CN117221822A

CN117221822A - Network optimization method, device, electronic equipment and readable storage medium

Info

Publication number: CN117221822A
Application number: CN202311218889.6A
Authority: CN
Inventors: 章安然
Original assignee: Vivo Software Technology Co Ltd
Current assignee: Vivo Software Technology Co Ltd
Priority date: 2023-09-20
Filing date: 2023-09-20
Publication date: 2023-12-12

Abstract

The application discloses a network optimization method, a network optimization device, electronic equipment and a readable storage medium, and belongs to the technical field of communication. The network optimization method comprises the following steps: acquiring at least two first resident cell sequences, wherein the first resident cell sequences are resident cell sequences corresponding to historical track information of electronic equipment; under the condition that the position of the electronic equipment moves, acquiring a second resident cell sequence, wherein the second resident cell sequence is a resident cell sequence corresponding to the current track information of the electronic equipment; determining a third resident cell sequence in the at least two first resident cell sequences according to the second resident cell sequences; acquiring a first cell and a first cell subsequence in a third resident cell sequence, wherein the first cell subsequence is a pre-cell sequence of the first cell in the third resident cell sequence; and under the condition that the second resident cell sequence is matched with the first cell subsequence, carrying out network optimization on the electronic equipment according to the abnormal cell information of the first cell.

Description

Network optimization method, device, electronic equipment and readable storage medium

Technical Field

The application belongs to the technical field of communication, and particularly relates to a network optimization method, a network optimization device, electronic equipment and a readable storage medium.

Background

Users often use rich mobile phone applications, such as map navigation, browsing news, short video, games, etc., during travel activities, which are very dependent on the use of data traffic. In the application process of the mobile phone, the mobile network is cut off, the system is switched and the like, so that poor application experience is brought to a user.

In the related technology, a cloud big data-based mode is used for predicting that a user enters a network abnormal high-emission area, and the predicted network abnormal high-emission area can be taken effect only by being issued to the electronic equipment of the user, so that larger time delay exists.

Disclosure of Invention

The embodiment of the application aims to provide a network optimization method, a network optimization device, electronic equipment and a readable storage medium, which solve the problem of large delay in predicting an abnormally high network area.

In a first aspect, an embodiment of the present application provides a network optimization method, including: acquiring at least two first resident cell sequences, wherein the first resident cell sequences are resident cell sequences corresponding to historical track information of electronic equipment; under the condition that the position of the electronic equipment moves, acquiring a second resident cell sequence, wherein the second resident cell sequence is a resident cell sequence corresponding to the current track information of the electronic equipment; determining a third resident cell sequence in the at least two first resident cell sequences according to the second resident cell sequences, wherein the historical track information corresponding to the third resident cell sequence is matched with the current track information; acquiring a first cell and a first cell subsequence in a third resident cell sequence, wherein the first cell is an abnormal cell, and the first cell subsequence is a preposed cell sequence of the first cell in the third resident cell sequence; and under the condition that the second resident cell sequence is matched with the first cell subsequence, carrying out network optimization on the electronic equipment according to the abnormal cell information of the first cell.

In a second aspect, an embodiment of the present application provides a network optimization apparatus, including: the acquisition module is used for acquiring at least two first resident cell sequences, wherein the first resident cell sequences are resident cell sequences corresponding to the historical track information of the electronic equipment; the acquisition module is used for acquiring a second resident cell sequence under the condition that the position of the electronic equipment moves, wherein the second resident cell sequence is a resident cell sequence corresponding to the current track information of the electronic equipment; the determining module is used for determining a third resident cell sequence in the at least two first resident cell sequences according to the second resident cell sequences, and the historical track information corresponding to the third resident cell sequence is matched with the current track information; the acquisition module is used for acquiring a first cell and a first cell subsequence in the third resident cell sequence, wherein the first cell is an abnormal cell, and the first cell subsequence is a prepositive cell sequence of the first cell in the third resident cell sequence; and the processing module is used for carrying out network optimization on the electronic equipment according to the abnormal cell information of the first cell under the condition that the second resident cell sequence is matched with the first cell subsequence.

In a third aspect, embodiments of the present application provide an electronic device comprising a processor and a memory storing a program or instructions executable on the processor, the program or instructions implementing the steps of the method as in the first aspect when executed by the processor.

In a fourth aspect, embodiments of the present application provide a readable storage medium having stored thereon a program or instructions which when executed by a processor perform the steps of the method as in the first aspect.

In a fifth aspect, embodiments of the present application provide a chip comprising a processor and a communication interface coupled to the processor for running a program or instructions implementing the steps of the method as in the first aspect.

In a sixth aspect, embodiments of the present application provide a computer program product stored in a storage medium, the program product being executable by at least one processor to implement a method as in the first aspect.

In the embodiment of the application, a plurality of first resident cell sequences matched with the multiple historical track information of the user are obtained, and a third resident cell sequence matched with the second resident cell sequence of the current track information of the user is searched in the plurality of first resident cell sequences, so that the user can search the network abnormal information corresponding to the historical track information when carrying out the current track information, and the matching property of the abnormal condition of the judged network and the abnormal condition of the personal network of the user is improved. The second resident cell sequence corresponding to the current traveling behavior is compared with the first cell subsequence arranged in front of the abnormal cell, so that the accuracy of judging whether the network abnormal situation occurs in the current traveling behavior can be improved, the second resident cell sequence is not required to be compared with the complete first resident cell sequence, the compared data quantity can be reduced, the power consumption generated in the comparison process is effectively reduced, and the problem that the larger time delay exists in the predicted network abnormal high-occurrence area is solved.

Drawings

FIG. 1 illustrates a flow diagram of a network optimization method provided by some embodiments of the application;

fig. 2 illustrates a mapping diagram of a fourth resident cell sequence, a first location sequence, and a first resident cell sequence provided by some embodiments of the application;

FIG. 3 illustrates a block diagram of a network optimization device provided by some embodiments of the application;

FIG. 4 illustrates a block diagram of an electronic device provided by some embodiments of the application;

fig. 5 is a schematic diagram of a hardware structure of an electronic device according to some embodiments of the present application.

Detailed Description

The technical solutions of the embodiments of the present application will be clearly described below with reference to the drawings in the embodiments of the present application, and it is apparent that the described embodiments are some embodiments of the present application, but not all embodiments. All other embodiments, which are obtained by a person skilled in the art based on the embodiments of the present application, fall within the scope of protection of the present application.

The terms first, second and the like in the description and in the claims, are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the terms so used are interchangeable under appropriate circumstances such that the embodiments of the application are capable of operation in sequences other than those illustrated or otherwise described herein, and that the objects identified by "first," "second," etc. are generally of a type not limited to the number of objects, for example, the first object may be one or more. Furthermore, in the description and claims, "and/or" means at least one of the connected objects, and the character "/", generally means that the associated object is an "or" relationship.

The network optimization method, the network optimization device, the electronic device and the readable storage medium provided by the embodiment of the application are described in detail below with reference to fig. 1 to 5 through specific embodiments and application scenarios thereof.

In some embodiments of the present application, a network optimization method is provided, as shown in fig. 1, where the network optimization method includes:

102, acquiring at least two first resident cell sequences, wherein the first resident cell sequences are resident cell sequences corresponding to historical track information of electronic equipment;

in the embodiment of the application, the at least two first resident cell sequences are sequences of network cells where the electronic equipment resides when the user uses the historical track information of the electronic equipment, and the at least two first resident cell sequences correspond to the at least two historical track information of the electronic equipment used by the user one by one.

It should be noted that, the first residence community sequence includes a plurality of network communities through which the history track information passes, and the plurality of network communities through which the history track information passes are arranged according to the passing sequence in the first residence community sequence. And the first resident cell sequence stores not only the cell information of the network cell but also abnormal cell information, namely the cell information of the network cell marked as frequent occurrence of network abnormality.

104, under the condition that the position of the electronic equipment moves, acquiring a second resident cell sequence, wherein the second resident cell sequence is a resident cell sequence corresponding to the current track information of the electronic equipment;

in the embodiment of the application, the second resident cell sequence is a network cell sequence where the electronic equipment resides when the user uses the current track information of the electronic equipment. The arrangement order of the network cells in the second resident cell sequence is matched with the order of the current track information.

Step 106, determining a third resident cell sequence in the at least two first resident cell sequences according to the second resident cell sequences;

wherein, the historical track information corresponding to the third resident cell sequence is matched with the current track information.

In the embodiment of the application, after the second resident cell sequence of the current track information is acquired, the current track information of the second resident cell sequence is compared with the historical track information corresponding to at least two first resident cell sequences, so that the historical track information matched with the current track information can be determined, and the first resident cell sequence corresponding to the historical track information is determined as the third resident cell sequence.

Illustratively, the plurality of historical track information includes a shift-in trip, a return-home trip, a school-in trip, etc. of the user, each of the historical track information corresponding to one of the first resident cell sequences. When the user executes the current track information by using the electronic equipment, the first resident cell sequence matched with the current track information can be determined based on the generated second resident cell sequence.

Step 108, a first cell and a first cell subsequence in a third resident cell sequence are obtained, wherein the first cell is an abnormal cell, and the first cell subsequence is a prepositive cell sequence of the first cell in the third resident cell sequence;

in the embodiment of the application, the first cell is an abnormal cell in the third resident cell sequence, the first cell subsequence is a partial sequence in the third resident cell sequence, namely, the first cell subsequence comprises a part of network cells in the third resident cell sequence, and the network cells in the first cell subsequence are all leading cells of the first cell, namely, the travel of the history track information passes through the network cells in the first cell subsequence first and then passes through the first cell.

Step 110, in the case that the second resident cell sequence is matched with the first cell sub-sequence, network optimization is performed on the electronic device according to the abnormal cell information of the first cell.

In the embodiment of the application, under the condition that the second resident cell sequence corresponding to the current track information is matched with the first cell subsequence, the condition that the current track information possibly has network abnormality is determined, the corresponding abnormality information of the first cell is extracted, and accordingly, the network optimization processing such as preloading and the like is carried out on the electronic equipment of the user in advance.

In the embodiment of the application, the third resident cell sequence stores the cell information of the network cells through which the plurality of historical track information passes, and the abnormal network cells are marked in the third resident cell sequence, wherein the abnormal cell information is the information of the network cells which are frequently abnormal in the third resident cell sequence. After the abnormal cell information is determined, predicting whether the electronic equipment is about to enter an abnormal network cell according to the position information in the abnormal cell information, so that the network of the electronic equipment is optimized in advance.

When the electronic equipment runs the application program, the electronic equipment is determined to enter the network abnormal area through the abnormal cell information, and the electronic equipment is controlled to preload the network content of the running application program, so that the network of the electronic equipment is optimized, and the network utilization experience of a user in a subsequent period of time is improved.

In some embodiments of the application, acquiring at least two first camping cell sequences comprises: acquiring at least two fourth resident cell sequences corresponding to each history track information in at least two history track information; and acquiring a first resident cell sequence corresponding to each historical track information by carrying out aggregation processing on at least two fourth resident cell sequences corresponding to each historical track information.

In the embodiment of the application, the fourth resident cell sequence is a cell sequence obtained by recording when the user uses the electronic equipment to carry out the historical track information, and each historical track information corresponds to a plurality of fourth resident cell sequences. Each historical track information may be repeated multiple times, for example: in a period of time, the user needs to perform the shift-in journey many times, the same shift-in journey many times corresponds to one piece of history track information, and each time the shift-in journey is performed, the electronic equipment records and obtains a fourth resident cell sequence.

For example, if the number of times of a certain historical track information of the user is N, N fourth resident Cell sequences are recorded, each fourth resident Cell sequence is denoted as List < Cell >, and N identical historical track information is corresponding to N lists < Cell >, namely list_1, list_2 … and list_n.

It should be noted that, when the user performs the same historical track information multiple times, even if the user passes through the same area in the geographic location, the connected cells may be different, and there is a certain randomness, and even if the user is located in the same geographic location, the network cell in which the electronic device is located may also switch multiple factors in one bit, so that the switching of the cells does not necessarily represent that the geographic location of the user changes, and therefore, there is a certain randomness in the fourth resident cell obtained by multiple records of the same historical track information record.

In the embodiment of the application, the randomness in the fourth resident cell sequences obtained by recording the same historical track information can be eliminated by carrying out aggregation treatment on the fourth resident cell sequences, so that the obtained first resident cell can truly reflect the position change of the user, the accuracy of searching the third resident cell in the subsequent network optimization process is improved, and the accuracy of judging the occurrence of network abnormal conditions is improved.

In some embodiments of the present application, the aggregation processing is performed on at least two fourth resident cell sequences corresponding to each historical track information to obtain a first resident cell sequence corresponding to each historical track information, including: mapping at least two fourth resident cell sequences into at least two first position sequences through a first mapping relation; determining a location random coefficient between any two pieces of first location information in at least two first location sequences, and a residence time of each piece of first location information; determining a position information set in at least two first position sequences through the position random coefficient and the residence time, wherein at least two second position information in the position information set are matched; generating a second mapping relation through the position information set and the first mapping relation; and mapping at least two first position sequences into a first resident cell sequence through a second mapping relation.

In the embodiment of the application, in the process of aggregating at least two fourth resident cell sequences, the at least two fourth resident cell sequences need to be mapped to actual positions, and at least two first position sequences are obtained. Determining whether different network cells are in the same geographic position by determining the front-to-back position relation of the different network cells in the fourth resident cell sequence and the resident duration.

In the embodiment of the present application, the first mapping relationship is a preset mapping relationship, and, for example, one network cell in the fourth resident cell sequence is mapped to one piece of first location information in the first location sequence, that is, the fourth resident cell sequence corresponds to the first location sequence one by one.

Illustratively, the fourth resident Cell sequence is denoted as List < Cell >, the Set of network cells in the plurality of List < Cell > is extracted, and the Set of network cells is denoted as Set < Cell >. Each Cell in the network Cell set (i.e., the network Cell) is re-labeled with its ID (i.e., the first location information), the Cell-to-ID mapping is denoted as (cell_id_map), and each List < Cell > may be re-labeled as List < ID >, where List < ID > is the first location sequence.

In the implementation of the application, the position random coefficient and the residence time can reflect the consistency of any two first position information in the first position sequence, so that the position information with higher consistency can be found in the first position sequence through the position random coefficient and the residence time, and a position information set is generated, namely, the second position information in the position information set is the position information with higher consistency.

Illustratively, the process of determining the location random coefficients is described in detail below:

fig. 2 is a schematic diagram illustrating mapping of a fourth resident Cell sequence, a first location sequence, and a first resident Cell sequence according to some embodiments of the present application, as shown in fig. 2, where List < Cell > is the fourth resident Cell sequence, list < ID > is the first location sequence, and List < MCell > is the first resident Cell sequence.

By traversing each first position sequence List < ID >, each ID records a sequence relationship according to a preset quantity relationship. Illustratively, the number relationship is 3 positional relationships for each ID, the first position sequence List < ID > is ID_1, ID_2, ID_3, ID_4, and the order of the records is <1,2>, <1,3>, <2,4>, <3,4>.

When the position random coefficient of any two pieces of first position information in the first position sequence List < ID > is calculated, the number of occurrences of the two pieces of first position information is determined. For example: the two first position information are id_i and id_j, the sequence relation of the two is < i, j > and is counted as M1, and the sequence relation of the two is counted as M2, and then the position random coefficient between the two is calculated by the following relation (1).

Wherein order is _coe For the position random coefficients abs () is calculated as taking the absolute value.

It should be noted that order _coe The smaller the value, the more random the two first location information are.

In the embodiment of the application, by setting the coefficient threshold value for the position random coefficient, if the position random coefficient between the two pieces of first position information is smaller than or equal to the coefficient threshold value, the positions of the two pieces of first position information are judged to be consistent, and the two pieces of first position information are added to the position information set.

Since the residence time of the user in a certain first location information is very short, it is determined that the first location information is substantially identical to the location of the next adjacent first location information.

In the embodiment of the application, by setting the duration threshold for the residence time, when the residence time of the first position information is smaller than or equal to the duration threshold, the position of the first position information is judged to be consistent with the position of the adjacent first position information, so that the two first position information is added to the position information set.

Illustratively, the first subset, obtainable by the random coefficients, is denoted MSet (i, j), and the second subset, obtainable by the dwell time, is denoted MSet (j, k). Combining the first subset and the second subset to obtain a position information set MSet (i, j, k).

In the embodiment of the application, the location information set includes a plurality of pieces of second location information with higher consistency, and a second mapping relation is generated according to the plurality of pieces of second location information with higher consistency and a plurality of corresponding network cells, wherein the second mapping relation is that the plurality of network cells correspond to one piece of second location information, so that a plurality of first location sequences can be mapped into one first resident cell sequence through the second mapping relation.

In the embodiment of the application, the fourth resident cell sequence is mapped into the first position sequence through the first mapping relation, the second mapping relation can be obtained through the aggregation processing mode, the first position sequence can be mapped into the first resident cell sequence through the second mapping relation, and the obtained first resident cell sequence eliminates the position randomness of the fourth resident cell sequence through two-layer mapping conversion, so that the accuracy of judging the abnormal condition of the network in the follow-up process is improved.

In some implementations of the application, after acquiring the at least two first camping cell sequences, further comprising: and obtaining the similarity between at least two first resident cell sequences, and deleting at least two first resident cell sequences with lower similarity.

In the embodiment of the application, after a plurality of first resident cell sequences are obtained, the common first resident cell sequences are screened and stored, and the first resident cell sequences with lower similarity are not common sequences, so that the first resident cell sequences are deleted to avoid occupying storage space.

Illustratively, sequences having a similarity higher than the threshold are deleted as similar sequences by comparing the similarity to each other for the plurality of first camping cell sequences.

Illustratively, by comparing the similarity to the plurality of first camping cell sequences in pairs, a sequence having a similarity higher than a threshold value is regarded as a similar sequence. And generating sequence sets comprising two similar sequences after judging that the two sequences are similar sequences and generating a plurality of sequence sets, merging the sets pairwise until no intersection exists between all the sets, taking the set with the largest sequence in the sets as a common route, and deleting the rest parts.

Illustratively, the identification model is constructed using time information and location information of historical track information corresponding to two similar resident cell sequences. Wherein the time information includes: whether working day, travel time period, etc. The specific modeling process is described below:

First, feature extraction is performed, the history track information is L, and the travel time (start_time), whether the travel day (is_work) and the corresponding first resident cell sequence (List < MCell >) in L are extracted, and the difference features extracted by the two pieces of travel information are as follows in table 1:

TABLE 1

The similarity calculation method for table 1 includes, but is not limited to: similarity of both sets (Jaccard similarity), edit distance of both lists, edit distance of both intersections.

And secondly, setting a label, wherein the history track information of the user is the same line twice, the label is 1, and otherwise, the label is 0.

In some embodiments of the application, acquiring the first cell and the first cell subsequence in the third resident cell sequence comprises: marking a first cell in a third resident cell sequence according to historical network anomaly information of the electronic equipment; and extracting the first cell subsequence in the third resident cell sequence according to the resident cell number or resident duration.

In the embodiment of the application, the historical network anomaly information is network anomaly information obtained by reading the Internet surfing record corresponding to the historical track information of the user, and the network anomaly information comprises information of network cells with anomalies, so that the first cell with anomalies in the third resident cell sequence can be extracted according to the information.

Illustratively, the first cell in the third resident cell sequence List < MCell > is labeled AbnCell, and the first cell subsequence in front is labeled pre_list. Specifically, a plurality of latest travel records of the user are read, and the abnormal times of a certain cell in the third resident cell sequence are reported to reach the preset times, then the network cell is marked as a first cell AbnCell, and the corresponding position of the first cell is marked as a Pre-position pre_mcells. After obtaining the Pre-position pre_mcells, the corresponding first cell sub-sequence pre_list is searched forward based on the Pre-position pre_mcells.

In the embodiment of the application, after determining the first cell with the abnormality in the third resident cell sequence, the first cell subsequence prepositioned in the first cell is searched forward through the preset resident cell number or preset resident duration.

Illustratively, if the first cell subsequence is determined according to the number k of resident cells, k network cells are fixedly fetched from the abnormal first cell forward in the third resident cell sequence to generate the first cell subsequence.

Illustratively, the first cell subsequence is determined from the duration of camping, for example: and if the residence time is 1 minute, determining the network cell passing within 1 minute before entering the abnormal first cell as a first cell subsequence.

It should be noted that, the first cell subsequences found according to the number of camping cells and the camping duration may be different, for example: the third camping cell sequence is A, B, C, D, E, and the abnormal first cell is E. The number of resident cells is 2, the first cell subsequence is C, D, the resident time is 30 seconds, and the first cell subsequence is D.

In the embodiment of the application, the abnormal first cell in the third resident cell sequence can be accurately found through the historical network abnormal information, and the first cell subsequence of the front first cell is found through the resident cell number or resident time length, so that the first cell subsequence is the front sequence of the first cell, and the abnormal situation of the network can be predicted in advance through comparing the first cell subsequence with the second resident cell sequence.

In some embodiments of the present application, before performing network optimization on the electronic device according to the cell information of the first cell, in a case where the second resident cell sequence matches the first cell sub-sequence, the method further includes: obtaining the similarity of at least part of cells in the second resident cell sequence and the first cell subsequence; and determining that the second resident cell sequence matches the first cell subsequence based on the similarity being greater than a similarity threshold.

In the embodiment of the application, when judging whether the second resident cell sequence is matched with the first cell subsequence, the second resident cell sequence and the first cell subsequence can be determined to be matched by judging the similarity of at least part of cells in the second resident cell and the first cell subsequence, and when the similarity is higher than the similarity threshold value, the second resident cell sequence and the first cell subsequence are determined to be matched.

Illustratively, a first target cell sequence in the second resident cell sequence is extracted, and a second target cell sequence of the first cell subsequence is extracted, wherein the resident durations of the first target cell sequence and the second target cell sequence are similar. And if the similarity between the first target cell sequence and the second target cell sequence is higher than a similarity threshold, determining that the second resident cell sequence is matched with the first cell subsequence.

Illustratively, the similarity of all network cells in the second resident cell sequence to all network cells in the first cell sub-sequence is determined. In the event that the similarity is above a similarity threshold, then it is determined that the second resident cell sequence matches the first cell subsequence.

In the embodiment of the application, whether the second resident cell sequence is matched with the first cell subsequence or not is determined through the numerical relation between the similarity of at least part of cells in the second resident cell sequence and the first cell subsequence and the similarity threshold value, so that the accuracy of determining whether the second resident cell sequence is matched with the first cell subsequence or not is improved, and the accuracy of predicting the abnormal condition of the network in advance is improved.

According to the network optimization method provided by the embodiment of the application, the execution main body can be a network optimization device. In the embodiment of the application, a network optimization device is taken as an example to execute a network optimization method, and the network optimization device provided by the embodiment of the application is described.

In some embodiments of the present application, a network optimization device is provided, and fig. 3 shows a block diagram of the network optimization device provided in some embodiments of the present application. As shown in fig. 3, the network optimization apparatus 300 includes:

the acquiring module 302 is configured to acquire at least two first resident cell sequences, where the first resident cell sequences are resident cell sequences corresponding to historical track information of the electronic device;

the acquiring module 302 is configured to acquire a second residence cell sequence under a situation that a position of the electronic device moves, where the second residence cell sequence is a residence cell sequence corresponding to current track information of the electronic device;

a determining module 304, configured to determine a third resident cell sequence from the at least two first resident cell sequences according to the second resident cell sequence, where historical track information corresponding to the third resident cell sequence matches with current track information;

an obtaining module 302, configured to obtain a first cell and a first cell subsequence in a third resident cell sequence, where the first cell is an abnormal cell, and the first cell subsequence is a pre-cell sequence of the first cell in the third resident cell sequence;

And a processing module 306, configured to perform network optimization on the electronic device according to the abnormal cell information of the first cell when the second resident cell sequence is matched with the first cell subsequence.

In some embodiments of the present application, the obtaining module 302 is configured to obtain at least two fourth camping cell sequences corresponding to each of the at least two historical track information;

and the processing module 306 is configured to obtain a first resident cell sequence corresponding to each historical track information by performing aggregation processing on at least two fourth resident cell sequences corresponding to each historical track information.

In the embodiment of the application, the randomness in the fourth resident cell sequences obtained by recording the same historical track information can be eliminated by carrying out aggregation treatment on the fourth resident cell sequences, so that the obtained first resident cell can truly reflect the position change of the user, the accuracy of searching the third resident cell in the subsequent network optimization process is improved, and the accuracy of judging the occurrence of network abnormal conditions is improved. In some embodiments of the present application, the processing module 306 is configured to map at least two fourth camping cell sequences into at least two first location sequences through the first mapping relationship;

a determining module 304, configured to determine a location random coefficient between any two pieces of first location information in at least two first location sequences, and a residence time of each piece of first location information;

A determining module 304, configured to determine, by using the location random coefficient and the residence time, a set of location information in at least two first location sequences, where at least two second location information in the set of location information are matched;

the processing module 306 is configured to generate a second mapping relationship through the location information set and the first mapping relationship;

a processing module 306, configured to map at least two first location sequences into a first camping cell sequence through the second mapping relationship.

In some embodiments of the present application, the processing module 306 is configured to mark the first cell in the third resident cell sequence according to the historical network anomaly information of the electronic device;

a processing module 306, configured to extract the first cell subsequence in the third resident cell sequence according to the number of resident cells or the resident duration.

In some embodiments of the present application, the obtaining module 302 is configured to obtain a similarity between the second camping cell sequence and at least a portion of the cells in the first cell subsequence;

a determining module 304 is configured to determine that the second resident cell sequence matches the first cell subsequence based on the similarity being greater than a similarity threshold.

The network optimization device in the embodiment of the application can be electronic equipment or a component in the electronic equipment, such as an integrated circuit or a chip. The electronic device may be a terminal, or may be other devices than a terminal. Illustratively, the electronic device may be a mobile phone, a tablet computer, a notebook computer, a palm computer, a vehicle-mounted electronic device, a mobile internet appliance (Mobile Internet Device, MID), an augmented reality (augmented reality, AR)/Virtual Reality (VR) device, a robot, a wearable device, an ultra-mobile personal computer (ultra-mobile personal computer, UMPC), a netbook or a personal digital assistant (personal digital assistant, PDA), or the like, and may also be a server, a network attached storage (Network Attached Storage, NAS), a personal computer (personal computer, PC), a Television (TV), a teller machine, a self-service machine, or the like.

The network optimization device in the embodiment of the application can be a device with an operating system. The operating system may be an Android operating system, an iOS operating system, or other possible operating systems, and the embodiment of the present application is not limited specifically.

The network optimization device provided by the embodiment of the application can realize each process realized by the embodiment of the method, and in order to avoid repetition, the description is omitted.

Optionally, the embodiment of the present application further provides an electronic device, which includes the network optimization device in any of the embodiments, so that the electronic device has all the advantages of the network optimization device in any of the embodiments, and will not be described in detail herein.

Optionally, an embodiment of the present application further provides an electronic device, fig. 4 shows a block diagram of a structure of the electronic device according to an embodiment of the present application, as shown in fig. 4, an electronic device 400 includes a processor 402, a memory 404, and a program or an instruction stored in the memory 404 and capable of running on the processor 402, where the program or the instruction implements each process of the above-mentioned network optimization method embodiment when executed by the processor 402, and the process can achieve the same technical effect, and is not repeated herein.

The electronic device in the embodiment of the application includes the mobile electronic device and the non-mobile electronic device.

Fig. 5 is a schematic hardware structure of an electronic device implementing an embodiment of the present application.

The electronic device 500 includes, but is not limited to: radio frequency unit 501, network module 502, audio output unit 503, input unit 504, sensor 505, display unit 506, user input unit 507, interface unit 508, memory 509, and processor 510.

Those skilled in the art will appreciate that the electronic device 500 may further include a power source (e.g., a battery) for powering the various components, and that the power source may be logically coupled to the processor 510 via a power management system to perform functions such as managing charging, discharging, and power consumption via the power management system. The electronic device structure shown in fig. 5 does not constitute a limitation of the electronic device, and the electronic device may include more or less components than shown, or may combine certain components, or may be arranged in different components, which are not described in detail herein.

A processor 510, configured to obtain at least two first resident cell sequences, where the first resident cell sequences are resident cell sequences corresponding to historical track information of the electronic device;

the processor 510 is configured to obtain a second resident cell sequence when the location of the electronic device moves, where the second resident cell sequence is a resident cell sequence corresponding to current track information of the electronic device;

a processor 510, configured to determine a third resident cell sequence from the at least two first resident cell sequences according to the second resident cell sequence, where historical track information corresponding to the third resident cell sequence matches with current track information;

A processor 510, configured to obtain a first cell and a first cell subsequence in the third resident cell sequence, where the first cell is an abnormal cell, and the first cell subsequence is a pre-cell sequence of the first cell in the third resident cell sequence;

and a processor 510, configured to perform network optimization on the electronic device according to the abnormal cell information of the first cell in the case that the second resident cell sequence matches with the first cell subsequence.

Further, the processor 510 is configured to obtain at least two fourth camping cell sequences corresponding to each of the at least two historical track information;

the processor 510 is configured to obtain a first resident cell sequence corresponding to each historical track information by performing aggregation processing on at least two fourth resident cell sequences corresponding to each historical track information.

In some embodiments of the present application, the processor 510 is configured to map the at least two fourth camping cell sequences into at least two first location sequences through a first mapping relationship;

a processor 510 for determining a location random coefficient between any two first location information in the at least two first location sequences, and a residence time of each first location information;

A processor 510 for determining a set of location information in at least two first location sequences, at least two second location information in the set of location information being matched, by a location random coefficient and a residence time;

a processor 510, configured to generate a second mapping relationship through the location information set and the first mapping relationship;

the processor 510 is configured to map at least two first location sequences into a first camping cell sequence through a second mapping relationship.

In some embodiments of the present application, the processor 510 is configured to tag the first cell in the third resident cell sequence according to historical network anomaly information for the electronic device;

a processor 510 is configured to extract the first cell subsequence in the third camping cell sequence according to the number of camping cells or the camping duration.

In some embodiments of the present application, the processor 510 is configured to obtain a similarity of the second resident cell sequence and at least a portion of the cells in the first cell subsequence;

a processor 510 is configured to determine that the second resident cell sequence matches the first cell subsequence based on the similarity being greater than a similarity threshold.

It should be appreciated that in embodiments of the present application, the input unit 504 may include a graphics processor (Graphics Processing Unit, GPU) 5041 and a microphone 5042, the graphics processor 5041 processing image data of still pictures or video obtained by an image capturing device (e.g., a camera) in a video capturing mode or an image capturing mode. The display unit 506 may include a display panel 5061, and the display panel 5061 may be configured in the form of a liquid crystal display, an organic light emitting diode, or the like. The user input unit 507 includes at least one of a touch panel 5071 and other input devices 5072. Touch panel 5071, also referred to as a touch screen. Touch panel 5071 may include two parts, a touch detection device and a touch controller. Other input devices 5072 may include, but are not limited to, a physical keyboard, function keys (e.g., volume control keys, switch keys, etc.), a trackball, a mouse, a joystick, and so forth, which are not described in detail herein.

The memory 509 may be used to store software programs as well as various data. The memory 509 may mainly include a first storage area storing programs or instructions and a second storage area storing data, wherein the first storage area may store an operating system, application programs or instructions (such as a sound playing function, an image playing function, etc.) required for at least one function, and the like. Further, the memory 509 may include volatile memory or nonvolatile memory, or the memory 509 may include both volatile and nonvolatile memory. The nonvolatile Memory may be a Read-Only Memory (ROM), a Programmable ROM (PROM), an Erasable PROM (EPROM), an Electrically Erasable EPROM (EEPROM), or a flash Memory. The volatile memory may be random access memory (Random Access Memory, RAM), static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double Data Rate SDRAM (ddr SDRAM), enhanced SDRAM (Enhanced SDRAM), synchronous DRAM (SLDRAM), and Direct RAM (DRRAM). Memory 509 in embodiments of the present application includes, but is not limited to, these and any other suitable types of memory.

Processor 510 may include one or more processing units; optionally, the processor 510 integrates an application processor that primarily processes operations involving an operating system, user interface, application programs, etc., and a modem processor that primarily processes wireless communication signals, such as a baseband processor. It will be appreciated that the modem processor described above may not be integrated into the processor 510.

The embodiment of the application also provides a readable storage medium, and the readable storage medium stores a program or an instruction, which when executed by a processor, implements each process of the above method embodiment, and can achieve the same technical effects, so that repetition is avoided, and no further description is provided herein.

The processor is a processor in the electronic device in the above embodiment. Readable storage media include computer readable storage media such as computer readable memory ROM, random access memory RAM, magnetic or optical disks, and the like.

The embodiment of the application further provides a chip, the chip comprises a processor and a communication interface, the communication interface is coupled with the processor, the processor is used for running programs or instructions, the processes of the network optimization method embodiment can be realized, the same technical effects can be achieved, and the repetition is avoided, and the description is omitted here.

It should be understood that the chips referred to in the embodiments of the present application may also be referred to as system-on-chip chips, chip systems, or system-on-chip chips, etc.

Embodiments of the present application provide a computer program product stored in a storage medium, where the program product is executed by at least one processor to implement the respective processes of the network optimization method embodiments described above, and achieve the same technical effects, and for avoiding repetition, a detailed description is omitted herein.

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 one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element. Furthermore, it should be noted that the scope of the methods and apparatus in the embodiments of the present application is not limited to performing the functions in the order shown or discussed, but may also include performing the functions in a substantially simultaneous manner or in an opposite order depending on the functions involved, e.g., the described methods may be performed in an order different from that described, and various steps may be added, omitted, or combined. Additionally, features described with reference to certain examples may be combined in other examples.

From the above description of the embodiments, it will be clear to those skilled in the art that the above-described embodiment method may be implemented by means of software plus a necessary general hardware platform, but of course may also be implemented by means of hardware, but in many cases the former is a preferred embodiment. Based on such understanding, the technical solution of the present application may be embodied essentially or in part in the form of a computer software product stored on a storage medium (e.g. ROM/RAM, magnetic disk, optical disk) comprising instructions for causing a terminal (which may be a mobile phone, a computer, a server, or a network device, etc.) to perform the method of the embodiments of the present application.

The embodiments of the present application have been described above with reference to the accompanying drawings, but the present application is not limited to the above-described embodiments, which are merely illustrative and not restrictive, and many forms may be made by those having ordinary skill in the art without departing from the spirit of the present application and the scope of the claims, which are to be protected by the present application.

Claims

1. A method of network optimization, comprising:

acquiring at least two first resident cell sequences, wherein the first resident cell sequences are resident cell sequences corresponding to historical track information of electronic equipment;

under the condition that the position of the electronic equipment moves, acquiring a second resident cell sequence, wherein the second resident cell sequence is a resident cell sequence corresponding to the current track information of the electronic equipment;

determining a third resident cell sequence in at least two first resident cell sequences according to the second resident cell sequences, wherein the historical track information corresponding to the third resident cell sequence is matched with the current track information;

acquiring a first cell and a first cell subsequence in the third resident cell sequence, wherein the first cell is an abnormal cell, and the first cell subsequence is a preposed cell sequence of the first cell in the third resident cell sequence;

and under the condition that the second resident cell sequence is matched with the first cell subsequence, carrying out network optimization on the electronic equipment according to the abnormal cell information of the first cell.

2. The network optimization method according to claim 1, wherein the acquiring at least two first camping cell sequences comprises:

Acquiring at least two fourth resident cell sequences corresponding to each of the historical track information in at least two historical track information;

and acquiring the first resident cell sequence corresponding to each history track information by carrying out aggregation processing on at least two fourth resident cell sequences corresponding to each history track information.

3. The network optimization method according to claim 2, wherein the obtaining the first resident cell sequence corresponding to each historical track information by performing aggregation processing on at least two fourth resident cell sequences corresponding to each historical track information includes:

mapping at least two fourth resident cell sequences into at least two first position sequences through a first mapping relation;

determining a position random coefficient between any two pieces of first position information in at least two first position sequences and residence time of each piece of first position information;

determining a set of position information in at least two first position sequences through the position random coefficient and the residence time, wherein at least two second position information in the set of position information are matched;

Generating a second mapping relation through the position information set and the first mapping relation;

and mapping at least two first position sequences into the first resident cell sequence through the second mapping relation.

4. A network optimization method according to any one of claims 1 to 3, characterized in that the acquiring the first cell and the first cell sub-sequence in the third resident cell sequence comprises:

marking the first cell in the third resident cell sequence according to the historical network anomaly information of the electronic equipment;

and extracting the first cell subsequence in the third resident cell sequence according to the resident cell number or resident duration.

5. A network optimization method according to any one of claims 1 to 3, wherein, in the case where the second resident cell sequence matches the first cell sub-sequence, before performing network optimization on the electronic device according to the abnormal cell information of the first cell, the method further comprises:

obtaining the similarity of the second resident cell sequence and at least part of cells in the first cell subsequence;

and determining that the second resident cell sequence is matched with the first cell subsequence based on the similarity being greater than a similarity threshold.

6. A network optimization device, comprising:

the acquisition module is used for acquiring at least two first resident cell sequences, wherein the first resident cell sequences are resident cell sequences corresponding to historical track information of the electronic equipment;

the acquiring module is configured to acquire a second residence cell sequence under the condition that the position of the electronic device moves, where the second residence cell sequence is a residence cell sequence corresponding to current track information of the electronic device;

the determining module is used for determining a third resident cell sequence in at least two first resident cell sequences according to the second resident cell sequences, and the historical track information corresponding to the third resident cell sequence is matched with the current track information;

the acquisition module is configured to acquire a first cell and a first cell subsequence in the third resident cell sequence, where the first cell is an abnormal cell, and the first cell subsequence is a pre-cell sequence of the first cell in the third resident cell sequence;

and the processing module is used for carrying out network optimization on the electronic equipment according to the abnormal cell information of the first cell under the condition that the second resident cell sequence is matched with the first cell subsequence.

7. The network optimization device of claim 6, wherein,

the acquisition module is used for acquiring at least two fourth resident cell sequences corresponding to each of the historical track information in at least two historical track information;

the processing module is configured to obtain the first resident cell sequence corresponding to each historical track information by performing aggregation processing on at least two fourth resident cell sequences corresponding to each historical track information.

8. The network optimization device of claim 7, wherein,

the processing module is configured to map at least two fourth camping cell sequences into at least two first location sequences through a first mapping relationship;

the determining module is used for determining a position random coefficient between any two pieces of first position information in at least two first position sequences and residence time of each piece of first position information;

the determining module is used for determining a set of position information in at least two first position sequences according to the position random coefficient and the residence time, and at least two second position information in the set of position information are matched;

The processing module is used for generating a second mapping relation through the position information set and the first mapping relation;

and the processing module is used for mapping at least two first position sequences into the first resident cell sequence through the second mapping relation.

9. The network optimization device according to any one of claims 6 to 8, characterized in that,

the processing module is used for marking the first cell in the third resident cell sequence according to the historical network abnormality information of the electronic equipment;

the processing module is configured to extract the first cell subsequence in the third resident cell sequence according to the number of resident cells or resident duration.

10. The network optimization device of claim 9, wherein the network optimization device,

the acquisition module is used for acquiring the similarity between the second resident cell sequence and at least part of cells in the first cell subsequence;

and the determining module is used for determining that the second resident cell sequence is matched with the first cell subsequence based on the similarity being greater than a similarity threshold.

11. An electronic device, comprising:

A processor and a memory storing a program or instructions executable on the processor, which when executed by the processor, implement the steps of the method of any one of claims 1 to 5.

12. A readable storage medium having stored thereon a program or instructions which when executed by a processor implement the steps of the method according to any of claims 1 to 5.