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WO2019061663A1 - Wi-Fi热点部署优化方法、服务器及存储介质 - Google Patents

Wi-Fi热点部署优化方法、服务器及存储介质 Download PDF

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Publication number
WO2019061663A1
WO2019061663A1 PCT/CN2017/108771 CN2017108771W WO2019061663A1 WO 2019061663 A1 WO2019061663 A1 WO 2019061663A1 CN 2017108771 W CN2017108771 W CN 2017108771W WO 2019061663 A1 WO2019061663 A1 WO 2019061663A1
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WO
WIPO (PCT)
Prior art keywords
hotspot
hotspots
business circle
business
user
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Application number
PCT/CN2017/108771
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English (en)
French (fr)
Inventor
金新
王建明
肖京
Original Assignee
平安科技（深圳）有限公司
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Publication of WO2019061663A1 publication Critical patent/WO2019061663A1/zh

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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W16/00—Network planning, e.g. coverage or traffic planning tools; Network deployment, e.g. resource partitioning or cells structures
    • H04W16/18—Network planning tools
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W24/00—Supervisory, monitoring or testing arrangements
    • H04W24/02—Arrangements for optimising operational condition
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W48/00—Access restriction; Network selection; Access point selection
    • H04W48/16—Discovering, processing access restriction or access information

Definitions

  • the present application relates to the field of computer technologies, and in particular, to a Wi-Fi hotspot deployment optimization method, a server, and a computer readable storage medium.
  • Wi-Fi Wireless-Fidelity
  • Wi-Fi Wireless-Fidelity
  • the characteristics of control and restraint, blindness and randomness are very obvious.
  • Consumer electronics products such as mobile phones, game consoles, and portable computers are becoming more and more popular.
  • the richness of terminal products will make the penetration rate of Wi-Fi higher and higher. It is of substantial significance to strengthen the coverage of Wi-Fi signals.
  • the person in the field determines whether the current location of the to-be-arranged space exists by calculating the coverage area of the hot spot, and if so, adjusts the location of the hot spot toward the blind spot outside the coverage area of the hot spot, or at a position close to the blind spot.
  • a hot spot is arranged until the entire location of the space to be laid is covered by at least one hot spot, thereby optimizing the arrangement of the Wi-Fi hotspot.
  • Wi-Fi hotspot deployment needs to be optimized, it is necessary to consider the traffic volume in the business circle and the user satisfaction with the Wi-Fi hotspot in the business circle, so as to be based on user satisfaction and within the business circle.
  • the traffic aggregation area determines what problems exist in the deployment of existing Wi-Fi hotspots. Therefore, how to make full use of Wi-Fi hotspots, adjust the location of existing Wi-Fi hotspot deployments, and increase the signal coverage of Wi-Fi hotspots.
  • the crowd improving the user's online experience, is an urgent problem to be solved.
  • the present application provides a Wi-Fi hotspot deployment optimization method, a server, and a computer readable storage medium, the main purpose of which is to understand the user's satisfaction with the Wi-Fi hotspot in the business circle and the distribution of Wi-Fi hotspots in the business circle.
  • An optimization scheme for Wi-Fi hotspot deployment in the business circle is given to enhance the user experience of Wi-Fi hotspots.
  • the present application provides a server, including: a memory, a processor, and a Wi-Fi hotspot deployment optimization program stored on the memory, where the optimization program is executed by the processor to implement the following steps:
  • the collecting step collecting the positioning information of the first mobile terminal at the current location and the Wi-Fi hotspot list scanned by the first mobile terminal at the current location, where the positioning information includes the current location of the first mobile terminal Latitude and longitude;
  • a first calculating step calculating a latitude and longitude hash value of the current location of the first mobile terminal, taking a result of the first preset level of the hash value, and scanning the first mobile terminal at the current location according to the result of the first preset level
  • the list of Wi-Fi hotspots is aggregated in multiple business districts;
  • a second calculating step calculating a latitude and longitude hash value of the current location of the first mobile terminal, taking a result of the second preset level of the hash value, and determining, according to the result of the second preset level, the Wi- in the plurality of shopping districts a concentrated area of Fi hotspots;
  • a third calculating step reading historical data of the Wi-Fi hotspot in the Wi-Fi hotspot list in a preset time, and calculating, by the first user of the first mobile terminal, the Wi-Fi hotspot in the multiple shopping districts User satisfaction;
  • the number of first users in the plurality of business circles is equal to the number of first users in the first business circle and the user satisfaction is the highest, according to the number of Wi-Fi hotspots in the second business circle and the number of first users Correlation relationship, determine the number of Wi-Fi hotspots that need to be adjusted to improve the user satisfaction of the first business circle.
  • the traffic flow aggregation area and the Wi-Fi hotspot concentration area in the first business circle it is determined that the user satisfaction of the first business circle needs to be adjusted.
  • the location of the Wi-Fi hotspot is determined that the user satisfaction of the first business circle needs to be adjusted.
  • the present application further provides a Wi-Fi hotspot deployment optimization method, where the method includes:
  • the acquiring step collecting the positioning information of the first mobile terminal at the current location and the Wi-Fi hotspot list scanned by the first mobile terminal at the current location, where the positioning information includes the latitude and longitude of the current location of the first mobile terminal;
  • a first calculating step calculating a latitude and longitude hash value of the current location of the first mobile terminal, taking a result of the first preset level of the hash value, and scanning the first mobile terminal at the current location according to the result of the first preset level
  • the list of Wi-Fi hotspots is aggregated in multiple business districts;
  • a second calculating step calculating a latitude and longitude hash value of the current location of the first mobile terminal, taking a result of the second preset level of the hash value, and determining, according to the result of the second preset level, the Wi- in the plurality of shopping districts a concentrated area of Fi hotspots;
  • a third calculating step reading historical data of the Wi-Fi hotspot in the Wi-Fi hotspot list in a preset time, and calculating, by the first user of the first mobile terminal, the Wi-Fi hotspot in the multiple shopping districts User satisfaction;
  • the number of first users in the plurality of business circles is equal to the number of first users in the first business circle and the user satisfaction is the highest, according to the number of Wi-Fi hotspots in the second business circle and the number of first users Correlation relationship, determine the number of Wi-Fi hotspots that need to be adjusted to improve the user satisfaction of the first business circle.
  • the traffic flow aggregation area and the Wi-Fi hotspot concentration area in the first business circle it is determined that the user satisfaction of the first business circle needs to be adjusted. of The location of the Wi-Fi hotspot.
  • the present application further provides a computer readable storage medium, where the Wi-Fi hotspot deployment optimization program is stored, and the optimized program is executed by the processor to implement the foregoing Steps to deploy an optimization method for Wi-Fi hotspots.
  • the method, the server, and the computer readable storage medium provided by the present application calculate the user's satisfaction with the Wi-Fi hotspots of each business circle by acquiring historical data of all Wi-Fi hotspots scanned by the mobile terminal. According to the calculation result, the optimization scheme of Wi-Fi hotspot deployment in the business circle with lower user satisfaction is given according to the calculation result, and the Wi-Fi hotspot coverage population is increased, and the user's Internet access is increased. Experience.
  • FIG. 1 is a schematic diagram of a preferred embodiment of a server of the present application.
  • FIG. 2 is a schematic block diagram of a preferred embodiment of the Wi-Fi hotspot deployment optimization program of FIG. 1;
  • FIG. 3 is a flowchart of a preferred embodiment of a Wi-Fi hotspot deployment optimization method according to the present application.
  • the application provides a server 1.
  • a server 1 Referring to FIG. 1, a schematic diagram of a preferred embodiment of the server 1 of the present application is shown.
  • the server 1 includes a memory 11, a processor 12, a network interface 13, and a communication bus 14.
  • the communication bus 14 is used to implement connection communication between these components.
  • Server 1 can be a rack server, a blade server, a tower server, or a rack server.
  • the network interface 13 may include a standard wired interface, a wireless interface (such as a WI-FI interface). Usually used to connect to mobile terminals.
  • the server 1 connects the plurality of first mobile terminals 21 and the second mobile terminal 22 through the network interface 13.
  • the first mobile terminal 21 and the second mobile terminal 22 may be terminal devices having a wireless local area network configuration and display function, such as a notebook, a tablet, a smart phone, and an e-book reader.
  • the memory 11 includes at least one type of readable storage medium.
  • the at least one type of readable storage medium may be a non-volatile storage medium such as a flash memory, a hard disk, a multimedia card, a card type memory, or the like.
  • the readable storage medium may be an internal storage unit of the server 1, such as a hard disk of the server 1.
  • the readable storage medium may also be an external storage device of the server 1, such as a plug-in hard disk equipped on the server 1, a smart memory card (SMC), and security. Digital (Secure Digital, SD) card, flash card (Flash Card), etc.
  • the readable storage medium of the memory 11 is generally used to store a Wi-Fi hotspot deployment optimization program installed in the server 1, a Wi-Fi hotspot collected by the first mobile terminal 21, and historical data of the user. Model files of pre-determined and updated logistic regression models, distribution of human traffic within each business district obtained from third parties, etc.
  • the memory 11 can also be used to temporarily store data that has been output or is about to be output.
  • the processor 12 may be a Central Processing Unit (CPU), microprocessor or other data processing chip for running program code or processing data stored in the memory 11, such as performing Wi-Fi. Hotspot deployment optimizer, etc.
  • CPU Central Processing Unit
  • microprocessor or other data processing chip for running program code or processing data stored in the memory 11, such as performing Wi-Fi. Hotspot deployment optimizer, etc.
  • FIG. 1 shows only server 1 with components 11-14 and Wi-Fi hotspot deployment optimizer 10, but it should be understood that not all illustrated components may be implemented, alternative implementations may be more or less Component.
  • the server 1 may further include a user interface
  • the user interface may include an input unit such as a keyboard
  • the optional user interface may further include a standard wired interface and a wireless interface.
  • the server 1 may further include a display, which may also be referred to as a display screen or a display unit.
  • a display may also be referred to as a display screen or a display unit.
  • it may be an LED display, a liquid crystal display, a touch liquid crystal display, an OLED (Organic Light-Emitting Diode) touch sensor, or the like.
  • the display is used to display information processed in the server 1 and a user interface for displaying visualizations.
  • the server 1 may further include a camera, an RF (Radio Frequency) circuit, a sensor, an audio circuit, and the like, and details are not described herein.
  • RF Radio Frequency
  • a Wi-Fi hotspot deployment optimizer 10 is stored in the memory 11, and the processor 12 performs the following steps when executing the Wi-Fi hotspot deployment optimization program 10 stored in the memory 11:
  • the acquiring step collecting the positioning information of the first mobile terminal 21 at the current location and the Wi-Fi hotspot list scanned by the first mobile terminal 21 at the current location, where the positioning information includes the latitude and longitude of the current location of the first mobile terminal 21;
  • a first calculating step calculating a hash value of the latitude and longitude of the current location of the first mobile terminal 21, taking a result of the first preset level of the hash value, and using the first preset level as a result of the first preset level
  • the list of Wi-Fi hotspots scanned by the location is aggregated in multiple business districts;
  • a second calculating step calculating a latitude and longitude hash value of the current position of the first mobile terminal 21, taking a result of the second preset level of the hash value, and determining, according to the result of the second preset level, the Wi in the plurality of business circles - the concentrated area of the Fi hotspot;
  • a third calculating step reading historical data of the Wi-Fi hotspot in the Wi-Fi hotspot list in a preset time, and calculating, by the first user of the first mobile terminal 21, Wi-Fi in the multiple shopping districts Hot user satisfaction;
  • the traffic aggregation area and the Wi-Fi hotspot concentration area determine the location of the Wi-Fi hotspot that needs to be adjusted to improve the user satisfaction of the first business circle.
  • each city has a plurality of different business districts, wherein the business circle refers to a certain range of areas, and the first user refers to accessing and using the Wi-Fi hotspots in each business circle through the first mobile terminal 21.
  • the second user refers to the staff of the Wi-Fi hotspot provider, and the second user knows the distribution of Wi-Fi hotspots in each business circle through the second mobile terminal 22 and the first user to Wi-Fi in each business circle
  • the user satisfaction of the hotspot is that the first mobile terminal 21 is installed with an APP for connecting to the Wi-Fi hotspot, and the APP can obtain location information (latitude and longitude) of the first mobile terminal 21 at the current location, and the second mobile terminal 22 is installed with Wi-Fi hotspot deployment optimizer 10 client program.
  • Hotspot vendors can use a variety of channels to understand the areas where the first users of each city's business districts gather, such as obtaining relevant data through third parties.
  • the APP scans the Wi-Fi hotspot list available at the current location through the first mobile terminal 21, and collects all Wi-Fi hotspots in the Wi-Fi hotspot list.
  • Historical data within a preset time (one week), including: Wi-Fi name, time of access, time of operation, operational status (connection success, connection failure, login success, login failure, etc.), frequency of access, availability by operator Etc. Then, the above historical data is sent to the server 1.
  • the server 1 extracts key historical data, such as Wi-Fi identification, time, location, connection operation, Internet access duration, number of successful connections, and number of connection failures, through the data collection technology (Extract-Transform-Load, ETL).
  • ETL Extract-Transform-Load
  • Wi-Fi hotspot the transmitting device of the Wi-Fi hotspot in the Wi-Fi hotspot list (hereinafter referred to as Wi-Fi hotspot) is also Located near the location of the first mobile terminal 21, the location of the Wi-Fi hotspot can be estimated based on the location information of the first mobile terminal 21.
  • the geohash algorithm can be used to calculate the latitude and longitude hash values, and the hash values of different levels can be different. For example, one to nine results can be taken, and the six-level result is expressed as a range of 1.2 km*1.2 km. (Approximately can be understood as the scope of a business circle), taking a seven-level result indicating a range of 152m * 152m.
  • the Wi-Fi hotspot lists scanned by the first mobile terminal at the current location are a, b, c, d, and e, respectively, assuming a first preset.
  • the level is six
  • the server 1 reads the latitude and longitude of the current position of the first mobile terminal A, B, C, D, E from the memory 11, performs a geohash operation, and calculates the latitude and longitude hash value of the current position of each first mobile terminal 21. If the six-level hash value of the first mobile terminal A, B, C, D, and E is the same, it is determined that the first mobile terminal A, B, C, D, and E are in the same business circle.
  • the Wi-Fi hotspots in the Wi-Fi lists a, b, c, d, and e are also located in the business circle.
  • the second calculation step is needed to determine the area of the Wi-Fi hotspot in the business circle.
  • the second calculating step includes: according to the second preset The result of the level, the Wi-Fi hotspot list in each business circle is divided into different regional blocks smaller than the business circle; in the Wi-Fi hotspot list in the different regional blocks, the Wi with the same Wi-Fi hotspot will be The -Fi hotspot list is merged to form a new Wi-Fi hotspot list; the different regional blocks in which the Wi-Fi hotspot list with the same Wi-Fi hotspot is located are merged to form a new regional block, and Wi-Fi in different new regional blocks is determined.
  • the second preset level is seven levels, performing a geohash operation on the latitude and longitude of the current location of the first mobile terminal A, B, C, D, and E, and taking a seven-level result of the hash value, wherein the first mobile terminal A, C The seven-level result is the same, the seventh-level results of the first mobile terminal B, E are the same, and the multiple Wi-Fi hotspots in the Wi-Fi hotspot list a, c are divided into the regional block P, and the Wi-Fi hotspot list b is The plurality of Wi-Fi hotspots in the e are divided into the area block Q, and the plurality of Wi-Fi hotspots in the Wi-Fi hotspot list d are separately divided into the area block O.
  • Wi-Fi hotspot list merges into one Wi-Fi hotspot list until there is no duplicate Wi-Fi hotspot between each Wi-Fi hotspot list in the same block, that is, if there is originally 20 in a certain block
  • a list of Wi-Fi hotspots, after deduplication, the number of Wi-Fi hotspot lists will be less than or equal to 20.
  • Wi-Fi hotspot it can be understood that, assuming that the transmitting device of a Wi-Fi hotspot is located between two points, and both points are within the coverage of the Wi-Fi hotspot, then different first mobile terminals located between the two points 21 can scan the Wi-Fi hotspot, that is, according to different latitude and longitude in the positioning information of the first mobile terminal 21, the same Wi-Fi hotspot may be divided into the middle lines of two adjacent different regional blocks. There is a nearby Wi-Fi hotspot, so this type of Wi-Fi hotspot needs to be further processed.
  • the area blocks P and Q are adjacent, and the same Wi-Fi hotspot exists in the Wi-Fi hotspot list in the area blocks P and Q, then the area blocks P and Q are merged into a new area block, and De-duxing the Wi-Fi hotspots in all Wi-Fi hotspot lists in this new block, maximizing the number of Wi-Fi hotspots in the new block; then, in multiple new blocks
  • the Wi-Fi hotspots are merged and de-duplicated until there are no duplicate Wi-Fi hotspots in all the blocks in the business circle and in the block.
  • Some of the new blocks have a large number of Wi-Fi hotspots, and some have few Wi-Fi.
  • the obtained distribution of each block and the number of Wi-Fi hotspots in each block are linked to the electronic map of the business circle, so that the area where the Wi-Fi hotspot is concentrated in the business circle can be determined.
  • S is the first user's satisfaction with the Wi-Fi hotspots in each business circle
  • C is the average connection success rate of Wi-Fi hotspots in each business circle
  • L is the average landing of Wi-Fi hotspots in each business circle.
  • Success rate T 1 is the average online time (in min) of Wi-Fi hotspots in each business circle
  • T 2 is the average connection of Wi-Fi hotspots in each business circle to power consumption (in s).
  • the abnormal values (such as the maximum value and the minimum value) need to be removed when calculating C, L, T 1 and T 2 , and the actual network time length is rounded when calculating T 1 , and the value is 20 minutes over 10 minutes. Take 10 minutes for less than 10 minutes.
  • the third calculating step includes: calculating, according to historical data of the Wi-Fi hotspot in each business circle within a preset time period and a predetermined logistic regression model, the first user to Wi- in each business circle. User satisfaction of Fi hotspots.
  • the Wi-Fi operator may collect a first user's user satisfaction sample for the Wi-Fi hotspot in the business circle through an operation questionnaire or a user scoring system, and the predetermined logistic regression model is used to calculate the first user to each business circle.
  • User satisfaction of the internal Wi-Fi hotspot, the model file of the logistic regression model is stored in the memory 11, which can collect the user satisfaction sample and the key historical data of the Wi-Fi hotspot in the business circle in the past three months. Offline training is available.
  • the advantage of the offline training model is that it utilizes a large amount of historical data, and the sample is sufficient.
  • the advantage of the online training model is that it can use the latest data, the model can adapt to the changes of real-time data, and the online model is more accurate in the case of large data distribution and historical gap.
  • the above-mentioned logistic regression model can be updated every three days. Then, the key historical data of the Wi-Fi hotspot in each business circle in the latest week is read from the memory 11, and the updated logistic regression model is input, and the user satisfaction of the first user to the Wi-Fi hotspot in the business circle is calculated.
  • an electronic map is installed on the second mobile terminal 22, and a map of all cities is displayed to the second user through the electronic map, and when the second user selects a certain city, the location and the merchants of all the business districts of the city are also displayed.
  • User satisfaction in Wi-Fi hotspots in the circle When a Wi-Fi hotspot provider needs to optimize the deployment of a Wi-Fi hotspot in a certain business circle to improve the user satisfaction of the Wi-Fi hotspot in the business circle, the business circle is selected, and the server 1 will respond to the click operation.
  • the server 1 filters out the user from the number of the first user and the current number of the first user of the business circle B (for example, a ⁇ 50).
  • the highest satisfaction business circle G according to the relationship between the number of Wi-Fi hotspots in the business circle G and the number of first users, determine whether the Wi-Fi hotspot should be increased or decreased in the business circle B, and then aggregate according to the traffic flow in the business circle B. The area is compared with the concentrated area of the Wi-Fi hotspot in the business circle B, and it is determined that the location of the Wi-Fi hotspot needs to be adjusted in the business circle B.
  • the number of Wi-Fi hotspots in the business circle G (for example, m) is significantly more than the number of Wi-Fi hotspots in the business circle B (for example, n), then the corresponding number (mn) needs to be added in the business circle B.
  • the Wi-Fi hotspot is equal to the number of Wi-Fi hotspots in the business district G; if the number of Wi-Fi hotspots in the business circle G is less than B or equivalent, the number of Wi-Fi hotspots does not cause the business circle B, G user
  • the main reason for the difference in satisfaction is to further consider whether the location of the Wi-Fi hotspot is reasonable.
  • the demand for the Wi-Fi hotspot is also relatively large. Then, after reading the distribution area of the traffic flow in the business circle B obtained from the third party from the memory 11 The comparison between the traffic aggregation area in the business circle B and the centralized area of the Wi-Fi hotspot in the business circle B obtained through the second calculation step is the same.
  • the first user aggregation area of circle B has fewer Wi-Fi hotspots, and there are more Wi-Fi hotspots in the area where there are fewer first users, so it is necessary to adjust some Wi-Fi hotspots from the area where the first user is less to the first user.
  • the quality problem may exist in the Wi-Fi hotspot in the business circle B. It is recommended to replace the Wi-Fi hotspot.
  • the server 1 displays the distribution of the Wi-Fi hotspots in the shopping circle to the second user through the first display area of the second mobile terminal 22, and displays the improvement in the business circle B in the second display area of the second mobile terminal 22.
  • Wi-Fi hotspot user satisfaction Wi-Fi hotspot adjustment program Wi-Fi hotspot adjustment program.
  • the server 1 proposed in the above embodiment calculates the user's satisfaction with the Wi-Fi hotspots of each business circle and the Wi-Fi hotspots in each business circle by acquiring the historical data of all the Wi-Fi hotspots scanned by the first mobile terminal 21.
  • an optimization scheme for Wi-Fi hotspot deployment in the business circle with lower user satisfaction is given, and the Wi-Fi hotspot coverage population is increased to improve the user's online experience.
  • the Wi-Fi hotspot deployment optimizer 10 may also be partitioned into one or more modules, one or more modules being stored in the memory 11 and being processed by one or more processors 12 implementations to complete this application.
  • a module as referred to in this application refers to a series of computer program instructions that are capable of performing a particular function.
  • FIG. 2 it is a block diagram of a preferred embodiment of the Wi-Fi hotspot deployment optimization program 10 of FIG.
  • the Wi-Fi hotspot deployment optimization program 10 can be divided into: an acquisition module 110, a first calculation module 120, a second calculation module 130, a third calculation module 140, and an optimization module 150, which are implemented by the modules 110-150.
  • the functions or operational steps are similar to the above, and are not described in detail here, exemplarily, for example:
  • the collecting module 110 is configured to collect the positioning information of the first mobile terminal 21 at the current location and the Wi-Fi hotspot list scanned by the first mobile terminal 21 at the current location, where the positioning information includes the latitude and longitude of the current location of the first mobile terminal 21. ;
  • the first calculation module 120 is configured to calculate a latitude and longitude hash value of the current location of the first mobile terminal 21, and take a result of the first preset level of the hash value, and the first mobile terminal according to the result of the first preset level 21
  • the list of Wi-Fi hotspots scanned at the current location is aggregated in multiple business districts;
  • the second calculation module 130 is configured to calculate a latitude and longitude hash value of the current location of the first mobile terminal 21, and take a result of the second preset level of the hash value, and determine the location according to the result of the second preset level. a concentrated area of Wi-Fi hotspots in multiple business districts;
  • the third calculation module 140 is configured to read historical data of the Wi-Fi hotspot in the Wi-Fi hotspot list in a preset time, and calculate a first user of the first mobile terminal 21 to be in the multiple shopping districts. User satisfaction of Wi-Fi hotspots; and
  • the optimization module 150 is configured to filter out a second business circle from the plurality of business circles in response to a click operation of the second user in the second mobile terminal 22 to select a first business circle from the plurality of shopping circles.
  • the number of first users in the plurality of business districts is equal to the number of first users in the first business circle and the user satisfaction is the highest, according to the number of Wi-Fi hotspots in the second business circle and the first user
  • the relationship between the numbers determines the number of Wi-Fi hotspots that need to be adjusted to improve the user satisfaction of the first business circle.
  • the traffic aggregation area and the Wi-Fi hotspot concentration area in the first business circle the first business circle user is determined to be improved. Satisfaction needs to adjust the location of the Wi-Fi hotspot.
  • the present application also provides a Wi-Fi hotspot deployment optimization method.
  • FIG. 3 it is a flowchart of a preferred embodiment of a Wi-Fi hotspot deployment optimization method of the present application.
  • the method can be performed by a server, which can be implemented by software and/or hardware.
  • the Wi-Fi hotspot deployment optimization method includes: Step S10 to Step S60.
  • step S10 the location information of the first mobile terminal at the current location and the Wi-Fi hotspot list scanned by the first mobile terminal at the current location are collected, where the location information includes the latitude and longitude of the current location of the first mobile terminal;
  • Step S20 calculating a latitude and longitude hash value of the current location of the first mobile terminal, taking a result of the first preset level of the hash value, and scanning the first mobile terminal at the current location according to the result of the first preset level.
  • Wi-Fi hotspot lists are aggregated across multiple business districts;
  • Step S30 calculating a latitude and longitude hash value of the current location of the first mobile terminal, taking a result of the second preset level of the hash value, and determining a Wi-Fi hotspot in the plurality of business circles according to the result of the second preset level Concentrated area
  • Step S40 Read historical data of the Wi-Fi hotspot in the Wi-Fi hotspot list in a preset time, and calculate a user of the first mobile terminal to the Wi-Fi hotspot in the multiple shopping districts. Satisfaction; and
  • Step S50 In response to the click operation of the second user selecting a first business circle from the plurality of shopping circles, the second user selects a second business circle from the plurality of business circles, the second business
  • the number of first users in the plurality of business circles is equal to the number of first users in the first business circle and the user satisfaction is the highest, according to the number of Wi-Fi hotspots in the second business circle and the number of first users Correlation relationship, determine the number of Wi-Fi hotspots that need to be adjusted to improve the user satisfaction of the first business circle.
  • the traffic flow aggregation area and the Wi-Fi hotspot concentration area in the first business circle it is determined that the user satisfaction of the first business circle needs to be adjusted.
  • the location of the Wi-Fi hotspot is determined that the user satisfaction of the first business circle needs to be adjusted.
  • each city has a plurality of different business districts, wherein the business circle refers to a certain range of regions, and the first user refers to accessing and using the Wi-Fi hotspots in each commercial circle through the first mobile terminal.
  • the second user refers to the staff of the Wi-Fi hotspot provider, and the second user passes the second mobile terminal. Solving the distribution of Wi-Fi hotspots in each business circle and the user satisfaction of the first user to Wi-Fi hotspots in each business circle.
  • the first mobile terminal is equipped with an APP for connecting to a Wi-Fi hotspot, and the APP can be obtained.
  • the location information (latitude and longitude) of the first mobile terminal at the current location, and the client program of the Wi-Fi hotspot deployment optimization program is installed on the second mobile terminal.
  • Hotspot vendors can use a variety of channels to understand the areas where the first users of each city's business districts gather, such as obtaining relevant data through third parties.
  • the APP scans the Wi-Fi hotspot list available at the current location through the first mobile terminal, and collects all the Wi-Fi hotspots in the Wi-Fi hotspot list.
  • Historical data within a preset time (one week), including: Wi-Fi name, time of access, time of operation, operation status (connection success, connection failure, login success, login failure, etc.), frequency of access, availability of operators, etc. And then send the above historical data to the server.
  • the server extracts key historical data, such as Wi-Fi identification, time, location, connection operation, Internet access duration, number of successful connections, and number of connection failures, through the data collection technology (Extract-Transform-Load, ETL).
  • ETL Extract-Transform-Load
  • Wi-Fi hotspot the transmitting device of the Wi-Fi hotspot (hereinafter referred to as Wi-Fi hotspot) in the Wi-Fi hotspot list is also located.
  • the location of the first mobile terminal is located, and therefore, the location of the Wi-Fi hotspot can be estimated according to the location information of the first mobile terminal.
  • the geohash algorithm can be used to calculate the latitude and longitude hash values, and the hash values of different levels can be different. For example, one to nine results can be taken, and the six-level result is expressed as a range of 1.2 km*1.2 km. (Approximately can be understood as the scope of a business circle), taking a seven-level result indicating a range of 152m * 152m.
  • the Wi-Fi hotspot lists scanned by the first mobile terminal at the current location are a, b, c, d, and e, respectively, assuming a first preset.
  • the level is six levels
  • the server reads the latitude and longitude of the current position of the first mobile terminal A, B, C, D, E from the memory, performs a geohash operation, calculates the latitude and longitude hash value of the current position of each first mobile terminal, and takes a hash.
  • the Wi-Fi hotspots in the Wi-Fi lists a, b, c, d, and e are also located in the business district.
  • the second calculation step is needed to determine the area of the Wi-Fi hotspot in the business circle.
  • the second calculating step includes: according to the second preset level.
  • the Wi-Fi hotspot list in each business district is divided into different regional blocks smaller than the business circle; in the Wi-Fi hotspot list in the different regional blocks, Wi-s with the same Wi-Fi hotspot will be The Fi hotspot list is merged to form a new Wi-Fi hotspot list; the different regional blocks in which the Wi-Fi hotspot list with the same Wi-Fi hotspot is located are merged to form a new regional block, and different Wi-Fi hotspots in the new local block are determined.
  • the number of Wi-Fi hotspots in each business district is based on the number of Wi-Fi hotspots in the new block of the business district.
  • the second preset level is seven
  • the seventh-level results of A and C are the same.
  • the seven-level results of the first mobile terminals B and E are the same.
  • the Wi-Fi hotspots in the Wi-Fi hotspot list a and c are divided into the regional block P, and Wi-Fi is used.
  • the plurality of Wi-Fi hotspots in the hotspot list b, e are divided into the regional block Q, and the plurality of Wi-Fi hotspots in the Wi-Fi hotspot list d are separately divided into the regional block O. If there are duplicate Wi-Fi hotspots in the Wi-Fi hotspot list in the same area block, deduplication is performed on the Wi-Fi hotspots in each of the two Wi-Fi hotspot lists, and the two Wis after deduplication are performed.
  • Wi-Fi hotspot list merges into one Wi-Fi hotspot list until there is no duplicate Wi-Fi hotspot between each Wi-Fi hotspot list in the same block, that is, if there is originally 20 in a certain block
  • a list of Wi-Fi hotspots, after deduplication, the number of Wi-Fi hotspot lists will be less than or equal to 20.
  • Wi-Fi hotspots can be scanned, that is, according to the latitude and longitude in different first mobile terminal positioning information, the same Wi-Fi hotspot may be divided into the vicinity of the middle line of two adjacent different regional blocks. Wi-Fi hotspots, therefore, need to further process this type of Wi-Fi hotspot.
  • the area blocks P and Q are adjacent, and the same Wi-Fi hotspot exists in the Wi-Fi hotspot list in the area blocks P and Q, then the area blocks P and Q are merged into a new area block, and De-duxing the Wi-Fi hotspots in all Wi-Fi hotspot lists in this new block, maximizing the number of Wi-Fi hotspots in the new block; then, in multiple new blocks
  • the Wi-Fi hotspots are merged and de-duplicated until there are no duplicate Wi-Fi hotspots in all the blocks in the business circle and in the block.
  • Some of the new blocks have a large number of Wi-Fi hotspots, and some have few Wi-Fi.
  • the obtained distribution of each block and the number of Wi-Fi hotspots in each block are linked to the electronic map of the business circle, so that the area where the Wi-Fi hotspot is concentrated in the business circle can be determined.
  • S is the first user's satisfaction with the Wi-Fi hotspots in each business circle
  • C is the average connection success rate of Wi-Fi hotspots in each business circle
  • L is the average landing of Wi-Fi hotspots in each business circle.
  • Success rate T 1 is the average online time (in min) of Wi-Fi hotspots in each business circle
  • T 2 is the average connection of Wi-Fi hotspots in each business circle to power consumption (in s).
  • the abnormal values (such as the maximum value and the minimum value) need to be removed when calculating C, L, T 1 and T 2 , and the actual network time length is rounded when calculating T 1 , and the value is 20 minutes over 10 minutes. Take 10 minutes for less than 10 minutes.
  • the third calculating step includes: calculating, according to historical data of the Wi-Fi hotspot in each business circle within a preset time period and a predetermined logistic regression model, the first user to Wi- in each business circle. User satisfaction of Fi hotspots.
  • the Wi-Fi operator may collect a first user's user satisfaction sample for the Wi-Fi hotspot in the business circle through an operation questionnaire or a user scoring system, and the predetermined logistic regression model is used to calculate the first user to each business circle.
  • User satisfaction of the internal Wi-Fi hotspot, the model file of the logistic regression model is stored in the memory, which can be collected through the collected user satisfaction sample and the key historical data of the Wi-Fi hotspot within the business circle in the past three months. Trained.
  • the advantage of the offline training model is that it utilizes a large amount of historical data, and the sample is sufficient.
  • the advantage of the online training model is that it can use the latest data, the model can adapt to the changes of real-time data, and the online model is more accurate in the case of large data distribution and historical gap.
  • the above-mentioned logistic regression model can be updated every three days. Then, the key historical data of the Wi-Fi hotspot in each business circle in the last week is read from the memory, and the updated logistic regression model is input, and the first user's user satisfaction with the Wi-Fi hotspot in the business circle is calculated.
  • an electronic map is installed on the second mobile terminal, and the map of all cities is displayed to the second user through the electronic map, and when the second user selects a certain city, the location and the business districts of all the business districts of the city are also displayed.
  • User satisfaction within the Wi-Fi hotspot When a Wi-Fi hotspot provider needs to optimize the deployment of Wi-Fi hotspots in a certain business circle to improve the user satisfaction of Wi-Fi hotspots in the business circle, select the business circle, and the server will respond to the click operation to For example, in the business circle B, the number of the first user is a, and the server selects the user satisfaction from the number of the first user and the current number of the first user of the business circle B (for example, a ⁇ 50).
  • the highest business circle G determines whether the Wi-Fi hotspot should be increased or decreased in the business circle B, and then according to the traffic flow area in the business circle B and The concentrated areas of the Wi-Fi hotspots in the business district B are compared, and it is determined that the location of the Wi-Fi hotspot needs to be adjusted in the business circle B.
  • the number of Wi-Fi hotspots in the business circle G (for example, m) is significantly more than the number of Wi-Fi hotspots in the business circle B (for example, n)
  • the corresponding number (mn) needs to be added in the business circle B.
  • the Wi-Fi hotspot is equal to the number of Wi-Fi hotspots in the business district G; if the number of Wi-Fi hotspots in the business circle G is less than B or equivalent, the number of Wi-Fi hotspots does not cause the business circle B,
  • the main reason for the difference in user satisfaction is to consider whether the location of the Wi-Fi hotspot is reasonable.
  • the demand for the Wi-Fi hotspot is also relatively large. Then, after reading the distribution area of the traffic flow in the business circle B obtained from the third party from the memory 11 The comparison between the traffic aggregation area in the business circle B and the centralized area of the Wi-Fi hotspot in the business circle B obtained through the second calculation step is the same.
  • the first user aggregation area of circle B has fewer Wi-Fi hotspots, and there are more Wi-Fi hotspots in the area where there are fewer first users, so it is necessary to adjust some Wi-Fi hotspots from the area where the first user is less to the first user.
  • Aggregation area if the first user aggregation area and the centralized area of the Wi-Fi hotspot are consistent, then Ming may be a quality problem in the Wi-Fi hotspot in the business district B. It is recommended to replace the Wi-Fi hotspot.
  • preset time and the like need to be preset parameters, which can be adjusted according to user needs.
  • the method further includes: displaying a concentrated area of the Wi-Fi hotspot in the first business circle on the electronic map of the second mobile terminal, and displaying Wi-Fi that needs to be adjusted to improve user satisfaction in the first business circle.
  • the server displays the distribution of the Wi-Fi hotspots in the business circle to the second user through the first display area of the second mobile terminal, and displays the Wi-Fi hotspot user in the business circle B in the second display area of the second mobile terminal. Satisfaction adjustment scheme for Wi-Fi hotspots.
  • the first display area and the second display area are only different to indicate that the two display areas are different.
  • the Wi-Fi hotspot deployment optimization method proposed by the foregoing embodiment obtains the historical data of all the Wi-Fi hotspots scanned by the first mobile terminal, and calculates the user's satisfaction with the Wi-Fi hotspots of each commercial circle, and the Wi in each business circle.
  • the aggregation area of the Fi hotspot is based on the calculation result, and the optimization scheme for the Wi-Fi hotspot deployment in the business circle with lower user satisfaction is given, and the Wi-Fi hotspot coverage population is increased to improve the user's online experience.
  • the embodiment of the present application further provides a computer readable storage medium, where the Wi-Fi hotspot deployment optimization program is stored, and when the optimization program is executed by the processor, the following operations are implemented:
  • the acquiring step collecting the positioning information of the first mobile terminal at the current location and the Wi-Fi hotspot list scanned by the first mobile terminal at the current location, where the positioning information includes the latitude and longitude of the current location of the first mobile terminal;
  • a first calculating step calculating a latitude and longitude hash value of the current location of the first mobile terminal, taking a result of the first preset level of the hash value, and scanning the first mobile terminal at the current location according to the result of the first preset level
  • the list of Wi-Fi hotspots is aggregated in multiple business districts;
  • a second calculating step calculating a latitude and longitude hash value of the current location of the first mobile terminal, taking a result of the second preset level of the hash value, and determining, according to the result of the second preset level, the Wi- in the plurality of shopping districts a concentrated area of Fi hotspots;
  • a third calculating step reading historical data of the Wi-Fi hotspot in the Wi-Fi hotspot list in a preset time, and calculating, by the first user of the first mobile terminal, the Wi-Fi hotspot in the multiple shopping districts User satisfaction;
  • the number of first users in the plurality of business circles is equal to the number of first users in the first business circle and the user satisfaction is the highest, according to the number of Wi-Fi hotspots in the second business circle and the number of first users Correlation relationship, determine the number of Wi-Fi hotspots that need to be adjusted to improve the user satisfaction of the first business circle.
  • the traffic flow aggregation area and the Wi-Fi hotspot concentration area in the first business circle it is determined that the user satisfaction of the first business circle needs to be adjusted.
  • the location of the Wi-Fi hotspot is determined that the user satisfaction of the first business circle needs to be adjusted.
  • the specific implementation manner of the computer-readable storage medium of the present application is substantially the same as the specific implementation manner of the foregoing Wi-Fi hotspot deployment optimization method, and details are not described herein again.
  • a disk including a number of instructions for causing a terminal device (which may be a mobile phone, a computer, a server, or a network device, etc.) to perform the methods described in the various embodiments of the present application.
  • a terminal device which may be a mobile phone, a computer, a server, or a network device, etc.

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Abstract

本申请提出一种Wi-Fi热点部署优化方法，该方法包括：采集第一移动终端定位信息及扫描到的Wi-Fi热点列表，定位信息包括第一移动终端的经纬度；将Wi-Fi热点列表聚合在多个商圈内；确定多个商圈内Wi-Fi热点的集中区域；计算第一用户对多个商圈内Wi-Fi热点的用户满意度；及，响应第二用户选择第一商圈的操作，筛选出第二商圈，根据该第二商圈内Wi-Fi热点及第一用户数量、第一商圈中人流量聚集区域和Wi-Fi热点集中区域，确定提高第一商圈用户满意度需要调整的Wi-Fi热点的数量和位置。本申请还提出一种服务器及计算机可读存储介质。本申请利用商圈内Wi-Fi热点的分布情况及用户满意度，给出商圈内Wi-Fi热点部署的优化方案，提升用户上网体验。

Description

Wi-Fi热点部署优化方法、服务器及存储介质
优先权申明
本申请基于巴黎公约申明享有2017年9月26日递交的申请号为CN201710883895.1、名称为“Wi-Fi热点部署优化方法、服务器及存储介质”的中国专利申请的优先权，该中国专利申请的整体内容以参考的方式结合在本申请中。
技术领域
本申请涉及计算机技术领域，尤其涉及一种Wi-Fi热点部署优化方法、服务器及计算机可读存储介质。
背景技术
随着科技的发展、社会的进步，无线Wi-Fi(无线保真，Wireless-Fidelity)技术在当前人们的生活中非常重要，但由于无线信号热点的分布具有随机性，缺少对热点位置的合理布控与约束，盲目性和随意性的特点表现的非常明显。手机、游戏机、便携式电脑等消费类电子产品越来越普及，终端产品丰富使Wi-Fi的普及率将会越来越高，加强实现Wi-Fi信号的全覆盖研究有着实质性意义。
目前，本领域人员通过计算热点的覆盖区域，判断当前待布设空间中是否存在位于，若是，则将热点的位置往靠近热点的覆盖区域外的盲点的方向进行调整，或者在靠近盲点的位置另外布设热点，直至所述待布设空间全部位置得到至少一个热点的覆盖，从而优化对Wi-Fi热点的布置。
然而，对于一个Wi-Fi热点部署需要优化的商圈而言，需要考虑该商圈内的人流量、用户对商圈内Wi-Fi热点的用户满意度，从而根据用户满意度及商圈内人流量聚集区域确定现有的Wi-Fi热点的部署存在哪些问题，因此，如何充分利用Wi-Fi热点、对现有的Wi-Fi热点部署的位置进行调整，增加Wi-Fi热点的信号覆盖人群，，提升用户的上网体验，是一个亟待解决的问题。
发明内容
本申请提供一种Wi-Fi热点部署优化方法、服务器及计算机可读存储介质，其主要目的在于，通过了解用户对商圈Wi-Fi热点的满意度及商圈内Wi-Fi热点的分布情况，给出商圈内Wi-Fi热点部署的优化方案，以提升用户对Wi-Fi热点的使用体验。
为实现上述目的，本申请提供一种服务器，该服务器包括：存储器、处理器，所述存储器上存储有Wi-Fi热点部署优化程序，该优化程序被所述处理器执行时实现如下步骤：
采集步骤：采集第一移动终端在当前位置的定位信息及第一移动终端在当前位置扫描到的Wi-Fi热点列表，所述定位信息包括第一移动终端当前位置 的经纬度；
第一计算步骤：计算第一移动终端当前位置的经纬度的hash值，取hash值的第一预设级别的结果，根据该第一预设级别的结果将所述第一移动终端在当前位置扫描到的Wi-Fi热点列表聚合在多个商圈内；
第二计算步骤：计算第一移动终端当前位置的经纬度的hash值，取hash值的第二预设级别的结果，根据该第二预设级别的结果，确定所述多个商圈内Wi-Fi热点的集中区域；
第三计算步骤：读取所述Wi-Fi热点列表中的Wi-Fi热点在预设时间内的历史数据，计算第一移动终端的第一用户对所述多个商圈内Wi-Fi热点的用户满意度；及
优化步骤：响应第二用户在第二移动终端从所述多个商圈中选择一个第一商圈的点击操作，从所述多个商圈中筛选出一个第二商圈，该第二商圈在所述多个商圈中第一用户数量与第一商圈的第一用户数量相当且用户满意度最高，根据该第二商圈内Wi-Fi热点数量与第一用户数量之间的关联关系，确定提高第一商圈用户满意度需要调整的Wi-Fi热点数量，根据第一商圈中人流量聚集区域和Wi-Fi热点集中区域，确定提高第一商圈用户满意度需要调整的Wi-Fi热点的位置。
此外，为实现上述目的，本申请还提供一种Wi-Fi热点部署优化方法，该方法包括：
采集步骤：采集第一移动终端在当前位置的定位信息及第一移动终端在当前位置扫描到的Wi-Fi热点列表，所述定位信息包括第一移动终端当前位置的经纬度；
第一计算步骤：计算第一移动终端当前位置的经纬度的hash值，取hash值的第一预设级别的结果，根据该第一预设级别的结果将所述第一移动终端在当前位置扫描到的Wi-Fi热点列表聚合在多个商圈内；
第二计算步骤：计算第一移动终端当前位置的经纬度的hash值，取hash值的第二预设级别的结果，根据该第二预设级别的结果，确定所述多个商圈内Wi-Fi热点的集中区域；
第三计算步骤：读取所述Wi-Fi热点列表中的Wi-Fi热点在预设时间内的历史数据，计算第一移动终端的第一用户对所述多个商圈内Wi-Fi热点的用户满意度；及
优化步骤：响应第二用户在第二移动终端从所述多个商圈中选择一个第一商圈的点击操作，从所述多个商圈中筛选出一个第二商圈，该第二商圈在所述多个商圈中第一用户数量与第一商圈的第一用户数量相当且用户满意度最高，根据该第二商圈内Wi-Fi热点数量与第一用户数量之间的关联关系，确定提高第一商圈用户满意度需要调整的Wi-Fi热点数量，根据第一商圈中人流量聚集区域和Wi-Fi热点集中区域，确定提高第一商圈用户满意度需要调整的 Wi-Fi热点的位置。
此外，为实现上述目的，本申请还提供一种计算机可读存储介质，所述计算机可读存储介质上存储有Wi-Fi热点部署优化程序，该优化程序被处理器执行时实现如上所述的Wi-Fi热点部署优化方法的步骤。
相较于现有技术，本申请提出的方法、服务器及计算机可读存储介质，通过获取移动终端扫描到的所有Wi-Fi热点的历史数据，计算用户对各商圈Wi-Fi热点的满意度、各商圈内Wi-Fi热点的聚集区域，根据计算结果，给出针对用户满意度较低的商圈内Wi-Fi热点部署的优化方案，增加Wi-Fi热点覆盖人群，提升用户的上网体验。
附图说明
图1为本申请服务器较佳实施例的示意图；
图2为图1中Wi-Fi热点部署优化程序较佳实施例的模块示意图；
图3为本申请Wi-Fi热点部署优化方法较佳实施例的流程图。
本申请目的的实现、功能特点及优点将结合实施例，参照附图做进一步说明。
具体实施方式
应当理解，此处所描述的具体实施例仅仅用以解释本申请，并不用于限定本申请。
本申请提供一种服务器1。参照图1所示，为本申请服务器1较佳实施例的示意图。在本实施例中，该服务器1包括存储器11、处理器12、网络接口13及通信总线14。其中，通信总线14用于实现这些组件之间的连接通信。
服务器1可以是机架式服务器、刀片式服务器、塔式服务器或机柜式服务器等。
网络接口13可以包括标准的有线接口、无线接口(如WI-FI接口)。通常用于连接移动终端。在本实施例中，服务器1通过网络接口13连接多个第一移动终端21、第二移动终端22。其中，所述第一移动终端21、第二移动终端22可以为笔记本、平板电脑、智能手机、电子书阅读器等具有无线局域网配置及显示功能的终端设备。
存储器11包括至少一种类型的可读存储介质。所述至少一种类型的可读存储介质可为如闪存、硬盘、多媒体卡、卡型存储器等的非易失性存储介质。在一些实施例中，所述可读存储介质可以是所述服务器1的内部存储单元，例如该服务器1的硬盘。在另一些实施例中，所述可读存储介质也可以是所述服务器1的外部存储设备，例如所述服务器1上配备的插接式硬盘，智能存储卡(Smart Media Card,SMC)，安全数字(Secure Digital,SD)卡，闪存卡(Flash Card)等。
在本实施例中，所述存储器11的可读存储介质通常用于存储安装于所述服务器1的Wi-Fi热点部署优化程序、第一移动终端21收集的Wi-Fi热点及用户的历史数据、预先确定好的及更新后的逻辑回归模型的模型文件、从第三方获取的各商圈内人流量的分布情况等。所述存储器11还可以用于暂时地存储已经输出或者将要输出的数据。
处理器12在一些实施例中可以是一中央处理器(Central Processing Unit,CPU)，微处理器或其他数据处理芯片，用于运行存储器11中存储的程序代码或处理数据，例如执行Wi-Fi热点部署优化程序等。
图1仅示出了具有组件11-14以及Wi-Fi热点部署优化程序10的服务器1，但是应理解的是，并不要求实施所有示出的组件，可以替代的实施更多或者更少的组件。
可选的，该服务器1还可以包括用户接口，用户接口可以包括输入单元比如键盘(Keyboard)，可选的用户接口还可以包括标准的有线接口、无线接口。
可选的，该服务器1还可以包括显示器，也可以称为显示屏或显示单元。在一些实施例中可以是LED显示器、液晶显示器、触控式液晶显示器以及OLED(Organic Light-Emitting Diode，有机发光二极管)触摸器等。所述显示器用于显示在服务器1中处理的信息以及用于显示可视化的用户界面。
可选地，该服务器1还可以包括摄像头、RF(Radio Frequency，射频)电路，传感器、音频电路等等，在此不再赘述。
在图1所示的实施例中，存储器11中存储有Wi-Fi热点部署优化程序10，处理器12执行存储器11中存储的Wi-Fi热点部署优化程序10时实现以下步骤：
采集步骤：采集第一移动终端21在当前位置的定位信息及第一移动终端21在当前位置扫描到的Wi-Fi热点列表，所述定位信息包括第一移动终端21当前位置的经纬度；
第一计算步骤：计算第一移动终端21当前位置的经纬度的hash值，取hash值的第一预设级别的结果，根据该第一预设级别的结果将所述第一移动终端21在当前位置扫描到的Wi-Fi热点列表聚合在多个商圈内；
第二计算步骤：计算第一移动终端21当前位置的经纬度的hash值，取hash值的第二预设级别的结果，根据该第二预设级别的结果，确定所述多个商圈内Wi-Fi热点的集中区域；
第三计算步骤：读取所述Wi-Fi热点列表中的Wi-Fi热点在预设时间内的历史数据，计算第一移动终端21的第一用户对所述多个商圈内Wi-Fi热点的用户满意度；及
优化步骤：响应第二用户在第二移动终端22从所述多个商圈中选择一个第一商圈的点击操作，从所述多个商圈中筛选出一个第二商圈，该第二商圈在所述多个商圈中第一用户数量与第一商圈的第一用户数量相当且用户满意 度最高，根据该第二商圈内Wi-Fi热点数量与第一用户数量之间的关联关系，确定提高第一商圈用户满意度需要调整的Wi-Fi热点数量，根据第一商圈中人流量聚集区域和Wi-Fi热点集中区域，确定提高第一商圈用户满意度需要调整的Wi-Fi热点的位置。
在本实施例中，每个城市有多个不同的商圈，其中，商圈指一定范围内的地区，第一用户指通过第一移动终端21访问并使用了各商圈内Wi-Fi热点的用户，第二用户指Wi-Fi热点供应商的工作人员，第二用户通过第二移动终端22了解各商圈内Wi-Fi热点的分布情况及第一用户对各商圈内Wi-Fi热点的用户满意度，第一移动终端21上安装有用于连接Wi-Fi热点的APP，该APP可获取第一移动终端21在当前位置的位置信息(经纬度)，第二移动终端22上安装有Wi-Fi热点部署优化程序10的客户端程序。热点供应商可以通过多种渠道了解各城市各商圈第一用户聚集的区域，例如通过第三方获取相关数据。
当需要对某商圈内Wi-Fi热点的部署进行优化时，该APP通过第一移动终端21扫描在当前位置可用的Wi-Fi热点列表，并收集Wi-Fi热点列表中所有Wi-Fi热点在预设时间(一周)内历史数据，包括：Wi-Fi的名称、被访问时间及时长、操作状态(连接成功、连接失败、登陆成功、登陆失败等)、被访问频次、是否运营商提供等，然后将上述历史数据发送至服务器1。服务器1对该历史数据经过数据仓库技术(Extract-Transform-Load，简称ETL)，抽取出关键历史数据，如Wi-Fi标识、时间、位置、连接操作、上网时长、连接成功次数、连接失败次数、重试次数、登陆成功次数、登陆失败次数等，保存至存储器11中，供后续进行用户满意度计算操作。
可以理解的是，当第一移动终端21在某一个位置扫描到可用的Wi-Fi热点列表，说明该Wi-Fi热点列表中的Wi-Fi热点的发射装置(以下简称Wi-Fi热点)也位于第一移动终端21所在位置附近，因此，可以根据第一移动终端21的定位信息估算Wi-Fi热点的位置。需要了解的是，采用geohash算法计算经纬度的hash值可取多级结果，不同级数的hash值表示的范围不同，例如：可取一到九级结果，取六级结果表示1.2km*1.2km的范围(大致可以理解成一个商圈的范围)，取七级结果表示152m*152m的范围。
以第一移动终端A、B、C、D、E为例，各第一移动终端在当前位置扫描到的Wi-Fi热点列表分别为a、b、c、d、e，假设第一预设级别为六级，服务器1从存储器11中读取第一移动终端A、B、C、D、E当前位置的经纬度，分别进行geohash运算，计算各第一移动终端21当前位置的经纬度的hash值，取hash值的六级结果，若第一移动终端A、B、C、D、E的六级hash值相同，则判断第一移动终端A、B、C、D、E同在一个商圈内，相应的，Wi-Fi列表a、b、c、d、e中的Wi-Fi热点也都位于该商圈内。
在初步确定了商圈内Wi-Fi热点之后，需要通过第二计算步骤确定该商圈内Wi-Fi热点集中的区域，具体地，所述第二计算步骤包括：根据该第二预设 级别的结果，将各商圈内Wi-Fi热点列表划分至比商圈小的不同区域块内；在所述不同区域块内的Wi-Fi热点列表中，将具有相同Wi-Fi热点的Wi-Fi热点列表合并形成新的Wi-Fi热点列表；将所述具有相同Wi-Fi热点的Wi-Fi热点列表所在的不同区域块合并形成新区域块，确定不同的新区域块内Wi-Fi热点的数量；及，根据该商圈不同的新区域块内Wi-Fi热点的数量，得到每个商圈内Wi-Fi热点集中的区域。
假设第二预设级别为七级，对上述第一移动终端A、B、C、D、E当前位置的经纬度进行geohash运算，取hash值的七级结果，其中，第一移动终端A、C的七级结果相同，第一移动终端B、E的七级结果相同，将Wi-Fi热点列表a、c中的多个Wi-Fi热点划分至区域块P中，将Wi-Fi热点列表b、e中的多个Wi-Fi热点划分至区域块Q中，Wi-Fi热点列表d中的多个Wi-Fi热点单独划分至区域块O中。若同一个区域块中的Wi-Fi热点列表中存在重复的Wi-Fi热点，对每两个Wi-Fi热点列表中的Wi-Fi热点进行去重操作，并将去重后的两个Wi-Fi热点列表合并成一个Wi-Fi热点列表，直到同一个区域块中的各Wi-Fi热点列表之间不存在重复的Wi-Fi热点，也就是说，若某个区域块中原本有20个Wi-Fi热点列表，经过去重之后，Wi-Fi热点列表数量会小于或等于20。
可以理解的是，假设某Wi-Fi热点的发射装置位于两点之间，且这两点均在该Wi-Fi热点的覆盖范围之内，那么位于该两点之间的不同第一移动终端21均能扫描到该Wi-Fi热点，也就是说，根据不同第一移动终端21定位信息中的经纬度，同一个Wi-Fi热点，可能会被划分到相邻的两个不同区域块中间线附近的Wi-Fi热点，因此，需要对这一类Wi-Fi热点进行进一步处理。假设区域块P、Q相邻，且位于区域块P、Q内的Wi-Fi热点列表中都存在同一个Wi-Fi热点，那么，将区域块P、Q合并成一个新区域块，同时，对这个新区域块中所有Wi-Fi热点列表中的Wi-Fi热点进行去重，最大化的确定所述新的区域块中Wi-Fi热点的数量；然后，对多个新的区域块内的Wi-Fi热点进行合并去重，直到该商圈内所有区域块之间、区域块内没有重复的Wi-Fi热点为止，这样会存在多种情况，新区域块的数量比较少，每个新区域块的范围不一样，每个新区域块中的Wi-Fi热点列表数量不一样，有的新区域块中的Wi-Fi热点数量很多，有的Wi-Fi数量很少。将得到的各区域块分布情况及各区域块中的Wi-Fi热点数量关联至该商圈的电子地图上，这样就可以确定该商圈内Wi-Fi热点集中的区域。
接下来，读取各商圈内Wi-Fi热点在预设时间(例如，一周)内的关键历史数据，计算第一用户对各商圈内Wi-Fi热点的用户满意度，具体地，根据最近一周内的历史数据，计算各商圈内Wi-Fi热点的各项用户指标，加权汇总成第一用户的用户满意度，计算公式为：
S＝C*L*T1/(T2 1/9)
其中，S为第一用户对各商圈内Wi-Fi热点的用户满意度，C为各商圈内 Wi-Fi热点的平均连接成功率，L为各商圈内Wi-Fi热点的平均登陆成功率，T1为各商圈内Wi-Fi热点的平均上网时长(单位为min)，T2为各商圈内Wi-Fi热点的平均连接成功耗时(单位为s)。为了使计算更准确，计算C、L、T1、T2时需去除异常值(例如最大值、最小值)，计算T1时对实际上网时长取整，超过10分钟取值为20分钟，不足10分钟取值10分钟。
在其他实施例中，所述第三计算步骤包括：根据各商圈内Wi-Fi热点在预设时间内的历史数据及预先确定的逻辑回归模型，计算第一用户对各商圈内Wi-Fi热点的用户满意度。
Wi-Fi运营商可通过运营调查问卷或者用户评分系统收集第一用户对商圈内Wi-Fi热点的用户满意度样本，所述预先确定的逻辑回归模型用于计算第一用户对各商圈内Wi-Fi热点的用户满意度，该逻辑回归模型的模型文件保存于存储器11中，其可通过收集的用户满意度样本及该商圈内Wi-Fi热点近三个月内的关键历史数据离线训练得到。
需要说明的是，离线训练模型的优点是利用已有大量的历史数据，样本充分。在线训练模型的优点是能够利用最新的数据，模型能适应实时数据的变化，在数据分布和历史差距较大的情形下，在线模型更准确。为了使后续计算得到的各商圈内Wi-Fi热点的用户满意度更准确，可每隔三天对上述逻辑回归模型进行更新。然后从存储器11中读取各商圈内Wi-Fi热点在最近一周的关键历史数据，输入更新后的逻辑回归模型，计算得到第一用户对该商圈内Wi-Fi热点的用户满意度。通过结合使离线训练和在线训练两种方式，可以取两者优点，提升模型的准确度，同时防止了在线环境下，数据量太少或者网络、系统问题导致的实时模型更新失败等问题。关于对该模型进行训练更新并使用它来计算各商圈Wi-Fi热点的用户满意度已经有成熟的计算方法，在此不再赘述。
进一步地，第二移动终端22上安装有电子地图，通过电子地图，向第二用户展示所有城市的地图，且当第二用户选择某城市后，还展示该城市所有商圈的位置及各商圈内Wi-Fi热点的用户满意度。当Wi-Fi热点供应商需要对某商圈内Wi-Fi热点的部署进行优化，提高该商圈内Wi-Fi热点的用户满意度时，选择该商圈，服务器1将响应该点击操作，以商圈B为例，目前第一用户数量为a，服务器1从第一用户数量和商圈B当前的第一用户数量相当(例如，a±50)的多个商圈中，筛选出用户满意度最高的商圈G，根据商圈G内Wi-Fi热点数量和第一用户数量的关系，判断商圈B中是否应增加或减少Wi-Fi热点，然后根据商圈B中人流量聚集区域与商圈B内Wi-Fi热点的集中区域进行比较，判断商圈B内需要调整Wi-Fi热点的位置。
具体地，如果商圈G内Wi-Fi热点数量(例如m个)明显多于商圈B内Wi-Fi热点数量(例如n个)，那么需要在商圈B内新增相应数量(m-n个)的Wi-Fi热点，直到与商圈G内Wi-Fi热点数量相当；如果商圈G内Wi-Fi热点的数量少于B或者相当，说明Wi-Fi热点的数量不是造成商圈B、G用户 满意度差异的主要原因，则需要进一步考虑Wi-Fi热点设置的位置是否合理。
可以理解的是，对于商圈中人流量较集中的区域，对Wi-Fi热点的需求也比较多，那么，从存储器11中读取从第三方获得的商圈B内人流量的分布区域后，比较商圈B内人流量聚集区域和通过第二计算步骤得到的商圈B内Wi-Fi热点的集中区域是否一致，若第一用户聚集区域和Wi-Fi热点的集中区域不一致，在商圈B的第一用户聚集区域Wi-Fi热点较少，第一用户较少的区域Wi-Fi热点较多，则需要将部分Wi-Fi热点从第一用户较少的区域调整到第一用户聚集区域，若第一用户聚集区域和Wi-Fi热点的集中区域一致，则说明可能是商圈B内Wi-Fi热点存在质量问题，建议更换Wi-Fi热点。
最后，服务器1通过第二移动终端22的第一显示区域向第二用户展示该商圈内Wi-Fi热点的分布情况，并在第二移动终端22的第二显示区域展示提高商圈B内Wi-Fi热点用户满意度的Wi-Fi热点的调整方案。
需要说明的是，对于不同的性质的商圈，需要区别对待，例如购物中心、学校、写字楼区、郊区等。本申请的方案适用于购物中心，但不一定适用于学校或其他地区，以偏远郊区为例，由于自身地区限制，不管增加Wi-Fi数量，还是更换Wi-Fi，对提高Wi-Fi热点的满意度评分并不会有显著地贡献。
上述实施例提出的服务器1，通过获取第一移动终端21扫描到的所有Wi-Fi热点的历史数据，计算用户对各商圈Wi-Fi热点的满意度、各商圈内Wi-Fi热点的聚集区域，根据计算结果，给出针对用户满意度较低的商圈内Wi-Fi热点部署的优化方案，增加Wi-Fi热点覆盖人群，提升用户的上网体验。
可选地，在其他的实施例中，Wi-Fi热点部署优化程序10还可以被分割为一个或者多个模块，一个或者多个模块被存储于存储器11中，并由一个或多个处理器12所执行，以完成本申请。本申请所称的模块是指能够完成特定功能的一系列计算机程序指令段。参照图2所示，是图1中Wi-Fi热点部署优化程序10较佳实施例的模块示意图。
所述Wi-Fi热点部署优化程序10可以被分割为：采集模块110、第一计算模块120、第二计算模块130、第三计算模块140及优化模块150，所述模块110-150所实现的功能或操作步骤均与上文类似，此处不再详述，示例性地，例如其中：
采集模块110，用于采集第一移动终端21在当前位置的定位信息及第一移动终端21在当前位置扫描到的Wi-Fi热点列表，所述定位信息包括第一移动终端21当前位置的经纬度；
第一计算模块120，用于计算第一移动终端21当前位置的经纬度的hash值，取hash值的第一预设级别的结果，根据该第一预设级别的结果将所述第一移动终端21在当前位置扫描到的Wi-Fi热点列表聚合在多个商圈内；
第二计算模块130，用于计算第一移动终端21当前位置的经纬度的hash值，取hash值的第二预设级别的结果，根据该第二预设级别的结果，确定所 述多个商圈内Wi-Fi热点的集中区域；
第三计算模块140，用于读取所述Wi-Fi热点列表中的Wi-Fi热点在预设时间内的历史数据，计算第一移动终端21的第一用户对所述多个商圈内Wi-Fi热点的用户满意度；及
优化模块150，用于响应第二用户在第二移动终端22从所述多个商圈中选择一个第一商圈的点击操作，从所述多个商圈中筛选出一个第二商圈，该第二商圈在所述多个商圈中第一用户数量与第一商圈的第一用户数量相当且用户满意度最高，根据该第二商圈内Wi-Fi热点数量与第一用户数量之间的关联关系，确定提高第一商圈用户满意度需要调整的Wi-Fi热点数量，根据第一商圈中人流量聚集区域和Wi-Fi热点集中区域，确定提高第一商圈用户满意度需要调整的Wi-Fi热点的位置。
此外，本申请还提供一种Wi-Fi热点部署优化方法。参照图3所示，为本申请Wi-Fi热点部署优化方法较佳实施例的流程图。该方法可以由一个服务器执行，该装置可以由软件和/或硬件实现。
在本实施例中，Wi-Fi热点部署优化方法包括：步骤S10～步骤S60。
步骤S10，采集第一移动终端在当前位置的定位信息及第一移动终端在当前位置扫描到的Wi-Fi热点列表，所述定位信息包括第一移动终端当前位置的经纬度；
步骤S20，计算第一移动终端当前位置的经纬度的hash值，取hash值的第一预设级别的结果，根据该第一预设级别的结果将所述第一移动终端在当前位置扫描到的Wi-Fi热点列表聚合在多个商圈内；
步骤S30，计算第一移动终端当前位置的经纬度的hash值，取hash值的第二预设级别的结果，根据该第二预设级别的结果，确定所述多个商圈内Wi-Fi热点的集中区域；
步骤S40，读取所述Wi-Fi热点列表中的Wi-Fi热点在预设时间内的历史数据，计算第一移动终端的第一用户对所述多个商圈内Wi-Fi热点的用户满意度；及
步骤S50，响应第二用户在第二移动终端从所述多个商圈中选择一个第一商圈的点击操作，从所述多个商圈中筛选出一个第二商圈，该第二商圈在所述多个商圈中第一用户数量与第一商圈的第一用户数量相当且用户满意度最高，根据该第二商圈内Wi-Fi热点数量与第一用户数量之间的关联关系，确定提高第一商圈用户满意度需要调整的Wi-Fi热点数量，根据第一商圈中人流量聚集区域和Wi-Fi热点集中区域，确定提高第一商圈用户满意度需要调整的Wi-Fi热点的位置。
在本实施例中，每个城市有多个不同的商圈，其中，商圈指一定范围内的地区，第一用户指通过第一移动终端访问并使用了各商圈内Wi-Fi热点的用户，第二用户指Wi-Fi热点供应商的工作人员，第二用户通过第二移动终端了 解各商圈内Wi-Fi热点的分布情况及第一用户对各商圈内Wi-Fi热点的用户满意度，第一移动终端上安装有用于连接Wi-Fi热点的APP，该APP可获取第一移动终端在当前位置的位置信息(经纬度)，第二移动终端上安装有Wi-Fi热点部署优化程序的客户端程序。热点供应商可以通过多种渠道了解各城市各商圈第一用户聚集的区域，例如通过第三方获取相关数据。
当需要对某商圈内Wi-Fi热点的部署进行优化时，该APP通过第一移动终端扫描在当前位置可用的Wi-Fi热点列表，并收集Wi-Fi热点列表中所有Wi-Fi热点在预设时间(一周)内历史数据，包括：Wi-Fi的名称、被访问时间及时长、操作状态(连接成功、连接失败、登陆成功、登陆失败等)、被访问频次、是否运营商提供等，然后将上述历史数据发送至服务器。服务器对该历史数据经过数据仓库技术(Extract-Transform-Load，简称ETL)，抽取出关键历史数据，如Wi-Fi标识、时间、位置、连接操作、上网时长、连接成功次数、连接失败次数、重试次数、登陆成功次数、登陆失败次数等，保存至存储器中，供后续进行用户满意度计算操作。
可以理解的是，当第一移动终端在某一个位置扫描到可用的Wi-Fi热点列表，说明该Wi-Fi热点列表中的Wi-Fi热点的发射装置(以下简称Wi-Fi热点)也位于第一移动终端所在位置附近，因此，可以根据第一移动终端的定位信息估算Wi-Fi热点的位置。需要了解的是，采用geohash算法计算经纬度的hash值可取多级结果，不同级数的hash值表示的范围不同，例如：可取一到九级结果，取六级结果表示1.2km*1.2km的范围(大致可以理解成一个商圈的范围)，取七级结果表示152m*152m的范围。
以第一移动终端A、B、C、D、E为例，各第一移动终端在当前位置扫描到的Wi-Fi热点列表分别为a、b、c、d、e，假设第一预设级别为六级，服务器从存储器中读取第一移动终端A、B、C、D、E当前位置的经纬度，分别进行geohash运算，计算各第一移动终端当前位置的经纬度的hash值，取hash值的六级结果，若第一移动终端A、B、C、D、E的六级hash值相同，则判断第一移动终端A、B、C、D、E同在一个商圈内，相应的，Wi-Fi列表a、b、c、d、e中的Wi-Fi热点也都位于该商圈内。
在初步确定了商圈内Wi-Fi热点之后，需要通过第二计算步骤确定该商圈内Wi-Fi热点集中的区域，具体地，所述第二计算步骤包括：根据该第二预设级别的结果，将各商圈内Wi-Fi热点列表划分至比商圈小的不同区域块内；在所述不同区域块内的Wi-Fi热点列表中，将具有相同Wi-Fi热点的Wi-Fi热点列表合并形成新的Wi-Fi热点列表；将所述具有相同Wi-Fi热点的Wi-Fi热点列表所在的不同区域块合并形成新区域块，确定不同的新区域块内Wi-Fi热点的数量；及，根据该商圈不同的新区域块内Wi-Fi热点的数量，得到每个商圈内Wi-Fi热点集中的区域。
假设第二预设级别为七级，对上述第一移动终端A、B、C、D、E当前位置的经纬度进行geohash运算，取hash值的七级结果，其中，第一移动终端 A、C的七级结果相同，第一移动终端B、E的七级结果相同，将Wi-Fi热点列表a、c中的多个Wi-Fi热点划分至区域块P中，将Wi-Fi热点列表b、e中的多个Wi-Fi热点划分至区域块Q中，Wi-Fi热点列表d中的多个Wi-Fi热点单独划分至区域块O中。若同一个区域块中的Wi-Fi热点列表中存在重复的Wi-Fi热点，对每两个Wi-Fi热点列表中的Wi-Fi热点进行去重操作，并将去重后的两个Wi-Fi热点列表合并成一个Wi-Fi热点列表，直到同一个区域块中的各Wi-Fi热点列表之间不存在重复的Wi-Fi热点，也就是说，若某个区域块中原本有20个Wi-Fi热点列表，经过去重之后，Wi-Fi热点列表数量会小于或等于20。
可以理解的是，假设某Wi-Fi热点的发射装置位于两点之间，且这两点均在该Wi-Fi热点的覆盖范围之内，那么位于该两点之间的不同第一移动终端均能扫描到该Wi-Fi热点，也就是说，根据不同第一移动终端定位信息中的经纬度，同一个Wi-Fi热点，可能会被划分到相邻的两个不同区域块中间线附近的Wi-Fi热点，因此，需要对这一类Wi-Fi热点进行进一步处理。假设区域块P、Q相邻，且位于区域块P、Q内的Wi-Fi热点列表中都存在同一个Wi-Fi热点，那么，将区域块P、Q合并成一个新区域块，同时，对这个新区域块中所有Wi-Fi热点列表中的Wi-Fi热点进行去重，最大化的确定所述新的区域块中Wi-Fi热点的数量；然后，对多个新的区域块内的Wi-Fi热点进行合并去重，直到该商圈内所有区域块之间、区域块内没有重复的Wi-Fi热点为止，这样会存在多种情况，新区域块的数量比较少，每个新区域块的范围不一样，每个新区域块中的Wi-Fi热点列表数量不一样，有的新区域块中的Wi-Fi热点数量很多，有的Wi-Fi数量很少。将得到的各区域块分布情况及各区域块中的Wi-Fi热点数量关联至该商圈的电子地图上，这样就可以确定该商圈内Wi-Fi热点集中的区域。
接下来，读取各商圈内Wi-Fi热点在预设时间(例如，一周)内的关键历史数据，计算第一用户对各商圈内Wi-Fi热点的用户满意度，具体地，根据最近一周内的历史数据，计算各商圈内Wi-Fi热点的各项用户指标，加权汇总成第一用户的用户满意度，计算公式为：
S＝C*L*T1/(T2 1/9)
其中，S为第一用户对各商圈内Wi-Fi热点的用户满意度，C为各商圈内Wi-Fi热点的平均连接成功率，L为各商圈内Wi-Fi热点的平均登陆成功率，T1为各商圈内Wi-Fi热点的平均上网时长(单位为min)，T2为各商圈内Wi-Fi热点的平均连接成功耗时(单位为s)。为了使计算更准确，计算C、L、T1、T2时需去除异常值(例如最大值、最小值)，计算T1时对实际上网时长取整，超过10分钟取值为20分钟，不足10分钟取值10分钟。
在其他实施例中，所述第三计算步骤包括：根据各商圈内Wi-Fi热点在预设时间内的历史数据及预先确定的逻辑回归模型，计算第一用户对各商圈内Wi-Fi热点的用户满意度。
Wi-Fi运营商可通过运营调查问卷或者用户评分系统收集第一用户对商圈内Wi-Fi热点的用户满意度样本，所述预先确定的逻辑回归模型用于计算第一用户对各商圈内Wi-Fi热点的用户满意度，该逻辑回归模型的模型文件保存于存储器中，其可通过收集的用户满意度样本及该商圈内Wi-Fi热点近三个月内的关键历史数据离线训练得到。
需要说明的是，离线训练模型的优点是利用已有大量的历史数据，样本充分。在线训练模型的优点是能够利用最新的数据，模型能适应实时数据的变化，在数据分布和历史差距较大的情形下，在线模型更准确。为了使后续计算得到的各商圈内Wi-Fi热点的用户满意度更准确，可每隔三天对上述逻辑回归模型进行更新。然后从存储器中读取各商圈内Wi-Fi热点在最近一周的关键历史数据，输入更新后的逻辑回归模型，计算得到第一用户对该商圈内Wi-Fi热点的用户满意度。通过结合使离线训练和在线训练两种方式，可以取两者优点，提升模型的准确度，同时防止了在线环境下，数据量太少或者网络、系统问题导致的实时模型更新失败等问题。关于对该模型进行训练更新并使用它来计算各商圈Wi-Fi热点的用户满意度已经有成熟的计算方法，在此不再赘述。
进一步地，第二移动终端上安装有电子地图，通过电子地图，向第二用户展示所有城市的地图，且当第二用户选择某城市后，还展示该城市所有商圈的位置及各商圈内Wi-Fi热点的用户满意度。当Wi-Fi热点供应商需要对某商圈内Wi-Fi热点的部署进行优化，提高该商圈内Wi-Fi热点的用户满意度时，选择该商圈，服务器将响应该点击操作，以商圈B为例，目前第一用户数量为a，服务器从第一用户数量和商圈B当前的第一用户数量相当(例如，a±50)的多个商圈中，筛选出用户满意度最高的商圈G，根据商圈G内Wi-Fi热点数量和第一用户数量的关系，判断商圈B中是否应增加或减少Wi-Fi热点，然后根据商圈B中人流量聚集区域与商圈B内Wi-Fi热点的集中区域进行比较，判断商圈B内需要调整Wi-Fi热点的位置。
具体地，如果商圈G内Wi-Fi热点数量(例如m个)明显多于商圈B内Wi-Fi热点数量(例如n个)，那么需要在商圈B内新增相应数量(m-n个)的Wi-Fi热点，直到与商圈G内Wi-Fi热点数量相当；如果商圈G内Wi-Fi热点的数量少于B或者相当，说明Wi-Fi热点的数量不是造成商圈B、G用户满意度差异的主要原因，则需要进一步考虑Wi-Fi热点设置的位置是否合理。
可以理解的是，对于商圈中人流量较集中的区域，对Wi-Fi热点的需求也比较多，那么，从存储器11中读取从第三方获得的商圈B内人流量的分布区域后，比较商圈B内人流量聚集区域和通过第二计算步骤得到的商圈B内Wi-Fi热点的集中区域是否一致，若第一用户聚集区域和Wi-Fi热点的集中区域不一致，在商圈B的第一用户聚集区域Wi-Fi热点较少，第一用户较少的区域Wi-Fi热点较多，则需要将部分Wi-Fi热点从第一用户较少的区域调整到第一用户聚集区域，若第一用户聚集区域和Wi-Fi热点的集中区域一致，则说 明可能是商圈B内Wi-Fi热点存在质量问题，建议更换Wi-Fi热点。
需要说明的是，所述预设时间等需要预先设置的参数，可根据用户需要进行调整。
在其他实施例中，该方法还包括：在第二移动终端的电子地图上展示第一商圈内Wi-Fi热点的集中区域，并展示提高第一商圈用户满意度需要调整的Wi-Fi热点数量及Wi-Fi热点的位置。
服务器通过第二移动终端的第一显示区域向第二用户展示该商圈内Wi-Fi热点的分布情况，并在第二移动终端的第二显示区域展示提高商圈B内Wi-Fi热点用户满意度的Wi-Fi热点的调整方案。其中，所述第一显示区域和第二显示区域仅为了表示两个显示区域不同。
上述实施例提出的Wi-Fi热点部署优化方法，通过获取第一移动终端扫描到的所有Wi-Fi热点的历史数据，计算用户对各商圈Wi-Fi热点的满意度、各商圈内Wi-Fi热点的聚集区域，根据计算结果，给出针对用户满意度较低的商圈内Wi-Fi热点部署的优化方案，增加Wi-Fi热点覆盖人群，提升用户的上网体验。
此外，本申请实施例还提出一种计算机可读存储介质，所述计算机可读存储介质上存储有Wi-Fi热点部署优化程序，该优化程序被处理器执行时实现如下操作：
采集步骤：采集第一移动终端在当前位置的定位信息及第一移动终端在当前位置扫描到的Wi-Fi热点列表，所述定位信息包括第一移动终端当前位置的经纬度；
第一计算步骤：计算第一移动终端当前位置的经纬度的hash值，取hash值的第一预设级别的结果，根据该第一预设级别的结果将所述第一移动终端在当前位置扫描到的Wi-Fi热点列表聚合在多个商圈内；
第二计算步骤：计算第一移动终端当前位置的经纬度的hash值，取hash值的第二预设级别的结果，根据该第二预设级别的结果，确定所述多个商圈内Wi-Fi热点的集中区域；
第三计算步骤：读取所述Wi-Fi热点列表中的Wi-Fi热点在预设时间内的历史数据，计算第一移动终端的第一用户对所述多个商圈内Wi-Fi热点的用户满意度；及
优化步骤：响应第二用户在第二移动终端从所述多个商圈中选择一个第一商圈的点击操作，从所述多个商圈中筛选出一个第二商圈，该第二商圈在所述多个商圈中第一用户数量与第一商圈的第一用户数量相当且用户满意度最高，根据该第二商圈内Wi-Fi热点数量与第一用户数量之间的关联关系，确定提高第一商圈用户满意度需要调整的Wi-Fi热点数量，根据第一商圈中人流量聚集区域和Wi-Fi热点集中区域，确定提高第一商圈用户满意度需要调整的Wi-Fi热点的位置。
本申请之计算机可读存储介质的具体实施方式与上述Wi-Fi热点部署优化方法的具体实施方式大致相同，在此不再赘述。
需要说明的是，在本文中，术语“包括”、“包含”或者其任何其他变体意在涵盖非排他性的包含，从而使得包括一系列要素的过程、装置、物品或者方法不仅包括那些要素，而且还包括没有明确列出的其他要素，或者是还包括为这种过程、装置、物品或者方法所固有的要素。在没有更多限制的情况下，由语句“包括一个……”限定的要素，并不排除在包括该要素的过程、装置、物品或者方法中还存在另外的相同要素。
上述本申请实施例序号仅仅为了描述，不代表实施例的优劣。通过以上的实施方式的描述，本领域的技术人员可以清楚地了解到上述实施例方法可借助软件加必需的通用硬件平台的方式来实现，当然也可以通过硬件，但很多情况下前者是更佳的实施方式。基于这样的理解，本申请的技术方案本质上或者说对现有技术做出贡献的部分可以以软件产品的形式体现出来，该计算机软件产品存储在如上所述的一个存储介质(如ROM/RAM、磁碟、光盘)中，包括若干指令用以使得一台终端设备(可以是手机，计算机，服务器，或者网络设备等)执行本申请各个实施例所述的方法。
以上仅为本申请的优选实施例，并非因此限制本申请的专利范围，凡是利用本申请说明书及附图内容所作的等效结构或等效流程变换，或直接或间接运用在其他相关的技术领域，均同理包括在本申请的专利保护范围内。

Claims (20)

  1. 一种Wi-Fi热点部署优化方法，应用于服务器，其特征在于，该方法包括：
    采集步骤：采集第一移动终端在当前位置的定位信息及第一移动终端在当前位置扫描到的Wi-Fi热点列表，所述定位信息包括第一移动终端当前位置的经纬度；
    第一计算步骤：计算第一移动终端当前位置的经纬度的hash值，取hash值的第一预设级别的结果，根据该第一预设级别的结果将所述第一移动终端在当前位置扫描到的Wi-Fi热点列表聚合在多个商圈内；
    第二计算步骤：计算第一移动终端当前位置的经纬度的hash值，取hash值的第二预设级别的结果，根据该第二预设级别的结果，确定所述多个商圈内Wi-Fi热点的集中区域；
    第三计算步骤：读取所述Wi-Fi热点列表中的Wi-Fi热点在预设时间内的历史数据，计算第一移动终端的第一用户对所述多个商圈内Wi-Fi热点的用户满意度；及
    优化步骤：响应第二用户在第二移动终端从所述多个商圈中选择一个第一商圈的点击操作，从所述多个商圈中筛选出一个第二商圈，该第二商圈在所述多个商圈中第一用户数量与第一商圈的第一用户数量相当且用户满意度最高，根据该第二商圈内Wi-Fi热点数量与第一用户数量之间的关联关系，确定提高第一商圈用户满意度需要调整的Wi-Fi热点数量，根据第一商圈中人流量聚集区域和Wi-Fi热点集中区域，确定提高第一商圈用户满意度需要调整的Wi-Fi热点的位置。
  2. 根据权利要求1所述的Wi-Fi热点部署优化方法，其特征在于，该方法还包括：
    展示步骤：在第二移动终端的电子地图上展示第一商圈内Wi-Fi热点的集中区域，并展示提高第一商圈用户满意度需要调整的Wi-Fi热点数量及Wi-Fi热点的位置。
  3. 根据权利要求2所述的Wi-Fi热点部署优化方法，其特征在于，所述第二计算步骤包括：
    根据该第二预设级别的结果，将各商圈内Wi-Fi热点列表划分至比商圈小的不同区域块内；
    在所述不同区域块内的Wi-Fi热点列表中，将具有相同Wi-Fi热点的Wi-Fi热点列表合并形成新的Wi-Fi热点列表；
    将所述具有相同Wi-Fi热点的Wi-Fi热点列表所在的不同区域块合并形成新区域块，确定不同的新区域块内Wi-Fi热点的数量；及
    根据该商圈不同的新区域块内Wi-Fi热点的数量，得到每个商圈内Wi-Fi 热点集中的区域。
  4. 根据权利要求3所述的Wi-Fi热点部署优化方法，其特征在于，所述第三计算步骤包括：
    根据预设时间内的历史数据，计算各商圈内Wi-Fi热点的各项用户指标，加权汇总成第一用户的用户满意度，计算公式为：
    S＝C*L*T1/(T2 1/9)
    其中，S为第一用户对各商圈内Wi-Fi热点的用户满意度，C为各商圈内Wi-Fi热点的平均连接成功率，L为各商圈内Wi-Fi热点的平均登陆成功率，T1为各商圈内Wi-Fi热点的平均上网时长，T2为各商圈内Wi-Fi热点的平均连接成功耗时。
  5. 根据权利要求4所述的Wi-Fi热点部署优化方法，其特征在于，所述第三计算步骤可以替换为：
    根据各商圈内Wi-Fi热点在预设时间内的历史数据及预先确定的逻辑回归模型，计算第一用户对各商圈内Wi-Fi热点的用户满意度。
  6. 根据权利要求5所述的Wi-Fi热点部署优化方法，其特征在于，所述优化步骤包括：
    当第一商圈内Wi-Fi热点数量少于第二商圈内Wi-Fi热点数量时，增加第一商圈内Wi-Fi热点数量；及
    获取第一商圈的人流量分布情况，当第一商圈中人流量聚集区域和Wi-Fi热点集中区域不一致时，将新增的Wi-Fi热点设置在第一商圈中人流量聚集区域。
  7. 根据权利要求4所述的Wi-Fi热点部署优化方法，其特征在于，所述优化步骤包括：
    当第一商圈内Wi-Fi热点数量少于第二商圈内Wi-Fi热点数量时，增加第一商圈内Wi-Fi热点数量；及
    获取第一商圈的人流量分布情况，当第一商圈中人流量聚集区域和Wi-Fi热点集中区域不一致时，将新增的Wi-Fi热点设置在第一商圈中人流量聚集区域。
  8. 一种服务器，其特征在于，所述服务器包括：存储器、处理器，所述存储器上存储有Wi-Fi热点部署优化程序，该优化程序被所述处理器执行时实现如下步骤：
    采集步骤：采集第一移动终端在当前位置的定位信息及第一移动终端在当前位置扫描到的Wi-Fi热点列表，所述定位信息包括第一移动终端当前位置的经纬度；
    第一计算步骤：计算第一移动终端当前位置的经纬度的hash值，取hash值的第一预设级别的结果，根据该第一预设级别的结果将所述第一移动终端在当前位置扫描到的Wi-Fi热点列表聚合在多个商圈内；
    第二计算步骤：计算第一移动终端当前位置的经纬度的hash值，取hash值的第二预设级别的结果，根据该第二预设级别的结果，确定所述多个商圈内Wi-Fi热点的集中区域；
    第三计算步骤：读取所述Wi-Fi热点列表中的Wi-Fi热点在预设时间内的历史数据，计算第一移动终端的第一用户对所述多个商圈内Wi-Fi热点的用户满意度；及
    优化步骤：响应第二用户在第二移动终端从所述多个商圈中选择一个第一商圈的点击操作，从所述多个商圈中筛选出一个第二商圈，该第二商圈在所述多个商圈中第一用户数量与第一商圈的第一用户数量相当且用户满意度最高，根据该第二商圈内Wi-Fi热点数量与第一用户数量之间的关联关系，确定提高第一商圈用户满意度需要调整的Wi-Fi热点数量，根据第一商圈中人流量聚集区域和Wi-Fi热点集中区域，确定提高第一商圈用户满意度需要调整的Wi-Fi热点的位置。
  9. 根据权利要求8所述的服务器，其特征在于，该优化程序被所述处理器执行时还实现如下步骤：
    展示步骤：在第二移动终端的电子地图上展示第一商圈内Wi-Fi热点的集中区域，并展示提高第一商圈用户满意度需要调整的Wi-Fi热点数量及Wi-Fi热点的位置。
  10. 根据权利要求9所述的服务器，其特征在于，所述第二计算步骤包括：
    根据该第二预设级别的结果，将各商圈内Wi-Fi热点列表划分至比商圈小的不同区域块内；
    在所述不同区域块内的Wi-Fi热点列表中，将具有相同Wi-Fi热点的Wi-Fi热点列表合并形成新的Wi-Fi热点列表；
    将所述具有相同Wi-Fi热点的Wi-Fi热点列表所在的不同区域块合并形成新的区域块，确定不同的新的区域块内Wi-Fi热点的数量；及
    根据该商圈不同的新的区域块内Wi-Fi热点的数量，得到每个商圈内Wi-Fi热点集中的区域。
  11. 根据权利要求10所述的服务器，其特征在于，所述第三计算步骤包括：
    根据预设时间内的历史数据，计算第一用户各商圈内Wi-Fi热点的各项用户指标，加权汇总成第一用户的用户满意度，计算公式为：
    S＝C*L*T1/(T2 1/9)
    其中，S为第一用户对各商圈内Wi-Fi热点的用户满意度，C为各商圈内Wi-Fi热点的平均连接成功率，L为各商圈内Wi-Fi热点的平均登陆成功率，T1为各商圈内Wi-Fi热点的平均上网时长，T2为各商圈内Wi-Fi热点的平均连接成功耗时。
  12. 根据权利要求11所述的服务器，其特征在于，所述第三计算步骤可以替换为：
    根据各商圈内Wi-Fi热点在预设时间内的历史数据及预先确定的逻辑回归模型，计算第一用户对各商圈内Wi-Fi热点的用户满意度。
  13. 根据权利要求12所述的服务器，其特征在于，所述优化步骤包括：
    当第一商圈内Wi-Fi热点数量少于第二商圈内Wi-Fi热点数量时，增加第一商圈内Wi-Fi热点数量；及
    获取第一商圈的人流量分布情况，当第一商圈中人流量聚集区域和Wi-Fi热点集中区域不一致时，将新增的Wi-Fi热点设置在第一商圈中人流量聚集区域。
  14. 根据权利要求11所述的服务器，其特征在于，所述优化步骤包括：
    当第一商圈内Wi-Fi热点数量少于第二商圈内Wi-Fi热点数量时，增加第一商圈内Wi-Fi热点数量；及
    获取第一商圈的人流量分布情况，当第一商圈中人流量聚集区域和Wi-Fi热点集中区域不一致时，将新增的Wi-Fi热点设置在第一商圈中人流量聚集区域。
  15. 一种计算机可读存储介质，其特征在于，所述计算机可读存储介质上存储有Wi-Fi热点部署优化程序，该优化程序被处理器执行时实现如下步骤：
    采集步骤：采集第一移动终端在当前位置的定位信息及第一移动终端在当前位置扫描到的Wi-Fi热点列表，所述定位信息包括第一移动终端当前位置的经纬度；
    第一计算步骤：计算第一移动终端当前位置的经纬度的hash值，取hash值的第一预设级别的结果，根据该第一预设级别的结果将所述第一移动终端在当前位置扫描到的Wi-Fi热点列表聚合在多个商圈内；
    第二计算步骤：计算第一移动终端当前位置的经纬度的hash值，取hash值的第二预设级别的结果，根据该第二预设级别的结果，确定所述多个商圈内Wi-Fi热点的集中区域；
    第三计算步骤：读取所述Wi-Fi热点列表中的Wi-Fi热点在预设时间内的历史数据，计算第一移动终端的第一用户对所述多个商圈内Wi-Fi热点的用户满意度；及
    优化步骤：响应第二用户在第二移动终端从所述多个商圈中选择一个第 一商圈的点击操作，从所述多个商圈中筛选出一个第二商圈，该第二商圈在所述多个商圈中第一用户数量与第一商圈的第一用户数量相当且用户满意度最高，根据该第二商圈内Wi-Fi热点数量与第一用户数量之间的关联关系，确定提高第一商圈用户满意度需要调整的Wi-Fi热点数量，根据第一商圈中人流量聚集区域和Wi-Fi热点集中区域，确定提高第一商圈用户满意度需要调整的Wi-Fi热点的位置。
  16. 根据权利要求15所述的计算机可读存储介质，其特征在于，该优化程序被处理器执行时还实现如下步骤：
    展示步骤：在第二移动终端的电子地图上展示第一商圈内Wi-Fi热点的集中区域，并展示提高第一商圈用户满意度需要调整的Wi-Fi热点数量及Wi-Fi热点的位置。
  17. 根据权利要求16所述的计算机可读存储介质，其特征在于，所述第二计算步骤包括：
    根据该第二预设级别的结果，将各商圈内Wi-Fi热点列表划分至比商圈小的不同区域块内；
    在所述不同区域块内的Wi-Fi热点列表中，将具有相同Wi-Fi热点的Wi-Fi热点列表合并形成新的Wi-Fi热点列表；
    将所述具有相同Wi-Fi热点的Wi-Fi热点列表所在的不同区域块合并形成新的区域块，确定不同的新的区域块内Wi-Fi热点的数量；及
    根据该商圈不同的新的区域块内Wi-Fi热点的数量，得到每个商圈内Wi-Fi热点集中的区域。
  18. 根据权利要求17所述的计算机可读存储介质，其特征在于，所述第三计算步骤包括：
    根据预设时间内的历史数据，计算第一用户各商圈内Wi-Fi热点的各项用户指标，加权汇总成第一用户的用户满意度，计算公式为：
    S＝C*L*T1/(T2 1/9)
    其中，S为第一用户对各商圈内Wi-Fi热点的用户满意度，C为各商圈内Wi-Fi热点的平均连接成功率，L为各商圈内Wi-Fi热点的平均登陆成功率，T1为各商圈内Wi-Fi热点的平均上网时长，T2为各商圈内Wi-Fi热点的平均连接成功耗时。
  19. 根据权利要求18所述的计算机可读存储介质，其特征在于，所述第三计算步骤可以替换为：
    根据各商圈内Wi-Fi热点在预设时间内的历史数据及预先确定的逻辑回归模型，计算第一用户对各商圈内Wi-Fi热点的用户满意度。
  20. 根据权利要求19所述的计算机可读存储介质，其特征在于，所述优化步骤包括：
    当第一商圈内Wi-Fi热点数量少于第二商圈内Wi-Fi热点数量时，增加第一商圈内Wi-Fi热点数量；及
    获取第一商圈的人流量分布情况，当第一商圈中人流量聚集区域和Wi-Fi热点集中区域不一致时，将新增的Wi-Fi热点设置在第一商圈中人流量聚集区域。
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