Model training method, device, electronic device and storage medium

Abstract

The present disclosure provides a model training method, device, equipment and medium, the method comprising: obtaining historical wireless fidelity WiFi scanning information and historical WiFi distribution information; the historical WiFi scanning information comprises multiple groups of WiFi scanning information, each group of WiFi scanning information comprises at least one first WiFi information, and the historical WiFi distribution information comprises the second WiFi information associated with each point of interest; for each point of interest, based on the second WiFi information associated with the point of interest and at least one first WiFi information in each group of WiFi scanning information, co-occurrence information corresponding to the point of interest is generated; for each co-occurrence information, based on the co-occurrence information and the label corresponding to the co-occurrence information, training sample data is generated; based on each training sample data, a model to be trained is trained to obtain a trained model; the trained model is used to determine the corresponding point of interest location information based on the current WiFi scanning information. The embodiments of the present disclosure are conducive to improving the accuracy of the positioning results.

Description

Technical Field

[0001] The present disclosure relates to the field of positioning technology, and in particular to a model training method, a model training device, an electronic device, and a computer storage medium. Background Art

[0002] WiFi fingerprint positioning technology is one of the important technologies for achieving point of interest (POI) positioning. It is widely used in the industry because it does not require manual deployment of equipment and has good spatial distribution characteristics. Common fingerprint positioning technologies usually determine the user's current point of interest based on the cross-information between the WiFi information scanned by the user's electronic device and the collected historical WiFi fingerprint information. If there is an error in the cross-information, it will affect the credibility of the final positioning result. Therefore, how to improve the credibility of the positioning result is a problem that needs to be solved urgently. Summary of the invention

[0003] The embodiments of the present disclosure at least provide a model training method, device, electronic device and storage medium, which can improve the credibility of positioning results.

[0004] The present disclosure provides a model training method, including:

[0005] Acquire historical wireless fidelity WiFi scanning information and historical WiFi distribution information; the historical WiFi scanning information includes multiple groups of WiFi scanning information, each group of WiFi scanning information includes at least one first WiFi information, and the historical WiFi distribution information includes second WiFi information associated with each point of interest;

[0006] For each point of interest, based on the second WiFi information associated with the point of interest and at least one first WiFi information in each group of WiFi scanning information, generate co-occurrence information corresponding to the point of interest; the co-occurrence information is used to characterize the scanning state of each first WiFi information in each group relative to the second WiFi information associated with the point of interest;

[0007] For each co-occurrence information, generating training sample data based on the co-occurrence information and a label corresponding to the co-occurrence information; the label corresponding to the co-occurrence information is determined based on preset offline behavior information associated with the point of interest corresponding to the co-occurrence information;

[0008] Based on each training sample data, the model to be trained is trained to obtain a trained model; the trained model is used to determine the corresponding point of interest positioning information based on the current WiFi scanning information.

[0009] In the disclosed embodiment, co-occurrence information corresponding to the point of interest is constructed based on historical WiFi distribution information and historical WiFi scanning information. Since the co-occurrence information represents the scanning status of the historically scanned WiFi information relative to the second WiFi information associated with the point of interest, training the model to be trained by generating training sample data based on the co-occurrence information can enhance the model's memory of the association relationship between the WiFi scanning information and the point of interest, and obtain a trained model. Furthermore, the point of interest can be predicted based on the trained model, thereby improving the credibility of the positioning result.

[0010] In a possible implementation, each point of interest has corresponding point of interest identification information; and generating co-occurrence information corresponding to the point of interest based on the second WiFi information associated with the point of interest and at least one first WiFi information in each set of WiFi scan information includes:

[0011] For each point of interest, based on the second WiFi information associated with the point of interest and the first WiFi information in each group, generate scanning distribution identification information corresponding to the point of interest;

[0012] Based on the point of interest identification information of the point of interest and the scan distribution identification information corresponding to the point of interest, the co-occurrence information corresponding to the point of interest is generated.

[0013] In the disclosed embodiment, co-occurrence information corresponding to the point of interest is generated based on the unique point of interest identification information of the point of interest and the scanning distribution identification information corresponding to the point of interest. In this way, the co-occurrence information has the characteristics of the point of interest and the corresponding associated WiFi characteristics. In this way, when the model is trained based on the co-occurrence information in the subsequent steps, the model can learn the association between the point of interest and the corresponding WiFi, which is beneficial to improving the accuracy of the model.

[0014] In a possible implementation, the first WiFi information includes a first WiFi and a signal strength of the first WiFi, the second WiFi information includes a second WiFi and a signal strength of the second WiFi, and there is a distribution order between the second WiFis associated with each point of interest, and the distribution order is determined by the signal strength of each second WiFi;

[0015] The step of generating, for each point of interest, scanning distribution identification information corresponding to the point of interest based on the second WiFi information associated with the point of interest and the first WiFi information in each group, includes:

[0016] For each second WiFi associated with each point of interest, generating sub-scan distribution identification information based on the second WiFi and each first WiFi in each group;

[0017] The sub-scanning distribution identification information is spliced ​​according to the distribution order between the second WiFis associated with the points of interest to generate the scanning distribution identification information.

[0018] In the disclosed embodiment, since each point of interest is associated with at least one second WiFi, corresponding sub-scan distribution identification information can be generated for each second WiFi, and then the individual sub-scan distribution identification information can be spliced ​​to obtain scanning distribution identification information. In this way, the accuracy of the scanning distribution identification information can be improved, thereby improving the accuracy of the subsequent generation of training sample data, and further improving the accuracy of model training.

[0019] In a possible implementation, the sub-scan distribution identification information corresponds to a scan flag bit and multiple signal strength flag bits, and different signal strength flag bits are used to represent different signal strengths; the generating of the sub-scan distribution identification information for each second WiFi associated with each point of interest based on the second WiFi and each first WiFi in each group includes:

[0020] For each second WiFi, when there is a target first WiFi with the same media access control address MAC address as the second WiFi, determine that the value of the scan flag is 1;

[0021] Determine a target signal strength flag corresponding to the signal strength of the target first WiFi from the signal strength flags, and determine the value of the target signal strength flag to be 1, and determine the values ​​of other signal strength flags except the target signal strength flag to be 0;

[0022] The sub-scan distribution identification information is generated based on the value of the scan flag bit and the value of the signal strength flag bit.

[0023] In the disclosed embodiment, the sub-scanning distribution identification information is determined by determining the value of the scanning flag bit and the value of the signal strength flag bit, which is helpful to improve the accuracy of the sub-scanning distribution identification information.

[0024] In a possible implementation, the format of the co-occurrence information is a character string format; and for each piece of co-occurrence information, generating training sample data based on the co-occurrence information and a label corresponding to the co-occurrence information includes:

[0025] For each piece of co-occurrence information, feature processing is performed on the co-occurrence information in the string format to generate a feature vector corresponding to the co-occurrence information;

[0026] Acquire preset offline behavior information associated with the point of interest corresponding to the co-occurrence information, and determine a true value corresponding to the tag based on the preset offline behavior information;

[0027] The training sample data is generated based on the feature vector and the true value corresponding to the label.

[0028] In the disclosed embodiment, by converting the co-occurrence information in the string format into a feature vector, it is convenient to perform model training in the subsequent steps. In addition, the true value of the feature vector is determined based on the preset offline behavior information, and the true value is calibrated for the feature vector, which can improve the accuracy of the training sample data.

[0029] In a possible implementation, determining the true value corresponding to the tag based on the preset offline behavior information includes:

[0030] In the event of a preset offline behavior, the true value corresponding to the label is determined to be 1; or,

[0031] When the preset offline behavior does not occur, the true value corresponding to the label is determined to be 0.

[0032] In the disclosed embodiment, if a preset offline behavior occurs, it means that the preset offline behavior has been performed within the point of interest, and the true value corresponding to the label is determined to be 1. If the preset offline behavior does not occur, it means that the preset offline behavior has not been performed within the point of interest, and the true value corresponding to the label is determined to be 0. The accuracy of the true value determination can be improved, and the accuracy of the training sample data can be improved.

[0033] In a possible implementation, for each piece of co-occurrence information, performing feature processing on the co-occurrence information in a string format to generate a feature vector corresponding to the co-occurrence information includes:

[0034] Numbering each co-occurrence information in the string format according to a preset numbering method, and constructing a feature dictionary based on the numbers corresponding to each co-occurrence information; the feature dictionary is used to convert the co-occurrence information in the string format into a feature vector;

[0035] For each co-occurrence information in a string format, one-hot encoding is performed on the co-occurrence information in the string format based on the feature dictionary to generate a feature vector corresponding to the co-occurrence information.

[0036] In the disclosed embodiment, the co-occurrence information in string format is converted into features by constructing a feature dictionary, which is conducive to improving the efficiency of subsequent model training. In addition, since the one-hot encoding can represent the corresponding feature vector through a state code, the logic of feature conversion can be simplified by the one-hot encoding method.

[0037] In a possible implementation, performing one-hot encoding processing on the co-occurrence information in the string format based on the feature dictionary to generate a feature vector corresponding to the co-occurrence information includes:

[0038] Determine the number of numbers contained in the feature dictionary and create a zero vector having a vector length of the number;

[0039] For each piece of co-occurrence information, according to the number corresponding to the co-occurrence information in the feature dictionary, the value of the target index bit in the zero vector that is the same as the number is modified to 1, and a feature vector corresponding to the co-occurrence information is generated.

[0040] In the disclosed embodiment, for each piece of co-occurrence information, the target index bit in the zero vector is modified based on the serial number of the co-occurrence information in the feature dictionary, so that the accuracy of the feature vector can be improved.

[0041] In a possible implementation, the step of training the model to be trained based on each training sample data to obtain a trained model includes:

[0042] Input each training sample data into the model to be trained to obtain a prediction result corresponding to each training sample data;

[0043] Adjusting the model parameters of the model to be trained based on the prediction result corresponding to each training sample data and the label of each training sample data;

[0044] Repeat the above process until the training results meet the preset requirements to obtain the trained model.

[0045] In the disclosed embodiment, the model is supervisedly trained based on the prediction result corresponding to each training sample data and the label of each training sample data. In this way, the performance of the model training can be improved, thereby improving the prediction accuracy of the model.

[0046] The present disclosure provides a method for locating a point of interest, including:

[0047] Obtain current WiFi scanning information of the current device and associated WiFi information corresponding to each point of interest corresponding to the area where the current device is located;

[0048] For each point of interest, generating current co-occurrence information based on the current WiFi scan information and the associated WiFi information corresponding to the point of interest;

[0049] Input each current co-occurrence information into a trained model to obtain the interest point positioning information of the current device; wherein the trained model is obtained through an embodiment of the above-mentioned model training method.

[0050] In the disclosed embodiment, the location information of the points of interest of the current device is determined based on the trained model, so that the accuracy of the location of the points of interest can be improved.

[0051] The present disclosure provides a model training device, including:

[0052] An information acquisition module, used to acquire historical wireless fidelity WiFi scanning information and historical WiFi distribution information; the historical WiFi scanning information includes multiple groups of WiFi scanning information, each group of WiFi scanning information includes at least one first WiFi information, and the historical WiFi distribution information includes second WiFi information associated with each point of interest;

[0053] An information generation module, configured to generate, for each point of interest, co-occurrence information corresponding to the point of interest based on the second WiFi information associated with the point of interest and at least one first WiFi information in each group of WiFi scanning information; the co-occurrence information is used to characterize a scanning state of each first WiFi information in each group relative to the second WiFi information associated with the point of interest;

[0054] A sample generation module, for generating training sample data for each co-occurrence information based on the co-occurrence information and a label corresponding to the co-occurrence information; the label corresponding to the co-occurrence information is determined based on preset offline behavior information associated with the point of interest corresponding to the co-occurrence information;

[0055] The model training module is used to train the model to be trained based on various training sample data to obtain a trained model; the trained model is used to determine the corresponding point of interest positioning information based on the current WiFi scanning information.

[0056] In a possible implementation manner, each point of interest has corresponding point of interest identification information; the information generation module is specifically used to:

[0057] For each point of interest, based on the second WiFi information associated with the point of interest and the first WiFi information in each group, generate scanning distribution identification information corresponding to the point of interest;

[0058] Based on the point of interest identification information of the point of interest and the scan distribution identification information corresponding to the point of interest, the co-occurrence information corresponding to the point of interest is generated.

[0059] In a possible implementation, the first WiFi information includes a first WiFi and a signal strength of the first WiFi, the second WiFi information includes a second WiFi and a signal strength of the second WiFi, there is a distribution order between the second WiFis associated with each point of interest, and the distribution order is determined by the signal strength of each second WiFi; the information generation module is specifically used to:

[0060] For each second WiFi associated with each point of interest, generating sub-scan distribution identification information based on the second WiFi and each first WiFi in each group;

[0061] The sub-scanning distribution identification information is spliced ​​according to the distribution order between the second WiFis associated with the points of interest to generate the scanning distribution identification information.

[0062] In a possible implementation manner, the sub-scan distribution identification information corresponds to a scan flag bit and a plurality of signal strength flag bits, and different signal strength flag bits are used to represent different signal strengths; the information generation module is specifically used to:

[0063] For each second WiFi, when there is a target first WiFi with the same media access control address MAC address as the second WiFi, determine that the value of the scan flag is 1;

[0064] Determine a target signal strength flag corresponding to the signal strength of the target first WiFi from the signal strength flags, and determine the value of the target signal strength flag to be 1, and determine the values ​​of other signal strength flags except the target signal strength flag to be 0;

[0065] The sub-scan distribution identification information is generated based on the value of the scan flag bit and the value of the signal strength flag bit.

[0066] In a possible implementation, the format of the co-occurrence information is a character string format; and the sample generation module is specifically used to:

[0067] For each piece of co-occurrence information, feature processing is performed on the co-occurrence information in the string format to generate a feature vector corresponding to the co-occurrence information;

[0068] Acquire preset offline behavior information associated with the point of interest corresponding to the co-occurrence information, and determine a true value corresponding to the tag based on the preset offline behavior information;

[0069] The training sample data is generated based on the feature vector and the true value corresponding to the label.

[0070] In a possible implementation manner, the sample generation module is specifically used to:

[0071] In the event of a preset offline behavior, the true value corresponding to the label is determined to be 1; or,

[0072] When the preset offline behavior does not occur, the true value corresponding to the label is determined to be 0.

[0073] In a possible implementation manner, the sample generation module is specifically used to:

[0074] Numbering each co-occurrence information in the string format according to a preset numbering method, and constructing a feature dictionary based on the numbers corresponding to each co-occurrence information; the feature dictionary is used to convert the co-occurrence information in the string format into a feature vector;

[0075] For each co-occurrence information in a string format, one-hot encoding is performed on the co-occurrence information in the string format based on the feature dictionary to generate a feature vector corresponding to the co-occurrence information.

[0076] In a possible implementation manner, the sample generation module is specifically used to:

[0077] Determine the number of numbers contained in the feature dictionary and create a zero vector having a vector length of the number;

[0078] For each piece of co-occurrence information, according to the number corresponding to the co-occurrence information in the feature dictionary, the value of the target index bit in the zero vector that is the same as the number is modified to 1, and a feature vector corresponding to the co-occurrence information is generated.

[0079] In a possible implementation, the model training module is specifically used to:

[0080] Input each training sample data into the model to be trained to obtain a prediction result corresponding to each training sample;

[0081] Adjusting the model parameters of the model to be trained based on the prediction result corresponding to each training sample and the label of each training sample data;

[0082] Repeat the above process until the training results meet the preset requirements to obtain the trained model.

[0083] The present disclosure provides a device for locating a point of interest, including:

[0084] An acquisition module, used to acquire current WiFi scanning information of a current device and associated WiFi information corresponding to each point of interest corresponding to an area where the current device is located;

[0085] A generating module, configured to generate current co-occurrence information for each point of interest based on the current WiFi scanning information and the associated WiFi information corresponding to the point of interest;

[0086] The positioning module is used to input each current co-occurrence information into a trained model to obtain the interest point positioning information of the current device; wherein the trained model is obtained through an embodiment of the above-mentioned model training method.

[0087] An embodiment of the present disclosure also provides an electronic device, including: a processor, a memory and a bus, wherein the memory stores machine-readable instructions executable by the processor, and when the electronic device is running, the processor and the memory communicate through the bus, and when the machine-readable instructions are executed by the processor, the steps of the model training method described in any possible implementation manner described above or the steps of the point of interest positioning method described above are performed.

[0088] The embodiments of the present disclosure also provide a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the steps of the model training method described in any of the above possible implementation modes or the steps of the above point of interest positioning method are executed.

[0089] In order to make the above-mentioned objectives, features and advantages of the present disclosure more obvious and easy to understand, preferred embodiments are specifically cited below and described in detail with reference to the attached drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0090] In order to more clearly illustrate the technical solutions of the embodiments of the present disclosure, the following is a brief introduction to the drawings required for use in the embodiments. The drawings herein are incorporated into the specification and constitute a part of the specification. These drawings illustrate embodiments consistent with the present disclosure and are used together with the specification to illustrate the technical solutions of the present disclosure. It should be understood that the following drawings only illustrate certain embodiments of the present disclosure and should not be regarded as limiting the scope. For ordinary technicians in this field, other relevant drawings can also be obtained based on these drawings without creative work.

[0091] Figure 1 A flow chart of a model training method provided by an embodiment of the present disclosure is shown;

[0092] Figure 2 A flow chart showing a method for generating co-occurrence information provided by an embodiment of the present disclosure is shown;

[0093] Figure 3 A schematic diagram showing a process of generating sub-scan distribution identification information provided by an embodiment of the present disclosure;

[0094] Figure 4 A flow chart of a method for generating a training data sample provided by an embodiment of the present disclosure is shown;

[0095] Figure 5 A flow chart showing a method for processing features of co-occurrence information provided by an embodiment of the present disclosure is shown;

[0096] Figure 6 A flow chart of a method for locating a point of interest provided by an embodiment of the present disclosure is shown;

[0097] Figure 7 A schematic diagram of a model training device provided by an embodiment of the present disclosure is shown;

[0098] Figure 8 A schematic diagram of a device for locating a point of interest provided by an embodiment of the present disclosure is shown;

[0099] Fig. 9 A schematic diagram of an electronic device provided by an embodiment of the present disclosure is shown. DETAILED DESCRIPTION

[0100] In order to make the purpose, technical scheme and advantages of the embodiments of the present disclosure clearer, the technical scheme in the embodiments of the present disclosure will be clearly and completely described below in conjunction with the drawings in the embodiments of the present disclosure. Obviously, the described embodiments are only part of the embodiments of the present disclosure, rather than all of the embodiments. The components of the embodiments of the present disclosure generally described and shown in the drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present disclosure provided in the drawings is not intended to limit the scope of the present disclosure for protection, but merely represents the selected embodiments of the present disclosure. Based on the embodiments of the present disclosure, all other embodiments obtained by those skilled in the art without making creative work belong to the scope of protection of the present disclosure.

[0101] It should be noted that similar reference numerals and letters denote similar items in the following drawings, and therefore, once an item is defined in one drawing, further definition and explanation thereof is not required in subsequent drawings.

[0102] The term "and / or" herein only describes an association relationship, indicating that three relationships may exist. For example, A and / or B may represent the following three situations: A exists alone, A and B exist at the same time, and B exists alone. In addition, the term "at least one" herein represents any combination of at least two of any one or more of a plurality of. For example, including at least one of A, B, and C may represent including any one or more elements selected from the set consisting of A, B, and C.

[0103] It is understandable that before using the technical solutions disclosed in the embodiments of the present disclosure, the types, scope of use, usage scenarios, etc. of the personal information involved in the present disclosure should be informed to the user and the user's authorization should be obtained in an appropriate manner in accordance with relevant laws and regulations.

[0104] Research has found that common fingerprint positioning technologies usually determine the user's current point of interest based on the cross-information between the WiFi information scanned by the user's electronic device and the WiFi fingerprint information collected historically. The accuracy of the cross-information calculation will directly affect the credibility of the final positioning result. Therefore, how to improve the accuracy of the cross-information and thus improve the credibility of the positioning result is a problem that needs to be solved urgently.

[0105] In the field of personalized information recommendation, information scanning services, and online advertising, the click-through rate (CTR) prediction model is one of the important technologies used to learn and predict user feedback information, where the user feedback information can be information about user clicks, favorites, or purchases. The CTR model achieves the above-mentioned information recommendation and other functions through its memory ability, where the memory ability refers to the ability of the model to directly learn and use the "co-occurrence frequency" of requests and historical fingerprints in historical data. Generally speaking, models such as collaborative filtering models and logistic regression models have strong "memory capabilities". Due to the simple structure of these models, historical data can often directly affect the recommendation results, that is, the model can learn the distribution characteristics of historical data and use its own memory to predict the results.

[0106] Based on the above research, the present disclosure provides a model training method, device, electronic device and storage medium, firstly obtaining historical wireless fidelity WiFi scanning information and historical WiFi distribution information; the historical WiFi scanning information includes multiple groups of WiFi scanning information, each group of WiFi scanning information includes at least one first WiFi information, and the historical WiFi distribution information includes the second WiFi information associated with each point of interest; secondly, for each point of interest, based on the second WiFi information associated with the point of interest and at least one first WiFi information in each group of WiFi scanning information, co-occurrence information corresponding to the point of interest is generated; the co-occurrence information is used to characterize the scanning state of each first WiFi information in each group relative to the second WiFi information associated with the point of interest; then, for each co-occurrence information, based on the co-occurrence information and the label corresponding to the co-occurrence information, training sample data is generated; the label corresponding to the co-occurrence information is determined based on the preset offline behavior information associated with the point of interest corresponding to the co-occurrence information; finally, based on each training sample data, the model to be trained is trained to obtain a trained model; the trained model is used to determine the corresponding point of interest positioning information based on the current WiFi scanning information.

[0107] In the disclosed embodiment, corresponding co-occurrence information is constructed based on historical WiFi distribution information and historical WiFi scanning information. Since the co-occurrence information represents the scanning status of the WiFi information scanned by each first user through the electronic device relative to at least one WiFi information covering the point of interest, therefore, training the model by generating training sample data based on the co-occurrence information can enhance the model's memory of the association between the WiFi scanning information and the point of interest, and obtain a trained model. Furthermore, the point of interest can be predicted based on the trained model, thereby improving the credibility of the positioning result.

[0108] To facilitate understanding of this embodiment, the execution subject of the model training method provided by the embodiment of the present disclosure is first introduced in detail. The execution subject of the model training method provided by the embodiment of the present disclosure is an electronic device. In this embodiment, the electronic device is a server, which can be an independent physical server, or a server cluster or distributed system composed of multiple physical servers, or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud storage, big data and artificial intelligence platforms. In other embodiments, the electronic device can also be a terminal device. Among them, the terminal device can be a mobile device, a user terminal, a handheld device, a computing device, a wearable device, etc. In addition, the model training method can also be implemented by a processor calling a computer-readable instruction stored in a memory.

[0109] The model training method provided in the embodiment of the present application is described in detail below with reference to the accompanying drawings. Figure 1 FIG. 1 is a flowchart of a model training method provided in an embodiment of the present disclosure, wherein the method includes steps S101 to S104, wherein:

[0110] S101, obtaining historical wireless fidelity WiFi scanning information and historical WiFi distribution information; the historical WiFi scanning information includes multiple groups of WiFi scanning information, each group of WiFi scanning information includes at least one first WiFi information, and the historical WiFi distribution information includes second WiFi information associated with each point of interest.

[0111] The historical WiFi scanning information may be WiFi information scanned by an electronic device, that is, may refer to multiple groups of WiFi scanning information scanned by different electronic devices, and since WiFi signals have corresponding coverage ranges, each group of WiFi scanning information includes at least one first WiFi information. The first WiFi information includes a first WiFi and a signal strength of the first WiFi, and each first WiFi corresponds to a point of interest.

[0112] The electronic device may be a terminal device, such as a smart phone, a tablet computer, or a smart watch, etc., which is not limited here. It should be understood that the historical WiFi information is the WiFi information collected in the historical time (such as the previous month).

[0113] The historical WiFi distribution information includes second WiFi information associated with each point of interest, the second WiFi information includes the second WiFi and the signal strength of the second WiFi, and there is a distribution order between the second WiFi associated with each point of interest, and the distribution order is determined by the signal strength of each second WiFi.

[0114] The historical WiFi fingerprint distribution information may be WiFi distribution information in a point of interest (POI) dimension constructed based on historical WiFi scanning information, that is, the historical WiFi fingerprint distribution information may be a WiFi distribution constructed based on multiple sets of WiFi scanning information scanned for each point of interest within a historical time period, and since the signal strengths of the second WiFis are different, for each point of interest, there is a corresponding distribution order between the second WiFis associated with the point of interest.

[0115] Exemplarily, for point of interest A, its corresponding second WiFi may include W1, W2, and W3, wherein W1, W2, and W3 have different distances relative to point of interest A, resulting in different signal strengths of the second WiFis associated with point of interest A. For example, the signal strengths between W1, W2, and W3 are W1>W2>W3, and the distribution order between the second WiFis may be W1, W2, and W3.

[0116] In the embodiment of the present disclosure, the point of interest may refer to a store in a target location, which may be a shopping mall, an office building, etc. For example, the point of interest may be a store in a shopping mall. Each point of interest has corresponding unique point of interest identification information, which may be identification information composed of numbers, letters, etc.

[0117] S102: For each point of interest, based on the second WiFi information associated with the point of interest and at least one first WiFi information in each group of WiFi scanning information, generate co-occurrence information corresponding to the point of interest; the co-occurrence information is used to characterize a scanning state of each first WiFi information in each group relative to the second WiFi information associated with the point of interest.

[0118] The scanning status may refer to a scanned state and a not scanned state.

[0119] Here, for each point of interest, the second WiFi information associated with the point of interest can be compared with at least one first WiFi information in each group of WiFi scanning information. In this way, it can be determined whether there is first WiFi information in the at least one first WiFi information that is the same as the second WiFi information associated with the point of interest, and further the scanning status of each first WiFi information relative to the second WiFi information associated with the point of interest can be determined.

[0120] Optionally, for step S102, when generating co-occurrence information corresponding to each point of interest based on the second WiFi information associated with the point of interest and at least one first WiFi information in each set of WiFi scan information, refer to Figure 2 , may include the following steps S1021-S1022:

[0121] S1021: For each point of interest, based on the second WiFi information associated with the point of interest and the first WiFi information in each group, generate scanning distribution identification information corresponding to the point of interest.

[0122] In this step, for each point of interest, the second WiFi information associated with the point of interest and the first WiFi information in each group of WiFi scanning information may be compared, thereby generating scanning distribution identification information corresponding to the point of interest.

[0123] Specifically, for each point of interest, when generating the scanning distribution identification information corresponding to the point of interest based on the second WiFi information associated with the point of interest and the first WiFi information in each group, the following steps (1)-(2) may be included:

[0124] (1) For each second WiFi associated with each point of interest, sub-scan distribution identification information is generated based on the second WiFi and each first WiFi in each group.

[0125] The sub-scan distribution identification information corresponds to a scan flag and multiple signal strength flags, and the scan flag is used to indicate whether the corresponding second WiFi is scanned. The signal strength flag is used to indicate signal strength, and different signal strength flags correspond to different signal strengths. In this embodiment, the signal strength flag includes 8 bits, for example, -70dBm to -60dBm corresponds to signal strength flag 0, and -60dBm to -50dBm corresponds to signal strength flag 1.

[0126] Specifically, for each second WiFi, a scan flag bit can be determined based on whether the first WiFi in each group of first WiFi information is the same as the second WiFi, and a signal strength flag bit can be determined based on the signal strength of each first WiFi in the first WiFi information, and sub-scan distribution identification information can be generated based on the scan flag bit and the signal strength flag bit. Specifically, the following steps (a) to (c) can be included:

[0127] (a) For each second WiFi, when there is a target first WiFi with the same media access control address MAC address as the second WiFi, determine that the value of the scan flag is 1.

[0128] (b) determining a target signal strength flag corresponding to the signal strength of the target first WiFi from the signal strength flags, and determining the value of the target signal strength flag to be 1, and determining the values ​​of other signal strength flags except the target signal strength flag to be 0.

[0129] (c) generating the sub-scan distribution identification information based on the value of the scan flag and the value of the signal strength flag.

[0130] In this implementation manner, when determining the sub-scan distribution identification information according to the value of the scan flag bit and the value of the signal strength flag bit, it can be determined according to a generation formula of the sub-scan distribution identification information, as shown in formula (1):

[0131] ID n = Tag scan *(2 8 +2 I ) (1)

[0132] Among them, ID n Sub-scan distribution identification information, scan flag Tag scan Used to indicate that there is a target first WiFi in the first WiFi information that has the same media access control address MAC address as the second WiFi. If so, the value of the scan flag is determined to be 1; if not, the value of the scan flag is determined to be 0; I represents the index value of the signal bucket into which the signal strength of the target first WiFi falls, and the signal bucket refers to the signal range.

[0133] For example, see Figure 3 , is a schematic diagram of a process for generating sub-scan distribution identification information provided by an embodiment of the present disclosure. Figure 3 As shown in , there are 5 second WiFis (W1, W2, W3, W4, and W5) distributed at the point of interest POI-1. For each second WiFi (taking W1 as an example), W1 is compared with each first WiFi in each group of first WiFi information respectively. If there is a target first WiFi with the same MAC address as W1, the value of the scan flag is determined to be 1. Then, according to the signal strength of the target first WiFi, the target signal strength flag corresponding to its signal strength is determined, and the value of the target signal strength flag is assigned to 1, and the values ​​of other signal strength flags are assigned to 0. In this way, a binary code 100010000 with a length of 9 is obtained, and then the binary code is converted into a decimal integer to obtain the sub-scan distribution identification information 272.

[0134] Similarly, for W2, the corresponding sub-scan distribution identification information bit can be determined to be 000, the corresponding sub-scan distribution identification information for W3 is 272, the corresponding sub-scan distribution identification information for W4 is 258, and the corresponding sub-scan distribution identification information for W5 is 288.

[0135] (2) Each sub-scanning distribution identification information is spliced ​​according to the distribution order between the second WiFi associated with the point of interest to generate the scanning distribution identification information.

[0136] Here, after obtaining the distribution identification information of each sub-scan, the second WiFi can be spliced ​​according to the distribution order of each sub-scan. Figure 3 As shown, the obtained sub-scan distribution identification information are 272, 000, 272, 258 and 288 respectively, which are concatenated to obtain the scan distribution identification information 272000272258288.

[0137] S1022: Generate the co-occurrence information corresponding to the point of interest based on the point of interest identification information of the point of interest and the scan distribution identification information corresponding to the point of interest.

[0138] It can be understood that after obtaining the scanning distribution identification information, the co-occurrence information corresponding to the point of interest can be generated based on the point of interest identification information and the scanning distribution identification information corresponding to the point of interest. Specifically, the point of interest identification information and the scanning distribution identification information corresponding to the point of interest can be spliced ​​to generate co-occurrence information. Here, the format of the co-occurrence information is a string format.

[0139] For example, the interest point identification information is 22535659086281011, and the scan distribution identification information corresponding to the interest point is 272000272258288, then the co-occurrence information is: 272000272258288_22535659086281011.

[0140] It should be noted that the above example is described with reference to one point of interest and a set of first WiFi scan information. Therefore, in the actual process of generating co-occurrence information, for each point of interest, multiple co-occurrence information can be determined based on multiple sets of first WiFi scan information.

[0141] S103, for each co-occurrence information, generating training sample data based on the co-occurrence information and a label corresponding to the co-occurrence information; the label corresponding to the co-occurrence information is determined based on preset offline behavior information associated with the point of interest corresponding to the co-occurrence information.

[0142] The preset offline behavior information may refer to whether a preset offline behavior occurs. For example, the preset offline behavior may refer to offline consumption behavior, such as coupon redemption and the like.

[0143] It should be noted that when generating training sample data, for each first WiFi, if no preset offline behavior occurs in any point of interest corresponding to the first WiFi, the corresponding co-occurrence information will not be used to generate training sample data. For example, the first WiFi in the first WiFi scanning information includes W11, W12, ..., W19, which correspond to points of interest POI1, POI2, ..., POI9, respectively. If no offline behavior occurs at points of interest POI0, POI2, ..., POI9, the corresponding co-occurrence information will not be used to generate training sample data.

[0144] Optionally, for step S103, when generating training sample data for each co-occurrence information based on the co-occurrence information and the label corresponding to the co-occurrence information, refer to Figure 4 , which may include the following S1031 to S1033:

[0145] S1031 : For each piece of co-occurrence information, perform feature processing on the co-occurrence information in the string format to generate a feature vector corresponding to the co-occurrence information.

[0146] It should be understood that since the co-occurrence information is in a string format, it needs to be converted into a corresponding feature vector in order to facilitate model training in subsequent steps.

[0147] Optionally, for each piece of co-occurrence information, feature processing is performed on the co-occurrence information in the string format to generate a feature vector corresponding to the co-occurrence information. Figure 5 , including the following S10311~S10312:

[0148] S10311, number each co-occurrence information in the string format according to a preset numbering method, and construct a feature dictionary based on the numbers corresponding to each co-occurrence information; the feature dictionary is used to convert the co-occurrence information in the string format into a feature vector.

[0149] Specifically, each piece of co-occurrence information may be uniformly numbered so that each piece of co-occurrence information uniquely corresponds to an integer. For example, see Table 1, which shows the correspondence between the co-occurrence information and the number.

[0150] Table 1

[0151] Co-occurrence information Feature dictionary 272000272258288_22535659086281011 0 272000256258288_22535659086282098 1 272288272258288_22535659086281035 2 … … 272000272258288_22535659086098765 n

[0152] It should be noted that when generating the feature dictionary, some co-occurrence information with low frequency (for example, less than 5 times) will not be numbered.

[0153] S10312: For each piece of co-occurrence information in the string format, perform one-hot encoding processing on the co-occurrence information in the string format based on the feature dictionary to generate a feature vector corresponding to the co-occurrence information.

[0154] Wherein, the feature vector is a high-dimensional sparse feature vector.

[0155] After generating the above-mentioned feature dictionary, the co-occurrence information in the string format can be encoded. In the embodiment of the present disclosure, a one-hot encoding method is adopted, wherein the one-hot encoding is also called One-hot encoding, and the method is to use an N-bit state register to encode N states, each state has an independent register bit, and only one register bit is valid at any time.

[0156] Optionally, when performing one-hot encoding processing on the co-occurrence information in the string format based on the feature dictionary to generate a feature vector corresponding to the co-occurrence information, the number of numbers contained in the feature dictionary can be first determined, and a zero vector with a vector length of the number can be created. Then, for each co-occurrence information, according to the number corresponding to the co-occurrence information in the feature dictionary, the value of the target index bit in the zero vector that is the same as the number is modified to 1 to generate a feature vector corresponding to the co-occurrence information.

[0157] For example, please refer to Table 2, which shows the conversion process of the feature vector obtained by performing feature conversion on the co-occurrence information based on the feature dictionary.

[0158] Table 2

[0159]

[0160] Based on Table 2, if the number of numbers in the feature dictionary is 9, a zero vector with a vector length of 9 is created. For each co-occurrence information (for example, 272288272258288_22535659086281035), the number corresponding to the co-occurrence information is determined to be 2 in the feature dictionary, and the value of the register bit with index bit 2 in the zero vector is modified to 1, generating a feature vector 001000000 corresponding to the co-occurrence information.

[0161] S1032: Obtain preset offline behavior information associated with the point of interest corresponding to the co-occurrence information, and determine a true value corresponding to the tag based on the preset offline behavior information.

[0162] Wherein, when a preset offline behavior occurs, the true value corresponding to the label is determined to be 1.

[0163] When the preset offline behavior does not occur, the true value corresponding to the label is determined to be 0.

[0164] S1033: Generate the training sample data based on the feature vector and the true value corresponding to the label.

[0165] In this way, after the feature vector is determined, the true value of each feature vector determined in the above embodiment can be calibrated to obtain training sample data.

[0166] S104, training the model to be trained based on each training sample data to obtain a trained model; the trained model is used to determine the corresponding point of interest positioning information based on the current WiFi scanning information.

[0167] Among them, the model to be trained may refer to a logistic regression model. In other implementations, the model to be trained may also be other models, which is not limited here.

[0168] It can be understood that after obtaining the training sample data, the model to be trained can be trained based on the training sample data (or the training sample data can be fitted based on the logistic regression model to be trained) to obtain a trained model. Here, since each training sample data has a corresponding label, the model to be trained can be supervised.

[0169] In the disclosed embodiment, corresponding co-occurrence information is constructed based on historical WiFi distribution information and historical WiFi scanning information. Since the co-occurrence information represents the scanning status of each group of scanned first WiFi scanning information relative to at least one second WiFi information associated with the point of interest, therefore, training the model by generating training sample data based on the co-occurrence information can enhance the model's memory of the association relationship between the WiFi scanning information and the point of interest, and obtain a trained model. Furthermore, the point of interest can be predicted based on the trained model, thereby improving the credibility of the positioning result.

[0170] Optionally, when the model to be trained is trained based on the training sample data to obtain a trained model, each training sample data can be input into the model to be trained to obtain a prediction result corresponding to each training sample data, and then, based on the prediction result corresponding to each training sample data and the label of each training sample data, the model parameters of the model are adjusted. Specifically, the loss function can be set in advance, and the loss value between the prediction result corresponding to each training sample data and the label of each training sample data is calculated, and the model parameters of the model are adjusted based on the loss value. In this way, the above process is repeated until the training result meets the preset requirements to obtain the trained model.

[0171] See also Figure 6 , is a method for locating a point of interest provided by an embodiment of the present disclosure, the method comprising S601 to S603:

[0172] S601, obtaining current WiFi scanning information of a current device and associated WiFi information corresponding to each point of interest corresponding to an area where the current device is located.

[0173] Exemplarily, current WiFi scanning information may be obtained through the current electronic device, wherein the current WiFi scanning information includes at least one WiFi currently scanned and the signal strength of each WiFi.

[0174] Here, the area where the current device is located can be determined according to the location information of the current device, and the associated WiFi information corresponding to each point of interest can be pre-constructed.

[0175] S602: For each point of interest, generate current co-occurrence information based on the current WiFi scan information and associated WiFi information corresponding to the point of interest.

[0176] In this step, the current co-occurrence information is obtained by combining the current WiFi scan information with the POI identification information of each POI respectively, and performing one-hot encoding (One-hot) on the combined information, wherein the current co-occurrence information is in vector form.

[0177] S603: Input each current co-occurrence information into the trained model to obtain the interest point positioning information of the current device.

[0178] Wherein, the trained model is obtained by any of the model training methods described above.

[0179] It can be understood that after inputting each current co-occurrence information into the trained model, the probabilities corresponding to each point of interest corresponding to the area where the current device is located can be output. Therefore, in the process of POI positioning, it is only necessary to input the current co-occurrence information in the form of a vector obtained above into the trained model, and the model can output the probabilities corresponding to each point of interest. That is, according to the probabilities corresponding to each point of interest, the point of interest positioning information of the current device can be determined. Specifically, the point of interest corresponding to the maximum probability can be determined as the point of interest where the current device is located.

[0180] In some implementations, after determining the point of interest where the current device is located, information recommendations (such as coupon recommendations, product recommendations, online advertising, etc.) can be made to users who want to use the current device based on the point of interest where the device is located.

[0181] Those skilled in the art will appreciate that, in the above method of specific implementation, the order in which the steps are written does not imply a strict execution order and does not constitute any limitation on the implementation process. The specific execution order of the steps should be determined by their functions and possible internal logic.

[0182] Based on the same inventive concept, a model training device corresponding to the model training method is also provided in the embodiment of the present disclosure. Since the principle of solving the problem by the device in the embodiment of the present disclosure is similar to the above-mentioned model training method in the embodiment of the present disclosure, the implementation of the device can refer to the implementation of the method, and the repeated parts will not be repeated.

[0183] Reference Figure 7 FIG. 7 is a schematic diagram of a model training device 700 provided in an embodiment of the present disclosure, wherein the device includes:

[0184] The information acquisition module 701 is used to acquire historical wireless fidelity WiFi scanning information and historical WiFi distribution information; the historical WiFi scanning information includes multiple groups of WiFi scanning information, each group of WiFi scanning information includes at least one first WiFi information, and the historical WiFi distribution information includes second WiFi information associated with each point of interest;

[0185] The information generating module 702 is used to generate, for each point of interest, co-occurrence information corresponding to the point of interest based on the second WiFi information associated with the point of interest and at least one first WiFi information in each group of WiFi scanning information; the co-occurrence information is used to represent the scanning state of each first WiFi information in each group relative to the second WiFi information associated with the point of interest;

[0186] The sample generation module 703 is used to generate training sample data for each co-occurrence information based on the co-occurrence information and a label corresponding to the co-occurrence information; the label corresponding to the co-occurrence information is determined based on the preset offline behavior information associated with the point of interest corresponding to the co-occurrence information;

[0187] The model training module 704 is used to train the model to be trained based on various training sample data to obtain a trained model; the trained model is used to determine the corresponding point of interest positioning information based on the current WiFi scanning information.

[0188] In a possible implementation, each point of interest has corresponding point of interest identification information; the information generation module 702 is specifically used to:

[0189] For each point of interest, based on the second WiFi information associated with the point of interest and the first WiFi information in each group, generate scanning distribution identification information corresponding to the point of interest;

[0190] Based on the point of interest identification information of the point of interest and the scan distribution identification information corresponding to the point of interest, the co-occurrence information corresponding to the point of interest is generated.

[0191] In a possible implementation, the first WiFi information includes a first WiFi and a signal strength of the first WiFi, the second WiFi information includes a second WiFi and a signal strength of the second WiFi, and there is a distribution order between the second WiFis associated with each point of interest, and the distribution order is determined by the signal strength of each second WiFi; the information generation module 702 is specifically used to:

[0192] For each second WiFi associated with each point of interest, generating sub-scan distribution identification information based on the second WiFi and each first WiFi in each group;

[0193] The sub-scanning distribution identification information is spliced ​​according to the distribution order between the second WiFis associated with the points of interest to generate the scanning distribution identification information.

[0194] In a possible implementation manner, the sub-scan distribution identification information corresponds to a scan flag bit and a plurality of signal strength flag bits, and different signal strength flag bits are used to represent different signal strengths; the information generation module 702 is specifically used to:

[0195] For each second WiFi, when there is a target first WiFi with the same media access control address MAC address as the second WiFi, determine that the value of the scan flag bit is 1;

[0196] Determine a target signal strength flag corresponding to the signal strength of the target first WiFi from the signal strength flags, and determine the value of the target signal strength flag to be 1, and determine the values ​​of other signal strength flags except the target signal strength flag to be 0;

[0197] The sub-scan distribution identification information is generated based on the value of the scan flag bit and the value of the signal strength flag bit.

[0198] In a possible implementation manner, the format of the co-occurrence information is a string format; the sample generation module 703 is specifically used to:

[0199] For each piece of co-occurrence information, feature processing is performed on the co-occurrence information in the string format to generate a feature vector corresponding to the co-occurrence information;

[0200] Acquire preset offline behavior information associated with the point of interest corresponding to the co-occurrence information, and determine a true value corresponding to the tag based on the preset offline behavior information;

[0201] The training sample data is generated based on the feature vector and the true value corresponding to the label.

[0202] In a possible implementation manner, the sample generation module 703 is specifically configured to:

[0203] In the event of a preset offline behavior, the true value corresponding to the label is determined to be 1; or,

[0204] When the preset offline behavior does not occur, the true value corresponding to the label is determined to be 0.

[0205] In a possible implementation manner, the sample generation module 703 is specifically configured to:

[0206] Numbering each co-occurrence information in the string format according to a preset numbering method, and constructing a feature dictionary based on the numbers corresponding to each co-occurrence information; the feature dictionary is used to convert the co-occurrence information in the string format into a feature vector;

[0207] For each co-occurrence information in a string format, one-hot encoding is performed on the co-occurrence information in the string format based on the feature dictionary to generate a feature vector corresponding to the co-occurrence information.

[0208] In a possible implementation manner, the sample generation module 703 is specifically configured to:

[0209] Determine the number of numbers contained in the feature dictionary and create a zero vector having a vector length of the number;

[0210] For each piece of co-occurrence information, according to the number corresponding to the co-occurrence information in the feature dictionary, the value of the target index bit in the zero vector that is the same as the number is modified to 1, and a feature vector corresponding to the co-occurrence information is generated.

[0211] In a possible implementation, the model training module 704 is specifically used to:

[0212] Input each training sample data into the model to be trained to obtain a prediction result corresponding to each training sample;

[0213] Adjusting the model parameters of the model to be trained based on the prediction result corresponding to each training sample and the label of each training sample data;

[0214] Repeat the above process until the training results meet the preset requirements to obtain the trained model.

[0215] See also Figure 8 , is a point of interest positioning device provided by an embodiment of the present disclosure, the point of interest positioning device comprising:

[0216] An acquisition module 801 is used to acquire current WiFi scanning information of a current device and associated WiFi information corresponding to each point of interest corresponding to an area where the current device is located;

[0217] A generating module 802, configured to generate current co-occurrence information for each point of interest based on the current WiFi scanning information and the associated WiFi information corresponding to the point of interest;

[0218] The positioning module 803 is used to input each current co-occurrence information into a trained model to obtain the interest point positioning information of the current device; wherein the trained model is obtained through an embodiment of the above-mentioned model training method.

[0219] For descriptions of the processing flow of each module in the device and the interaction flow between each module, reference may be made to the relevant descriptions in the above method embodiment, which will not be described in detail here.

[0220] Based on the same technical concept, the embodiment of the present disclosure also provides an electronic device. Fig. 9 , which is a schematic diagram of the structure of an electronic device 900 provided in an embodiment of the present disclosure, including a processor 901, a memory 902, and a bus 903. The memory 902 is used to store execution instructions, including a memory 9021 and an external memory 9022; the memory 9021 is also called an internal memory, which is used to temporarily store the operation data in the processor 901 and the data exchanged with the external memory 9022 such as a hard disk. The processor 901 exchanges data with the external memory 9022 through the memory 9021.

[0221] In the embodiment of the present application, the memory 902 is specifically used to store the application code for executing the solution of the present application, and the execution is controlled by the processor 901. That is, when the electronic device 900 is running, the processor 901 communicates with the memory 902 through the bus 903, so that the processor 901 executes the application code stored in the memory 902, and then executes the method described in any of the above embodiments.

[0222] Among them, the memory 902 can be, but is not limited to, random access memory (Random Access Memory, RAM), read only memory (Read Only Memory, ROM), programmable read-only memory (Programmable Read-Only Memory, PROM), erasable programmable read-only memory (Erasable Programmable Read-Only Memory, EPROM), electrically erasable read-only memory (Electric Erasable Programmable Read-Only Memory, EEPROM), etc.

[0223] Processor 901 may be an integrated circuit chip with signal processing capabilities. The above-mentioned processor may be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it may also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components. The disclosed methods, steps and logic block diagrams in the embodiments of the present invention may be implemented or executed. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0224] It is to be understood that the structure illustrated in the embodiment of the present application does not constitute a specific limitation on the electronic device 900. In other embodiments of the present application, the electronic device 900 may include more or fewer components than shown in the figure, or combine some components, or split some components, or arrange the components differently. The components shown in the figure may be implemented in hardware, software, or a combination of software and hardware.

[0225] The present disclosure also provides a computer-readable storage medium on which a computer program is stored, and when the computer program is executed by a processor, the steps of model training in the above method embodiment are executed. The storage medium may be a volatile or non-volatile computer-readable storage medium.

[0226] The embodiments of the present disclosure also provide a computer program product, which carries a program code. The instructions included in the program code can be used to execute the model training steps in the above method embodiment. For details, please refer to the above method embodiment, which will not be repeated here.

[0227] The computer program product may be implemented in hardware, software or a combination thereof. In one optional embodiment, the computer program product is implemented as a computer storage medium. In another optional embodiment, the computer program product is implemented as a software product, such as a software development kit (SDK).

[0228] Those skilled in the art can clearly understand that, for the convenience and simplicity of description, the specific working process of the system and device described above can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here. In the several embodiments provided in the present disclosure, it should be understood that the disclosed system, device and method can be implemented in other ways. The device embodiments described above are merely schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some communication interfaces, and the indirect coupling or communication connection of the device or unit can be electrical, mechanical or other forms.

[0229] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0230] In addition, each functional unit in each embodiment of the present disclosure may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0231] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a non-volatile computer-readable storage medium that is executable by a processor. Based on this understanding, the technical solution of the present disclosure, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for enabling an electronic device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present disclosure. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory, a random access memory, a magnetic disk or an optical disk.

[0232] Finally, it should be noted that the above-described embodiments are only specific implementation methods of the present disclosure, which are used to illustrate the technical solutions of the present disclosure, rather than to limit them. The protection scope of the present disclosure is not limited thereto. Although the present disclosure is described in detail with reference to the above-described embodiments, ordinary technicians in the field should understand that any technician familiar with the technical field can still modify the technical solutions recorded in the above-described embodiments within the technical scope disclosed in the present disclosure, or can easily think of changes, or make equivalent replacements for some of the technical features therein; and these modifications, changes or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present disclosure, and should be included in the protection scope of the present disclosure. Therefore, the protection scope of the present disclosure should be based on the protection scope of the claims.

Claims

1. A model training method, characterized in that: include: Acquire historical wireless fidelity WiFi scanning information and historical WiFi distribution information; the historical WiFi scanning information includes multiple groups of WiFi scanning information, each group of WiFi scanning information includes at least one first WiFi information, and the historical WiFi distribution information includes second WiFi information associated with each point of interest; For each point of interest, based on the second WiFi information associated with the point of interest and at least one first WiFi information in each group of WiFi scanning information, generate co-occurrence information corresponding to the point of interest; the co-occurrence information is used to characterize the scanning state of each first WiFi information in each group relative to the second WiFi information associated with the point of interest; For each co-occurrence information, generating training sample data based on the co-occurrence information and a label corresponding to the co-occurrence information; the label corresponding to the co-occurrence information is determined based on preset offline behavior information associated with the point of interest corresponding to the co-occurrence information; Based on each training sample data, the model to be trained is trained to obtain a trained model; the trained model is used to determine the corresponding point of interest location information based on the current WiFi scanning information; The first WiFi information includes a first WiFi and a signal strength of the first WiFi, and the second WiFi information includes a second WiFi and a signal strength of the second WiFi.

2. The method according to claim 1, characterized in that Each point of interest has corresponding point of interest identification information; the generating co-occurrence information corresponding to the point of interest based on the second WiFi information associated with the point of interest and at least one first WiFi information in each set of WiFi scan information includes: For each point of interest, based on the second WiFi information associated with the point of interest and the first WiFi information in each group, generate scanning distribution identification information corresponding to the point of interest; Based on the point of interest identification information of the point of interest and the scan distribution identification information corresponding to the point of interest, the co-occurrence information corresponding to the point of interest is generated.

3. The method according to claim 2, characterized in that There is a distribution order between the second WiFis associated with each point of interest, and the distribution order is determined by the signal strength of each of the second WiFis; The step of generating, for each point of interest, scanning distribution identification information corresponding to the point of interest based on the second WiFi information associated with the point of interest and the first WiFi information in each group, includes: For each second WiFi associated with each point of interest, generating sub-scan distribution identification information based on the second WiFi and each first WiFi in each group; Splicing each sub-scanning distribution identification information according to the distribution order between the second WiFi associated with the point of interest to generate the scanning distribution identification information; The sub-scan distribution identification information corresponds to a scan flag bit and a plurality of signal strength flag bits.

4. The method according to claim 3, characterized in that Different signal strength flags are used to represent different signal strengths; for each second WiFi associated with each point of interest, generating sub-scan distribution identification information based on the second WiFi and each first WiFi in each group, including: For each second WiFi, when there is a target first WiFi with the same media access control address MAC address as the second WiFi, determine that the value of the scan flag bit is 1; Determine a target signal strength flag corresponding to the signal strength of the target first WiFi from the signal strength flags, and determine the value of the target signal strength flag to be 1, and determine the values ​​of other signal strength flags except the target signal strength flag to be 0; The sub-scan distribution identification information is generated based on the value of the scan flag bit and the value of the signal strength flag bit.

5. The method according to claim 1, characterized in that The format of the co-occurrence information is a string format; The step of generating training sample data for each piece of co-occurrence information based on the co-occurrence information and a label corresponding to the co-occurrence information includes: For each piece of co-occurrence information, feature processing is performed on the co-occurrence information in the string format to generate a feature vector corresponding to the co-occurrence information; Acquire preset offline behavior information associated with the point of interest corresponding to the co-occurrence information, and determine a true value corresponding to the tag based on the preset offline behavior information; The training sample data is generated based on the feature vector and the true value corresponding to the label.

6. The method according to claim 5, characterized in that The determining the true value corresponding to the tag based on the preset offline behavior information includes: When the preset offline behavior occurs, the true value corresponding to the label is determined to be 1; or, when the preset offline behavior does not occur, the true value corresponding to the label is determined to be 0.

7. The method according to claim 5, characterized in that The step of performing feature processing on the co-occurrence information in the string format for each piece of co-occurrence information to generate a feature vector corresponding to the co-occurrence information includes: Numbering each co-occurrence information in the string format according to a preset numbering method, and constructing a feature dictionary based on the numbers corresponding to each co-occurrence information; the feature dictionary is used to convert the co-occurrence information in the string format into a feature vector; For each co-occurrence information in a string format, one-hot encoding is performed on the co-occurrence information in the string format based on the feature dictionary to generate a feature vector corresponding to the co-occurrence information.

8. The method according to claim 7, characterized in that The one-hot encoding process is performed on the co-occurrence information in the string format based on the feature dictionary to generate a feature vector corresponding to the co-occurrence information, including: Determine the number of numbers contained in the feature dictionary and create a zero vector having a vector length of the number; For each piece of co-occurrence information, according to the number corresponding to the co-occurrence information in the feature dictionary, the value of the target index bit in the zero vector that is the same as the number is modified to 1, and a feature vector corresponding to the co-occurrence information is generated.

9. The method according to claim 1, characterized in that: The method of training the model to be trained based on each training sample data to obtain a trained model includes: Input each training sample data into the model to be trained to obtain a prediction result corresponding to each training sample; Adjusting the model parameters of the model to be trained based on the prediction result corresponding to each training sample and the label of each training sample data; Repeat the above process until the training results meet the preset requirements to obtain the trained model.

10. A method for locating a point of interest, characterized in that: include: Obtain current WiFi scanning information of the current device and associated WiFi information corresponding to each point of interest corresponding to the area where the current device is located; For each point of interest, generating current co-occurrence information based on the current WiFi scan information and the associated WiFi information corresponding to the point of interest; Input each current co-occurrence information into a trained model to obtain the point of interest positioning information of the current device; wherein the trained model is obtained by the model training method described in any one of claims 1-9.

11. A model training device, characterized in that: include: An information acquisition module, used to acquire historical wireless fidelity WiFi scanning information and historical WiFi distribution information; The historical WiFi scanning information includes multiple groups of WiFi scanning information, each group of WiFi scanning information includes at least one first WiFi information, and the historical WiFi distribution information includes second WiFi information associated with each point of interest; An information generation module, configured to generate, for each point of interest, co-occurrence information corresponding to the point of interest based on the second WiFi information associated with the point of interest and at least one first WiFi information in each group of WiFi scanning information; the co-occurrence information is used to characterize a scanning state of each first WiFi information in each group relative to the second WiFi information associated with the point of interest; A sample generation module, for generating training sample data for each co-occurrence information based on the co-occurrence information and a label corresponding to the co-occurrence information; the label corresponding to the co-occurrence information is determined based on preset offline behavior information associated with the point of interest corresponding to the co-occurrence information; A model training module is used to train the model to be trained based on each training sample data to obtain a trained model; the trained model is used to determine the corresponding point of interest location information based on the current WiFi scanning information; The first WiFi information includes a first WiFi and a signal strength of the first WiFi, and the second WiFi information includes a second WiFi and a signal strength of the second WiFi.

12. A point of interest positioning device, characterized in that: include: An acquisition module, used to acquire current WiFi scanning information of a current device and associated WiFi information corresponding to each point of interest corresponding to an area where the current device is located; A generating module, configured to generate current co-occurrence information for each point of interest based on the current WiFi scanning information and the associated WiFi information corresponding to the point of interest; A positioning module is used to input each current co-occurrence information into a trained model to obtain the point of interest positioning information of the current device; wherein the trained model is obtained by the model training method described in any one of claims 1-9.

13. An electronic device, characterized in that: include: A processor, a memory and a bus, wherein the memory stores machine-readable requests executable by the processor, and when the electronic device is running, the processor and the memory communicate through the bus, and when the machine-readable request is executed by the processor, the model training method according to any one of claims 1 to 9 or the steps of the model training method according to claim 10 are performed.

14. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, which, when executed by a processor, executes the model training method according to any one of claims 1 to 9 or the steps of the model training method according to claim 10.

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