CN112015776B - Passenger travel analysis application method based on cloud computing technology - Google Patents
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Abstract
The invention provides a passenger travel analysis application method based on a cloud computing technology, which is different from the prior art, and only predicts the passenger travel situation in a single area and a single time period, can obtain the estimated passenger travel times in any time period according to the historical passenger travel people flow, the area temperature, the area wind speed and the area precipitation of passenger traffic points in different areas, and can also correct travel time and weather factors of the estimated passenger travel times, so that the finally obtained estimated passenger travel times can comprehensively and accurately reflect the passenger appearance situations in any area and any time, and the pre-judgment and reliability of the passenger travel are improved.
Description
Technical Field
The invention relates to the technical field of travel big data processing, in particular to a passenger travel analysis application method based on a cloud computing technology.
Background
With the development of social economy and the improvement of living standard, residents can choose to travel to different areas in different time periods. Because of the huge number of the base numbers of the passengers in China, if a large number of passengers travel to the same area in the same period, the traffic pressure of the corresponding area can be increased, and meanwhile, the corresponding area can become crowded. If the travel mode, travel time period or travel destination of the passengers can be effectively and accurately predicted and analyzed, the traffic pressure and the passenger reception pressure of the corresponding region can be relieved by adopting targeted measures. The prior art only predicts the travel number of passengers in a specific holiday time based on the historical number of passengers in a hot tourist attraction, but the manner cannot comprehensively and accurately calculate the travel times of the passengers in all areas in a specified time period, which seriously reduces the reliability of analysis and prediction of the travel of the passengers.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides a passenger travel analysis application method based on a cloud computing technology, which is characterized in that the passenger travel analysis application method based on the cloud computing technology is used for obtaining the passenger history travel image information corresponding to different passenger traffic points in a preset area, generating corresponding historical passenger travel people flow according to the passenger history travel image information, carrying out weather monitoring on the preset area so as to obtain a plurality of different weather parameters of the preset area at preset time, determining the weather grade evaluation value of the preset area, obtaining the preliminary estimated passenger travel people of the preset area according to the historical passenger travel people flow, and correcting the preliminary estimated passenger travel people according to the new weather grade evaluation value, the holiday time distribution and the preset event occurrence time distribution, thereby obtaining the final estimated passenger travel people of the preset area; therefore, the passenger travel analysis application method of the cloud computing technology is different from the prior art, the passenger travel situation prediction is only carried out for a single area and a single time period, the estimated passenger travel times in any time period can be obtained according to the historical passenger travel people flow, the area temperature, the area wind speed and the area precipitation of different area passenger traffic points, and the estimated passenger travel times can be corrected for travel time and weather factors, so that the finally obtained estimated passenger travel times can comprehensively and accurately reflect the passenger appearance situations in any area and any time, and the pre-judgment and reliability of the passenger travel are improved. Further, the technical scheme can send preset information to the preset terminal located in the preset area according to the obtained final expected traveler travel times of the preset area (the preset information can comprise the final expected traveler travel times of the preset area, or a travel service personnel allocation scheme generated according to the final expected traveler travel times of the preset area, or an equipment allocation scheme for the traveler generated according to the final expected traveler travel times of the preset area, and the like), so that a passenger management or service department in the preset area is helped to perform reasonable service personnel allocation or equipment allocation according to the preset information, and service efficiency and passenger satisfaction of the traveler are improved.
The invention provides a passenger travel analysis application method based on a cloud computing technology, which comprises the following steps:
step S1, acquiring historical travel image information of passengers corresponding to different passenger traffic points in a preset area, generating corresponding historical passenger travel traffic according to the historical travel image information of the passengers, and storing the historical passenger travel traffic to a cloud;
step S2, carrying out weather monitoring on the preset area so as to obtain a plurality of different weather parameters of the preset area at preset time, thereby determining weather grade evaluation values of the preset area, wherein the plurality of different weather parameters comprise temperature, wind speed and precipitation;
step S3, updating the weather grade evaluation value obtained in the step S2 according to the relative time relation between the planned travel time and the current actual time of the passenger, so as to obtain a new weather grade evaluation value;
step S4, obtaining preliminary estimated traveler travel times of the preset area according to the historical traveler travel times, and correcting the preliminary estimated traveler travel times according to the new weather grade evaluation value, the holiday time distribution and the predetermined event occurrence time distribution so as to obtain final estimated traveler travel times of the preset area;
and S5, generating preset information according to the final expected number of passengers traveling in the preset area, and sending the preset information to a preset terminal in the preset area.
Further, in the step S1, the historical travel image information of the passengers corresponding to different passenger traffic points in the preset area is obtained, the corresponding historical passenger travel traffic is generated according to the historical travel image information of the passengers, the historical passenger travel traffic is stored in the cloud terminal to be included,
step S101, arranging camera monitoring equipment at each passenger transportation point in the preset area, and shooting a plurality of pieces of passenger history travel image information corresponding to different areas inside the passenger transportation point through the camera monitoring equipment;
step S102, performing person identification processing on the historical travel image information of the plurality of passengers, so as to determine the personnel presence state information corresponding to each passenger transportation point;
step S103, generating corresponding historical passenger pedestrian traffic according to the personnel presence state information, and storing the historical passenger pedestrian traffic to a cloud;
further, in the step S101, an image capturing monitor device is arranged at each passenger transportation point in the preset area, and a plurality of pieces of passenger history travel image information corresponding to different areas inside the passenger transportation point are captured by the image capturing monitor device,
at least two camera monitoring devices are arranged at each passenger transportation point, so that a plurality of multi-angle passenger history trip image information corresponding to different areas inside the passenger transportation point are shot;
the method comprises the steps of,
in the step S102, the person recognition processing is performed on the plurality of passenger history travel image information, so as to determine that the person presence status information corresponding to each passenger traffic point specifically includes,
calculating parallax images corresponding to the multi-angle passenger history travel image information, and identifying corresponding person existence positions and person motion paths from the parallax images to serve as the person existence state information;
the method comprises the steps of,
in the step S103, corresponding historical passenger pedestrian traffic is generated according to the personnel presence status information, and the historical passenger pedestrian traffic is stored in the cloud terminal,
according to the person existence position and the person movement path, calculating historical passenger travel traffic corresponding to the passenger traffic point, and simultaneously storing the historical passenger travel traffic to a cloud;
further, in the step S2, weather monitoring is performed on the preset area, so as to obtain a plurality of different weather parameters of the preset area at a predetermined time, so as to determine weather level evaluation values of the preset area specifically includes,
step S201, arranging weather monitoring equipment at a plurality of different positions in the preset area, and obtaining the temperature, the wind speed and the precipitation of the corresponding positions at the preset moment through the weather monitoring equipment;
step S202, calculating the weather grade evaluation value of the preset area according to the temperature, wind speed and precipitation obtained in the step S201 and the following formula (1)
In the above formula (1), it is shown that at the predetermined time t 0 Weather level evaluation values of the preset area,indicating at a predetermined time t 0 Temperature value, T, of said predetermined region of (C) z The optimal temperature value suitable for the travel of passengers is represented, and the optimal temperature value is 25℃ ->Indicating at a predetermined time t 0 Wind power grade value, F of said predetermined region z Representing the optimal wind power grade value suitable for the traveling of passengers, wherein the optimal wind power grade value is 6 # -and # ->Indicating at a predetermined time t 0 Precipitation of said predetermined region, J z The optimal precipitation amount suitable for the traveling of the passengers is represented, and the value of the optimal precipitation amount is 400mm;
further, in the step S3, the weather grade evaluation value obtained in the step S2 is updated according to the relative time relationship between the planned travel time and the current actual time of the passenger, so as to obtain a new weather grade evaluation value specifically including,
updating the weather grade evaluation value obtained in the above step S2 according to the following formula (2)
In the above-mentioned formula (2),indicating at a new time t 1 New weather rating value after update,/-for (a) a new weather rating value after update>Indicated at time t 0 Temperature value of said preset area of +a, < >>Indicated at time t 0 Wind power level value of said predetermined area of +a, < >>Indicated at time t 0 Precipitation of +a in the predetermined area;
further, in the step S4, according to the historical passenger travel volume, a preliminary estimated passenger travel volume of the preset area is obtained, specifically, according to the historical passenger travel volume and the following formula (3), the preliminary estimated passenger travel volume is calculated and obtained
In the above formula (3), R t,d The corresponding preliminary estimated passenger travel times under the conditions that the passenger is estimated to travel at the time t and the travel place is d are represented,historical passenger pedestrian traffic at the same time t and the same place d on the i-th day of forward pushing on the current date, and n represents the total number of days of forward pushing from the current date;
further, in the step S4, the correcting the preliminary estimated traveler' S travel times in the preset area specifically includes,
performing corresponding correction processing according to the new weather level evaluation value, the holiday time distribution, the predetermined event occurrence time distribution and the following formula (4),
in the above formula (4), the corrected final estimated traveler travel time is represented, Z represents a holiday time point calibration value, z=1 when the estimated traveler travel time is located in the holiday time range, otherwise z= -1, Q represents a predetermined event occurrence time point calibration value, q=1 when the estimated traveler travel time is located in the time range where an emergency event or disaster event occurs, otherwise q= -1, u () represents a step function, the value of the step function is 1 when the value in the bracket of the step function is greater than 0, and the value of the step function is 0 when the value in the bracket of the step function is less than 0;
further, in the step S4, after determining the final estimated passenger trip number of the preset area, any one of the shift density, the ticketing period and the ticketing number is adjusted for the passenger traffic of three aspects of the sea Liu Tian according to the final estimated passenger trip number.
Compared with the prior art, the method for analyzing and applying the travel of the passengers in the cloud computing technology comprises the steps of acquiring historical travel image information of the passengers corresponding to different passenger traffic points in a preset area, generating corresponding historical travel people flow according to the historical travel image information of the passengers, carrying out weather monitoring on the preset area to obtain a plurality of different weather parameters of the preset area at preset time, determining weather grade evaluation values of the preset area according to the travel people flow of the historical passengers, obtaining preliminary estimated travel people of the preset area, and correcting the preliminary estimated travel people of the passengers according to the new weather grade evaluation values, holiday time distribution and preset event occurrence time distribution, so as to obtain final estimated travel people of the preset area; therefore, the passenger travel analysis application method of the cloud computing technology is different from the prior art, the passenger travel situation prediction is only carried out for a single area and a single time period, the estimated passenger travel times in any time period can be obtained according to the historical passenger travel people flow, the area temperature, the area wind speed and the area precipitation of different area passenger traffic points, and the estimated passenger travel times can be corrected for travel time and weather factors, so that the finally obtained estimated passenger travel times can comprehensively and accurately reflect the passenger appearance situations in any area and any time, and the pre-judgment and reliability of the passenger travel are improved.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims thereof as well as the appended drawings.
The technical scheme of the invention is further described in detail through the drawings and the embodiments.
Drawings
In order to more clearly illustrate the embodiments of the invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a schematic flow chart of a passenger travel analysis application method based on a cloud computing technology.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to fig. 1, a flow chart of a passenger travel analysis application method based on a cloud computing technology according to an embodiment of the present invention is shown. The passenger travel analysis application method based on the cloud computing technology comprises the following steps:
step S1, acquiring historical travel image information of passengers corresponding to different passenger traffic points in a preset area, generating corresponding historical passenger travel traffic according to the historical travel image information of the passengers, and storing the historical passenger travel traffic to a cloud;
step S2, carrying out weather monitoring on the preset area so as to obtain a plurality of different weather parameters of the preset area at preset time, thereby determining weather grade evaluation values of the preset area, wherein the plurality of different weather parameters comprise temperature, wind speed and precipitation;
step S3, updating the weather grade evaluation value obtained in the step S2 according to the relative time relation between the planned travel time and the current actual time of the passenger, so as to obtain a new weather grade evaluation value;
step S4, obtaining preliminary estimated traveler travel times of the preset area according to the historical traveler travel times, and correcting the preliminary estimated traveler travel times according to the new weather grade evaluation value, the holiday time distribution and the preset event occurrence time distribution so as to obtain final estimated traveler travel times of the preset area;
according to the method, corresponding historical passenger pedestrian flow, temperature, wind speed, precipitation and other data are processed in a cloud computing mode, so that passenger appearance data of any region in any time period are obtained, original data needed by cloud computing are easy to obtain and large in quantity, accuracy of cloud computing results can be guaranteed, and the method has universality for passenger appearance data of any region in any time period, can be widely applied to hot travel regions or non-hot travel regions, and therefore convenience in calculating the passenger appearance data of different regions is greatly simplified.
And S5, generating preset information according to the final expected number of passengers traveling in the preset area, and sending the preset information to a preset terminal in the preset area.
The preset information may include a final estimated traveler travel number of persons in the preset area, or a travel service personnel allocation scheme (the more persons are needed to be allocated) generated according to the final estimated traveler travel number of persons in the preset area, or an allocation scheme (the more persons are needed to be allocated) of equipment (such as public restrooms, drinking water equipment, vending machines, etc.) for the traveler generated according to the final estimated traveler travel number of persons in the preset area. The preset terminal may be a management client of a travel management department, a management client of a travel service department, or the like.
Further, in said step S5, obtaining travel service personnel and equipment numbers by utilizing said final estimated traveler' S travel times includes,
obtaining the number of travel service staffs according to the final expected number of passengers and the following formula (5),
in the above formula (5), B t,d Representing the number of staffs equipped, and B representing the lowest number of staffs equipped (the specific value is determined according to the regulations of each region, but the lowest value must not be lower than 5); INT []Representing a rounding function (the function value is equal to rounding the value in brackets).
The number of equipment items for the passenger is obtained from the final estimated passenger travel times and the following formula (6),
in the above-mentioned formula (6),indicating the number of equipment items of the o-th device for the passenger, P o Indicating the lowest equipment number of the o-th device for the passenger (specific value according to the regulations of each regionDefinite, but must not be lower than 2 at the lowest for each device);
and recording the number of the personnel allocated by the travel service personnel and the number of the equipment allocated by each type of equipment used by the passengers as preset information.
The method comprises the steps of obtaining historical travel image information of passengers corresponding to different passenger traffic points in a preset area, generating corresponding historical passenger travel people flow according to the historical travel image information of the passengers, carrying out weather monitoring on the preset area, so as to obtain a plurality of different weather parameters of the preset area at preset time, determining weather grade evaluation values of the preset area, obtaining preliminary estimated passenger travel people of the preset area according to the historical passenger travel people flow, and correcting the preliminary estimated passenger travel people according to the new weather grade evaluation values, holiday time distribution and preset event occurrence time distribution, so as to obtain final estimated passenger travel people of the preset area; therefore, the passenger travel analysis application method of the cloud computing technology is different from the prior art, the passenger travel situation prediction is only carried out for a single area and a single time period, the estimated passenger travel times in any time period can be obtained according to the historical passenger travel people flow, the area temperature, the area wind speed and the area precipitation of different area passenger traffic points, and the estimated passenger travel times can be corrected for travel time and weather factors, so that the finally obtained estimated passenger travel times can comprehensively and accurately reflect the passenger appearance situations in any area and any time, and the pre-judgment and reliability of the passenger travel are improved. Further, the technical scheme can send preset information to the preset terminal located in the preset area according to the obtained final expected traveler travel times of the preset area, so that a traveler management or service department in the preset area can reasonably carry out service personnel or equipment according to the preset information, and the service efficiency and the traveler satisfaction of the traveler are improved.
Preferably, in the step S1, the historical travel image information of the passengers corresponding to different passenger traffic points in the preset area is obtained, and the corresponding historical passenger travel traffic is generated according to the historical travel image information of the passengers, and the historical passenger travel traffic is stored in the cloud terminal,
step S101, arranging camera monitoring equipment at each passenger transportation point in the preset area, and shooting a plurality of pieces of passenger history travel image information corresponding to different areas inside the passenger transportation point through the camera monitoring equipment;
step S102, carrying out person identification processing on the historical travel image information of the plurality of passengers so as to determine the personnel presence state information corresponding to each passenger transportation point;
and step S103, generating corresponding historical passenger pedestrian traffic according to the personnel presence state information, and storing the historical passenger pedestrian traffic to the cloud.
Because the passenger traffic points are the necessary places for the passengers to travel, the historical passenger flow rate in the corresponding areas can be comprehensively and accurately obtained by shooting the operation state images of the passenger traffic points, and thus the difficulty in obtaining the historical passenger flow rate can be effectively reduced.
Preferably, in the step S101, an image capturing monitoring device is disposed at each passenger transportation point in the preset area, and a plurality of pieces of passenger history travel image information corresponding to different areas inside the passenger transportation point are captured by the image capturing monitoring device,
at least two camera monitoring devices are arranged at each passenger transportation point, so that a plurality of multi-angle passenger history travel image information corresponding to different areas inside the passenger transportation point are shot;
the method comprises the steps of,
in the step S102, the person recognition processing is performed on the plurality of passenger history travel image information, so as to determine that the person presence status information corresponding to each of the passenger traffic points specifically includes,
calculating parallax images corresponding to the multi-angle passenger history travel image information, and identifying corresponding person existence positions and person movement paths from the parallax images to serve as person existence state information;
the method comprises the steps of,
in the step S103, corresponding historical passenger pedestrian traffic is generated according to the personnel presence status information, and the historical passenger pedestrian traffic is stored in the cloud terminal,
and counting the historical passenger pedestrian traffic corresponding to the passenger traffic point according to the person existence position and the person motion path, and storing the historical passenger pedestrian traffic to the cloud.
Through setting up at least two monitoring facilities of making a video recording and carry out the multi-angle to passenger traffic point and shoot, can avoid effectively having the shooting view angle dead zone of passenger traffic point in shooting process to guarantee the calculation accuracy nature of historical passenger's trip people flow.
Preferably, in the step S2, weather monitoring is performed on the preset area, so as to obtain a plurality of different weather parameters of the preset area at a predetermined time, so as to determine weather level evaluation values of the preset area specifically includes,
step S201, arranging weather monitoring equipment at a plurality of different positions in the preset area, and obtaining the temperature, the wind speed and the precipitation of the corresponding positions at the preset moment through the weather monitoring equipment;
step S202, calculating the weather grade evaluation value of the preset area according to the temperature, wind speed and precipitation obtained in the step S201 and the following formula (1)
In the above formula (1), it is shown that at the predetermined time t 0 Is a weather level evaluation value of the preset area,indicating at a predetermined time t 0 Temperature value, T, of the predetermined region z Representing the most suitable for the travel of passengersOptimal temperature value of 25℃ @,>indicating at a predetermined time t 0 Wind power level value of the preset region, F z Representing the optimal wind power grade value suitable for the travel of passengers, wherein the optimal wind power grade value is 6 # -, and # ->Indicating at a predetermined time t 0 Precipitation of the preset region J z The optimal precipitation amount suitable for the traveler to travel is represented, and the value of the optimal precipitation amount is 400mm.
Because whether the traveler travels in a row is generally influenced by weather factors, the weather grade evaluation value of the determined preset area is calculated through the formula (1), whether the weather state of the preset area is suitable for the traveler travel can be effectively measured, and therefore an effective reference standard is provided for predicting the number of people traveling by the traveler.
Preferably, in the step S3, the weather level evaluation value obtained in the step S2 is updated according to the relative time relationship between the planned travel time and the current actual time of the passenger, so that the new weather level evaluation value specifically includes,
updating the weather grade evaluation value obtained in the above step S2 according to the following formula (2)
In the above-mentioned formula (2),indicating at a new time t 1 New weather rating value after update,/-for (a) a new weather rating value after update>Indicated at time t 0 Temperature value of the predetermined region of +a, < + >>Indicated at time t 0 The wind power level value of the preset area of +a,indicated at time t 0 Precipitation of +a in the predetermined area.
Because weather parameters can change along with the passage of time, the weather grade evaluation value is updated in real time according to the relative time relation between the planned travel time and the current actual time of the passengers, whether the weather state of the preset area is suitable for the passengers to travel or not can be ensured to be more accurately measured by the updated weather grade evaluation value, and therefore effective reference can be provided for the prediction of the travel times of the passengers due to the fact that the weather grade evaluation value changes along with the time.
Preferably, in the step S4, a preliminary estimated traveler travel time in the preset area is obtained according to the historical traveler travel time, specifically, the preliminary estimated traveler travel time is calculated according to the historical traveler travel time and the following formula (3)
In the above formula (3), R t,d The corresponding preliminary estimated passenger travel times under the conditions that the passenger is estimated to travel at the time t and the travel place is d are represented,the historical passenger pedestrian traffic at the same time t and the same place d on the i-th day of forward pushing on the current date is represented, and n represents the total number of days of forward pushing from the current date.
Through the formula (3), the preliminary estimated passenger travel times obtained through accurate prediction can be accurately predicted under the action of fully considering the historical passenger travel times and different weather parameters, so that the preliminary estimated passenger travel times can be ensured to be closer to the actual situation to the greatest extent, and meanwhile, the calculation complexity and the calculation workload of the preliminary estimated passenger travel times are reduced.
Preferably, in this step S4, the correction of the preliminary estimated traveler' S travel times in the predetermined area comprises in particular,
corresponding correction processing is performed based on the new weather level evaluation value, the holiday time distribution, the predetermined event occurrence time distribution and the following formula (4),
in the above formula (4), the corrected final estimated traveler travel time is represented by Z representing a holiday time point calibration value, z=1 when the traveler estimated travel time is located in the holiday time range, otherwise z= -1, Q represents a predetermined event occurrence time point calibration value, q=1 when the traveler estimated travel time is located in the time range where an emergency event or disaster event occurs, otherwise q= -1, u () represents a step function, the value of the step function is 1 when the value in the step function bracket is greater than 0, and the value of the step function is 0 when the value in the step function bracket is less than 0.
The beneficial effects of the technical scheme are as follows: the method comprises the steps that (1) is utilized to obtain the pre-travel times meeting the travel time and place of the passengers, and aims to analyze historical passenger travel traffic data through the formula so as to predict the pre-travel times meeting the travel time and place of the passengers, and compared with the steps without the formula and the steps, the steps and the formulas can intuitively predict the pre-travel times so as to take the configuration of staff such as stations, ticket halls and the like as a reference; the weather grade value of the place when the passenger goes out is obtained by using the formula (2), so that the weather condition of the place when the passenger goes out is analyzed through weather forecast, the pre-travel times meeting the travel time and the place of the passenger are corrected according to the weather condition, the weather grade value is updated and corrected in real time by combining the formula (3) in the step A3, the pre-travel times can be predicted more accurately by adding the weather factors compared with the case without the formula and the step, the travel analysis of the passenger is more accurate, and finally the corrected pre-travel times are obtained by using the formula (4), so that the number of stations can be increased according to the corrected pre-travel times, the corresponding configuration is carried out on staff such as a ticket vending hall, and the travel conditions of the passenger are analyzed through the maximized step and the formula, and the method are accurate and efficient.
The appearance of the passengers is influenced by weather factors, holiday time distribution and scheduled event occurrence time distribution (such as public health events or natural disasters) are also influenced, and the preliminary estimated passenger travel times are corrected under the condition that the holiday time distribution and the scheduled event occurrence time distribution are fully considered, so that the obtained final estimated passenger travel times are more matched with actual conditions, and the prediction accuracy of the final estimated passenger travel times is improved.
Preferably, in the step S4, after determining the final estimated passenger trip number of the preset area, any one of the shift density, the ticketing period and the ticketing quantity is further adjusted according to the final estimated passenger trip number of the passenger traffic of three aspects of the sea Liu Tian.
According to the final estimated passenger occurrence number, any one of the shift density, the ticketing period and the ticketing quantity is adjusted for the passenger traffic of the sea Liu Tian, so that the traffic capacity of the passenger traffic can be effectively improved, the passenger visiting pressure of a preset area can be relieved, and the travel experience of the passenger can be improved to the greatest extent.
As can be seen from the foregoing embodiments, the method for applying the cloud computing technology to travel analysis of passengers includes obtaining historical travel image information of passengers corresponding to different passenger traffic points in a preset area, generating corresponding historical passenger travel traffic according to the historical travel image information of the passengers, performing weather monitoring on the preset area, thereby obtaining a plurality of different weather parameters of the preset area at predetermined time, determining weather grade evaluation values of the preset area, obtaining preliminary estimated passenger travel times of the preset area according to the historical passenger travel traffic, and correcting the preliminary estimated passenger travel times according to the new weather grade evaluation values, holiday time distribution and predetermined event occurrence time distribution, thereby obtaining final estimated passenger travel times of the preset area; therefore, the passenger travel analysis application method of the cloud computing technology is different from the prior art, the passenger travel situation prediction is only carried out for a single area and a single time period, the estimated passenger travel times in any time period can be obtained according to the historical passenger travel people flow, the area temperature, the area wind speed and the area precipitation of different area passenger traffic points, and the estimated passenger travel times can be corrected for travel time and weather factors, so that the finally obtained estimated passenger travel times can comprehensively and accurately reflect the passenger appearance situations in any area and any time, and the pre-judgment and reliability of the passenger travel are improved. Further, the technical scheme can send preset information to the preset terminal located in the preset area according to the obtained final expected traveler travel times of the preset area (the preset information can comprise the final expected traveler travel times of the preset area, or a travel service personnel allocation scheme generated according to the final expected traveler travel times of the preset area, or an equipment allocation scheme for the traveler generated according to the final expected traveler travel times of the preset area, and the like), so that a passenger management or service department in the preset area is helped to perform reasonable service personnel allocation or equipment allocation according to the preset information, and service efficiency and passenger satisfaction of the traveler are improved.
It will be apparent to those skilled in the art that various modifications and variations can be made to the present invention without departing from the spirit or scope of the invention. Thus, it is intended that the present invention also include such modifications and alterations insofar as they come within the scope of the appended claims or the equivalents thereof.
Claims (7)
1. The passenger travel analysis application method based on the cloud computing technology is characterized by comprising the following steps of:
step S1, acquiring historical travel image information of passengers corresponding to different passenger traffic points in a preset area, generating corresponding historical passenger travel traffic according to the historical travel image information of the passengers, and storing the historical passenger travel traffic to a cloud;
step S2, performing weather monitoring on the preset area to obtain a plurality of different weather parameters of the preset area at a preset moment so as to determine weather grade evaluation values of the preset area, wherein the plurality of different weather parameters comprise temperature, wind speed and precipitation, and the method specifically comprises the following steps:
step S201, arranging weather monitoring equipment at a plurality of different positions in the preset area, and obtaining the temperature, the wind speed and the precipitation of the corresponding positions at the preset moment through the weather monitoring equipment;
step S202, calculating the weather grade evaluation value of the preset area according to the temperature, wind speed and precipitation obtained in the step S201 and the following formula (1)
In the above formula (1), it is shown that at the predetermined time t 0 Weather level evaluation values of the preset area,indicating at a predetermined time t 0 Temperature value, T, of said predetermined region of (C) z The optimal temperature value suitable for the travel of passengers is represented, and the optimal temperature value is 25℃ ->Indicating at a predetermined time t 0 Wind power grade value, F of said predetermined region z Representing the optimal wind power grade value suitable for the traveling of passengers, wherein the optimal wind power grade value is 6 # -and # ->Indicating at a predetermined time t 0 Precipitation of said predetermined region, J z The optimal precipitation amount suitable for the traveling of the passengers is represented, and the value of the optimal precipitation amount is 400mm;
step S3, updating the weather grade evaluation value obtained in the step S2 according to the relative time relation between the planned travel time and the current actual time of the passenger, so as to obtain a new weather grade evaluation value;
step S4, obtaining preliminary estimated traveler travel times of the preset area according to the historical traveler travel times, and correcting the preliminary estimated traveler travel times according to the new weather grade evaluation value, holiday time distribution and preset event occurrence time distribution so as to obtain final estimated traveler travel times of the preset area;
and S5, generating preset information according to the final expected number of passengers traveling in the preset area, and sending the preset information to a preset terminal in the preset area.
2. The cloud computing technology-based passenger travel analysis application method as claimed in claim 1, wherein:
in the step S1, the historical travel image information of the passengers corresponding to different passenger traffic points in a preset area is obtained, corresponding historical passenger travel traffic is generated according to the historical travel image information of the passengers, the historical passenger travel traffic is stored in a cloud terminal,
step S101, arranging camera monitoring equipment at each passenger transportation point in the preset area, and shooting a plurality of pieces of passenger history travel image information corresponding to different areas inside the passenger transportation point through the camera monitoring equipment;
step S102, performing person identification processing on the historical travel image information of the plurality of passengers, so as to determine the personnel presence state information corresponding to each passenger transportation point;
and step S103, generating corresponding historical passenger pedestrian traffic according to the personnel presence state information, and storing the historical passenger pedestrian traffic to a cloud.
3. The application method for passenger travel analysis based on cloud computing technology as claimed in claim 2, wherein:
in the step S101, a camera monitoring device is arranged at each passenger transportation point in the preset area, and a plurality of pieces of passenger history travel image information corresponding to different areas inside the passenger transportation point are shot by the camera monitoring device,
at least two camera monitoring devices are arranged at each passenger transportation point, so that a plurality of multi-angle passenger history trip image information corresponding to different areas inside the passenger transportation point are shot;
the method comprises the steps of,
in the step S102, the person recognition processing is performed on the plurality of passenger history travel image information, so as to determine that the person presence status information corresponding to each passenger traffic point specifically includes,
calculating parallax images corresponding to the multi-angle passenger history travel image information, and identifying corresponding person existence positions and person motion paths from the parallax images to serve as the person existence state information;
the method comprises the steps of,
in the step S103, corresponding historical passenger pedestrian traffic is generated according to the personnel presence status information, and the historical passenger pedestrian traffic is stored in the cloud terminal,
and counting the historical passenger pedestrian traffic corresponding to the passenger traffic point according to the person existence position and the person motion path, and storing the historical passenger pedestrian traffic to a cloud.
4. The cloud computing technology-based passenger travel analysis application method as claimed in claim 3, wherein:
in the step S3, the weather grade evaluation value obtained in the step S2 is updated according to the relative time relationship between the planned travel time and the current actual time of the passenger, so as to obtain a new weather grade evaluation value specifically including,
updating the weather grade evaluation value obtained in the above step S2 according to the following formula (2)
In the above-mentioned formula (2),indicating at a new time t 1 New weather rating value after update,/-for (a) a new weather rating value after update>Indicated at time t 0 Temperature value of said preset area of +a, < >>Indicated at time t 0 Wind power level value of said predetermined area of +a, < >>Indicated at time t 0 And (c) precipitation of the preset area of +a.
5. The cloud computing technology-based passenger travel analysis application method of claim 4, wherein:
in the step S4, according to the historical passenger travel volume, a preliminary estimated passenger travel volume of the preset area is obtained, specifically, according to the historical passenger travel volume and the following formula (3), the preliminary estimated passenger travel volume is obtained by calculation
In the above formula (3), R t,d The corresponding preliminary estimated passenger travel times under the conditions that the passenger is estimated to travel at the time t and the travel place is d are represented,the historical passenger pedestrian traffic at the same time t and the same place d on the i-th day of forward pushing on the current date is represented, and n represents the total number of days of forward pushing from the current date.
6. The cloud computing technology-based passenger travel analysis application method of claim 5, wherein:
in the step S4, the correcting of the preliminary estimated traveler' S travel times in the preset area specifically includes,
performing corresponding correction processing according to the new weather level evaluation value, the holiday time distribution, the predetermined event occurrence time distribution and the following formula (4),
in the above-mentioned formula (4),and Z represents a holiday time point calibration value, Z=1 when the estimated travel time point of the passenger is in a holiday time range, Z= -1, Q represents a scheduled event occurrence time point calibration value, Q=1 when the estimated travel time point of the passenger is in an emergency event or disaster event occurrence time range, Q= -1, u () represents a step function, when the numerical value in a bracket of the step function is greater than 0, the value of the step function is 1, and when the numerical value in the bracket of the step function is less than or equal to 0, the value of the step function is 0.
7. The cloud computing technology-based passenger travel analysis application method of claim 6, wherein:
in the step S4, after determining the final estimated passenger trip number of the preset area, any one of the shift density, the ticketing period and the ticketing number is further adjusted for the passenger traffic of the three aspects of sea Liu Tian according to the final estimated passenger trip number.
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| CN107240289A (en) * | 2017-07-24 | 2017-10-10 | 济南博图信息技术有限公司 | A kind of bus routes optimum management method and system |
| CN107526083A (en) * | 2017-10-18 | 2017-12-29 | 国网新疆电力公司电力科学研究院 | A kind of strong convection wind scale Forecasting Methodology based on weather radar data |
| CN108932686A (en) * | 2018-05-09 | 2018-12-04 | 哈尔滨商业大学 | A kind of tourist famous-city tourist flow analysis method based on big data |
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| CN107526083A (en) * | 2017-10-18 | 2017-12-29 | 国网新疆电力公司电力科学研究院 | A kind of strong convection wind scale Forecasting Methodology based on weather radar data |
| CN108932686A (en) * | 2018-05-09 | 2018-12-04 | 哈尔滨商业大学 | A kind of tourist famous-city tourist flow analysis method based on big data |
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