CN116266946A - Method, device and equipment for determining base station position - Google Patents
Method, device and equipment for determining base station position Download PDFInfo
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- CN116266946A CN116266946A CN202111552890.3A CN202111552890A CN116266946A CN 116266946 A CN116266946 A CN 116266946A CN 202111552890 A CN202111552890 A CN 202111552890A CN 116266946 A CN116266946 A CN 116266946A
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- H04W64/003—Locating users or terminals or network equipment for network management purposes, e.g. mobility management locating network equipment
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Abstract
The invention discloses a method, a device and equipment for determining the position of a base station, wherein the method for determining the position of the base station comprises the following steps: acquiring position sequence information of at least one mobile terminal; inputting the position sequence information of the at least one mobile terminal into a preset position prediction model for prediction processing to obtain a predicted position of the at least one mobile terminal; clustering the mobile terminals according to the predicted position of the at least one mobile terminal to obtain a clustering result; and determining the address of the base station according to the clustering result. By the method, the position of the mobile terminal can be rapidly predicted, the change trend of the number of the regional mobile terminals is known, cluster analysis is carried out by combining the prediction result, the site selection is carried out according to the requirement, and the site selection accuracy of the base station is improved.
Description
Technical Field
The present invention relates to the field of communications technologies, and in particular, to a method, an apparatus, and a device for determining a base station position.
Background
In the prior art, the site selection of the base station is usually performed on site according to the aggregation condition of a large number of user terminals after the large-scale activities are finished, the specific configuration of the base station can be determined only by the estimated number of engineers, and the positions of the base stations are always uniformly distributed.
In the prior art, the base station site selection scheme always starts site selection after the high load problem occurs, and the processing efficiency is low; the manual site selection is generally uniformly distributed according to default configuration, and the position and configuration adjustment of the base station cannot be performed according to the people stream condition; a large number of personnel are needed to participate, and an emergency guarantee point may be in missing state, so that a large amount of manpower and material resources are needed to be consumed.
Disclosure of Invention
In view of the foregoing, embodiments of the present invention are provided to provide a method, apparatus, and device for determining a location of a base station that overcomes or at least partially solves the foregoing problems.
According to an aspect of an embodiment of the present invention, there is provided a method for determining a base station position, the method including:
acquiring position sequence information of at least one mobile terminal;
inputting the position sequence information of the at least one mobile terminal into a preset position prediction model for prediction processing to obtain a predicted position of the at least one mobile terminal;
clustering the mobile terminals according to the predicted position of the at least one mobile terminal to obtain a clustering result;
and determining the address of the base station according to the clustering result.
According to another aspect of the embodiment of the present invention, there is provided a base station position determining apparatus, including:
the acquisition module is used for acquiring the position sequence information of at least one mobile terminal;
the prediction module is used for inputting the position sequence information of the at least one mobile terminal into a preset position prediction model for prediction processing to obtain a predicted position of the at least one mobile terminal;
the clustering module is used for clustering the mobile terminals according to the predicted position of the at least one mobile terminal to obtain a clustering result;
and the determining module is used for determining the address for establishing the base station according to the clustering result.
According to yet another aspect of an embodiment of the present invention, there is provided a computing device including: the device comprises a processor, a memory, a communication interface and a communication bus, wherein the processor, the memory and the communication interface complete communication with each other through the communication bus;
the memory is used for storing at least one executable instruction, and the executable instruction enables the processor to execute the operation corresponding to the method for determining the position of the base station.
According to still another aspect of the embodiments of the present invention, there is provided a computer storage medium having at least one executable instruction stored therein, the executable instruction causing a processor to perform operations corresponding to the above-described base station location determination method.
According to the scheme provided by the embodiment of the invention, the position sequence information of at least one mobile terminal is acquired; inputting the position sequence information of the at least one mobile terminal into a preset position prediction model for prediction processing to obtain a predicted position of the at least one mobile terminal; clustering the mobile terminals according to the predicted position of the at least one mobile terminal to obtain a clustering result; and determining the address of the base station according to the clustering result. Therefore, the position of the mobile terminal can be rapidly predicted, the change trend of the number of the regional mobile terminals is known, cluster analysis is carried out by combining the prediction result, the site selection is carried out according to the requirement, and the site selection accuracy of the base station is improved.
The foregoing description is only an overview of the technical solutions of the embodiments of the present invention, and may be implemented according to the content of the specification, so that the technical means of the embodiments of the present invention can be more clearly understood, and the following specific implementation of the embodiments of the present invention will be more apparent.
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 designate like parts throughout the figures. In the drawings:
fig. 1 shows a flowchart of a method for determining a base station location according to an embodiment of the present invention;
fig. 2 is a flowchart of a specific implementation method of a method for determining a base station location according to another embodiment of the present invention;
FIG. 3 is a schematic diagram of a preset position prediction model according to an embodiment of the present invention;
fig. 4 is a schematic diagram showing the effect of predicting a mobile position of a terminal by a preset position prediction model in an embodiment of the present invention;
fig. 5 is a schematic structural diagram of a base station position determining apparatus according to an embodiment of the present invention;
FIG. 6 illustrates a schematic diagram of a computing device provided by an embodiment of the present invention.
Detailed Description
Exemplary embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the present invention are shown in the drawings, it should be understood that the present invention 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 invention to those skilled in the art.
Fig. 1 shows a flowchart of a method for determining a base station location according to an embodiment of the present invention. As shown in fig. 1, the method comprises the steps of:
and step 14, determining the address of the base station according to the clustering result.
In this embodiment of the present invention, the predicted position of the at least one mobile terminal is obtained by inputting the position sequence information of the at least one mobile terminal into a preset position prediction model for prediction processing; clustering the mobile terminals according to the predicted position of the at least one mobile terminal to obtain a clustering result; determining an address for establishing a base station according to the clustering result; therefore, the position of the mobile terminal can be rapidly predicted, the change trend of the number of the regional mobile terminals can be known, cluster analysis is carried out by combining the prediction result, the site selection is carried out according to the requirement, and the site selection accuracy of the base station is improved.
In an alternative embodiment of the present invention, the preset position prediction model is trained by the following procedure:
step 21, obtaining a training set comprising movement track data of a plurality of terminals;
step 22, inputting the movement track data into a target model for processing to obtain at least one hidden state and at least one observation state;
step 23, obtaining a probability matrix under an implicit state, a transition state matrix of the terminal between the implicit states and a transition matrix between the implicit state and an observed state;
and step 24, obtaining the preset position prediction model according to the probability matrix in the hidden state, the transition state matrix of the terminal between the hidden states and the transition matrix from the hidden state to the observation state.
Here, the movement track data of the plurality of terminals in the training set may be obtained according to measurement report MR data of the user and OTT (including user information and latitude and longitude information acquired from the navigation APP) data for drawing the movement track of the user; specifically, the points extracted from OTT are used for correlating with MR data, the MR with accurate correlation is used for establishing a feature library, and feature matching is carried out on the MR to be positioned to finish positioning;
specifically, MR data (terminal measurement report information) is acquired from a wireless side by soft acquisition, OTT data is acquired from a core network side by a probe, the MR data (initial residence time, residence time) and OTT data (longitude and latitude data) are subjected to cleaning, association and other processes, the MR sample points containing the longitude and latitude after association are sent to a fingerprint server, data of a plurality of days are accumulated, and a fingerprint library is created after the sample points are sufficiently rich. And (3) inputting the MR points to be positioned and carrying out feature matching with the fingerprint library, outputting the MR points containing longitude and latitude information after positioning, and finally realizing position positioning.
Here, the MR and OTT data are associated by reporting time, S1APID (index field in S1AP message in UE context), enb ID (evolved node ID), etc. fields, to obtain MR sample points containing longitude and latitude, where IMSI (international mobile subscriber identity) corresponds to u i Longitude and latitude correspond to l ij The initial MR reporting time at a certain position corresponds to the observation time t ij Reporting the corresponding residence time d of the MR total duration at a certain position ij ；
The user is at each location point s ij Can be represented by a quadruple, i.e. s ij ＝(u i ，l ij ，t ij D) a step of (d); wherein u is i An identity representing user i; l (L) ij The position of the user i at the moment j is shown; t is t ij Indicating that user i arrives at this location l ij D represents that user i is at location l ij The elements in the sequence being ordered by time stamp;
during the observation period T, s will be ij According to the time stamp l ij Ordering is performed, the user movement trace data can be expressed as being represented by a position point, namely a state variable s ij A sequence of formations, completing the construction of the user's movement track, which describes the user's movement between a plurality of positionsAs well as the situation where it is standing at each location.
As shown in fig. 2, based on the obtained whole network user movement track data, a preset position prediction model of the next position of the user is constructed by using an intelligent algorithm. The model can be described in terms of five elements, including 2 state sets and 3 probability matrices, and fig. 2 is an example of a model, specifically including:
implicit state S: and generally cannot be obtained by direct observation. s (t) represents hidden states at time t, each hidden state represents a user stay position and a start time stamp and stay time corresponding to the position, and s (t) ∈ { s1, s2, s3} is in the model shown in fig. 2.
Observable state O: associated with implicit states in the model, can be obtained by direct observation. The observable state represents the position information of each stop of the user and the start time and duration of the stop corresponding to the position, and o (t) represents the observable state at the time t, and in the model shown in fig. 2, o (t) ∈ { o1, o2, o3}.
Initial state probability vector pi: 1×n probability matrix of hidden states at initial time t=1, where N represents the hidden state number, pi= [ p(s) 1 )，p(s 2 )，…，p(s n )]. Each element p(s) i ) Representing the probability that the user is present in a particular implicit state. s (t) represents hidden states at the time t, and each hidden state represents user stay position information, and a start time stamp and a stay time length corresponding to the stay position.
Implicit state transition probability matrix a: the transition probability between each hidden state in the representation model is an N x N matrix. Wherein A is ij ＝P(s j，t+1 |s i，t ) Indicating at time t that the state is s i Under the condition of (1), the state is s at time t+1 j Is a probability of (2).
Hybrid matrix B: representing transition probabilities between hidden and observable states in a model as an N matrix, B ij ＝P(o i，t+1 |s j，t ) 1.ltoreq.i.ltoreq.M, 1.ltoreq.j.ltoreq.N, indicating that at time t, the implicit state is s j At time t+1, the observation state is o i Is a probability of (2). VariableThe quantity M represents the number of observable states.
A pre-set position prediction model (i.e., HMM) model is represented by a λ= { a, B, pi } triplet. The initial state probability vector pi, the hidden state transition probability matrix A and the hybrid matrix B are obtained through a parameter learning process.
In the embodiment, MR data and OTT data are adopted to construct user motion trail data, and then an intelligent algorithm is utilized to predict the position of the terminal, so that the position of the mobile terminal can be rapidly predicted, and the change trend of the number of regional mobile terminals can be known.
In an alternative embodiment of the present invention, the step 12 may include:
step 121, constructing a hash table, where the hash table includes: a hash key value and a hash value, wherein the hash key value is a sequence consisting of the first k-1 positions in a subsequence with the length of t1, the hash value is a linked list, and each node in the linked list comprises a k element and a probability value of a predicted position corresponding to the subsequence, and k is more than or equal to 2 and less than or equal to Y;
step 122, the position sequence of the mobile terminal with length L is set as ts 1 ，ts 2 ，…，ts L ]Searching in a hash table as a target hash key value, and if the target hash key value exists in the hash table, obtaining a predicted position from the linked list; if the target hash key value does not exist in the hash table, subtracting 1 from the length of the position sequence of the mobile terminal with the length L, and searching in the hash table again as the target hash key value until a linked list containing at least one predicted position is obtained, wherein L is more than or equal to 1 and less than or equal to Y-1;
and step 123, determining the predicted positions corresponding to the maximum value of the probabilities respectively corresponding to the plurality of predicted positions contained in the linked list as final predicted positions.
In this embodiment, a hash table is constructed to store the following information prior to position prediction: knowing the sequence of k-1 (2. Ltoreq.k. Ltoreq.10) places before, all possible next access places, and the probability that the corresponding probability takes on the value prob, prob being the predicted position.
Table 1 describes an example of a hash table pair. In the hash table, a sequence consisting of the first k-1 elements in a subsequence with the length of t1 is used as a hash key, the value of the hash table is a linked list, and each node in the linked list comprises the kth element and the prob of the subsequence.
Table 1:
after constructing the hash table, the sequence [ ts ] consisting of the last accessed l places 1 ，ts 2 ，…，ts l ](1 is less than or equal to l is less than or equal to 9) is a key value key, and the next position to be accessed by the user is predicted by looking up the hash table
If feasible, the length l of the sequence of initialized most recently accessed places may be up to 9, equal to the maximum length of the key in the hash table. Once the key is not present in the hash table, the length of the sequence decreases.
Specifically, in position prediction, if the sequence of length l [ ts ] 1 ，ts 2 ，…，ts l ]In the hash table, the locations that the user may access are obtained from the linked list that is returned. Otherwise, the first element in the sequence is removed, a sequence with the length of l-1 is obtained, and the lookup operation of the hash table is continuously executed until a linked list is returned.
If the returned linked list contains a plurality of possible positions, the position corresponding to the highest prob value is determined as the position to be accessed by the user
As shown in fig. 3, a flow of an embodiment of the present invention includes:
acquiring movement track data of the whole-network mobile terminal according to the MR data and the OTT data;
constructing a position prediction model based on the movement track data;
inputting a position sequence of the terminal in the last period of time into the position prediction model to obtain a predicted position of the terminal;
clustering is carried out based on the predicted position of the terminal to obtain a plurality of clustering clusters;
and determining the center of the cluster as the site building address of the base station.
The specific algorithm flow is as follows:
input: [ ts ] 1 ，ts 2 ，…，ts l ]A sequence of l places recently accessed, namely, constructing a hash table (seqpattern);
and (3) outputting: predicting a next position
1：Len＝l；placeSeq＝[ts 1 ，ts 2 ，…，ts l ](inputting a sequence of recently accessed/sites)
2：wherelendo
3: key=placeseq (key sequence to determine hash table)
4：if placeSeq in seqpatternthen
5：Linkedlist[possible place，prob]＝seqpattern.get(key)
6: ts= Select place of maxprob (Linkedlist) (the selection list contains the highest prob value among a plurality of possible positions)
7：break
8：else
9：len＝len-1，placeSeq＝[ts 1 ，ts 2 ，…，ts l ]
10：end if
11：end while
12：
(return to the predicted position).
As shown in fig. 4, a comparison of the locus formed by the predicted position and the true locus is shown.
After the model is built, the prediction accuracy is tested, and the prediction accuracy (prediction accuracy) is the ratio of the correct number of predictions for a user to the number of predictions that the user tries.
The next position of the user is predicted by using the model described above with the sequence formed by the movement track data of the user as input, and the result shows that the prediction accuracy of almost 60% of the users exceeds 80%.
In an optional embodiment of the present invention, clustering the mobile terminals according to the predicted position of the at least one mobile terminal to obtain a clustering result may include:
and clustering the mobile terminals according to the predicted position of the at least one mobile terminal, the coverage distance of the base station and the number of the terminals supported by the base station to obtain a clustering result.
Specifically, this step may include:
step a, firstly, determining radius r and base station capacity, wherein r can be the effective coverage average distance of a base station, and the base station capacity min Points can be the number of terminals which can be supported by the base station;
starting from an arbitrary data point that has not been visited, centering on the data point, whether the number of data points contained in a circle with r as a radius is greater than or equal to the base station capacity, if so, the data point is marked as a center point (central point), otherwise, the data point is marked as a noise point (noise point).
Step b, repeating step a, if a noise point exists in a circle with a radius at a certain center point, the data point is marked as an edge point, and otherwise the data point is still a noise point. Repeating step a until all data points have been accessed, thus obtaining a plurality of clusters.
In an alternative embodiment of the present invention, step 14 may include:
the center point of the cluster is determined as the address of the established base station. Thus, cluster analysis is carried out by combining the prediction results, and the site selection is carried out according to the requirement, so that the site selection accuracy of the base station is improved, and the site construction cost is reduced.
According to the embodiment of the invention, MR data are acquired from a wireless side, OTT data are acquired from a core network side by using a probe, the MR data and the OTT data are subjected to cleaning, association and other processes, and MR sample points containing longitude and latitude after association are stored in a target database; inputting an MR point to be positioned and a target database for feature matching, outputting the positioned MR point containing longitude and latitude information, and finally realizing position positioning; recording a user movement track according to the flow data; constructing a position prediction model, taking a moving sequence of a user as input, and predicting the next position of the user by using the position prediction model; selecting a mobile terminal prediction position library as a data source, and carrying out density-based cluster analysis by combining the coverage distance of base stations and the number of supported terminals to obtain a plurality of clusters, wherein the number of clusters is the number of determined base stations, and the central point of the clusters is the base station point selection position. Therefore, the mobile terminal position can be rapidly predicted based on the constructed position prediction model, the change trend of the number of the regional mobile terminals is known, cluster analysis is carried out by combining the prediction result, the site selection is carried out as required, the manual site selection is omitted, and the site selection accuracy of the base station is improved.
Fig. 5 shows a base station position determining apparatus 50 according to an embodiment of the present invention, where the apparatus includes:
an obtaining module 51, configured to obtain location sequence information of at least one mobile terminal;
the prediction module 52 is configured to input the position sequence information of the at least one mobile terminal into a preset position prediction model for performing prediction processing, so as to obtain a predicted position of the at least one mobile terminal;
a clustering module 53, configured to cluster the mobile terminals according to the predicted position of the at least one mobile terminal, to obtain a clustering result;
and the determining module 54 is configured to determine, according to the clustering result, an address for establishing the base station.
Optionally, the preset position prediction model is trained by the following process:
acquiring a training set comprising movement track data of a plurality of terminals;
inputting the movement track data into a target model for processing to obtain at least one hidden state and at least one observation state;
obtaining a probability matrix under an implicit state, a transition state matrix of a terminal between the implicit states and a transition matrix between the implicit states and an observation state;
and obtaining the preset position prediction model according to the probability matrix in the hidden state, the transition state matrix of the terminal between the hidden states and the transition matrix from the hidden state to the observation state.
Optionally, the movement track data of the plurality of terminals is defined by a state variable s ij A sequence of constructs; s is(s) ij ＝(u i ，l ij ，t ij ，d)；
Wherein u is i An identity representing user i; l (L) ij The position of the user i at the moment j is shown; t is t ij Indicating that user i arrives at this location l ij D represents that user i is at location l ij The elements in the sequence are ordered by time stamp.
Optionally, the probability matrix in the implicit state pi= [ p(s) 1 )，p(s 2 )，…，p(s n )]Element p(s) i ) The probability that the user appears in one hidden state is represented, s (t) represents the hidden state at the time t, and each hidden state represents the user stay position information, and the start time stamp and the stay time corresponding to the stay position.
Optionally, the transition state matrix of the terminal in the implicit state is a, and element a in a ij ＝P(s j，t+1 |s i，t ) Indicating at time t that the state is s i Under the condition of (1), the state is s at time t+1 j Is a probability of (2).
Optionally, the transition matrix B between the hidden state and the observed state is an nxn matrix, the elements B in B ij ＝P(o i，t+1 |s j，t ) 1.ltoreq.i.ltoreq.M, 1.ltoreq.j.ltoreq.N, indicating that at time t, the implicit state is s j At time t+1, the observation state is o i The variable M represents the number of observed states.
Optionally, inputting the position sequence information of the at least one mobile terminal into a preset position prediction model for prediction processing to obtain a predicted position of the at least one mobile terminal, including:
constructing a hash table, the hash table comprising: a hash key value and a hash value, wherein the hash key value is a sequence consisting of the first k-1 positions in a subsequence with the length of t1, the hash value is a linked list, and each node in the linked list comprises a k element and a probability value of a predicted position corresponding to the subsequence, and k is more than or equal to 2 and less than or equal to Y;
position sequence of mobile terminal with length L [ ts ] 1 ，ts 2 ，…，ts L ]Searching in a hash table as a target hash key value, and if the target hash key value exists in the hash table, obtaining a predicted position from the linked list; if the target hash key value does not exist in the hash table, subtracting 1 from the length of the position sequence of the mobile terminal with the length L, and searching in the hash table again as the target hash key value until a linked list containing at least one predicted position is obtained, wherein L is more than or equal to 1 and less than or equal to Y-1;
and determining the predicted positions corresponding to the maximum values of the probabilities respectively corresponding to the plurality of predicted positions contained in the linked list as final predicted positions.
It should be noted that, the embodiment of the apparatus is an apparatus corresponding to the embodiment of the method, and all the implementation manners in the embodiment of the method are applicable to the embodiment of the apparatus, so that the same technical effects can be achieved.
The embodiment of the invention provides a non-volatile computer storage medium, which stores at least one executable instruction, and the computer executable instruction can execute the cell optimization method in any of the above method embodiments.
FIG. 6 illustrates a schematic diagram of a computing device according to an embodiment of the present invention, and the embodiment of the present invention is not limited to a specific implementation of the computing device.
As shown in fig. 6, the computing device may include: a processor (processor), a communication interface (Communications Interface), a memory (memory), and a communication bus.
Wherein: the processor, communication interface, and memory communicate with each other via a communication bus. A communication interface for communicating with network elements of other devices, such as clients or other servers, etc. A processor, configured to execute a program, and specifically may perform relevant steps in the foregoing embodiment of a method for optimizing a cell of a computing device.
In particular, the program may include program code including computer-operating instructions.
The processor may be a central processing unit, CPU, or specific integrated circuit ASIC (Application Specific Integrated Circuit), or one or more integrated circuits configured to implement embodiments of the present invention. The one or more processors included by the computing device may be the same type of processor, such as one or more CPUs; but may also be different types of processors such as one or more CPUs and one or more ASICs.
And the memory is used for storing programs. The memory may comprise high-speed RAM memory or may further comprise non-volatile memory, such as at least one disk memory.
The program may be specifically adapted to cause a processor to perform the method of optimizing a cell in any of the method embodiments described above. The specific implementation of each step in the procedure may refer to corresponding steps and corresponding descriptions in the units in the above-mentioned cell optimization method embodiment, which are not repeated herein. It will be clear to those skilled in the art that, for convenience and brevity of description, specific working procedures of the apparatus and modules described above may refer to corresponding procedure descriptions in the foregoing method embodiments, which are not repeated herein.
The algorithms or displays presented herein are not inherently related to any particular computer, virtual system, or other apparatus. Various general-purpose systems may also be used with the teachings herein. The required structure for a construction of such a system is apparent from the description above. In addition, embodiments of the present invention are not directed to any particular programming language. It will be appreciated that the teachings of embodiments of the present invention described herein may be implemented in a variety of programming languages, and the above description of specific languages is provided for disclosure of enablement and best mode of the embodiments of the present invention.
In the description provided herein, numerous specific details are set forth. However, it is understood that embodiments of the invention may be practiced without these specific details. In some instances, well-known methods, structures and techniques have not been shown in detail in order not to obscure an understanding of this description.
Similarly, it should be appreciated that in the above description of exemplary embodiments of the invention, various features of the embodiments of the invention are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the disclosure and aiding in the understanding of one or more of the various inventive aspects. However, the disclosed method should not be construed as reflecting the intention that: i.e., an embodiment of the invention that is claimed, requires more features than are expressly recited in each claim. Rather, as the following claims reflect, inventive aspects lie in less than all features of a single foregoing disclosed embodiment. Thus, the claims following the detailed description are hereby expressly incorporated into this detailed description, with each claim standing on its own as a separate embodiment of this invention.
Those skilled in the art will appreciate that the modules in the apparatus of the embodiments may be adaptively changed and disposed in one or more apparatuses different from the embodiments. The modules or units or components of the embodiments may be combined into one module or unit or component and, furthermore, they may be divided into a plurality of sub-modules or sub-units or sub-components. Any combination of all features disclosed in this specification (including any accompanying claims, abstract and drawings), and all of the processes or units of any method or apparatus so disclosed, may be used in combination, except insofar as at least some of such features and/or processes or units are mutually exclusive. Each feature disclosed in this specification (including any accompanying claims, abstract and drawings), may be replaced by alternative features serving the same, equivalent or similar purpose, unless expressly stated otherwise.
Furthermore, those skilled in the art will appreciate that while some embodiments herein include some features but not others included in other embodiments, combinations of features of different embodiments are meant to be within the scope of the invention and form different embodiments. For example, in the following claims, any of the claimed embodiments can be used in any combination.
Various component embodiments of the invention may be implemented in hardware, or in software modules running on one or more processors, or in a combination thereof. Those skilled in the art will appreciate that some or all of the functionality of some or all of the components according to embodiments of the present invention may be implemented in practice using a microprocessor or Digital Signal Processor (DSP). Embodiments of the present invention may also be implemented as a device or apparatus program (e.g., a computer program and a computer program product) for performing a portion or all of the methods described herein. Such a program embodying the embodiments of the present invention may be stored on a computer readable medium, or may have the form of one or more signals. Such signals may be downloaded from an internet website, provided on a carrier signal, or provided in any other form.
It should be noted that the above-mentioned embodiments illustrate rather than limit the invention, and that those skilled in the art will be able to design alternative embodiments without departing from the scope of the appended claims. In the claims, any reference signs placed between parentheses shall not be construed as limiting the claim. The word "comprising" does not exclude the presence of elements or steps not listed in a claim. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. Embodiments of the invention may be implemented by means of hardware comprising several distinct elements, and by means of a suitably programmed computer. In the unit claims enumerating several means, several of these means may be embodied by one and the same item of hardware. The use of the words first, second, third, etc. do not denote any order. These words may be interpreted as names. The steps in the above embodiments should not be construed as limiting the order of execution unless specifically stated.
Claims (10)
1. A method for determining a location of a base station, the method comprising:
acquiring position sequence information of at least one mobile terminal;
inputting the position sequence information of the at least one mobile terminal into a preset position prediction model for prediction processing to obtain a predicted position of the at least one mobile terminal;
clustering the mobile terminals according to the predicted position of the at least one mobile terminal to obtain a clustering result;
and determining the address of the base station according to the clustering result.
2. The method for determining a base station position according to claim 1, wherein the preset position prediction model is trained by:
acquiring a training set comprising movement track data of a plurality of terminals;
inputting the movement track data into a target model for processing to obtain at least one hidden state and at least one observation state;
obtaining a probability matrix under an implicit state, a transition state matrix of a terminal between the implicit states and a transition matrix between the implicit states and an observation state;
and obtaining the preset position prediction model according to the probability matrix in the hidden state, the transition state matrix of the terminal between the hidden states and the transition matrix from the hidden state to the observation state.
3. The method for determining the position of a base station according to claim 2, wherein the movement trace data of the plurality of terminals is defined by a state variable s ij A sequence of constructs; s is(s) ij ＝(u i ，l ij ，t ij ，d)；
Wherein u is i An identity representing user i; l (L) ij The position of the user i at the moment j is shown; t is t ij Indicating that user i arrives at this location l ij D represents that user i is at location l ij The elements in the sequence are ordered by time stamp.
4. The method according to claim 2, characterized in that the probability matrix in the implicit state pi= [ p (s 1 )，p(s 2 )，…，p(s n )]Element p(s) i ) The probability that the user appears in one hidden state is represented, s (t) represents the hidden state at the time t, and each hidden state represents the user stay position information, and the start time stamp and the stay time corresponding to the stay position.
5. The method for determining the position of a base station according to claim 2, wherein the transition state matrix of the terminal in the implicit state is a, and element a in a is a ij ＝P(s j，t+1 |s i，t ) Indicating at time t that the state is s i Under the condition of (1), the state is s at time t+1 j Is a probability of (2).
6. The method according to claim 2, wherein the transition matrix B between the hidden state and the observed state is an nxn matrix, and the element B in B is ij ＝P(o i，t+1 |s j，t ) 1.ltoreq.i.ltoreq.M, 1.ltoreq.j.ltoreq.N, indicating that at time t, the implicit state is s j At time t+1, the observation state is o i The variable M represents the number of observed states.
7. The method for determining a base station position according to claim 1, wherein inputting the position sequence information of the at least one mobile terminal into a preset position prediction model for prediction processing to obtain a predicted position of the at least one mobile terminal comprises:
constructing a hash table, the hash table comprising: a hash key value and a hash value, wherein the hash key value is a sequence consisting of the first k-1 positions in a subsequence with the length of t1, the hash value is a linked list, and each node in the linked list comprises a k element and a probability value of a predicted position corresponding to the subsequence, and k is more than or equal to 2 and less than or equal to Y;
position sequence of mobile terminal with length L [ ts ] 1 ，ts 2 ，…，ts L ]Searching in a hash table as a target hash key value, and if the target hash key value exists in the hash table, obtaining a predicted position from the linked list; if the target hash key value does not exist in the hash table, subtracting 1 from the length of the position sequence of the mobile terminal with the length L, and searching in the hash table again as the target hash key value until a linked list containing at least one predicted position is obtained, wherein L is more than or equal to 1 and less than or equal to Y-1;
and determining the predicted positions corresponding to the maximum values of the probabilities respectively corresponding to the plurality of predicted positions contained in the linked list as final predicted positions.
8. A base station position determining apparatus, the apparatus comprising:
the acquisition module is used for acquiring the position sequence information of at least one mobile terminal;
the prediction module is used for inputting the position sequence information of the at least one mobile terminal into a preset position prediction model for prediction processing to obtain a predicted position of the at least one mobile terminal;
the clustering module is used for clustering the mobile terminals according to the predicted position of the at least one mobile terminal to obtain a clustering result;
and the determining module is used for determining the address for establishing the base station according to the clustering result.
9. A computing device, comprising: the device comprises a processor, a memory, a communication interface and a communication bus, wherein the processor, the memory and the communication interface complete communication with each other through the communication bus;
the memory is configured to store at least one executable instruction that causes the processor to perform operations corresponding to the method for determining a location of a base station according to any one of claims 1 to 7.
10. A computer storage medium having stored therein at least one executable instruction for causing a processor to perform operations corresponding to the method of determining a location of a base station as claimed in any one of claims 1 to 7.
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