CN110910191A - Car pooling order generation method and equipment - Google Patents
Car pooling order generation method and equipment Download PDFInfo
- Publication number
- CN110910191A CN110910191A CN201910835830.9A CN201910835830A CN110910191A CN 110910191 A CN110910191 A CN 110910191A CN 201910835830 A CN201910835830 A CN 201910835830A CN 110910191 A CN110910191 A CN 110910191A
- Authority
- CN
- China
- Prior art keywords
- experience
- passenger
- car
- carpooling
- data
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 61
- 238000011176 pooling Methods 0.000 title claims description 32
- 238000004422 calculation algorithm Methods 0.000 claims abstract description 21
- 238000004364 calculation method Methods 0.000 claims abstract description 18
- 238000010801 machine learning Methods 0.000 claims abstract description 18
- 238000000605 extraction Methods 0.000 claims description 4
- 238000013461 design Methods 0.000 description 8
- 238000010586 diagram Methods 0.000 description 6
- 238000004891 communication Methods 0.000 description 5
- 239000000284 extract Substances 0.000 description 4
- 238000010606 normalization Methods 0.000 description 3
- 238000004088 simulation Methods 0.000 description 3
- 230000000694 effects Effects 0.000 description 2
- 230000003287 optical effect Effects 0.000 description 2
- 238000012545 processing Methods 0.000 description 2
- 238000012549 training Methods 0.000 description 2
- 238000013136 deep learning model Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000002093 peripheral effect Effects 0.000 description 1
- 238000011002 quantification Methods 0.000 description 1
- 230000003068 static effect Effects 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q30/00—Commerce
- G06Q30/06—Buying, selling or leasing transactions
- G06Q30/0601—Electronic shopping [e-shopping]
- G06Q30/0633—Lists, e.g. purchase orders, compilation or processing
- G06Q30/0635—Processing of requisition or of purchase orders
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q30/00—Commerce
- G06Q30/06—Buying, selling or leasing transactions
- G06Q30/0601—Electronic shopping [e-shopping]
- G06Q30/0605—Supply or demand aggregation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q30/00—Commerce
- G06Q30/06—Buying, selling or leasing transactions
- G06Q30/0645—Rental transactions; Leasing transactions
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/40—Business processes related to the transportation industry
Landscapes
- Business, Economics & Management (AREA)
- Accounting & Taxation (AREA)
- Finance (AREA)
- General Business, Economics & Management (AREA)
- Engineering & Computer Science (AREA)
- Marketing (AREA)
- Strategic Management (AREA)
- Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Economics (AREA)
- Development Economics (AREA)
- Health & Medical Sciences (AREA)
- General Health & Medical Sciences (AREA)
- Human Resources & Organizations (AREA)
- Primary Health Care (AREA)
- Tourism & Hospitality (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The embodiment of the invention provides a method and equipment for generating a car sharing order, wherein the method comprises the steps of receiving a first car sharing request sent by a first passenger; acquiring and extracting first estimated data of each car sharing experience factor of a first passenger and second estimated data of a second passenger according to first to-be-shared data and a first car sharing request; determining a first carpooling experience degree of the first passenger and a second carpooling experience degree of the second passenger according to first pre-estimated data, second pre-estimated data and preset weights of all carpooling experience factors; the preset weight of each car sharing experience factor is determined through a machine learning algorithm according to historical car sharing data; and judging whether the first carpooling experience degree and the second carpooling experience degree meet preset conditions, if so, establishing a carpooling association between the first to-be-carpooled data and the carpooling request so as to generate a carpooling order according to the carpooling association. The embodiment of the invention can improve the calculation accuracy of the carpooling experience degree, thereby improving the rationality of the carpooling order.
Description
Technical Field
The embodiment of the invention relates to the technical field of data processing, in particular to a method and equipment for generating a car pooling order.
Background
With the continuous development of internet information technology, sharing economy is developed, and sharing single cars, sharing automobiles, car sharing travel and the like provide more convenient, economic and environment-friendly travel modes for consumers. If a car sharing trip mode is adopted, a car sharing system can establish a car sharing order according to the car sharing experience of passengers, the car sharing experience is a very important factor influencing the selection of car sharing trip of the passengers, and how to accurately calculate the car sharing experience of the passengers to generate a reasonable car sharing order is a problem to be solved urgently at present.
The ride share experience factors influencing the ride share experience are many, such as detour distance, detour time and the like. In the existing method for calculating the carpooling experience degree in the carpooling system, an independent threshold value is set for each experience factor, and if one experience factor exceeds the threshold value, the experience is considered to be poor. When all experience factors are within the threshold, the experience is considered to be ok.
However, the experience of the passengers cannot be accurately reflected by the calculation method of the car pooling experience by adopting a cutting strategy, and a reasonable car pooling order cannot be generated, so that fewer passengers select car pooling for traveling are required.
Disclosure of Invention
The embodiment of the invention provides a method and equipment for generating a car sharing order, which are used for improving the calculation accuracy of the car sharing experience degree and the rationality of the car sharing order.
In a first aspect, an embodiment of the present invention provides a method for generating a car pool order, including:
receiving a first car sharing request sent by a first passenger; the car sharing request comprises scheduled departure time, a departure place and a destination;
acquiring first to-be-pieced list data, and extracting first pre-estimated data of all the carpooling experience factors of a first passenger and second pre-estimated data of all the carpooling experience factors of a second passenger according to the first to-be-pieced list data and the first carpooling request; the first to-be-pieced list data is generated according to a car-sharing request of a second passenger in the manned vehicle;
determining a first carpooling experience degree of the first passenger and a second carpooling experience degree of the second passenger according to the first pre-estimated data, the second pre-estimated data and the preset weight of each carpooling experience factor; the weight of each preset car sharing experience factor is determined through a machine learning algorithm according to historical car sharing data;
and judging whether the first carpooling experience degree and the second carpooling experience degree meet preset conditions, if so, establishing a carpooling association between the first to-be-carpooled order data and the carpooling request, and generating a carpooling order according to the carpooling association.
In one possible design, the ride share experience factors include at least two of: the method comprises the following steps of detouring distance, detouring time, driving receiving distance, driving receiving time, driving sharing distance, driving sharing time, sequential splicing, turning and receiving, travel car splicing times, friend waiting time, driving receiving delay time, driver cancellation rate and car splicing, wherein the car splicing causes the car to enter a congested road section, and the car splicing causes the car splicing to be incapable of walking on an overhead or a loop.
In a possible design, before determining the first ride share experience of the first passenger and the second ride share experience of the second passenger according to the first pre-estimated data, the second pre-estimated data, and a preset weight of each ride share experience factor, the method further includes:
obtaining a plurality of carpooling order data within preset time and storing the carpooling order data in a database as historical carpooling data;
and determining the weight of each carpooling experience factor through a machine learning algorithm according to the historical carpooling data.
In one possible design, the determining the first ride share experience of the first passenger and the second ride share experience of the second passenger according to the first pre-estimated data, the second pre-estimated data, and a preset weight of each ride share experience factor includes:
and determining the first carpooling experience degree of the first passenger through weighted summation according to the first pre-estimated data and the weight of each preset carpooling experience factor.
In a possible design, before determining the first ride share experience of the first passenger and the second ride share experience of the second passenger according to the first pre-estimated data, the second pre-estimated data, and a preset weight of each ride share experience factor, the method further includes:
acquiring a first historical experience of a first passenger;
determining a first car sharing experience degree of the first passenger and a second car sharing experience degree of the second passenger according to the first pre-estimated data, the second pre-estimated data and the preset weight of each car sharing experience factor, and including:
and determining a first car sharing experience degree of the first passenger and a second car sharing experience degree of the second passenger according to the first pre-estimated data, the second pre-estimated data, the preset weight of each car sharing experience factor and the first historical experience degree.
In one possible design, after the establishing a car-sharing association between the first data to be assembled and the car-sharing request to generate a car-sharing order according to the car-sharing association, the method further includes:
extracting actual data of each carpool experience factor of the carpool request according to the carpool order;
determining the actual car sharing experience degree of the first passenger corresponding to the car sharing order according to the actual data of the car sharing experience factors and the preset weight of the car sharing experience factors;
and correcting the historical experience of the first passenger according to the actual car sharing experience to obtain a new historical experience so as to participate in the generation calculation of the next car sharing order of the first passenger.
In a possible design, after determining whether the first ride share experience and the second ride share experience satisfy a preset condition, the method further includes:
if the first passenger's first car sharing request does not meet the first data to be shared, the car sharing association between the first car sharing request of the first passenger and the first data to be shared is not established, and the second data to be shared is obtained, so that whether the first car sharing request of the first passenger can establish the car sharing association with the second data to be shared is determined.
In a second aspect, an embodiment of the present invention provides a car pooling order generating device, including:
the receiving module is used for receiving a first car sharing request sent by a first passenger; the car sharing request comprises scheduled departure time, a departure place and a destination;
the first extraction module is used for acquiring first to-be-pieced list data and extracting first pre-estimated data of all the car-sharing experience factors of a first passenger and second pre-estimated data of all the car-sharing experience factors of a second passenger according to the first to-be-pieced list data and the first car-sharing request; the first to-be-pieced list data is generated according to a car-sharing request of a second passenger in the manned vehicle;
the first determining module is used for determining a first car sharing experience degree of the first passenger and a second car sharing experience degree of the second passenger according to the first estimated data, the second estimated data and the preset weight of each car sharing experience factor; the weight of each preset car sharing experience factor is determined through a machine learning algorithm according to historical car sharing data;
and the judging module is used for judging whether the first carpooling experience degree and the second carpooling experience degree meet preset conditions or not, if so, establishing a carpooling association between the first to-be-carpooled order data and the carpooling request so as to generate a carpooling order according to the carpooling association.
In a third aspect, an embodiment of the present invention provides a car pooling order generating device, including: at least one processor and memory;
the memory stores computer-executable instructions;
the at least one processor executes computer-executable instructions stored by the memory to cause the at least one processor to perform the method as set forth in the first aspect above and in various possible designs of the first aspect.
In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, in which computer-executable instructions are stored, and when a processor executes the computer-executable instructions, the method according to the first aspect and various possible designs of the first aspect are implemented.
The method for generating a car-sharing order and the device thereof provided by this embodiment acquire first data to be pieced together by receiving a first car-sharing request sent by a first passenger, where the car-sharing request includes a scheduled departure time, a departure place and a destination, and extract first estimated data of each car-sharing experience factor of the first passenger and second estimated data of each car-sharing experience factor of a second passenger according to the first data to be pieced together and the first car-sharing request, where the first data to be pieced together is generated according to a car-sharing request of the second passenger in a manned vehicle, and determines a first car-sharing experience of the first passenger and a second car-sharing experience of the second passenger according to the first data, the second estimated data and weights of preset car-sharing experience factors, where the weights of the preset car-sharing experience factors are determined by a machine learning algorithm according to historical car-sharing data, judging whether the first car sharing experience degree and the second car sharing experience degree meet preset conditions or not, if so, establishing the first data to be shared with car sharing association between car sharing requests so as to generate car sharing orders according to the car sharing association, improving the calculation accuracy of the car sharing experience degree and further improving the rationality of the car sharing orders, and further improving the car sharing traveling experience of passengers.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to these drawings without creative efforts.
Fig. 1 is a schematic view of an application scenario of a car pool order generation method according to an embodiment of the present invention;
fig. 2 is a schematic flow chart of a method for generating a carpool order according to an embodiment of the present invention;
fig. 3 is a schematic flow chart of a method for generating a carpool order according to another embodiment of the present invention;
fig. 4 is a schematic structural diagram of a car pool order generating device according to an embodiment of the present invention;
fig. 5 is a schematic structural diagram of a car pool order generating device according to another embodiment of the present invention;
fig. 6 is a schematic hardware structure diagram of a car pool order generating device according to still another embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Fig. 1 is a schematic view of an application scenario of the car pool order generation method according to an embodiment of the present invention. As shown in fig. 1, the application scenario includes a terminal device 101 held by a passenger and an in-vehicle device 102 corresponding to a vehicle to be taken to participate in a car sharing, where the in-vehicle device 102 may be a handheld terminal of a taxi driver, such as a mobile phone, or may be a terminal device installed on a vehicle, as long as the terminal device can communicate with the terminal device 101 held by the passenger, and this embodiment is not limited thereto.
In a specific implementation process, when a passenger has a car sharing demand, the car sharing request can be sent to a background server in communication connection with the vehicle-mounted device 102 through an APP application loaded on the terminal device 101, and the background server combines the car sharing request with an existing car sharing request in a vehicle to be received to obtain a simulation trip. Calculating estimated data of the car sharing experience factors of the passengers according to the simulation journey, comparing the estimated data of the car sharing experience factors with a preset threshold value, and judging whether a car sharing request sent by the terminal device 101 of the passenger is pre-distributed to the vehicle-mounted device 102 according to a comparison result, namely establishing an association relationship between the terminal device 101 and the vehicle-mounted device 102. After the judgment is completed for both the car sharing request issued by the terminal device 101 and the existing car sharing requests of the respective waiting vehicles. And determining a final order based on the judgment result, namely each association relationship, pushing the generated final order to the terminal equipment 101 of the passenger and the vehicle-mounted equipment 102 of the vehicle to be received, wherein the passenger can wait for the vehicle to be received after selecting and confirming the order pushing interface on the terminal equipment 101, and a driver of the vehicle to be received can receive and send the passenger according to the travel of the car pooling order after receiving and confirming the car pooling order through the vehicle-mounted equipment 102.
Therefore, the calculation of the car sharing experience degree is particularly important in the process, and the candidate vehicles are selected for the passengers in the car sharing order generation process. However, in the conventional calculation of the car sharing experience degree, a cutting method is adopted, that is, if each car sharing experience factor is smaller than a preset threshold, a positive comparison result is obtained, that is, the association relationship between the car sharing request and the vehicle-mounted device 102 can be performed, and if each car sharing experience factor exceeds the preset threshold, a negative comparison result is obtained, that is, the association relationship between the car sharing request and the vehicle-mounted device 102 cannot be performed. That is to say, when only one car-sharing experience factor just exceeds the threshold value and all other experience factors are very good in performance, a negative comparison result is still obtained, so that the comparison result is not reasonable, and reasonable quantification of experience is difficult by adopting the method. Based on this, the embodiment of the invention provides a method for generating a car pooling order, so as to improve the calculation accuracy of the car pooling experience degree and further improve the rationality of the car pooling order.
The technical solution of the present invention will be described in detail below with specific examples. The following several specific embodiments may be combined with each other, and details of the same or similar concepts or processes may not be repeated in some embodiments.
Fig. 2 is a flowchart illustrating a method for generating a car pool order according to an embodiment of the present invention. As shown in fig. 2, the method includes:
201. receiving a first car sharing request sent by a first passenger; the carpool request comprises a scheduled departure time, a departure place and a destination.
In practical applications, the executing subject of the embodiment may be a server or a vehicle-mounted terminal for generating an order in the car pooling system, and the vehicle-mounted terminal is taken as an example for description below.
Specifically, when the first passenger has a car sharing demand, the car sharing request can be generated through terminal devices such as a mobile phone and a tablet according to the expected departure time, the address of the departure place and the address of the destination, and the car sharing request is sent to a background server or a vehicle-mounted terminal of the car sharing system. And the vehicle-mounted terminal receives the car sharing request. The ride share request may include the following information: the departure time, departure location and destination address are predetermined. The number of passengers expected by the first passenger and other requirements may also be included.
202. Acquiring first to-be-pieced list data, and extracting first pre-estimated data of all the carpooling experience factors of a first passenger and second pre-estimated data of all the carpooling experience factors of a second passenger according to the first to-be-pieced list data and the first carpooling request; the first data to be pieced together is generated according to a car-sharing request of a second passenger in the manned vehicle.
Optionally, each ride share experience factor includes at least two of the following: the method comprises the following steps of detouring distance, detouring time, driving receiving distance, driving receiving time, driving sharing distance, driving sharing time, sequential splicing, turning and receiving, travel car splicing times, friend waiting time, driving receiving delay time, driver cancellation rate and car splicing, wherein the car splicing causes the car to enter a congested road section, and the car splicing causes the car splicing to be incapable of walking on an overhead or a loop.
In practical application, the vehicle-mounted terminal generates first to-be-pieced list data according to the received car-sharing request of the second passenger in the corresponding vehicle. The data for the to-be-pieced together may include information on a scheduled departure time of the second passenger, an address of a departure place and a destination, and the like. The second passenger is all the passengers who the vehicle-mounted terminal has determined to receive. The number of the second passengers is not limited in this embodiment. The second passenger may include only the first passenger automatically assigned by the server and may also include the second passenger subsequently identified by the ride share order generation method.
203. Determining a first carpooling experience degree of the first passenger and a second carpooling experience degree of the second passenger according to the first pre-estimated data, the second pre-estimated data and the preset weight of each carpooling experience factor; and the weight of each preset car sharing experience factor is determined through a machine learning algorithm according to historical car sharing data.
Optionally, the first ride share experience of the first passenger may be determined by weighted summation according to the first pre-estimated data and a preset weight of each ride share experience factor.
And determining the second carpooling experience degree of the second passenger through weighted summation according to the second pre-estimated data and the preset weight of each carpooling experience factor.
For example, assume that only three ride share experience factors, namely, detour distance, pickup distance, and ride-sharing distance, are considered. The weights of these 3 factors obtained beforehand by the machine learning algorithm are: 0.5,0.2,0.3. Assuming that after a first passenger and a second passenger are pieced together (the first car-sharing request of the first passenger is distributed to the to-be-received vehicle where the second passenger is located, that is, the car-sharing request is combined with the first to-be-pieced data), the detour distance of the first passenger is 500 meters, and the detour distance is normalized to a value between 0 and 1 by a normalization algorithm (the normalization algorithm is various, in the embodiment, a linear normalization algorithm is exemplarily adopted), and the corresponding normalized detour distance is 0.83; the pickup distance of a first passenger is 2000 m, the pickup distance is normalized to a value between 0 and 1, and the correspondingly obtained normalized pickup distance is 0.375; the common multiplication distance of the first passenger is 4000 meters, the common multiplication distance is normalized to a numerical value between 0 and 1, and the corresponding normalized common multiplication distance is 0.8; then, the normalized values of the three car sharing experience factors are comprehensively considered, and the car sharing experience degree of the first passenger corresponding to the current trip can be obtained as follows: 0.5 × 0.83 +0.2 × 0.375+0.3 × 0.8 ═ 0.73; the carpooling experience of the second passenger can also be calculated in the same way.
204. And judging whether the first carpooling experience degree and the second carpooling experience degree meet preset conditions, if so, establishing a carpooling association between the first to-be-carpooled order data and the carpooling request, and generating a carpooling order according to the carpooling association.
In practical application, the first car-sharing experience of the first passenger obtained through calculation is compared with a first preset threshold, the second car-sharing experience is compared with a second preset threshold, and if the first car-sharing experience of the first passenger is higher than the first preset threshold and the first car-sharing experience of the second passenger is higher than the second preset threshold, a first car-sharing request of the first passenger and the first to-be-shared data are established. Optionally, the first preset threshold and the second preset threshold may be equal, and may also be set as different data according to car sharing characteristics of different passengers (for example, some passengers are more inclined to and select car sharing to go out, and the preset threshold corresponding to the passenger may be adjusted down to prompt the passenger to quickly form a list), which is not limited in this embodiment.
The method for generating a carpool order provided by this embodiment obtains first data to be pieced together by receiving a first carpool request sent by a first passenger, where the carpool request includes a scheduled departure time, a departure place, and a destination, and extracts first estimated data of each carpool experience factor of the first passenger and second estimated data of each carpool experience factor of a second passenger according to the first data to be pieced together and the first carpool request, where the first data to be pieced together is generated according to a carpool request of a second passenger in a manned vehicle, and determines a first carpool experience degree of the first passenger and a second carpool experience degree of the second passenger according to the first estimated data, the second estimated data, and weights of preset carpool experience factors, where the weights of the preset carpool experience factors are determined by a machine learning algorithm according to historical carpool data, judging whether the first car sharing experience degree and the second car sharing experience degree meet preset conditions or not, if so, establishing the first data to be shared with car sharing association between car sharing requests so as to generate car sharing orders according to the car sharing association, improving the calculation accuracy of the car sharing experience degree and further improving the rationality of the car sharing orders, and further improving the car sharing traveling experience of passengers.
Fig. 3 is a flowchart illustrating a method for generating a car pool order according to another embodiment of the present invention. As shown in fig. 3, based on the above embodiment, the present embodiment describes in detail the process of calculating the weight of each ride share experience factor and generating a ride share order in combination with the historical experience degree, and the method includes:
301. and acquiring a plurality of carpool order data within preset time and storing the carpool order data in a database as historical carpool data.
In practical application, before the method for generating a car pool order provided by this embodiment is adopted, a car pool order is generated by an existing method (for example, a one-off threshold comparison method described in the background art), a plurality of car pool order data (for example, car pool order data generated by a drip-drop car-making) within a preset time period are collected, and then the car pool order data including the car pool experience factor is sampled from the collected car pool order data and stored in a database as historical car pool data.
302. And determining the weight of each carpooling experience factor through a machine learning algorithm according to the historical carpooling data.
In this embodiment, the learning of the memorability weight of the existing machine learning algorithm may be selected. Such as LR models, deep learning models. The weight learning algorithm belongs to the prior art, and is not described herein again. In addition, in the present embodiment, what kind of machine learning algorithm is selected is not limited as long as weight learning can be performed.
303. Receiving a first car sharing request sent by a first passenger; the carpool request comprises a scheduled departure time, a departure place and a destination.
304. Acquiring first to-be-pieced list data, and extracting first pre-estimated data of all the carpooling experience factors of a first passenger and second pre-estimated data of all the carpooling experience factors of a second passenger according to the first to-be-pieced list data and the first carpooling request; the first to-be-pieced list data is generated according to a car-sharing request of a second passenger in the manned vehicle;
305. Acquiring a first historical experience of a first passenger;
in practical application, experience of the car sharing passengers can be stored, and the historical experience is obtained by integrating the experience of the car sharing travel of the passengers all the time. Optionally, after the passenger completes the car sharing trip, the background server may calculate the car sharing experience of the passenger on the trip, and update the current historical experience with the newly generated car sharing experience. Therefore, the historical experience stored in the database is the comprehensive value of the carpooling experience of all carpooling trips before the current trip.
306. And determining a first car sharing experience degree of the first passenger and a second car sharing experience degree of the second passenger according to the first pre-estimated data, the second pre-estimated data, the preset weight of each car sharing experience factor and the first historical experience degree.
Optionally, obtaining the pre-carpooling experience degree of the first passenger through weighted summation according to the first pre-estimated data and the preset weight of each carpooling experience factor; and obtaining a first carpooling experience degree according to the pre-carpooling experience degree and the first historical experience degree.
Specifically, a first carpooling experience degree can be obtained according to the pre-carpooling experience degree and the first historical experience degree through the following formula:
ESP＝α*ESH+(1-α)*ESP(1)
the ESP is the pre-carpooling experience, the ESH is the first historical experience, and α is the weight of the first historical experience.
According to the second estimated data and the preset weight of each car sharing experience factor, obtaining a second car sharing experience degree of a second passenger through weighted summation;
it is understood that a second historical experience of the second passenger may also be obtained for more accurate calculation of the second ride share experience of the second passenger. And obtaining a second carpooling experience degree of a second passenger through weighted summation according to the second pre-estimated data, the preset weight of each carpooling experience factor and a second historical experience degree.
307. Judging whether the first carpooling experience degree and the second carpooling experience degree meet preset conditions or not, if so, establishing a carpooling association between the first to-be-carpooled order data and the carpooling request, and generating a carpooling order according to the carpooling association; if the first passenger's first car sharing request does not meet the first data to be shared, the car sharing association between the first car sharing request of the first passenger and the first data to be shared is not established, and the second data to be shared is obtained, so that whether the first car sharing request of the first passenger can establish the car sharing association with the second data to be shared is determined.
In practical application, the first car-sharing experience of the first passenger obtained through calculation is compared with a first preset threshold, the second car-sharing experience is compared with a second preset threshold, and if the first car-sharing experience of the first passenger is higher than the first preset threshold and the first car-sharing experience of the second passenger is higher than the second preset threshold, a first car-sharing request of the first passenger and the first to-be-shared data are established. Optionally, the first preset threshold and the second preset threshold may be equal, and may also be set as different data according to car sharing characteristics of different passengers (for example, some passengers are more inclined to and select car sharing to go out, and the preset threshold corresponding to the passenger may be adjusted down to prompt the passenger to quickly form a list), which is not limited in this embodiment.
And if any value of the first carpooling experience degree and the second carpooling experience degree is smaller than the corresponding threshold value, not establishing the association relation between the first carpooling request and the first to-be-mosaiced sheet data. And continuously acquiring second to-be-pieced list data of other to-be-received vehicles to judge whether the association relation between the first carpooling request and the second to-be-pieced list data can be established.
Specifically, after the first car sharing request and the data of the to-be-shared sheets of each to-be-shared vehicle are judged whether to complete the establishment of the association relationship, one vehicle with the highest experience comprehensive value (the sum or average of the car sharing experience values of all passengers on the car after sharing) is selected from the to-be-shared vehicles corresponding to the data of the to-be-shared sheets of which the association relationship is established with the first car sharing request to generate the car sharing order.
308. And extracting actual data of each carpool experience factor of the carpool request according to the carpool order.
309. And determining the actual car sharing experience degree of the first passenger corresponding to the car sharing order according to the actual data of the car sharing experience factors and the preset weight of the car sharing experience factors.
310. And correcting the historical experience of the first passenger according to the actual car sharing experience to obtain a new historical experience so as to participate in the generation calculation of the next car sharing order of the first passenger.
In practical application, after the first passenger completes the car sharing trip, the actual data of each car sharing experience factor, such as an actual detour distance, an actual driving receiving distance, an actual riding sharing distance, and the like, can be acquired according to the car sharing trip. And then the actual car sharing experience degree is calculated according to the actual data. And according to the actual car sharing experience, the historical experience of the first passenger is corrected.
Specifically, the historical experience of the first passenger can be modified according to the actual car sharing experience through the following formula:
ESH＝β*ESA+(1-β)*ESH (2)
wherein, ESA is the actual car sharing experience, ESH is the first historical experience, and β is the weight of the first historical experience.
According to the car pooling order generation method provided by the embodiment, the weight of each car pooling experience factor is obtained through the offline simulation environment and the machine learning method training, the car pooling experience degree of the current travel is obtained through the weighted summation of the car pooling experience factors, meanwhile, the historical car pooling experience degree of the passenger is comprehensively considered, a more comprehensive and reasonable car pooling experience degree is obtained, car pooling experience can be reasonably quantized, the calculation accuracy of the car pooling experience degree is improved, the rationality of the car pooling order is further improved, and therefore the car pooling travel experience of the passenger is improved.
Fig. 4 is a schematic structural diagram of a car pool order generating device according to an embodiment of the present invention. As shown in fig. 4, the car pool order generating apparatus 40 includes: a receiving module 401, a first extracting module 402, a first determining module 403 and a judging module 404.
A receiving module 401, configured to receive a first car pooling request sent by a first passenger; the car sharing request comprises scheduled departure time, a departure place and a destination;
a first extraction module 402, configured to obtain first to-be-pieced sheet data, and extract first pre-estimated data of each car-sharing experience factor of a first passenger and second pre-estimated data of each car-sharing experience factor of a second passenger according to the first to-be-pieced sheet data and the first car-sharing request; the first to-be-pieced list data is generated according to a car-sharing request of a second passenger in the manned vehicle;
a first determining module 403, configured to determine a first ride share experience of the first passenger and a second ride share experience of the second passenger according to the first pre-estimated data, the second pre-estimated data, and a preset weight of each ride share experience factor; the weight of each preset car sharing experience factor is determined through a machine learning algorithm according to historical car sharing data;
a determining module 404, configured to determine whether the first carpooling experience degree and the second carpooling experience degree meet a preset condition, and if so, establish a carpooling association between the first to-be-carpooled order data and the carpooling request, so as to generate a carpooling order according to the carpooling association.
The carpooling order generation device provided by the embodiment of the invention receives a first carpooling request sent by a first passenger through a receiving module, wherein the carpooling request comprises scheduled departure time, a departure place and a destination, a first extraction module obtains first to-be-mosaiced data and extracts first estimated data of all carpooling experience factors of the first passenger and second estimated data of all carpooling experience factors of a second passenger according to the first to-be-mosaiced data and the first carpooling request, the first to-be-mosaiced data is generated according to the carpooling request of the second passenger in a manned vehicle, a first determination module determines a first carpooling experience degree of the first passenger and a second carpooling experience degree of the second passenger according to the first estimated data, the second estimated data and preset weights of all carpooling experience factors, and the preset weights of all carpooling experience factors are determined through a machine learning algorithm according to historical carpooling data, the judgment module judges whether the first car sharing experience degree and the second car sharing experience degree meet preset conditions or not, if yes, the first data to be shared and car sharing association between car sharing requests are established, a car sharing order is generated according to the car sharing association, the calculation accuracy of the car sharing experience degree can be improved, the rationality of the car sharing order is further improved, and the car sharing travel experience of passengers is improved.
Fig. 5 is a schematic structural diagram of a car pool order generating device according to still another embodiment of the present invention. As shown in fig. 5, the car pool order generating apparatus 40 further includes: a communication module 404 and a training module 405.
Optionally, each ride share experience factor includes at least two of the following: the method comprises the following steps of detouring distance, detouring time, driving receiving distance, driving receiving time, driving sharing distance, driving sharing time, sequential splicing, turning and receiving, travel car splicing times, friend waiting time, driving receiving delay time, driver cancellation rate and car splicing, wherein the car splicing causes the car to enter a congested road section, and the car splicing causes the car splicing to be incapable of walking on an overhead or a loop.
Optionally, the apparatus further comprises:
a first obtaining module 405, configured to obtain multiple pieces of car pooling order data within a preset time and store the obtained data in a database as historical car pooling data;
and a second determining module 406, configured to determine, according to the historical car-sharing data, a weight of each car-sharing experience factor through a machine learning algorithm.
Optionally, the first determining module is specifically configured to:
and determining the first carpooling experience degree of the first passenger through weighted summation according to the first pre-estimated data and the weight of each preset carpooling experience factor.
Optionally, the apparatus further comprises:
a second obtaining module 407, configured to obtain a first historical experience of the first passenger;
the first determining module is specifically configured to:
and determining a first car sharing experience degree of the first passenger and a second car sharing experience degree of the second passenger according to the first pre-estimated data, the second pre-estimated data, the preset weight of each car sharing experience factor and the first historical experience degree.
Optionally, the apparatus further comprises:
a second extracting module 408, configured to extract, according to the car pooling order, actual data of each car pooling experience factor of the car pooling request;
a third determining module 409, configured to determine, according to the actual data of each carpool experience factor and a preset weight of each carpool experience factor, an actual carpool experience degree of the first passenger corresponding to the carpool order;
and a correcting module 410, configured to correct the historical experience of the first passenger according to the actual car-sharing experience to obtain a new historical experience, so as to participate in generation and calculation of a next car-sharing order of the first passenger.
Optionally, the determining module is specifically configured to:
and when the first car sharing experience degree and the second car sharing experience degree do not meet the preset conditions, the car sharing association between the first car sharing request of the first passenger and the first data to be shared is not established, and the second data to be shared is obtained, so that whether the first car sharing request of the first passenger can establish the car sharing association with the second data to be shared is determined.
The car pooling order generating device provided by the embodiment of the invention can be used for executing the method embodiment, the implementation principle and the technical effect are similar, and the embodiment is not repeated herein.
Fig. 6 is a schematic hardware structure diagram of a car pool order generating device according to still another embodiment of the present invention. As shown in fig. 6, the car pool order generating apparatus 60 provided in the present embodiment includes: at least one processor 601 and memory 602. The ride share order generation apparatus 60 further comprises a communication component 603. The processor 601, the memory 602, and the communication section 603 are connected by a bus 604.
In a specific implementation, the at least one processor 601 executes computer-executable instructions stored by the memory 602 to cause the at least one processor 601 to perform a car pool order generation method as performed by the car pool order generation apparatus 60 described above.
When the experience calculation of the embodiment is performed by the server, the communication component 603 may send the ride share request and/or the historical experience to the server.
For a specific implementation process of the processor 601, reference may be made to the above method embodiments, which implement the principle and the technical effect similarly, and details of this embodiment are not described herein again.
In the embodiment shown in fig. 6, it should be understood that the Processor may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), etc. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of a method disclosed in connection with the present invention may be embodied directly in a hardware processor, or in a combination of the hardware and software modules within the processor.
The memory may comprise high speed RAM memory and may also include non-volatile storage NVM, such as at least one disk memory.
The bus may be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, an Extended ISA (EISA) bus, or the like. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of illustration, the buses in the figures of the present application are not limited to only one bus or one type of bus.
The application also provides a computer-readable storage medium, wherein computer-executable instructions are stored in the computer-readable storage medium, and when a processor executes the computer-executable instructions, the method for generating a car pool order, which is executed by the car pool order generating device, is realized.
The application also provides a computer-readable storage medium, wherein computer-executable instructions are stored in the computer-readable storage medium, and when a processor executes the computer-executable instructions, the method for generating a car pool order, which is executed by the car pool order generating device, is realized.
The computer-readable storage medium may be implemented by any type of volatile or non-volatile memory device or combination thereof, such as Static Random Access Memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic or optical disk. Readable storage media can be any available media that can be accessed by a general purpose or special purpose computer.
An exemplary readable storage medium is coupled to the processor such the processor can read information from, and write information to, the readable storage medium. Of course, the readable storage medium may also be an integral part of the processor. The processor and the readable storage medium may reside in an Application Specific Integrated Circuits (ASIC). Of course, the processor and the readable storage medium may also reside as discrete components in the apparatus.
Those of ordinary skill in the art will understand that: all or a portion of the steps of implementing the above-described method embodiments may be performed by hardware associated with program instructions. The program may be stored in a computer-readable storage medium. When executed, the program performs steps comprising the method embodiments described above; and the aforementioned storage medium includes: various media that can store program codes, such as ROM, RAM, magnetic or optical disks.
Finally, it should be noted that: the above embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; while the invention has been described in detail and with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; and the modifications or the substitutions do not make the essence of the corresponding technical solutions depart from the scope of the technical solutions of the embodiments of the present invention.
Claims (10)
1. A method for generating a car pooling order is characterized by comprising the following steps:
receiving a first car sharing request sent by a first passenger; the car sharing request comprises scheduled departure time, a departure place and a destination;
acquiring first to-be-pieced list data, and extracting first pre-estimated data of all the carpooling experience factors of a first passenger and second pre-estimated data of all the carpooling experience factors of a second passenger according to the first to-be-pieced list data and the first carpooling request; the first to-be-pieced list data is generated according to a car-sharing request of a second passenger in the manned vehicle;
determining a first carpooling experience degree of the first passenger and a second carpooling experience degree of the second passenger according to the first pre-estimated data, the second pre-estimated data and the preset weight of each carpooling experience factor; the weight of each preset car sharing experience factor is determined through a machine learning algorithm according to historical car sharing data;
and judging whether the first carpooling experience degree and the second carpooling experience degree meet preset conditions, if so, establishing a carpooling association between the first to-be-carpooled order data and the carpooling request, and generating a carpooling order according to the carpooling association.
2. The method of claim 1, wherein the ride share experience factors include at least two of: the method comprises the following steps of detouring distance, detouring time, driving receiving distance, driving receiving time, driving sharing distance, driving sharing time, sequential splicing, turning and receiving, travel car splicing times, friend waiting time, driving receiving delay time, driver cancellation rate and car splicing, wherein the car splicing causes the car to enter a congested road section, and the car splicing causes the car splicing to be incapable of walking on an overhead or a loop.
3. The method of claim 2, wherein before determining the first ride share experience of the first passenger and the second ride share experience of the second passenger based on the first pre-estimate data, the second pre-estimate data, and the preset weights of the ride share experience factors, further comprising:
obtaining a plurality of carpooling order data within preset time and storing the carpooling order data in a database as historical carpooling data;
and determining the weight of each carpooling experience factor through a machine learning algorithm according to the historical carpooling data.
4. The method of claim 1, wherein determining the first ride share experience of the first passenger based on the first pre-estimated data and a preset weight of each ride share experience factor comprises:
and determining the first carpooling experience degree of the first passenger through weighted summation according to the first pre-estimated data and the weight of each preset carpooling experience factor.
5. The method of any one of claims 1-4, wherein before determining the first ride share experience of the first passenger and the second ride share experience of the second passenger based on the first forecast data, the second forecast data, and a preset weight of each ride share experience factor, further comprising:
acquiring a first historical experience of a first passenger;
determining a first carpooling experience degree of the first passenger according to the first pre-estimated data and the preset weight of each carpooling experience factor, wherein the determining comprises the following steps:
and determining a first car sharing experience degree of the first passenger and a second car sharing experience degree of the second passenger according to the first pre-estimated data, the second pre-estimated data, the preset weight of each car sharing experience factor and the first historical experience degree.
6. The method of claim 5, wherein after establishing the carpool association between the first to-be-carpooled data and the carpool request to generate a carpool order according to the carpool association, the method further comprises:
extracting actual data of each carpool experience factor of the carpool request according to the carpool order;
determining the actual car sharing experience degree of the first passenger corresponding to the car sharing order according to the actual data of the car sharing experience factors and the preset weight of the car sharing experience factors;
and correcting the historical experience of the first passenger according to the actual car sharing experience to obtain a new historical experience so as to participate in the generation calculation of the next car sharing order of the first passenger.
7. The method according to any one of claims 1-4, wherein after determining whether the first ride share experience and the second ride share experience satisfy a preset condition, the method further comprises:
if the first passenger's first car sharing request does not meet the first data to be shared, the car sharing association between the first car sharing request of the first passenger and the first data to be shared is not established, and the second data to be shared is obtained, so that whether the first car sharing request of the first passenger can establish the car sharing association with the second data to be shared is determined.
8. A carpool order generating apparatus, comprising:
the receiving module is used for receiving a first car sharing request sent by a first passenger; the car sharing request comprises scheduled departure time, a departure place and a destination;
the first extraction module is used for acquiring first to-be-pieced list data and extracting first pre-estimated data of all the car-sharing experience factors of a first passenger and second pre-estimated data of all the car-sharing experience factors of a second passenger according to the first to-be-pieced list data and the first car-sharing request; the first to-be-pieced list data is generated according to a car-sharing request of a second passenger in the manned vehicle;
the first determining module is used for determining a first car sharing experience degree of the first passenger and a second car sharing experience degree of the second passenger according to the first estimated data, the second estimated data and the preset weight of each car sharing experience factor; the weight of each preset car sharing experience factor is determined through a machine learning algorithm according to historical car sharing data;
and the judging module is used for judging whether the first carpooling experience degree and the second carpooling experience degree meet preset conditions or not, if so, establishing a carpooling association between the first to-be-carpooled order data and the carpooling request so as to generate a carpooling order according to the carpooling association.
9. A carpool order generating apparatus, comprising: at least one processor and memory;
the memory stores computer-executable instructions;
the at least one processor executing the computer-executable instructions stored by the memory causes the at least one processor to perform the ride share order generation method of any of claims 1-7.
10. A computer-readable storage medium having computer-executable instructions stored thereon, which when executed by a processor, implement the ride share order generation method of any of claims 1 to 7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910835830.9A CN110910191A (en) | 2019-09-05 | 2019-09-05 | Car pooling order generation method and equipment |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910835830.9A CN110910191A (en) | 2019-09-05 | 2019-09-05 | Car pooling order generation method and equipment |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN110910191A true CN110910191A (en) | 2020-03-24 |
Family
ID=69814494
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910835830.9A Pending CN110910191A (en) | 2019-09-05 | 2019-09-05 | Car pooling order generation method and equipment |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110910191A (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111768018A (en) * | 2020-08-07 | 2020-10-13 | 腾讯科技（深圳）有限公司 | Data processing method and device and computer readable storage medium |
| CN113344658A (en) * | 2021-05-25 | 2021-09-03 | 深圳依时货拉拉科技有限公司 | Method for continuous carpooling in journey, computer readable storage medium and computer device |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105678601A (en) * | 2015-12-31 | 2016-06-15 | 百度在线网络技术（北京）有限公司 | Order sending method and device |
| CN109478275A (en) * | 2017-06-16 | 2019-03-15 | 北京嘀嘀无限科技发展有限公司 | The system and method for distributing service request |
| CN109583605A (en) * | 2017-09-29 | 2019-04-05 | 北京嘀嘀无限科技发展有限公司 | Share-car method and device, computer equipment and readable storage medium storing program for executing |
-
2019
- 2019-09-05 CN CN201910835830.9A patent/CN110910191A/en active Pending
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105678601A (en) * | 2015-12-31 | 2016-06-15 | 百度在线网络技术（北京）有限公司 | Order sending method and device |
| CN109478275A (en) * | 2017-06-16 | 2019-03-15 | 北京嘀嘀无限科技发展有限公司 | The system and method for distributing service request |
| CN109583605A (en) * | 2017-09-29 | 2019-04-05 | 北京嘀嘀无限科技发展有限公司 | Share-car method and device, computer equipment and readable storage medium storing program for executing |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111768018A (en) * | 2020-08-07 | 2020-10-13 | 腾讯科技（深圳）有限公司 | Data processing method and device and computer readable storage medium |
| CN113344658A (en) * | 2021-05-25 | 2021-09-03 | 深圳依时货拉拉科技有限公司 | Method for continuous carpooling in journey, computer readable storage medium and computer device |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110348589B (en) | Car pooling method and device, computer equipment and storage medium | |
| CN110118567B (en) | Travel mode recommendation method and device | |
| US20200210905A1 (en) | Systems and Methods for Managing Networked Vehicle Resources | |
| CN109308537B (en) | Site recommendation method and device | |
| CN111861643A (en) | Riding position recommendation method and device, electronic equipment and storage medium | |
| US20200104889A1 (en) | Systems and methods for price estimation using machine learning techniques | |
| CN110910191A (en) | Car pooling order generation method and equipment | |
| CN111144603A (en) | Service information pushing method and device, electronic equipment and storage medium | |
| CN111784018A (en) | Resource scheduling method and device, electronic equipment and storage medium | |
| CN111861081A (en) | Order allocation method and device, electronic equipment and storage medium | |
| CN112862214A (en) | Parking service recommendation method, device, medium and server based on big data | |
| CN111476389A (en) | Method and device for pre-estimating order receiving waiting time | |
| CN112329965A (en) | Riding service scheduling method and device, electronic equipment and storage medium | |
| CN111859172B (en) | Information pushing method, device, electronic equipment and computer readable storage medium | |
| CN116663811A (en) | Scheduling matching method and device for reciprocating dynamic carpooling of inter-city passenger transport | |
| CN113254771B (en) | Bank outlet recommendation method and device | |
| WO2021031636A1 (en) | Real-time order travel vehicle-based real-time order assignment method and apparatus | |
| CN112106021A (en) | Method and device for providing vehicle navigation simulation environment | |
| CN110458310A (en) | Net about vehicle order processing method, equipment and terminal device | |
| CN110853333A (en) | Reservation order dispatching method and device based on real-time single-stroke vehicle | |
| CN110910202A (en) | Order processing method and device | |
| US10989546B2 (en) | Method and device for providing vehicle navigation simulation environment | |
| CN111105284B (en) | Order processing method and device, M-layer order processing model, electronic equipment and storage medium | |
| CN112529602A (en) | Data processing method and device, readable storage medium and electronic equipment | |
| CN111881375A (en) | Method and device for distributing in-route orders, electronic equipment and readable storage medium |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20200324 |