CN115879733A - Service demand element processing system oriented to unmanned passenger service scene - Google Patents
Service demand element processing system oriented to unmanned passenger service scene Download PDFInfo
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Abstract
The invention discloses a service demand element processing system for an unmanned passenger service scene, and relates to the technical field of rail transit. The method comprises the following steps: the passenger demand data acquisition module is used for acquiring trip data aiming at different trip scenes, the passenger demand analysis and mining module is used for analyzing and mining the trip data to generate passenger trip guiding data, and the passenger trip guiding data is used for guiding the trip according to the trip guiding data and evaluating the trip process. The service demand element processing system for the unmanned passenger service scene is realized, the passenger demand can be more objectively described, the service can be guided, the intelligent, personalized and customized passenger service can be realized, and the travel experience of passengers can be improved. The invention can fully utilize the value behind the data in practical application, fully considers the requirements of passengers and provides more abundant and diversified information feedback and service values.
Description
Technical Field
The invention relates to the technical field of rail transit, in particular to a service demand factor processing system facing to an unmanned passenger service scene.
Background
In recent years, rail transit is gradually one of main transportation means in modern cities, has the advantages of safety, high speed and high efficiency, and plays an increasingly important role in the urban modernization process. At present, the urban rail transit in China generally adopts a form that a ticket booth and a vehicle control room are used as a customer service center to serve passengers. Such forms of service are inefficient and tend to consume significant human resources. Meanwhile, the rail transit serving devices should avoid contact between people as much as possible so as to reduce the spread of viruses and guarantee the life safety of people. Therefore, it is urgent to realize unmanned passenger service at a rail transit station.
The prior unmanned passenger service technology mainly focuses on providing specific services at a rail transit station, and the services are mechanical and similar to ticket vending machines. The service provided by the method has the advantages of complete service information and complete service functions, but has the defect of lack of personalization. Lack of collection and analysis of passenger demand information makes it difficult to accurately formulate a personalized service scheme for each passenger, and it is difficult to improve the service experience of the passenger despite the improved level of intelligence of the service. At present, it is difficult to achieve the goal of increasing passenger satisfaction with service, and improvements are still needed.
Disclosure of Invention
The invention aims to solve the problem that the existing unmanned passenger service technology is difficult to accurately set up a personalized service scheme for each passenger, and provides a service demand element processing system facing an unmanned passenger service scene in order to solve the technical problem.
In a first aspect, a service requirement element processing system facing an unmanned passenger service scene is provided, which includes: the system comprises a passenger demand data acquisition module and a passenger demand analysis and mining module, wherein the passenger demand data acquisition module is used for acquiring travel data aiming at different travel scenes, the passenger demand analysis and mining module is used for analyzing and mining the travel data to generate passenger travel guide data, and the passenger travel guide data is used for guiding the passenger to travel and evaluating the travel process;
wherein the travel scene comprises: an inbound scene, a consultation scene, a ticket buying scene, a security check scene, a ticket checking scene, a waiting scene, a riding scene and an outbound scene.
In a possible implementation of the first aspect, when the travel scene is an inbound scene, the passenger demand data collection module is configured to collect passenger flow information of a rail transit station, and collect welcome information and commercial promotion information of the rail transit station; the passenger demand analysis and mining module is used for sending the passenger flow information to a passenger terminal, collecting feedback opinions of passengers according to the welcome information and the commercial promotion information, and modifying or replacing the welcome information and the commercial promotion information according to the feedback opinions.
In a possible implementation of the first aspect, when the travel scene is a consultation scene, the passenger demand data acquisition module is configured to acquire consultation data of a passenger; the passenger demand analysis and mining module is used for processing the consultation data, analyzing a satisfaction point and an dissatisfaction point of a passenger in a consultation scene, and analyzing and predicting problems in the consultation scene according to the satisfaction point and the dissatisfaction point.
In a possible implementation of the first aspect, when the travel scene is a ticket purchasing scene, the passenger demand data collection module is configured to collect passenger ticketing data and passenger opinions about ticketing services; the passenger demand analysis and mining module is used for processing and analyzing the ticketing data and the opinion of the ticketing service.
In a possible implementation of the first aspect, when the travel scene is a security check scene, the passenger demand data acquisition module is configured to acquire article safety information and epidemic situation prevention and control data of articles carried by passengers; the passenger demand analysis and mining module is used for processing and analyzing the article safety information, generating dangerous article prompt information and sending the dangerous article prompt information to the passenger terminal, and generating epidemic situation prompt information according to the epidemic situation prevention and control data and sending the epidemic situation prompt information to the passenger terminal.
In a possible implementation of the first aspect, when the travel scene is a ticket checking scene, the passenger demand data acquisition module is configured to acquire the number of people coming into and going out of the station and the opinion of the passenger on the ticket checking scene; the passenger demand analysis and mining module is used for sending the number of people entering and leaving the station to the terminals of passengers and analyzing the problems to be improved in the ticket checking scene according to the opinions of the passengers on the ticket checking scene.
In a possible implementation of the first aspect, when the travel scene is a waiting scene, the passenger demand data acquisition module is configured to acquire passenger information, station management data, train operation data, and comments of passengers on the waiting scene; the passenger demand analysis and mining module is used for analyzing the passenger information, the station management data, the train operation data and the opinions of the passengers on the waiting scene and determining the problems to be improved in the waiting scene.
In a possible implementation of the first aspect, when the travel scene is a riding scene, the passenger demand data acquisition module is used for geographic information data of passengers; the passenger demand analysis and mining module is used for analyzing the riding mileage and the riding willingness of the passenger according to the geographic information data.
In a possible implementation of the first aspect, when the travel scene is an outbound scene, the passenger demand data collection module is configured to collect a starting position, an end position and a transfer intermediate station of a passenger trip; the passenger demand analysis and mining module is used for generating a travel recommended route according to the starting position, the destination position and the transfer intermediate station and sending the travel recommended route to a passenger terminal.
In one possible implementation of the first aspect, the passenger demand analysis and mining module is specifically configured to generate passenger travel guidance data by a Dial algorithm and a Dijkstra algorithm.
The service demand element processing system for the unmanned passenger service scene is realized, the demand of the passenger on the service in the unmanned scene is considered, and the current unmanned service level is combined, so that the passenger demand can be more objectively described, the service is guided, the intelligent, personalized and customized passenger service is realized, and the passenger travel experience is improved. The invention can fully utilize the value behind the data in practical application, fully considers the requirements of passengers and provides more abundant and diversified information feedback and service values.
Advantages of additional aspects of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention.
Drawings
FIG. 1 is a schematic structural diagram of a service requirement element processing system according to an embodiment of the present invention.
Detailed Description
The principles and features of this invention are described below in conjunction with the following drawings, which are set forth to illustrate, but are not to be construed to limit the scope of the invention.
As shown in fig. 1, a schematic structural diagram is provided for an embodiment of the service requirement element processing system of the present invention, and the service requirement element processing system for an unmanned passenger service scenario includes: the system comprises a passenger demand data acquisition module and a passenger demand analysis and mining module, wherein the passenger demand data acquisition module is used for acquiring travel data aiming at different travel scenes, the passenger demand analysis and mining module is used for analyzing and mining the travel data to generate passenger travel guide data, the passengers are guided to travel according to the travel guide data, and the travel process is evaluated;
wherein, the trip scene includes: an inbound scene, a consultation scene, a ticket buying scene, a security check scene, a ticket checking scene, a waiting scene, a riding scene and an outbound scene.
The passenger demand analysis and excavation module can be used for completing the analysis of the travel path of the passenger by combining the GPS signal analysis of the passenger before entering the station through the statistics of the information of entering and leaving the station, so that the travel path preference of the passenger is obtained, meanwhile, the travel path of the passenger can be optimized, and the optimal degree of the path is guaranteed.
The passenger demand analysis and mining module can analyze and mine data through a passenger portrait technology and a Kano model technology, the passenger portrait technology can expand and construct passenger portraits through obtaining passenger portrait basic information and the passenger portraits, data support is provided for deep analysis of travel rules of rail transit passengers, and basic data are provided for accurate and differentiated individual services and extended services of the passengers. The requirement of passengers for obtaining quality service is met, and the urban rail transit operation service level is improved.
The Kano model technique classifies quality of service elements into 5 categories, basic (M category), desired (O category), attractive (A category), non-essential (I category), and reverse (R category). The classification of each quality element can be investigated by designing a Kano questionnaire and determined by a Kano evaluation chart. The Kano questionnaire is a structural questionnaire and comprises a front question and a back question, different answers to the same question form the frequency of the attribute, statistical data in a Kano evaluation table are sorted, and the Kano type with the largest total frequency is used as the Kano type of the element.
The embodiment realizes the service demand element processing system facing to the service scene of the unmanned passengers, takes the 'station', 'vehicle', 'person' 3 elements as the core, combs the service demands of the passengers under the station and train environment, and combines the current unmanned service level by considering the demands of the passengers on the service under the unmanned scene, thereby more objectively describing the passenger demands, guiding the service, realizing the intelligent, personalized and customized passenger service, and improving the travel experience of the passengers. The embodiment can fully utilize the value behind the data in practical application, fully considers the requirements of passengers, and provides more abundant and diversified information feedback and service values.
Optionally, in some possible embodiments, when the travel scene is an arrival scene, the passenger demand data acquisition module is configured to acquire passenger flow information of a rail transit station, and acquire welcome information and commercial promotion information of the rail transit station; the passenger demand analysis and mining module is used for sending passenger flow information to a passenger terminal, collecting feedback opinions of passengers according to the welcome information and the commercial promotion information, and modifying or replacing the welcome information and the commercial promotion information according to the feedback opinions.
It should be noted that the passenger flow information refers to the boarding and alighting amount of the rail transit station, i.e., the sum of the inbound passenger flow, the outbound passenger flow and the transfer passenger flow, wherein the inbound passenger flow and the outbound passenger flow can be obtained by collecting the data of the passengers entering and exiting the station in the inbound gate and the outbound gate of the rail transit station, and the transfer passenger flow can be calculated according to the information of the passengers transfer in the charging system.
It should be noted that the welcome information refers to station entrance broadcast, and specifically, may be welcome information adopted by the rail transit station in a traditional service mode, so as to analyze the opinion of passengers at a later stage, and the broadcast information may be implemented by collecting relevant data in the central broadcast control terminal.
The commercial promotion information is divided into commercial advertisements and public service advertisements according to types, the quantity can be counted manually and respectively, the passenger opinions can be collected in a questionnaire form, and the questionnaire is distributed on a terminal held by the passenger when the passenger leaves the station to count the satisfaction degree and opinion suggestion of the passenger on the services respectively.
Optionally, in some possible embodiments, when the travel scene is a consultation scene, the passenger demand data acquisition module is configured to acquire consultation data of a passenger; the passenger demand analysis and mining module is used for processing the consultation data, analyzing the satisfaction points and the dissatisfaction points of the passengers in the consultation scene, and analyzing and predicting the problems in the consultation scene according to the satisfaction points and the dissatisfaction points.
The consultation data can be obtained from the processing terminal of the passenger service window, and the accumulated consultation data can be directly read by accessing the processing terminal.
It should be noted that the passenger requirement analysis and mining module may classify the passenger consultation questions according to keywords, for example: the method comprises the steps of selecting directions, buying tickets, a certain number of trains and the like, then sorting according to the occurrence frequency of keywords, extracting the top ten questions as the most likely problems, and generating answers in advance to push to a user terminal.
Optionally, in some possible embodiments, when the travel scene is a ticket purchasing scene, the passenger demand data acquisition module is used for acquiring the ticketing data of the passenger and the opinion of the passenger on the ticketing service; the passenger demand analysis and mining module is used for processing and analyzing the ticketing data and the opinion of the ticketing service.
For example, passenger opinions about the ticketing service may be collected in the form of questionnaires.
It should be noted that after the ticketing data of the passenger is collected, the ticketing data is analyzed to determine when and where the passenger takes the vehicle, so as to generate ticketing discount information for the passenger, and the discount information is pushed to the terminal of the passenger such as a mobile phone.
Optionally, in some possible embodiments, when the travel scene is a security check scene, the passenger demand data acquisition module is configured to acquire article security information and epidemic situation prevention and control data of articles carried by passengers; the passenger demand analysis and mining module is used for processing and analyzing the article safety information, generating dangerous article prompt information and sending the dangerous article prompt information to the passenger terminal, and generating epidemic situation prompt information according to the epidemic situation prevention and control data and sending the epidemic situation prompt information to the passenger terminal.
It should be noted that the article safety information can be obtained from a terminal of a security check platform at a station, and the types of dangerous articles collected by taking the day as a unit according to the security check of the rail transit station include firearms, ammunition, flammable and explosive articles, corrosive articles, control tools and other belt-limiting wines and the like and the quantity thereof. Then according to the frequency of various dangerous goods, the passenger terminal is prompted at irregular time to prevent the passenger from taking the dangerous goods by mistake.
If the epidemic situation occurs in the place, prompt information can be generated in time and sent to the passenger terminal.
Optionally, in some possible embodiments, when the travel scene is a ticket checking scene, the passenger demand data acquisition module is configured to acquire the number of people entering and leaving the station and the opinions of the passengers on the ticket checking scene; the passenger demand analysis and mining module is used for sending the number of people getting in and out of the station to the terminals of passengers, and analyzing the problems to be improved in the ticket checking scene according to the opinions of the passengers on the ticket checking scene.
It should be noted that the number of people entering and leaving the station refers to the amount of distribution, i.e. the passenger flow entering the station and the passenger flow leaving the station.
The opinion of the passenger on the ticket checking scene can be acquired in the form of a questionnaire, so that the opinion of the passenger on the ticket checking scene can be acquired, and therefore improvement measures can be provided in a targeted manner to improve the ticket checking scene.
Optionally, in some possible embodiments, when the travel scene is a waiting scene, the passenger demand data acquisition module is configured to acquire passenger information, station management data, train operation data, and comments of passengers on the waiting scene; the passenger demand analysis and mining module is used for analyzing passenger information, station management data, train operation data and the opinions of passengers on the waiting scene and determining the problem to be improved in the waiting scene.
It should be understood that the passenger information refers to personal information such as age, sex, number of travelers, and travel information such as departure place, arrival place, riding time of the passenger.
The station management data refers to information such as guidance methods and current limit methods for passengers.
The train operation data refers to information such as operation interval, operation time, station stay time and the like of each train pass.
It should be noted that the opinions of the passengers about the waiting scene can be obtained in the form of a questionnaire, for example, so as to obtain the opinions of the passengers about the waiting scene, and thus, improvement measures can be provided specifically to improve the waiting scene.
Optionally, in some possible embodiments, when the travel scene is a riding scene, the passenger demand data acquisition module is used for geographic information data of passengers; the passenger demand analysis and mining module is used for analyzing the riding mileage and riding willingness of the passenger according to the geographic information data.
The geographic information data refers to information of passengers entering and leaving the station, and the driving mileage can be determined according to the information of the passengers entering and leaving the station, for example, a passenger enters the station at the station a and leaves the station at the station B, and if the passenger can reach the station directly, the mileage is the track mileage from the station a to the station B. If the station can not reach directly and the transfer station can be C or D, respectively counting the sum of the subway mileage from A to C, C to B and the sum of the subway mileage from A to D, D to B, comparing the two and taking the minimum value. The riding willingness can be counted whether the passenger wants to take the vehicle again by setting a questionnaire at the exit.
Optionally, in some possible embodiments, when the travel scene is an outbound scene, the passenger demand data acquisition module is configured to acquire a starting position, an end position and a transfer intermediate station of passenger travel; the passenger demand analysis and mining module is used for generating a travel recommended route according to the starting position, the end position and the transfer intermediate station and sending the travel recommended route to a passenger terminal.
Alternatively, in some possible embodiments, the passenger demand analysis and mining module may collect passenger behavior data by cooperating with the relevant departments.
In addition, the arrival and departure time of the bus can be acquired through cooperation with a bus company, the number of the bus lines for transfer after passengers exit is acquired, and the difference between the taking numbers of nearby buses before and after the setting of the rail transit station can be acquired.
In addition, the system can cooperate with a meteorological department to acquire all-day and all-time weather condition data.
It should be noted that the arrival and departure times of buses at the nearest time can be reported to the passengers, including a plurality of bus stations near the rail transit station, so as to help the passengers to reasonably arrange the travel time and the travel plan. Simultaneously, an intelligent guide module is provided, the weather of the day is reported to the passenger according to the weather information collected previously, and the traveling experience of the passenger is improved.
Optionally, in some possible embodiments, the passenger demand analysis and mining module may include an intelligent guidance module for generating passenger travel guidance data through a Dial algorithm and a Dijkstra algorithm.
Firstly, a network model A [ i, j ] of a station is established by using an adjacency matrix A:
a [ i, j ] = M stations i and j have subway M connection
0 stations i and j have no subway direct connection
Where i and j refer to any two sites in the matrix.
Then, the Dijkstra shortest path algorithm is used for the sites i and j respectively to calculate the shortest paths from the site i to the starting point O and from the site j to the end point D.
Then, by defining conditions r (i) < r (j), and s (i) > s (j), a path for which validity is checked is defined as a valid path.
Wherein r (i) represents the shortest path from the station i to the starting point O, r (j) represents the shortest path from the station j to the starting point O, s (i) represents the shortest path from the station i to the end point D, and s (j) represents the shortest path from the station j to the end point D. If the two formulas are satisfied, the path is defined as an effective path.
Step four: and continuously searching the next effective path until all path searching is completed.
The Dijkstra algorithm is explained below.
Step 1: initializing, marking the labels of all nodes except the root node as infinity, enabling the front node to be empty, and placing the root node into E, and enabling E = { E }, v = E, l (E) =0,p (E) =0. For each v (i) ≠ e, let l (v) i )＝+∞，p(v i )＝0。
Step 2: examine each of the commands (v) * ，v j ) Is epsilon of A and
node v of j 。
If l (v) j )>l(v * )+w(v * ，v j ) Then l (v) is processed j ) Modified as l (v) * )+w(v * ，v j ) P (v) j ) Modified as v * 。
If l (v) j )≤l(v * )+w(v * ，v j ) Then l (v) j ) And p (v) j ) And is not changed. And (4) turning to the step 3 until all the adjacent nodes are marked.
And 3, step 3: for all of
Comparison l (v) j ) Find the value of l (v) j ) The smallest node, denoted v * V is to be * Put into E and deleted from S.
And 4, step 4: if it is used
And (5) finishing the algorithm, otherwise, turning to the step 2.
It should be noted that the generalized cost function can be used to represent the comprehensive cost of passengers for rail transit, and the most reasonable riding route is the route with the least generalized cost. Suppose there are W valid paths between the nth OD pair, let
For the cost value on the w-th valid path from the start point to the end point, which is a defined cost estimate plus a random error term, then->
Wherein it is present>
Which is a random error term, is expected to be equal to 0. Then the generalized path cost>
Is the sum of a series of sub-terms multiplied by a weight. />
Wherein it is present>
Is a pending parameter that represents the weight of the ith path attribute.
On the basis, the influence of the riding time, the transfer times and the transfer waiting time on the traveling of the passengers is combined. Then the
Wherein +>
Representing the ride time of the nth OD on the w-th path,
indicates the number of transfers of the nth OD to the w-th path>
And (4) the waiting time of the nth OD for the w route is shown.
The following description is made with reference to specific examples.
Suppose a subway line, a longitudinal subway is a first line, a transverse subway is a second line, and a cross point is a transfer station. The congestion degree of transfer stations affects transfer time, wherein the transfer time of stations with smooth congestion degree is 1 minute, and the congestion station is 2 minutes.
The first step is as follows: if a certain station B on the first line starts and a certain station D on the second line arrives, all stations can establish a matrix A [ i, j ] during the BD.
The second step is that: all the sites are numbered from 1 to n, so that the station i and the station j are paired into n (n-1) types. If one of the sites i is arbitrarily selected, the shortest path between the site i and the departure point B can be calculated: except for a root node starting point B, node labels of other stations, namely nodes 1-n, are infinite, the root node is placed in E, and E = { E }, v = E, l (E) =0,p (E) =0, wherein l (E) represents the generalized cost from the root node to a node i, and p (E) represents a precursor node of an i node on the current shortest path. For each v (i) ≠ e, letl (vi) = + ∞, p (vi) =0. When the station j is (v, vj) epsilon A and
the node of (c). For each satisfactory site j, the calculation is performed: if l (vj)>l (v) + w (v, vj), then l (vj) is modified to l (v) + w (v, vj), and p (vj) is modified to v. If l (vj) ≦ l (v) + w (v, vj), l (vj) and p (vj) do not change. For all>
And comparing the values of l (vj), finding out the node with the minimum l (vj), marking the node as v, and putting the v into E. If->
The algorithm ends. At this time, the shortest path from the station i, j to the starting point B can be obtained.
The third step: repeating the above algorithm, changing the starting point B into the end point D, and then obtaining the shortest path from the station i, j to the end point D.
The fourth step: if r (i) < r (j) and s (i) > s (j) at this time, it is an effective path, and it is recorded as OM. (r (i) represents the shortest path from site i to origin O, r (j) represents the shortest path from site j to origin O, s (i) represents the shortest path from site i to destination D, and s (j) represents the shortest path from site j to destination D)
The fifth step: and repeating the second, third and fourth steps to search all effective paths.
And a sixth step: and adding congestion waiting time caused by different congestion degrees of transfer stations to the obtained effective path time, and finally comparing to obtain the path with the shortest time. Wherein the degree of congestion can be estimated based on previously obtained traffic conditions.
Optionally, in some possible embodiments, the passenger demand analysis and mining module may further include: and the consumption mode analysis module is used for analyzing and acquiring the preference of the consumption mode of the passenger according to the ticket business and consumption data acquired according to the ticket purchasing scene information so as to analyze the consumption mode.
Optionally, in some possible embodiments, the passenger demand analysis and mining module may further include: the health care module is used for aiming at accidents except sudden major diseases and sudden accidents of passengers in a rail transit system, such as injured people caused by damage of internal objects in the rail transit system, injured people caused by fighting and stepping, and guaranteeing the life safety of the passengers.
For example, an ARM9 microprocessor can be used as a core chip, and an embedded Linux operating system is combined to communicate with a GSM base station through GPRS equipment, so that monitoring, remote communication and real-time data transmission of a high-speed moving target are realized. Thereby realizing the functions of alarming and calling the rescue vehicle and the fire department reliably at high speed.
Optionally, in some possible embodiments, the passenger demand analysis and mining module may further include: passenger guide module in the station through passenger demand analysis with the passenger trip guide data that the module analysis of excavating obtained, rationally guides the passenger to get into the train, guarantees the inside orderliness in station.
It should be understood that, because the rail transit station is mostly located underground, and the underground environment mostly lacks GNSS signals, it is difficult to implement integrated connection of ground and underground. Accurate positioning is difficult to achieve using conventional navigation approaches that rely on global navigation satellite systems. The invention provides a positioning signal by installing a radio frequency matrix base station in a tunnel. When the passenger got into track traffic station, radio frequency signal was caught to the cell-phone, and APP judges automatically and gets into the underground scope, switches positioning signal to radio frequency signal from the satellite signal. Meanwhile, accurate navigation of each passenger is completed by means of the path algorithm through distance information of the radio frequency matrix base stations, and reasonable guidance is achieved.
Optionally, in some possible embodiments, the passenger demand analysis and mining module may further include: the passenger opinion feedback module is used for continuously improving passenger service behaviors according to collected passenger feedback opinions, improving passenger travel experience, and finally evaluating and researching passenger satisfaction through a Kano model.
For example, ten service elements, such as ticket prices, infrastructure, in-car environment, convenience service, catering shopping, punctuation rate, security inspection flow, waiting order, indication marks and ticket booking convenience degree, are Kano classified by using a Kano model, SII and DDI values of each service element are calculated, and the current service of the rail transit is evaluated. Meanwhile, the key service elements are determined more accurately through the strategy priority sequence of different service elements under the same Kano attribute. Meanwhile, the strategy priorities are sequenced, and decision support is provided for more effective optimization service.
Passenger demand data acquisition module can adopt the questionnaire mode to set up the problem of two positive and negative directions to ten service element of fare, infrastructure, car internal environment, convenient service, food and beverage shopping, punctuate rate, security inspection flow, order of waiting, indicator, ticket booking convenience, for example set up three problem to infrastructure:
1. if there is this facility, your satisfaction is: a. high, b, high, c, zero, d, low, e, low;
2. without this facility, your satisfaction is: a. high, b, high, c, zero, d, low, e, low;
3. do you currently be satisfied with the facility: a. satisfactory, b, general, c, unsatisfactory.
Meanwhile, a questionnaire is issued in a random sampling mode, for the first question and the second question, different answers are counted by referring to a table 1, and the elements are divided into basic elements (M types), expected elements (O types), attractive elements (A types), unnecessary elements (I types) and reverse elements (R types) according to the frequency of the different answers. Calculating a passenger satisfaction coefficient (SII) and a dissatisfaction coefficient (DDI) according to the investigation result, wherein the formulas are as follows:
SII＝(A+O)/(A+O+M+I)
DDI＝-(M+O)/(A+O+M+I)
the closer the absolute value of the result is to 1, the more important and improved the result is.
For the third problem, an item a is selected to be marked with 100 points, an item b is marked with 70 points, an item c is marked with 50 points, frequency numbers of the three items abc are counted and marked with xyz respectively, and the frequency numbers are calculated according to a formula:
satisfaction = (x 100+ y 70+ z 50)/(x + y + z) 100%
The passenger's satisfaction with each service can be derived.
TABLE 1
It should be understood that the above embodiments are product embodiments corresponding to the previous method embodiments, and the description of the product embodiments may refer to the description of the previous method embodiments, and will not be repeated herein.
It is understood that any combination of the above embodiments can be made by those skilled in the art without departing from the spirit of the present invention, and the scope of the present invention is also within the scope of the present invention.
The reader should understand that in the description of this specification, reference to the description of the terms "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above are not necessarily intended to refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, various embodiments or examples and features of different embodiments or examples described in this specification can be combined and combined by one skilled in the art without contradiction.
In the several embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other ways. For example, the above-described method embodiments are merely illustrative, and for example, the division of steps into only one logical functional division may be implemented in practice in another way, for example, multiple steps may be combined or integrated into another step, or some features may be omitted, or not implemented.
The above method, if implemented in the form of software functional units and sold or used as a stand-alone product, can be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention essentially or partly contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: various media capable of storing program codes, such as a usb disk, a removable hard disk, a Read-only memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disk.
While the invention has been described with reference to specific embodiments, it will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (10)
1. A service requirement element processing system oriented to an unmanned passenger service scene is characterized by comprising: the system comprises a passenger demand data acquisition module and a passenger demand analysis and mining module, wherein the passenger demand data acquisition module is used for acquiring travel data aiming at different travel scenes, the passenger demand analysis and mining module is used for analyzing and mining the travel data to generate passenger travel guide data, and the passenger travel guide data is used for guiding the passenger to travel and evaluating the travel process;
wherein the travel scene comprises: the system comprises an entrance scene, a consultation scene, a ticket purchasing scene, a security check scene, a ticket checking scene, a waiting scene, a riding scene and an exit scene.
2. The service demand element processing system for the unmanned passenger service scenario as claimed in claim 1, wherein when the travel scenario is an inbound scenario, the passenger demand data collection module is configured to collect passenger flow information of a rail transit station, and collect welcome information and commercial promotion information of the rail transit station; the passenger demand analysis and mining module is used for sending the passenger flow information to a passenger terminal, collecting feedback opinions of passengers according to the welcome information and the commercial promotion information, and modifying or replacing the welcome information and the commercial promotion information according to the feedback opinions.
3. The service requirement element processing system for the unmanned passenger service scene as claimed in claim 1, wherein when the travel scene is a consultation scene, the passenger requirement data collecting module is configured to collect consultation data of passengers; the passenger demand analysis and mining module is used for processing the consultation data, analyzing a satisfaction point and an unsatisfied point of a passenger in a consultation scene, and analyzing and predicting problems appearing in the consultation scene according to the satisfaction point and the unsatisfied point.
4. The system for processing service requirement elements for an unmanned passenger service scenario according to claim 1, wherein when the travel scenario is a ticket buying scenario, the passenger requirement data collection module is configured to collect ticketing data of passengers and opinions of passengers on ticketing services; the passenger demand analysis and mining module is used for processing and analyzing the ticketing data and the opinion of the ticketing service.
5. The service demand element processing system oriented to the unmanned passenger service scene as claimed in claim 1, wherein when the travel scene is a security inspection scene, the passenger demand data collection module is configured to collect article security information and epidemic prevention and control data of articles carried by passengers; the passenger demand analysis and mining module is used for processing and analyzing the goods safety information, generating dangerous goods prompt information and sending the dangerous goods prompt information to the passenger terminal, and generating epidemic situation prompt information according to the epidemic situation prevention and control data and sending the dangerous goods prompt information to the passenger terminal.
6. The service requirement element processing system for the unmanned passenger service scene as claimed in claim 1, wherein when the travel scene is a ticket checking scene, the passenger requirement data collecting module is used for collecting the number of people coming in and going out of the station and the opinion of the passenger on the ticket checking scene; the passenger demand analysis and mining module is used for sending the number of people entering and leaving the station to the terminals of passengers and analyzing the problems to be improved in the ticket checking scene according to the opinions of the passengers on the ticket checking scene.
7. The service requirement element processing system for the unmanned passenger service scene as claimed in claim 1, wherein when the travel scene is a waiting scene, the passenger requirement data collection module is configured to collect passenger information, station management data, train operation data and passenger opinions about the waiting scene; the passenger demand analysis and mining module is used for analyzing the passenger information, the station management data, the train operation data and the opinions of passengers on the waiting scene and determining the problems to be improved in the waiting scene.
8. The service requirement element processing system for the unmanned passenger service scenario of claim 1, wherein when the travel scenario is a riding scenario, the passenger requirement data acquisition module is used for geographic information data of passengers; the passenger demand analysis and mining module is used for analyzing the riding mileage and the riding willingness of the passenger according to the geographic information data.
9. The service demand element processing system for the unmanned passenger service scene as claimed in claim 1, wherein when the travel scene is an outbound scene, the passenger demand data collection module is configured to collect a starting position, an ending position and a transfer intermediate station of a passenger trip; the passenger demand analysis and mining module is used for generating a travel recommended route according to the starting position, the end position and the transfer intermediate station and sending the travel recommended route to a passenger terminal.
10. The service requirement element processing system for the unmanned passenger service scenario of claim 1, wherein the passenger requirement analysis and mining module is specifically configured to generate passenger travel guidance data through a Dial algorithm and a Dijkstra algorithm.
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