CN111368862A - Method for distinguishing indoor and outdoor marks, training method and device of classifier and medium - Google Patents
Method for distinguishing indoor and outdoor marks, training method and device of classifier and medium Download PDFInfo
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Abstract
The invention discloses a distinguishing method of indoor and outdoor marks, a training method of a classifier, equipment and a medium, wherein the distinguishing method comprises the following steps: collecting measurement report data of a target user; inputting the measurement report data of the target user into a random forest classifier for classifying indoor and outdoor labels of the user; and determining the indoor and outdoor marks of the target user according to the classification calculation of the random forest classifier. The method and the device effectively reduce the misjudgment rate in the aspect of determining the indoor and outdoor marks of the user, and effectively ensure the real-time performance in the process of determining the indoor and outdoor marks of the user.
Description
Technical Field
The invention relates to the field of communication, in particular to a method for distinguishing indoor and outdoor marks, a method for training a classifier, equipment and a medium.
Background
In the mobile internet era, the life style and behavior habits of people are changed by intelligent terminals. People are accustomed to finding shopping malls, hospitals, banks, even friends, etc. by Location Based Services (LBS), where some mobile services occur indoors and some outdoors. How to accurately judge whether a mobile service user is located indoors or outdoors is important for a specific room. For example: the method can solve the problem of how to accurately identify the deep coverage concerned by operators by distinguishing indoor and outdoor users, and accordingly, an accurate station adding scheme is customized. If the indoor coverage is insufficient, adding an indoor substation; if the outdoor coverage is insufficient, adding an outdoor station: for the old or children needing to be cared for, whether the old or children are in a room or an area can be judged through indoor and outdoor distinguishing; and access to the network within the company, no access to company information once leaving the office building, etc.
The requirement analysis of the application has high requirements on real-time performance and accuracy of indoor and outdoor differentiation of the mobile service. However, the prior art has the problems of low efficiency, high misjudgment rate and incapability of ensuring real-time performance in the aspect of judging indoor and outdoor distinction of the mobile user.
Disclosure of Invention
In order to overcome the above-mentioned drawbacks, the technical problem to be solved by the present invention is to provide a method for distinguishing indoor and outdoor labels, a method for training a classifier, a device and a medium thereof, so as to at least solve the problem of high false rate in determining indoor and outdoor labels of a user.
In order to solve the above technical problem, an embodiment of the present invention provides a method for distinguishing indoor and outdoor tags of a user, including:
collecting measurement report data of a target user;
inputting the measurement report data of the target user into a random forest classifier for classifying indoor and outdoor labels of the user;
and determining the indoor and outdoor marks of the target user according to the classification calculation of the random forest classifier.
In order to solve the technical problem, the training method of the random forest classifier in the embodiment of the invention comprises the following steps:
extracting a training data set from the collected measurement report data of the sample user in the target area and the actual indoor and outdoor marks corresponding to each piece of training data;
inputting the training data set into a preset random forest classification model for training;
in the training process, searching the optimal model parameters of the random forest classification model through grids;
and taking the random forest classification model corresponding to the optimal model parameter as the random forest classifier.
In order to solve the above technical problem, a communication node device in an embodiment of the present invention includes a memory storing an indoor and outdoor tagging program of a user, and a processor executing the computer program to implement the steps of the above distinguishing method.
In order to solve the technical problem, in an embodiment of the present invention, a training apparatus for a random forest classifier includes a memory and a processor, where the memory stores a training program for the random forest classifier, and the processor executes the computer program to implement the steps of the training method.
To solve the above technical problem, in an embodiment of the present invention, a computer-readable storage medium stores an indoor and outdoor tagging program for a user, and the computer program is executable by at least one processor to implement the steps of the above distinguishing method.
To solve the above technical problem, in an embodiment of the present invention, a computer-readable storage medium stores a training program of a random forest classifier, and the computer program is executable by at least one processor to implement the steps of the training method.
The embodiment of the invention has the following beneficial effects:
in the embodiments, the acquired MR data of the target user is input into the random forest classifier for classification calculation, so that the indoor and outdoor marks of the target user can be determined according to the classification calculation, the misjudgment rate is effectively reduced in the aspect of determining the indoor and outdoor marks of the user, and the real-time performance in the process of determining the indoor and outdoor marks of the user is effectively ensured by performing judgment based on the MR data.
The foregoing description is only an overview of the technical solutions of the present invention, and the embodiments of the present invention are described below in order to make the technical means of the present invention more clearly understood and to make the above and other objects, features, and advantages of the present invention more clearly understandable.
Drawings
Various other advantages and benefits will become apparent to those of ordinary skill in the art upon reading the following detailed description of the preferred embodiments. The drawings are only for purposes of illustrating the preferred embodiments and are not to be construed as limiting the invention. Also, like reference numerals are used to refer to like parts throughout the drawings. In the drawings:
FIG. 1 is a flow chart of a method for distinguishing between indoor and outdoor tags of a user according to an embodiment of the present invention;
FIG. 2 is a flow chart of a method for optionally distinguishing between user indoor and outdoor signs in an embodiment of the present invention;
fig. 3 is a diagram of the predicted effect of the indoor and outdoor labels of the target user in the embodiment of the present invention.
Detailed Description
Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.
In the following description, suffixes such as "module", "component", or "unit" used to denote elements are used only for facilitating the explanation of the present invention, and have no specific meaning in itself. Thus, "module", "component" or "unit" may be used mixedly.
The use of prefixes such as "first," "second," etc. to distinguish between elements is merely intended to facilitate the description of the invention and has no particular meaning in and of themselves.
Example one
The embodiment of the invention provides a method for distinguishing indoor and outdoor marks of a user, which comprises the following steps of:
s101, collecting Measurement Report data (MR) of a target user;
s102, inputting the measurement report data of the target user into a random forest classifier for classifying indoor and outdoor mark positions _ real of the user;
s103, determining indoor and outdoor marks of the target user according to the classification calculation of the random forest classifier.
The target user refers to a user to be positioned, and the user generally refers to a mobile user. The MR records wireless measurement information such as serving cell id (identity), rsrp (test power value), (LTE reference signal received quality rsrq), ta _ calc (time delay), aoa (incident angle), start time (start time), end time (end time), imsi (international mobile subscriber identity) and the like of a mobile subscriber in a service process. The MR data of the target user collected in the embodiment of the present invention includes aoa (incident angle), ta _ calc (time delay), rsrp (test power value), tadltvalue (downlink time delay), and time _ difference (time difference-start time). The indoor and outdoor marks are used for marking that a user is indoors or outdoors, and can also be described as indoor or outdoor marks and indoor and outdoor marks.
The method in the embodiment of the invention can be applied to a communication node side, such as a base station side; in the determination process, the base station may acquire the MR data of the target user in real time, so the MR data in the embodiment of the present invention may also be described as real-time MR data. Since the determination is performed by the classification calculation of the random forest classifier, the determination is also a prediction process.
According to the embodiment of the invention, the acquired MR data of the target user is input into the random forest classifier for classification calculation, so that the indoor and outdoor marks of the target user can be determined according to the classification calculation, the misjudgment rate is effectively reduced in the aspect of determining the indoor and outdoor marks of the user, and the real-time performance in the process of determining the indoor and outdoor marks of the user is effectively ensured by performing judgment based on the MR data.
On the basis of the above embodiments, several specific and alternative embodiments are given below to refine and optimize the embodiments of the present invention, so that the implementation of the scheme of the embodiments of the present invention is more convenient and accurate. In addition, the following embodiments may be arbitrarily combined with each other without conflict.
In order to effectively ensure real-time performance in determining indoor and outdoor labels of users, in some embodiments, before inputting the measurement report data of the target user into a random forest classifier for classifying indoor and outdoor labels of users, the method includes:
collecting measurement report data of sample users in a target area and indoor or outdoor labels corresponding to each measurement report data;
extracting a training data set from the collected measurement report data of the sample user in the target area and the actual indoor and outdoor marks corresponding to each piece of training data;
inputting the training data set into a preset random forest classification model for training;
in the training process, searching the optimal model parameters of the random forest classification model through a GridSearchCV grid;
and taking the random forest classification model corresponding to the optimal model parameter as the random forest classifier.
The target area can be a designated area, and the model parameters can include the number n _ estimators of the decision trees and the calculation attribute criterion; the random forest classification model can be realized through Python codes, and can be simply called as a model in the embodiment of the invention. Certainly, before the training data set and the actual indoor and outdoor labels corresponding to each piece of training data are input into a preset random forest classification model for training, in order to improve the prediction accuracy of the indoor and outdoor labels of the user, the collected measurement report data of the sample user in the target area and the indoor or outdoor labels corresponding to each piece of measurement report data can be used as original data, data preprocessing is performed on the original data, and abnormal data are removed.
In the prediction process, characteristics such as aoa (incident angle), ta _ calc (time delay), rsrp (test power value), tadltvalue (downlink time delay), time _ difference and the like in a training data set are extracted as independent variables X, corresponding position mark _ real (indoor and outdoor marks) are set as dependent variables Y, and the independent variables X are used for determining the indoor and outdoor marks Y; that is to say, each training data in the training data set is set as an independent variable, and the actual indoor and outdoor labels corresponding to each training data set as dependent variables determined by the independent variables, so that a 0-1 classification problem can be seen, the complexity of the training process of the random forest classifier can be effectively reduced, and the prediction accuracy of the indoor and outdoor labels of the user can be effectively improved.
In the prediction process, the random forest classification model obtained by verification training can be predicted through the test data set, and the prediction accuracy of the obtained indoor and outdoor marks of the user is ensured through prediction verification. Namely, the original data is subjected to data preprocessing, abnormal data are removed, a data set is obtained by extracting characteristic values from the original data from which the abnormal data are removed, and the data set is divided into a training data set and a testing data set. And continuously inputting the test data set into the trained random forest model for cross prediction verification until a relatively better model is found as a final random forest classification model. That is, the taking the random forest classification model corresponding to the optimal model parameter as the random forest classifier may include:
extracting a test data set from the sample measurement report data;
inputting the test data set into a random forest classification model corresponding to the optimal model parameter for prediction verification;
determining the minimum mean square error between the prediction verification result and the actual indoor and outdoor marks correspondingly arranged in the test data set;
when the mean square error is not larger than a preset threshold value, taking a random forest classification model corresponding to the optimal model parameter as the random forest classifier;
and searching the optimal model parameters of the random forest classification model again through the grids when the mean square error is larger than the threshold value.
Example two
Based on the first embodiment, the embodiment of the present invention provides a specific method for distinguishing indoor and outdoor tags of a user, as shown in fig. 2, the method mainly includes two stages: the method comprises an off-line stage and an on-line stage, wherein the off-line stage is mainly used for training a random forest classifier, and the on-line stage is mainly used for predicting a target in real time and comprises the following steps:
step 201, acquiring MR data of a sample user in a target area.
Selecting a designated area, and acquiring 12000 MR data reported by a user at a base station side. The MR data records wireless measurement information such as service cell id, ta _ calc, rsrp, rsrq, ta, aoa, mrtime, starttime, endtime, imsi and the like of a user in a service process, and a prestionmark _ real indoor and outdoor mark corresponding to each piece of measurement information.
And step 202, exception data processing.
And replacing abnormal data or null values of each field in the 12000 pieces of MR data with 0, and performing orthogonal normalization processing on the whole data matrix. And randomly selecting 75% of data in the data set as a training set, and respectively storing 25% of data in two files as a test set.
And step 203, selecting the MR data corresponding to the characteristic value.
Because the index terms of the MR record are more, the calculation and the accuracy of the whole model are influenced greatly. In order to improve the calculation and accuracy of the model, characteristic values such as aoa (incident angle), ta _ calc (time delay), rsrp (test power value), tadltvalue (downlink time delay), time _ difference (time-difference) and the like are selected as independent variables X, and corresponding posionmark _ real (indoor and outdoor marks) is set as dependent variables Y. Therefore, the problem is converted into a mathematical problem, the indoor and outdoor marks Y are determined by the environment variable X, the problem can be regarded as a 0-1 classification problem, and the random forest classification model has better accuracy and generalization in the embodiment of the invention. A random forest classification model is constructed through Python codes, and a training data set is input into a RandomForestClassifier model to start training.
And step 204, training the model to optimize model parameters.
Inputting the training data set into a random forest classifier model, and searching the number n _ estimators and the calculation attribute criterion of the optimal decision tree of the random forest classification algorithm through a GridSearchCV grid; and inputting the test data set into the trained model for cross validation. If the error is smaller, the model is selected, otherwise, the model parameters are continuously adjusted until the error of the model verification test data is small enough.
Step 205, measure model accuracy mechanism.
Inputting the test set data into a trained random forest classification model for cross prediction verification,
and the cross prediction verifies the minimum mean square error between the predicted value and the true value of the test set data. If the error is smaller, the model is better, otherwise, the error is poor. Recording the accuracy of the prediction data set of the model each time, selecting the model with the highest accuracy, and storing the model; when the mean square error is not larger than a preset threshold value, taking a random forest classification model corresponding to the optimal model parameter as the random forest classifier; and searching the optimal model parameters of the random forest classification model again through the grids when the mean square error is larger than the threshold value.
Step 206, acquiring real-time MR data of the target user.
Randomly selecting a target user in an area, collecting MR real-time data of a part of target users at a base station side, wherein the MR real-time data at least comprises a plurality of indexes of aoa (incident angle), ta _ calc (time delay), rsrp (test power value), tadltvalue (downlink time delay) and time _ difference (time difference-start time), and then actually predicting indoor and outdoor marks of the mobile users by using the indexes.
Step 207, real-time MR data preprocessing.
Abnormal or null data may exist in the real-time data, the abnormal data are replaced by 0, and a plurality of indexes corresponding to the training model are selected as characteristic values. The characteristic value data is subjected to orthogonal normalization processing, so that the phenomenon of over-fitting can be effectively avoided.
And step 208, inputting the real-time MR data into a random forest classifier for prediction.
As shown in fig. 3, the processed real-time MR data is input into a previously trained random forest classifier, and is subjected to fitting by the random forest classifier.
Step 209, the indoor and outdoor labeling results corresponding to the real-time MR data of the target users can be obtained.
The embodiment of the invention effectively improves the prediction accuracy of the indoor and outdoor marks of the user and effectively ensures the real-time performance in the process of determining the indoor and outdoor marks of the user.
EXAMPLE III
The embodiment of the invention provides a training method of a random forest classifier, which comprises the following steps:
extracting a training data set from the collected measurement report data of the sample user in the target area and the actual indoor and outdoor marks corresponding to each piece of training data;
inputting the training data set into a preset random forest classification model for training;
in the training process, searching the optimal model parameters of the random forest classification model through grids;
and taking the random forest classification model corresponding to the optimal model parameter as the random forest classifier.
The training process of the random forest classifier in the embodiment of the invention is the same as that of the first embodiment, and when the training process is specifically realized, the first embodiment can be referred to, so that the training process has corresponding technical effects.
Example four
An embodiment of the present invention provides a communication node device, which includes a memory and a processor, where the memory stores an indoor and outdoor tagging program of a user, and the processor executes the computer program to implement the steps of the method according to any one of the first embodiment and the second embodiment. Wherein the communication node device may be a base station or the like.
EXAMPLE five
The embodiment of the invention provides training equipment of a random forest classifier, which comprises a memory and a processor, wherein the memory stores a training program of the random forest classifier, and the processor executes the computer program to realize the steps of the method according to the third embodiment.
EXAMPLE six
An embodiment of the present invention provides a computer-readable storage medium, wherein the storage medium stores an indoor and outdoor tagging program for a user, and the computer program is executable by at least one processor to implement the steps of the method according to any one of the first embodiment and the second embodiment.
EXAMPLE seven
An embodiment of the present invention provides a computer-readable storage medium, wherein the storage medium stores a training program for a random forest classifier, and the computer program is executable by at least one processor to implement the steps of the method according to embodiment three.
It should be noted that the specific implementation of the third to seventh embodiments can refer to the first embodiment, and has corresponding technical effects.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
The above-mentioned serial numbers of the embodiments of the present invention are merely for description and do not represent the merits of the embodiments.
Through the above description of the embodiments, those skilled in the art will clearly understand that the method of the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but in many cases, the former is a better implementation manner. Based on such understanding, the technical solutions of the present invention may be embodied in the form of a software product, which is stored in a storage medium (such as ROM/RAM, magnetic disk, optical disk) and includes instructions for enabling a terminal (such as a mobile phone, a computer, a server, an air conditioner, or a network device) to execute the method according to the embodiments of the present invention.
While the present invention has been described with reference to the embodiments shown in the drawings, the present invention is not limited to the embodiments, which are illustrative and not restrictive, and it will be apparent to those skilled in the art that various changes and modifications can be made therein without departing from the spirit and scope of the invention as defined in the appended claims.
Claims (10)
1. A method for distinguishing indoor and outdoor labels of users, the method comprising:
collecting measurement report data of a target user;
inputting the measurement report data of the target user into a random forest classifier for classifying indoor and outdoor labels of the user;
and determining the indoor and outdoor marks of the target user according to the classification calculation of the random forest classifier.
2. The method of claim 1, wherein the inputting the measurement report data of the target user before the random forest classifier for classifying indoor and outdoor labels of the user comprises:
extracting a training data set from the collected measurement report data of the sample user in the target area and the actual indoor and outdoor marks corresponding to each piece of training data;
inputting the training data set into a preset random forest classification model for training;
in the training process, searching the optimal model parameters of the random forest classification model through grids;
and taking the random forest classification model corresponding to the optimal model parameter as the random forest classifier.
3. A method as claimed in claim 2, wherein before inputting the training data set into a preset random forest classification model for training, comprising:
and setting each training data in the training data set as an independent variable, and setting an actual indoor and outdoor mark corresponding to each training data as a dependent variable determined by the independent variable.
4. The method as claimed in claim 2, wherein the using the random forest classification model corresponding to the optimal model parameters as the random forest classifier comprises:
extracting a test data set from the sample measurement report data;
inputting the test data set into a random forest classification model corresponding to the optimal model parameter for prediction verification;
determining the minimum mean square error between the prediction verification result and the actual indoor and outdoor marks correspondingly arranged in the test data set;
when the mean square error is not larger than a preset threshold value, taking a random forest classification model corresponding to the optimal model parameter as the random forest classifier;
and searching the optimal model parameters of the random forest classification model again through the grids when the mean square error is larger than the threshold value.
5. The method of any of claims 1-4, wherein the measurement report data includes angle of incidence, time delay, test power value, downlink time delay, and time difference.
6. A training method of a random forest classifier is characterized by comprising the following steps:
extracting a training data set from the collected measurement report data of the sample user in the target area and the actual indoor and outdoor marks corresponding to each piece of training data;
inputting the training data set into a preset random forest classification model for training;
in the training process, searching the optimal model parameters of the random forest classification model through grids;
and taking the random forest classification model corresponding to the optimal model parameter as the random forest classifier.
7. A communication node arrangement, characterized in that the arrangement comprises a memory storing an indoor and outdoor signature program of a user and a processor executing the computer program for carrying out the steps of the method according to any one of claims 1-5.
8. Training device of a random forest classifier, characterized in that the device comprises a memory in which a training program of the random forest classifier is stored and a processor which executes the computer program to carry out the steps of the method as claimed in claim 6.
9. A computer-readable storage medium, characterized in that the storage medium stores an indoor-outdoor tagging program for a user, the computer program being executable by at least one processor to implement the steps of the method according to any one of claims 1-5.
10. A computer-readable storage medium, characterized in that the storage medium stores a training program of a random forest classifier, the computer program being executable by at least one processor to implement the steps of the method as claimed in claim 6.
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| CN201811595402.5A CN111368862A (en) | 2018-12-25 | 2018-12-25 | Method for distinguishing indoor and outdoor marks, training method and device of classifier and medium |
| PCT/CN2019/109438 WO2020134299A1 (en) | 2018-12-25 | 2019-09-30 | Indoor and outdoor label distinguishing method, training method and device of classifier and medium |
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| CN115082767A (en) * | 2021-03-15 | 2022-09-20 | ***通信集团福建有限公司 | Random forest model training method and device |
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| WO2020134299A1 (en) | 2020-07-02 |
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