CN113658029A - Bus comfort level query method, system and equipment for intelligent travel study and judgment - Google Patents
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Abstract
The invention provides a bus comfort level query method, system and device for intelligent travel study and judgment. The query method comprises the following steps: determining a target bus according to a bus comfort level query request from a user terminal, and acquiring intelligent sensing data corresponding to the target bus; establishing a first weight vector of a first influence factor of the comfort level of the target bus based on a first set evaluation factor set, constructing a comprehensive evaluation matrix of the first influence factor of the comfort level of the target bus based on intelligent sensing data, and forming a comfort level vector through the first weight vector and the comprehensive evaluation matrix; comfort information is generated using the comfort vector. And sending comfort degree information to the user terminal to respond to the inquiry request. The invention can take the multidimensional dynamic data influencing the comfort level of the bus as the basis, thereby achieving the purpose of reflecting the comfort level of the bus comprehensively, truly and in real time and providing intelligent traffic comfort level information inquiry service for users.
Description
Technical Field
The invention relates to the technical field of intelligent transportation, in particular to the technical field of bus comfort level query and acquisition.
Background
With the rapid development of economy and the continuous expansion of urban scale, higher requirements are put forward on public transport. Although bus routes and real-time bus position information can be inquired in the prior art in modes of an electronic map in a mobile phone and the like, information such as comfort degree of a passenger waiting for a bus to a target bus to be arrived and the like is lost. The method for dynamically monitoring the congestion degree inside the carriage to reflect the comfort degree of the bus has been proposed, but the method cannot reflect the actual situation of the bus comprehensively, truly and in real time, and cannot meet the requirement of passengers for obtaining the actual comfort degree information of the bus.
Disclosure of Invention
In order to solve the problem that comprehensive and real bus comfort degree information cannot be obtained in the prior art, the invention can provide a bus comfort degree query method, a system, equipment and a storage medium for intelligent travel research and judgment, so as to fulfill the aim of meeting comprehensive and reliable comfort degree information acquisition requests of passengers for target buses.
In order to achieve the technical purpose, the invention discloses a bus comfort inquiry method for intelligent travel research and judgment, which can include but is not limited to one or more of the following steps.
And determining the target bus according to the bus comfort inquiry request from the user terminal.
And acquiring intelligent sensing data corresponding to the target bus, wherein the intelligent sensing data comprises bus related data and external environment data.
And creating a first weight vector of a first influence factor of the comfort level of the target bus based on a first set evaluation factor set, wherein the first influence factor comprises bus running factors, in-bus environment factors, body attribute factors and external environment factors.
And constructing a comprehensive evaluation matrix of the first influence factor of the comfort level of the target bus based on the intelligent perception data.
And forming a comfort degree vector through the first weight vector and the comprehensive evaluation matrix.
Generating comfort information using the comfort vector.
And sending the comfort level information to the user terminal to respond to the query request.
Further, the constructing a comprehensive evaluation matrix of the first influence factor of the comfort level of the target bus based on the intelligent perception data comprises:
and creating a first single-factor evaluation vector of each first influence factor based on the intelligent perception data.
And constructing a comprehensive evaluation matrix through the first single-factor evaluation vectors of all the first influencing factors.
Further, the creating a first single-factor evaluation vector for each of the first influencing factors based on the smart perception data includes:
creating a second weight vector of a second influence factor of the comfort level of the target bus based on a second set of evaluation factors; wherein the second influence factor is subordinate to the first influence factor.
And constructing a single-factor evaluation matrix of a second influence factor of the comfort level of the target bus based on the intelligent perception data.
And forming a first single-factor evaluation vector through the second weight vector and the single-factor evaluation matrix.
Wherein, the second influence factor of being affiliated to the public transit operation factor includes: the vehicle running speed factor and the vehicle running stability factor, and the second influence factors subordinate to the in-vehicle environment factors comprise: the second influence factors belonging to the vehicle body attribute factors comprise: a power type factor and a service age factor, and the second influence factor subordinate to the external environment factor includes: ambient temperature factors, wind level factors, precipitation factors, and snowfall factors.
Further, the creating a second weight vector for a second influencing factor of the target bus comfort based on the smart perception data comprises:
and comparing the evaluation factors in the second set evaluation factor set, and creating a second weight vector of a second influence factor of the comfort degree of the target bus according to the comparison result.
Further, the constructing of the single-factor evaluation matrix of the second influence factor of the comfort level of the target bus based on the intelligent perception data comprises:
and creating a second single-factor evaluation vector of each second influence factor based on the intelligent perception data.
And forming a single-factor evaluation matrix through the second single-factor evaluation vector.
Further, the creating a second single-factor evaluation vector for each second influencing factor based on the smart perception data includes:
and mapping the intelligent perception data into second single-factor evaluation vectors of the second influence factors by utilizing a preset trapezoidal distribution function.
Further, the creating a first weight vector for a first influencing factor of the target bus comfort based on the smart perception data comprises:
and comparing the evaluation factors in the first set evaluation factor set, and creating a first weight vector of the first influence factor of the comfort degree of the target bus according to the comparison result.
In order to achieve the technical purpose, the invention can also provide an inquiry system for bus comfort level for intelligent travel research and judgment, wherein the inquiry system comprises but is not limited to a traffic service platform and a data center.
And the traffic service platform is used for determining a target bus according to the inquiry request of the bus comfort degree from the user terminal.
And the data center station is in communication connection with the traffic service platform and is used for acquiring intelligent sensing data corresponding to the target bus. The intelligent perception data comprises bus related data and external environment data.
The data center is also used for calling a comfort degree calculation model, calculating comfort degree information according to the comfort degree calculation model and the intelligent perception data,
the comfort degree calculation model is used for establishing a first weight vector of a first influence factor of the comfort degree of the target bus based on a first set evaluation factor set, constructing a comprehensive evaluation matrix of the first influence factor of the comfort degree of the target bus based on the intelligent perception data, passing through the first weight vector and the comprehensive evaluation matrix to form a comfort degree vector, and utilizing the comfort degree vector to generate comfort degree information, wherein the first influence factor comprises bus operation factors, in-vehicle environment factors, vehicle body attribute factors and external environment factors.
The traffic service platform is further configured to send the comfort level information to the user terminal to respond to the query request.
In order to achieve the above technical object, the present invention can also provide a computer device, which may include a memory and a processor, wherein the memory stores computer readable instructions, and the computer readable instructions, when executed by the processor, cause the processor to execute the steps of the bus comfort query method for intelligent travel study and judgment according to any embodiment of the present invention.
To achieve the above technical objects, the present invention may also provide a storage medium storing computer readable instructions, which when executed by one or more processors, cause the one or more processors to perform the steps of the method for inquiring bus comfort for intelligent travel study and judgment according to any embodiment of the present invention.
The invention has the beneficial effects that: the method can take the multidimensional first influence factors influencing the comfort level of the bus as the basis, pre-construct a comfort level calculation model, provide intelligent traffic comfort level information inquiry service for users, acquire real-time intelligent sensing data corresponding to the target bus based on the target bus information in the inquiry request, construct a comprehensive evaluation matrix of the first influence factors of the comfort level of the target bus based on the intelligent sensing data, acquire a first weight vector of the first influence factors of the comfort level of the target bus in the comfort level calculation model, form a comfort level vector through the first weight vector and the comprehensive evaluation matrix, and generate comfort level information by using the comfort level vector, thereby achieving the purpose of comprehensively, truly and real-timely reflecting the comfort level of the bus. Moreover, the invention can also provide visual quantized comfort level information for the user, guide public trip through the quantized comfort level information, provide a study and judgment basis for intelligent trip, fully meet the requirement of the user for acquiring the real comfort level information, and have better user experience and very high user satisfaction.
The bus comfort information can meet the requirements of real-time query and acquisition, and provides a research and judgment reference for the trip of a user based on comprehensive, real and real-time comfort information provided for passengers.
Drawings
Fig. 1 is a flow chart illustrating a method for querying bus comfort for intelligent travel research and judgment according to one or more embodiments of the present invention.
Fig. 2 is a schematic diagram illustrating an architecture of an overall system for acquiring bus comfort data in real time according to one or more embodiments of the present invention.
FIG. 3 is a schematic diagram illustrating a hierarchy of influencing factors influencing bus comfort in one or more embodiments of the present invention.
FIG. 4 is a schematic diagram illustrating the internal architecture of a computing device in accordance with one or more embodiments of the invention.
Detailed Description
The invention specifically provides a bus comfort inquiry method, a system, equipment and a storage medium for intelligent travel research and judgment, which are explained and explained in detail in the following by combining the drawings of the specification.
As shown in fig. 1, one or more embodiments of the present invention may specifically provide a method for inquiring bus comfort level for intelligent travel research and judgment, which is used for an intelligent transportation travel service platform. The query method comprehensively collects multiple factors influencing comfort level, and determines comprehensive evaluation indexes and evaluation results. Including, but not limited to, one or more of the following.
Optionally, some embodiments of the present invention further determine a target bus stop according to a query request for bus comfort level from a user terminal, and further can obtain related environmental data near the stop based on the target bus stop.
The vehicle running speed refers to the running speed of the target bus, and the larger the value of the vehicle running speed, the shorter the time for reaching the next station, and the higher the comfort level of passengers.
The vehicle running stability refers to the number of three-step speeds in a unit kilometer of a target bus, the larger the numerical value of the vehicle running stability is, the lower the comfort level of passengers is, and the forward processing is performed on the vehicle running stability index by the embodiment.
The in-vehicle temperature refers to the temperature in the target bus. Because the comfort level is decreased when the value is too large or too small according to the human body sensing temperature range, the present embodiment may perform the moderation processing on the human body sensing temperature range, for example, the processing is specifically performed according to a calculation manner of T2 being 1/(1+ | T1-a |), where T1 represents an actual temperature value, T2 represents standard data after the moderation processing, a is used to represent an optimal temperature value, and a is 25 ℃.
The ambient temperature refers to the ambient temperature outside the target bus. Because the comfort level is decreased when the value is too large or too small according to the human body sensing temperature range, the present embodiment may perform the moderation process on the human body sensing temperature range, for example, the process is specifically performed according to a calculation manner of T4 being 1/(1+ | T3-B |), wherein T3 represents an actual external temperature value, T4 represents standard data after the moderation process, and B represents an optimal temperature value, for example, B being 25 ℃.
The in-vehicle congestion degree refers to a congestion index in a target bus, and the larger the numerical value of the in-vehicle congestion degree is, the lower the comfort level of passengers is, and the forward processing is performed on the in-vehicle congestion degree index in the embodiment.
The in-vehicle noise refers to a noise value in the target bus, the larger the value of the in-vehicle noise is, the lower the comfort level of the passengers is, and the in-vehicle noise index is subjected to forward processing in the embodiment.
The number of the old people refers to the number of the old passengers in the target bus, and is obtained through card swiping data or video identification results of the old bus cards. In consideration of the specificity of the elderly, a large space needs to be provided, and therefore, the larger the numerical value, the lower the comfort of the passenger, and the present embodiment performs a forward processing on the evaluation index.
The power type refers to a power source of a target bus, and generally comprises a gas type and an electric type, wherein the electric type bus is high in comfort level.
The service age refers to the service life of the target bus, the larger the value of the service age is, the lower the comfort of passengers is, and the service age index is subjected to forward processing in the embodiment.
The external wind level refers to the external environmental wind level or wind speed of the target bus, and the larger the value of the external wind level is, the more comfortable the inside of the bus is, and the higher the psychological comfort level of passengers is.
The external precipitation or snowfall refers to the external precipitation or snowfall condition of the target bus, and is similar to the external wind level condition, and the larger the numerical value is, the higher the psychological comfort level of passengers is.
Next, the evaluation set of the comfort-related data of the present embodiment and the five-level threshold value in the evaluation set V ═ poor V are described by a table1D difference v2,General v3Good v4Very good v5}。
The invention discloses a method for establishing a first weight vector of a first influence factor of the comfort degree of a target bus based on a first set evaluation factor set, which comprises the following steps: and comparing the evaluation factors in the first set evaluation factor set, and creating a first weight vector of the first influence factor of the comfort degree of the target bus according to the comparison result. The above process may specifically include constructing a judgment matrix according to the comparison result, normalizing each column parameter in the judgment matrix by column, and performing an average processing after summing the normalized matrix by row to obtain the first weight vector.
This embodiment determines that the matrix a is (a)ij)n×n，aij＞0，aij＝Ci：Cj＝1/aji。
Wherein, CiA quantitative value C representing the importance of the ith evaluation factor in the first set of evaluation factorsjA quantized value representing the degree of importance of the jth evaluation factor in the first set of evaluation factors, aijThe element indicating the ith row and the jth column in the matrix a, and n indicates the number of evaluation factors in the first set evaluation factor set.
For example, the determination matrix of the present embodiment
The first weight vector T is obtained {0.27730.58800.06730.0673 }.
And 103, constructing a comprehensive evaluation matrix of the first influence factor of the comfort level of the target bus based on the intelligent sensing data.
In one embodiment of the invention, a first influence factor and a second influence factor of the bus comfort level are set based on an analytic hierarchy process, wherein the first influence factor is a first-layer evaluation factor, the second influence factor is a second-layer evaluation factor, and the second-layer evaluation factor is subordinate to the first-layer evaluation factor. Specifically, the method for constructing the comprehensive evaluation matrix of the first influence factor of the comfort level of the target bus based on the intelligent sensing data comprises the following steps: and establishing a first single-factor evaluation vector of each first influence factor based on the intelligent sensing data, wherein the first single-factor evaluation vector represents a vector of a first influence factor numerical range determined based on the second influence factor, and then establishing a comprehensive evaluation matrix through the first single-factor evaluation vectors of all the first influence factors.
Optionally, the creating a first single-factor evaluation vector of each first influence factor based on the intelligent sensing data in the embodiment of the present invention includes: creating a second weight vector of a second influence factor of the comfort level of the target bus based on a second set of evaluation factors; wherein the second influencing factor is subordinate to the first influencing factor; constructing a single-factor evaluation matrix of second influence factors of the comfort level of the target bus based on the intelligent sensing data, wherein the single-factor evaluation matrix represents a matrix of the numerical distribution condition of a plurality of second influence factors under a single first influence factor; and forming a first single-factor evaluation vector through the second weight vector and the single-factor evaluation matrix.
As shown in fig. 3, the invention specifically constructs a two-layer hierarchical structure model of the comfort level of the bus based on an analytic hierarchy process. The second influencing factor belonging to the bus running factor in the embodiment includes: the vehicle running speed factor and the vehicle running stability factor, and the second influence factors belonging to the environmental factors in the vehicle comprise: the second influence factors belonging to the vehicle body attribute factors comprise: the power type factor and the service age factor, and the second influence factor belonging to the external environment factor comprises: ambient temperature factors, wind level factors, precipitation factors, and snowfall factors.
Optionally, the present invention creates a target bus comfort based on a second set of evaluation factorsThe second weight vector of the second influence factor of degrees comprises: and comparing the set evaluation factor sets in the second set evaluation factor set, and creating a second weight vector of a second influence factor of the comfort level of the target bus according to the comparison result. Similar to the process of calculating the first weight vector, the determination matrix B in this embodiment is (B)ij)m×m，bij＞0，bij＝Pi：Pj＝1/bji. Wherein, PiA quantized value, P, representing the importance of the ith evaluation factor in the second set of evaluation factorsjA quantized value representing the degree of importance of the jth evaluation factor in the second set of evaluation factors, bijThe element in the ith row and the jth column in the matrix B is represented, and m represents the number of evaluation factors in the second set evaluation factor set. For example, the following second weight vector is obtained: {0.2500,0.7500},{0.2216,0.6263,0.0761,0.0761},{0.7500,0.2500},{0.0780,0.1046,0.4191,0.3982}.
In connection with the example of the first weight vector, the present embodiment may list the first weight vector of the first influencing factor and the second weight vector of the second influencing factor by the following table.
Optionally, the constructing of the single-factor evaluation matrix of the second influence factor of the comfort level of the target bus based on the intelligent sensing data in the embodiment of the present invention includes: and creating second single-factor evaluation vectors of the second influence factors based on the intelligent sensing data, and forming a single-factor evaluation matrix through all the second single-factor evaluation vectors. More specifically, the creating of the second single-factor evaluation vector of each second influence factor based on the smart sensing data in this embodiment includes: and mapping the intelligent perception data into second single-factor evaluation vectors of each second influence factor by using a preset trapezoidal distribution function, which is specifically described as follows.
The trapezoidal distribution function of one or more embodiments of the present invention is represented by, in particular, where ri1，ri2，ri3，ri4，ri5Denotes a second one-factor evaluation vector, x denotes the value of a second influencing factor, α1、α2、α3、α4、α5A threshold value representing five levels in the evaluation set as above.
In an example, the current speed per hour of an electric bus which is put into operation in this month is 45km/h, the speed of the electric bus is 5 times at a speed of about three kilometers, the temperature in the automobile is 25 ℃, the crowding degree is 0.2, the noise in the automobile is 25 decibels, no old people are in the automobile, the external temperature is 25 ℃, the external wind speed is 30km/h, and the external precipitation is 55 mm.
The second influencing factor is the vehicle speed, x is 45, α1＝10，α2＝20，α3＝30，α4＝40，α5When the evaluation vector is 50, the second one-factor evaluation vector RVehicle running speed＝{0 0 0 0.5 0.5}。
In the same way, RSmoothness of vehicle running＝{0 0 0 0.5 0.5}。
Obtaining a single-factor evaluation matrix through a second single-factor evaluation vector
After the single-factor evaluation matrix and the corresponding second weight vector { 0.25000.7500 } are determined, the first single-factor evaluation vector B corresponding to the bus running is formed in the embodimentPublic transport operation。
Similar to the obtaining of the first single-factor evaluation vector B corresponding to the bus operationPublic transport operationThe present embodiment determines the first one-factor evaluation vector B corresponding to the in-vehicle environment by the following equationIn-vehicle environmentFirst one-factor evaluation vector B corresponding to vehicle body attributeVehicle body PropertiesAnd a first one-factor evaluation vector B corresponding to the external environmentExternal environment。
On this basis, the comprehensive evaluation matrix R of the first influence factor of the comfort level of the target bus can be constructed based on the intelligent perception data.
And 104, forming a comfort level vector through the first weight vector and the comprehensive evaluation matrix. In specific implementation, some embodiments of the invention fuse the bus related data and the external environment data, and realize the generation process of the weight vector and the generation process of the comprehensive evaluation matrix through a bus comfort degree calculation model on the basis of data fusion so as to determine the first weight vector and the comprehensive evaluation matrix and further determine the comfort degree vector.
The present example synthesizes the evaluation matrix as above
For example, the first weight vector is given by T ═ {0.27730.58800.06730.0673} as above, and the comfort vector S can be calculated as follows:
Some embodiments of the invention can determine quantifiable comfort information according to a preset score calculation mode, and can calculate the comfort score by using a score definition vector and a comfort vector. The score definition vector is, for example, {20, 40, 60, 80, 100}, and the comfort score u is calculated as follows.
u＝20×0+40×0+60×0+80×0.13865+100×0.86125＝97.217
Based on the above-mentioned predetermined score calculation method, the current comfort level information of the embodiment of the present invention is specifically 97.217 scores.
Optionally, the maximum value may also be selected from the comfort level vectors as the final result according to the maximum membership rule, for example, 0.86125 may correspond to a preset evaluation set value or range, and the current in-vehicle comfort level information of the target bus is considered to be "good".
As shown in fig. 2, in the embodiment of the present invention, the bus data and the bus station environmental data acquired by the intelligent sensing device are uploaded to the data center station, and in the process, the acquired data may be uploaded to the IOT platform first and then uploaded to the data center station by the IOT (Internet of Things) platform. The intelligent sensing equipment comprises but is not limited to temperature sensing equipment, humidity sensing equipment, noise sensing equipment, video acquisition equipment and the like, and is arranged in a bus or a bus stop according to the acquisition purpose, the information of wind level, precipitation or snowfall amount can be determined through the meteorological information of the area where the current bus stop is located or the administrative area, and the information related to the bus body and the bus running information can be directly acquired through a data center. The data processing process can be realized by taking the constructed public transport comfort degree calculation model as a calculation engine, and the data processing result is fed back to the traffic service platform so as to respond to the request of the intelligent terminal; therefore, the method for studying and judging the comfort level of the bus in real time based on the intelligent traffic service platform can be provided.
As shown in fig. 2, based on the same technical concept as the query method, the invention can also provide a bus comfort query system for intelligent travel study and judgment. The query system in the invention includes but is not limited to a traffic service platform and a data center.
The traffic service platform is used for determining a target bus according to a bus comfort level query request from the user terminal.
The data center is in communication connection with the traffic service platform and used for acquiring intelligent sensing data corresponding to the target bus. The intelligent sensing data comprises bus related data and external environment data.
The data center is also used for calling a comfort degree calculation model, and the comfort degree information is calculated according to the comfort degree calculation model and the intelligent perception data.
The comfort degree calculation model is used for creating a first weight vector of a first influence factor of the comfort degree of the target bus based on a first set evaluation factor set, constructing a comprehensive evaluation matrix of the first influence factor of the comfort degree of the target bus based on intelligent perception data, forming a comfort degree vector through the first weight vector and the comprehensive evaluation matrix, and generating comfort degree information by utilizing the comfort degree vector, wherein the first influence factor comprises bus operation factors, in-vehicle environment factors, vehicle body attribute factors and external environment factors.
And the traffic service platform is also used for sending comfort level information to the user terminal so as to respond to the query request.
Optionally, the comfort level calculation model can be configured to compare the evaluation factors in the first set of evaluation factors, and create a first weight vector of the first influencing factor of the comfort level of the target bus according to the comparison result. Specifically, the comfort level calculation model is used for creating a first single-factor evaluation vector of each first influence factor based on the intelligent perception data, and is used for constructing a comprehensive evaluation matrix through the first single-factor evaluation vectors of all the first influence factors. More specifically, the comfort level calculation model can be used for creating a second weight vector of a second influence factor of the comfort level of the target bus based on a second set evaluation factor set, and is used for constructing a single-factor evaluation matrix of the second influence factor of the comfort level of the target bus based on the intelligent perception data; the comfort degree calculation model is used for forming a first single-factor evaluation vector through the second weight vector and the single-factor evaluation matrix. The second influencing factor in the present embodiment is subordinate to the first influencing factor. In this embodiment, the second influencing factors belonging to the bus running factors include a vehicle running speed factor and a vehicle running stability factor, the second influencing factors belonging to the in-vehicle environment factors include an in-vehicle temperature factor, an in-vehicle crowding factor, an in-vehicle noise factor and an old people number factor, the second influencing factors belonging to the vehicle body attribute factors include a power type factor and a service age factor, and the second influencing factors belonging to the external environment factors include an environment temperature factor, a wind level factor, a precipitation factor and a snowfall factor. Optionally, the comfort level calculation model can be configured to compare the evaluation factors in the second set of evaluation factors, and to create a second weight vector of the second influencing factor of the target bus comfort level from the comparison result. The comfort level calculation model may be used to create second one-factor evaluation vectors for each second influencing factor based on the smart perception data, and to form a one-factor evaluation matrix from all the second one-factor evaluation vectors. The comfort level calculation model in this embodiment is specifically configured to map the intelligent sensing data to second single-factor evaluation vectors of the second influence factors by using a preset trapezoidal distribution function.
In a preferred embodiment, the data center station further includes an IOT platform (internet of things platform), where an intelligent sensing device for collecting intelligent sensing data is connected to the IOT platform, and the IOT platform is compatible with the intelligent sensing data sent by each intelligent sensing device, and the compatibility of the data center station with each intelligent device is improved by setting the IOT platform.
Specifically, with the data query system according to the above embodiment, the intelligent transportation service platform obtains a query request from the user terminal, calls the real-time intelligent sensing data stored in the data center station corresponding to the query request according to the query request, and calls the bus comfort level calculation model preset in the calculation engine, the intelligent sensing data is uploaded to the data center station in real time, the data center station calculates the comfort level information in real time according to the obtained intelligent sensing data corresponding to the target bus and the pre-established comfort level calculation model, and sends the comfort level information to the user terminal that sent the query request to respond to the query request, in the above embodiment, the intelligent transportation service platform for receiving and processing the comfort level query request and the data center station for storing the intelligent transportation sensing data are deployed in a distributed manner, and the comfort level calculation model is stored and the calculation engine, the computing engine can be stored in the data center platform or can be independently arranged, and the computing engine, the data center platform and the intelligent traffic service platform are distributed and deployed, so that the computing flexibility and the computing efficiency of the system are improved.
As shown in fig. 4, based on the same technical concept as the method for inquiring the comfort level of the intelligent travel study and judgment bus according to the present invention, the present invention can further provide a computer device, where the computer device may include a memory and a processor, and the memory stores computer readable instructions, and when the computer readable instructions are executed by the processor, the processor executes the steps of the method for inquiring the comfort level of the intelligent travel study and judgment bus according to any embodiment of the present invention. The specific inquiry method for the comfort level of the bus is described in detail in this specification, and is not described herein again.
As shown in fig. 4, based on the same technical concept as the method for inquiring bus comfort level for intelligent travel study and judgment of the present invention, the present invention can also provide a storage medium storing computer readable instructions, which when executed by one or more processors, cause the one or more processors to perform the steps of the method for inquiring bus comfort level for intelligent travel study and judgment of any embodiment of the present invention. The specific inquiry method for the comfort level of the bus is described in detail in this specification, and is not described herein again.
The logic and/or steps represented in the flowcharts or otherwise described herein, such as an ordered listing of executable instructions that can be considered to implement logical functions, can be embodied in any computer-readable storage medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. For the purposes of this description, a "computer-readable storage medium" can be any means that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: an electrical connection (electronic device) having one or more wires, a portable computer cartridge (magnetic device), a Random Access Memory (RAM), a Read-Only Memory (ROM), an Erasable Programmable Read-Only Memory (EPROM-Only Memory, or flash Memory), an optical fiber device, and a portable Compact Disc Read-Only Memory (CDROM). Additionally, the computer-readable storage medium may even be paper or another suitable medium upon which the program is printed, as the program can be electronically captured, via for instance optical scanning of the paper or other medium, then compiled, interpreted or otherwise processed in a suitable manner if necessary, and then stored in a computer memory.
It should be understood that portions of the present invention may be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, the various steps or methods may be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, any one or combination of the following techniques, which are known in the art, may be used: a discrete logic circuit having a logic Gate circuit for implementing a logic function on a data signal, an application specific integrated circuit having an appropriate combinational logic Gate circuit, a Programmable Gate Array (PGA), a Field Programmable Gate Array (FPGA), or the like.
In the description herein, references to the description of the term "the present embodiment," "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., mean 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 present 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.
Furthermore, the terms "first", "second" and "first" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature. In the description of the present invention, "a plurality" means at least two, e.g., two, three, etc., unless specifically limited otherwise.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents and simplifications made in the spirit of the present invention are intended to be included in the scope of the present invention.
Claims (10)
1. The utility model provides an inquiry method that is used for bus comfort level that wisdom trip was studied and judged, is used for wisdom transportation trip service platform, its characterized in that includes:
determining a target bus according to a bus comfort inquiry request from a user terminal;
acquiring intelligent sensing data corresponding to the target bus, wherein the intelligent sensing data comprises bus related data and external environment data;
establishing a first weight vector of first influence factors of the comfort level of the target bus based on a first set evaluation factor set, wherein the first influence factors comprise bus running factors, in-bus environment factors, body attribute factors and external environment factors;
constructing a comprehensive evaluation matrix of a first influence factor of the comfort level of the target bus based on the intelligent perception data;
forming a comfort level vector through the first weight vector and the comprehensive evaluation matrix;
generating comfort information using the comfort vector;
and sending the comfort level information to the user terminal to respond to the query request.
2. The method for inquiring the comfort level of the bus according to claim 1, wherein the constructing a comprehensive evaluation matrix of the first influencing factor of the comfort level of the target bus based on the intelligent perception data comprises:
creating a first single-factor evaluation vector of each first influence factor based on the intelligent perception data;
and constructing a comprehensive evaluation matrix through the first single-factor evaluation vectors of all the first influencing factors.
3. The method for inquiring the comfort level of the bus according to claim 2, wherein the creating a first single-factor evaluation vector of each of the first influencing factors based on the smart perception data comprises:
creating a second weight vector of a second influence factor of the comfort level of the target bus based on a second set of evaluation factors; wherein the second influence factor is subordinate to the first influence factor;
constructing a single-factor evaluation matrix of a second influence factor of the comfort level of the target bus based on the intelligent perception data;
forming a first single-factor evaluation vector through the second weight vector and the single-factor evaluation matrix;
wherein, the second influence factor of being affiliated to the public transit operation factor includes: the vehicle running speed factor and the vehicle running stability factor, and the second influence factors subordinate to the in-vehicle environment factors comprise: the second influence factors belonging to the vehicle body attribute factors comprise: a power type factor and a service age factor, and the second influence factor subordinate to the external environment factor includes: ambient temperature factors, wind level factors, precipitation factors, and snowfall factors.
4. The method of claim 3, wherein the creating a second weight vector for a second influencing factor of the target bus comfort level based on the smart perception data comprises:
and comparing the evaluation factors in the second set evaluation factor set, and creating a second weight vector of a second influence factor of the comfort degree of the target bus according to the comparison result.
5. The method for inquiring the comfort level of the bus according to claim 3, wherein the constructing the single-factor evaluation matrix of the second influencing factor of the comfort level of the target bus based on the intelligent perception data comprises:
creating a second single-factor evaluation vector of each second influence factor based on the intelligent perception data;
and forming a single-factor evaluation matrix through the second single-factor evaluation vector.
6. The method for inquiring the comfort level of the bus according to claim 5, wherein the creating a second single-factor evaluation vector of each of the second influencing factors based on the smart perception data comprises:
and mapping the intelligent perception data into second single-factor evaluation vectors of the second influence factors by utilizing a preset trapezoidal distribution function.
7. The method of claim 1, wherein the creating a first weight vector of a first influencing factor of the target bus comfort level based on the smart perception data comprises:
and comparing the evaluation factors in the first set evaluation factor set, and creating a first weight vector of the first influence factor of the comfort degree of the target bus according to the comparison result.
8. The utility model provides an inquiry system that is used for bus comfort level that wisdom trip was studied and judged which characterized in that includes:
the traffic service platform is used for determining a target bus according to a bus comfort level query request from the user terminal;
the data center station is in communication connection with the traffic service platform and is used for acquiring intelligent sensing data corresponding to the target bus, and the intelligent sensing data comprises bus related data and external environment data;
the data center is also used for calling a comfort degree calculation model, calculating comfort degree information according to the comfort degree calculation model and the intelligent perception data,
the comfort degree calculation model is used for creating a first weight vector of a first influence factor of the comfort degree of the target bus based on a first set evaluation factor set, constructing a comprehensive evaluation matrix of the first influence factor of the comfort degree of the target bus based on the intelligent perception data, forming a comfort degree vector through the first weight vector and the comprehensive evaluation matrix, and generating comfort degree information by using the comfort degree vector, wherein the first influence factor comprises bus operation factors, in-vehicle environment factors, vehicle body attribute factors and external environment factors;
the traffic service platform is further configured to send the comfort level information to the user terminal to respond to the query request.
9. A computer device comprising a memory and a processor, the memory having stored therein computer readable instructions which, when executed by the processor, cause the processor to perform the steps of the method of querying bus comfort for intelligent travel study as claimed in any one of claims 1 to 7.
10. A storage medium storing computer readable instructions which, when executed by one or more processors, cause the one or more processors to perform the steps of the method of querying bus comfort for intelligent travel study as claimed in any one of claims 1 to 7.
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