracy of the Xgboost model of the intermediate state is greater than a specified threshold value, determining the Xgboost model of the intermediate state as the trained Xgboost model.
In a possible implementation manner, the training of the Xgboost model to be trained by using the training data to obtain the trained Xgboost model may further include:
determining the quantity of training data needing to be supplemented under the condition that the positioning accuracy of the Xgboost model of the intermediate state is smaller than or equal to the specified threshold; acquiring supplementary training data according to the quantity, wherein the acquisition position of the supplementary training data is different from the acquisition position of the positioning marking data; and training the Xgboost model of the intermediate state by adopting the supplemented training data to obtain the trained Xgboost model.
It is considered that the Xgboost model trained by using the training data (i.e. the Xgboost model of the intermediate state) may have a problem of insufficient positioning accuracy. Thus, the test data can be used to test the Xgboost model for the intermediate state. And determining whether to train for the second time or not based on the positioning accuracy obtained by the test and a specified threshold value.
In one example, the server may adopt a 10-fold cross-validation method to divide the positioning annotation data into 10 training sets and 10 testing sets, and store the 20 sets in the csv format respectively. Included in the training set is training data and included in the test set is test data. The server may also obtain the test data and the test data from the positioning annotation data in other manners, which is not limited in this disclosure. The specified threshold may be set as desired, for example, may be set to 95%.
The server can read the positioning annotation data (including wireless signals and floor labels) of the test set by using a Python pandas library, then call a prediction (predict) function of the xgbclasifier, take the wireless signals of the positioning annotation data in the test set as function parameters, and compare the output result of the function with the floor labels of the positioning annotation data in the test set to obtain the positioning accuracy.
In case the positioning accuracy of the intermediate state Xgboost model is larger than a specified threshold, the positioning accuracy of the intermediate state Xgboost model indicating the intermediate state Xgboost model is high, and the intermediate state Xgboost model can be directly used for the prediction of the floor. Thus, the Xgboost model of the intermediate state may be determined as the trained Xgboost model.
When the positioning accuracy of the Xgboost model in the intermediate state is smaller than or equal to the specified threshold, it indicates that the positioning accuracy of the Xgboost model in the intermediate state is poor, and the Xgboost model in the intermediate state cannot be directly used for the pre-floor of the floor and needs to be trained for the second time. Therefore, in this case, the number of training data that needs to be supplemented may be determined, the supplemented training data may be obtained according to the number, and the trained Xgboost model of the intermediate state may be obtained by training the Xgboost model with the supplemented training data.
In one example, the terminal can prompt the number of training data needing to be supplemented to the acquisition personnel, so that the acquisition personnel can acquire the training data conveniently, and the user experience is improved. The acquisition position of the supplemented training data is different from the acquisition position of the positioning marking data, so that the positioning accuracy of the model can be effectively improved.
The process of acquiring the supplemented training data may refer to a process of acquiring positioning annotation data before, a process of training the Xgboost model in the intermediate state by using the supplemented training data, and a process of training the Xgboost model to be trained by using the training data before, which is not described herein again.
Because the server adopts the Python-trained Xgboost model as Python format, the format is not suitable for the terminal (such as a mobile phone, a tablet and the like). Thus, in embodiments of the present disclosure, the server may lead into the m2cgen library of Python. And then, calling an export _ to _ C function of the m2cgen library, wherein the parameter of the function is an Xgboost model in Python format, and the returned function is the Xgboost model in C + + format. The server may use this C + + formatted Xgboost model as the floor prediction model in step S12. In this way, the server may send the Xgboost model in C + + format to the terminal, and the terminal performs step S12. That is, floor location is performed locally by the terminal. Therefore, the flow can be saved, the positioning time can be saved, the terminal cannot be burdened, the light-weight floor positioning is realized, and the problem of inaccurate positioning caused by high visual similarity among different floors is solved.
It is understood that the above-mentioned method embodiments of the present disclosure can be combined with each other to form a combined embodiment without departing from the logic of the principle, which is limited by the space, and the detailed description of the present disclosure is omitted. Those skilled in the art will appreciate that in the above methods of the specific embodiments, the specific order of execution of the steps should be determined by their function and possibly their inherent logic.
In addition, the present disclosure also provides a positioning apparatus, an electronic device, a computer-readable storage medium, and a program, which can be used to implement any one of the positioning methods provided by the present disclosure, and the corresponding technical solutions and descriptions and corresponding descriptions in the method sections are not repeated.
Fig. 5 shows a block diagram of a positioning device according to an embodiment of the present disclosure. As shown in fig. 5, the positioning device 50 may include:
a signal obtaining module 51, configured to obtain a wireless signal received by a terminal at a current location, where the wireless signal includes strength information of multiple wireless access points;
and the signal input module 52 is configured to input the wireless signal received by the terminal at the current position into a pre-trained floor prediction model, so as to obtain a floor tag of the current position.
In one possible implementation, the apparatus further includes:
the model training module is used for training the Xgboost model;
the model training module is specifically configured to:
collecting positioning marking data, wherein the positioning marking data comprise wireless signals received by a terminal at a plurality of collecting positions and floor labels corresponding to the collecting positions;
acquiring training data from the positioning labeling data;
and training the Xgboost model to be trained by adopting the training data to obtain the trained Xgboost model.
In a possible implementation manner, the training of the Xgboost model to be trained by using the training data to obtain the trained Xgboost model includes:
training an Xgboost model to be trained by adopting the training data to obtain an Xgboost model in an intermediate state;
acquiring test data from the positioning marking data;
testing the Xgboost model of the intermediate state by adopting the test data to obtain the positioning accuracy of the Xgboost model of the intermediate state;
and under the condition that the positioning accuracy of the Xgboost model of the intermediate state is greater than a specified threshold value, determining the Xgboost model of the intermediate state as the trained Xgboost model.
In a possible implementation manner, the training of the Xgboost model to be trained by using the training data to obtain the trained Xgboost model further includes:
determining the quantity of training data needing to be supplemented under the condition that the positioning accuracy of the Xgboost model of the intermediate state is smaller than or equal to the specified threshold;
acquiring supplementary training data according to the quantity, wherein the acquisition position of the supplementary training data is different from the acquisition position of the positioning marking data;
and training the Xgboost model of the intermediate state by adopting the supplemented training data to obtain the trained Xgboost model.
In one possible implementation, the collecting the positioning annotation data includes:
determining the acquisition position of the positioning marking data;
and aiming at each acquisition position, acquiring a wireless signal received by the terminal at the acquisition position and a floor mark of a floor where the acquisition position is located, and determining the wireless signal received by the terminal at the acquisition position and the floor mark corresponding to the floor mark as the labeled positioning data of the acquisition position.
In a possible implementation manner, the acquiring the wireless signal received by the terminal at the collecting position and the floor identifier of the floor where the collecting position is located includes:
displaying a floor mark corresponding to each floor label and a position mark of each acquisition position on the terminal;
and under the condition that the acquisition control is triggered, acquiring a wireless signal, and respectively determining the acquired wireless signal and a floor label corresponding to the currently triggered floor identifier as the wireless signal received by the terminal at the acquisition position corresponding to the currently triggered position identifier and the floor label of the floor where the acquisition position is located.
In some embodiments, functions or modules included in the apparatus provided in the embodiments of the present disclosure may be used to execute the method described in the above method embodiments, and specific implementations and technical effects thereof may refer to the description of the above method embodiments, which are not described herein again for brevity.
Embodiments of the present disclosure also provide a computer-readable storage medium having stored thereon computer program instructions, which when executed by a processor, implement the above-mentioned method. The computer readable storage medium may be a non-volatile computer readable storage medium.
An embodiment of the present disclosure further provides an electronic device, including: a processor; a memory for storing processor-executable instructions; wherein the processor is configured to invoke the memory-stored instructions to perform the above-described method.
The embodiments of the present disclosure also provide a computer program product, which includes computer readable code, and when the computer readable code runs on a device, a processor in the device executes instructions for implementing the positioning method provided in any of the above embodiments.
The embodiments of the present disclosure also provide another computer program product for storing computer readable instructions, which when executed cause a computer to perform the operations of the positioning method provided in any of the above embodiments.
The electronic device may be provided as a terminal, server, or other form of device.
Fig. 6 illustrates a block diagram of an electronic device 800 in accordance with an embodiment of the disclosure. For example, the electronic device 800 may be a mobile phone, a computer, a digital broadcast terminal, a messaging device, a game console, a tablet device, a medical device, a fitness device, a personal digital assistant, or the like terminal.
Referring to fig. 6, electronic device 800 may include one or more of the following components: a processing component 802, a memory 804, a power component 806, a multimedia component 808, an audio component 810, an input/output (I/O) interface 812, a sensor component 814, and a communication component 816.
The processing component 802 generally controls overall operation of the electronic device 800, such as operations associated with display, telephone calls, data communications, camera operations, and recording operations. The processing components 802 may include one or more processors 820 to execute instructions to perform all or a portion of the steps of the methods described above. Further, the processing component 802 can include one or more modules that facilitate interaction between the processing component 802 and other components. For example, the processing component 802 can include a multimedia module to facilitate interaction between the multimedia component 808 and the processing component 802.
The memory 804 is configured to store various types of data to support operations at the electronic device 800. Examples of such data include instructions for any application or method operating on the electronic device 800, contact data, phonebook data, messages, pictures, videos, and so forth. The Memory 804 may be implemented by any type of volatile or non-volatile Memory device or combination thereof, such as Static Random-Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read-Only Memory (EPROM), Programmable Read-Only Memory (PROM), Read-Only Memory (ROM), magnetic Memory, flash Memory, magnetic or optical disk.
The power supply component 806 provides power to the various components of the electronic device 800. The power components 806 may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power for the electronic device 800.
The multimedia component 808 includes a screen that provides an output interface between the electronic device 800 and a user. In some embodiments, the screen may include a Liquid Crystal Display (LCD) and a Touch Panel (TP). If the screen includes a touch panel, the screen may be implemented as a touch screen to receive an input signal from a user. The touch panel includes one or more touch sensors to sense touch, slide, and gestures on the touch panel. The touch sensor may not only sense the boundary of a touch or slide action, but also detect the duration and pressure associated with the touch or slide operation. In some embodiments, the multimedia component 808 includes a front facing camera and/or a rear facing camera. The front camera and/or the rear camera may receive external multimedia data when the electronic device 800 is in an operation mode, such as a shooting mode or a video mode. Each front camera and rear camera may be a fixed optical lens system or have a focal length and optical zoom capability.
The audio component 810 is configured to output and/or input audio signals. For example, the audio component 810 includes a Microphone (MIC) configured to receive external audio signals when the electronic device 800 is in an operational mode, such as a call mode, a recording mode, and a voice recognition mode. The received audio signals may further be stored in the memory 804 or transmitted via the communication component 816. In some embodiments, audio component 810 also includes a speaker for outputting audio signals.
The I/O interface 812 provides an interface between the processing component 802 and peripheral interface modules, which may be keyboards, click wheels, buttons, etc. These buttons may include, but are not limited to: a home button, a volume button, a start button, and a lock button.
The sensor assembly 814 includes one or more sensors for providing various aspects of state assessment for the electronic device 800. For example, the sensor assembly 814 may detect an open/closed state of the electronic device 800, the relative positioning of components, such as a display and keypad of the electronic device 800, the sensor assembly 814 may also detect a change in the position of the electronic device 800 or a component of the electronic device 800, the presence or absence of user contact with the electronic device 800, orientation or acceleration/deceleration of the electronic device 800, and a change in the temperature of the electronic device 800. Sensor assembly 814 may include a proximity sensor configured to detect the presence of a nearby object without any physical contact. The sensor assembly 814 may also include a photosensor, such as a Complementary Metal Oxide Semiconductor (CMOS) or Charge-coupled Device (CCD) image sensor, for use in imaging applications. In some embodiments, the sensor assembly 814 may also include an acceleration sensor, a gyroscope sensor, a magnetic sensor, a pressure sensor, or a temperature sensor.
The communication component 816 is configured to facilitate wired or wireless communication between the electronic device 800 and other devices. The electronic device 800 may access a wireless network based on a communication standard, such as WiFi, 2G or 3G, or a combination thereof. In an exemplary embodiment, the communication component 816 receives a broadcast signal or broadcast related information from an external broadcast management system via a broadcast channel. In an exemplary embodiment, the communication component 816 further includes a Near Field Communication (NFC) module to facilitate short-range communications. For example, the NFC module may be implemented based on Radio Frequency Identification (RFID) technology, Infrared Data Association (IrDA) technology, Ultra Wide Band (UWB) technology, Bluetooth (BT) technology, and other technologies.
In an exemplary embodiment, the electronic Device 800 may be implemented by one or more Application Specific Integrated Circuits (ASICs), Digital Signal Processors (DSPs), Digital Signal Processing Devices (DSPDs), Programmable Logic Devices (PLDs), Field Programmable Gate Arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components for performing the above-described methods.
In an exemplary embodiment, a non-transitory computer-readable storage medium, such as the memory 804, is also provided that includes computer program instructions executable by the processor 820 of the electronic device 800 to perform the above-described methods.
Fig. 7 illustrates a block diagram of an electronic device 1900 in accordance with an embodiment of the disclosure. For example, the electronic device 1900 may be provided as a server. Referring to fig. 7, electronic device 1900 includes a processing component 1922 further including one or more processors and memory resources, represented by memory 1932, for storing instructions, e.g., applications, executable by processing component 1922. The application programs stored in memory 1932 may include one or more modules that each correspond to a set of instructions. Further, the processing component 1922 is configured to execute instructions to perform the above-described method.
The electronic device 1900 may also include a power component 1926 configured to perform power management of the electronic device 1900, a wired or wireless network interface 1950 configured to connect the electronic device 1900 to a network, and an input/output (I/O) interface 1958. The electronic device 1900 may operate based on an operating system stored in memory 1932, such as Windows Server, Mac OS XTM, UnixTM, LinuxTM, FreeBSDTM, or the like.
In an exemplary embodiment, a non-transitory computer readable storage medium, such as the memory 1932, is also provided that includes computer program instructions executable by the processing component 1922 of the electronic device 1900 to perform the above-described methods.
The present disclosure may be systems, methods, and/or computer program products. The computer program product may include a computer-readable storage medium having computer-readable program instructions embodied thereon for causing a processor to implement various aspects of the present disclosure.
The computer readable storage medium may be a tangible device that can hold and store the instructions for use by the instruction execution device. The computer readable storage medium is not limited to electronic, magnetic, optical, electromagnetic, semiconductor memory devices, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: a portable computer diskette, a hard disk, a Random Access Memory (RAM), a Read-only Memory (ROM), an erasable programmable Read-only Memory (EPROM or flash Memory), a static random-Access Memory (SRAM), a portable Compact Disc Read-only Memory (CD-ROM), a Digital Versatile Disc (DVD), a Memory stick, a floppy disk, a mechanical coding device, a punch card or an in-groove protrusion structure such as those having instructions stored thereon, and any suitable combination of the foregoing. Computer-readable storage media as used herein is not to be construed as transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission medium (e.g., optical pulses through a fiber optic cable), or electrical signals transmitted through electrical wires.
The computer-readable program instructions described herein may be downloaded from a computer-readable storage medium to a respective computing/processing device, or to an external computer or external storage device via a network, such as the internet, a local area network, a wide area network, and/or a wireless network. The network may include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers and/or edge servers. The network adapter card or network interface in each computing/processing device receives computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in a computer-readable storage medium in the respective computing/processing device.
The computer program instructions for carrying out operations of the present disclosure may be assembler instructions, Instruction Set Architecture (ISA) instructions, machine-related instructions, microcode, firmware instructions, state setting data, or source or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The computer-readable program instructions may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of Network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider). In some embodiments, the electronic circuitry can execute computer-readable program instructions to implement aspects of the present disclosure by utilizing state information of the computer-readable program instructions to personalize custom electronic circuitry, such as Programmable logic circuits, Field Programmable Gate Arrays (FPGAs), or Programmable Logic Arrays (PLAs).
Various aspects of the present disclosure are described herein with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the disclosure. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer-readable program instructions.
These computer-readable program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks. These computer-readable program instructions may also be stored in a computer-readable storage medium that can direct a computer, programmable data processing apparatus, and/or other devices to function in a particular manner, such that the computer-readable medium storing the instructions comprises an article of manufacture including instructions which implement the function/act specified in the flowchart and/or block diagram block or blocks.
The computer readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other devices to cause a series of operational steps to be performed on the computer, other programmable apparatus or other devices to produce a computer implemented process such that the instructions which execute on the computer, other programmable apparatus or other devices implement the functions/acts specified in the flowchart and/or block diagram block or blocks.
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of instructions, which comprises one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The computer program product may be embodied in hardware, software or a combination thereof. In an alternative embodiment, the computer program product is embodied in a computer storage medium, and in another alternative embodiment, the computer program product is embodied in a Software product, such as a Software Development Kit (SDK), or the like.
Having described embodiments of the present disclosure, the foregoing description is intended to be exemplary, not exhaustive, and not limited to the disclosed embodiments. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The terminology used herein is chosen in order to best explain the principles of the embodiments, the practical application, or improvements made to the technology in the marketplace, or to enable others of ordinary skill in the art to understand the embodiments disclosed herein.

Claims (14)

1. A method of positioning, the method comprising:
acquiring wireless signals received by a terminal at the current position, wherein the wireless signals comprise strength information of a plurality of wireless access points;
and inputting the wireless signal received by the terminal at the current position into a pre-trained floor prediction model to obtain a floor label of the current position.
2. The method of claim 1, wherein the floor prediction model comprises an Xgboost model, and wherein prior to the step of inputting the wireless signal received by the terminal at the current location into a pre-trained floor prediction model, the method further comprises: training an Xgboost model;
the step of training the Xgboost model comprises:
collecting positioning marking data, wherein the positioning marking data comprise wireless signals received by a terminal at a plurality of collecting positions and floor labels corresponding to the positions;
acquiring training data from the positioning labeling data;
and training the Xgboost model to be trained by adopting the training data to obtain the trained Xgboost model.
3. The method of claim 2, wherein using the training data to train the Xgboost model to be trained to obtain a trained Xgboost model comprises:
training an Xgboost model to be trained by adopting the training data to obtain an Xgboost model in an intermediate state;
acquiring test data from the positioning marking data;
testing the Xgboost model of the intermediate state by adopting the test data to obtain the positioning accuracy of the Xgboost model of the intermediate state;
and under the condition that the positioning accuracy of the Xgboost model of the intermediate state is greater than a specified threshold value, determining the Xgboost model of the intermediate state as the trained Xgboost model.
4. The method of claim 3, wherein using the training data to train the Xgboost model to be trained, obtaining a trained Xgboost model further comprises:
determining the quantity of training data needing to be supplemented under the condition that the positioning accuracy of the Xgboost model of the intermediate state is smaller than or equal to the specified threshold;
acquiring supplementary training data according to the quantity, wherein the acquisition position of the supplementary training data is different from the acquisition position of the positioning marking data;
and training the Xgboost model of the intermediate state by adopting the supplemented training data to obtain the trained Xgboost model.
5. The method of any of claims 2 to 4, wherein said acquiring positioning annotation data comprises:
determining the acquisition position of the positioning marking data;
and aiming at each acquisition position, acquiring a wireless signal received by the terminal at the acquisition position and a floor mark of a floor where the acquisition position is located, and determining the wireless signal received by the terminal at the acquisition position and the floor mark corresponding to the floor mark as the labeled positioning data of the acquisition position.
6. The method of claim 5, wherein obtaining the wireless signal received by the terminal at the acquisition location and the floor identification of the floor on which the acquisition location is located comprises:
displaying a floor mark corresponding to each floor label and a position mark of each acquisition position on the terminal;
and under the condition that the acquisition control is triggered, acquiring a wireless signal, and respectively determining the acquired wireless signal and a floor label corresponding to the currently triggered floor identifier as the wireless signal received by the terminal at the acquisition position corresponding to the currently triggered position identifier and the floor label of the floor where the acquisition position is located.
7. A positioning device, the device comprising:
the system comprises a signal acquisition module, a signal processing module and a signal processing module, wherein the signal acquisition module is used for acquiring wireless signals received by a terminal at the current position, and the wireless signals comprise the strength information of a plurality of wireless access points;
and the signal input module is used for inputting the wireless signals received by the terminal at the current position into a pre-trained floor prediction model to obtain the floor label of the current position.
8. The apparatus of claim 7, further comprising:
the model training module is used for training the Xgboost model;
the model training module is specifically configured to:
collecting positioning marking data, wherein the positioning marking data comprise wireless signals received by a terminal at a plurality of collecting positions and floor labels corresponding to the collecting positions;
acquiring training data from the positioning labeling data;
and training the Xgboost model to be trained by adopting the training data to obtain the trained Xgboost model.
9. The apparatus of claim 8, wherein using the training data to train an Xgboost model to be trained to obtain a trained Xgboost model comprises:
training an Xgboost model to be trained by adopting the training data to obtain an Xgboost model in an intermediate state;
acquiring test data from the positioning marking data;
testing the Xgboost model of the intermediate state by adopting the test data to obtain the positioning accuracy of the Xgboost model of the intermediate state;
and under the condition that the positioning accuracy of the Xgboost model of the intermediate state is greater than a specified threshold value, determining the Xgboost model of the intermediate state as the trained Xgboost model.
10. The apparatus of claim 9, wherein using the training data to train the Xgboost model to be trained, obtaining the trained Xgboost model further comprises:
determining the quantity of training data needing to be supplemented under the condition that the positioning accuracy of the Xgboost model of the intermediate state is smaller than or equal to the specified threshold;
acquiring supplementary training data according to the quantity, wherein the acquisition position of the supplementary training data is different from the acquisition position of the positioning marking data;
and training the Xgboost model of the intermediate state by adopting the supplemented training data to obtain the trained Xgboost model.
11. The apparatus of any one of claims 8 to 10, wherein said acquiring positioning annotation data comprises:
determining the acquisition position of the positioning marking data;
and aiming at each acquisition position, acquiring a wireless signal received by the terminal at the acquisition position and a floor mark of a floor where the acquisition position is located, and determining the wireless signal received by the terminal at the acquisition position and the floor mark corresponding to the floor mark as the labeled positioning data of the acquisition position.
12. The apparatus of claim 11, wherein obtaining the wireless signal received by the terminal at the collection location and the floor identification of the floor where the collection location is located comprises:
displaying a floor mark corresponding to each floor label and a position mark of each acquisition position on the terminal;
and under the condition that the acquisition control is triggered, acquiring a wireless signal, and respectively determining the acquired wireless signal and a floor label corresponding to the currently triggered floor identifier as the wireless signal received by the terminal at the acquisition position corresponding to the currently triggered position identifier and the floor label of the floor where the acquisition position is located.
13. An electronic device, comprising:
a processor;
a memory for storing processor-executable instructions;
wherein the processor is configured to invoke the memory-stored instructions to perform the method of any of claims 1 to 6.
14. A computer readable storage medium having computer program instructions stored thereon, which when executed by a processor implement the method of any one of claims 1 to 6.
CN202010479175.0A 2020-05-29 2020-05-29 Positioning method and device, electronic equipment and storage medium Pending CN111650554A (en)

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Application publication date: 20200911