Dynamic price adjustment method and system in online car-hailing order explosion scene

Abstract

The invention relates to the related field of online car-hailing scheduling management, and discloses a dynamic price adjustment method and system in an online car-hailing order-exploding scene, which perform price increase coefficient calculation through indexes of real-time big data statistics, refine to a honeycomb dimension, solve the problem that the price increase of the whole city is not fine enough, and improve the price increase efficiency. Meanwhile, the price increase coefficient is dynamically adjusted through the driver busy degree, and the problem that manual setting of the price adjustment coefficient is unreasonable is effectively solved.

Description

Technical Field

[0001] The present invention relates to the field of online car-hailing dispatch management, and specifically to a dynamic price adjustment method and system in the scenario of online car-hailing order explosion. Background Art

[0002] In the online car-hailing industry, in special situations such as rainy, snowy weather, and morning and evening rush hours, there will be a situation where the number of orders explodes while the transportation capacity is insufficient. In order to improve the willingness of drivers to go out and at the same time increase operating profits, online car-hailing companies generally temporarily adjust the order price.

[0003] The commonly used adjustment method in the prior art is that when the number of orders increases, the operation personnel manually configure the urban adjustment coefficient. When the user enters the starting and ending points on the online car-hailing passenger side, the configured coefficient will be increased on the basis of the basic price. Some price-sensitive users may give up placing an order when they see the price is too high, which alleviates the supply-demand pressure to a certain extent.

[0004] Although this method has a certain adjustment ability, it is not refined and real-time enough. When there is a local order explosion in the city, for example, a concert has just ended in a certain area, causing a local order explosion in the area. At this time, it is impossible to adjust the regional supply and demand through the city-wide adjustment coefficient. When most areas of the city have order explosions, there are still some areas where there are no order explosions. Raising the price of the whole city will suppress the demand in the areas without order explosions, resulting in insufficient driver orders; at the same time, the operation personnel need to monitor the market manually to configure the adjustment coefficient, which incurs certain labor costs. Summary of the Invention

[0005] The purpose of the present invention is to provide a dynamic price adjustment method and system in the scenario of online car-hailing order explosion to solve the problems raised in the above background art.

[0006] To achieve the above purpose, the present invention provides the following technical solutions:

[0007] A dynamic price adjustment method in the scenario of online car-hailing order explosion, comprising:

[0008] Establish a rated evaluation time period, and set data query nodes at intervals after the initial time node of each said evaluation time period;

[0009] At the said data query time node, obtain the order volume, response rate, and year-on-year increase rate of the order volume in the region within the previous evaluation time period, and compare and determine the price adjustment threshold based on multiple parameters to obtain the initial price adjustment coefficient;

[0010] After executing the initial price adjustment coefficient, evaluate the idle rate of the driver, and optimize the price adjustment coefficient based on the idle rate. The current price adjustment coefficient optimization adopts a price adjustment management with a low price adjustment span and a high price adjustment frequency.

[0011] As a further solution of the present invention: it further includes a regionalized management division step, specifically including;

[0012] Set the size of the honeycomb grid area according to management requirements, divide the urban management area through the size of the honeycomb grid, and obtain the urban management area composed of several closely connected honeycomb grids;

[0013] Perform big data real-time containment management on several orders and drivers in each honeycomb grid, and record the order volume, response rate, driver idle rate, and year-on-year increase rate of the order volume in the corresponding honeycomb grid within the evaluation time period.

[0014] As a further solution of the present invention: the initial price adjustment coefficient includes multiple step coefficients, which are respectively used to limit the price adjustment demand range, facilitating the rapid completion of the optimization adjustment of the initial price adjustment coefficient during the optimization process of the adjustment coefficient;

[0015] The price adjustment coefficient also includes a rated maximum value, which is used to limit the maximum price increase range.

[0016] As a further solution of the present invention: the step of comparing and determining the price adjustment threshold based on multiple parameters to obtain the initial price adjustment coefficient specifically includes:

[0017] If the order volume is greater than 50, the response rate is less than 10%, and the year-on-year increase rate of the order volume is greater than 200%, then the initial price adjustment coefficient is 10%;

[0018] If the order volume is greater than 100, the response rate is less than 10%, and the year-on-year increase rate of the order volume is greater than 300%, then the initial price adjustment coefficient is 15%;

[0019] If the order volume is greater than 200, the response rate is less than 10%, and the year-on-year increase rate of the order volume is greater than 500%, then the initial price adjustment coefficient is 20%.

[0020] As a further solution of the present invention: the step of optimizing the price adjustment coefficient based on the idle rate, where the current optimization of the price adjustment coefficient adopts a price adjustment management with a low price adjustment span and a high price adjustment frequency specifically includes:

[0021] If the idle rate of the driver is less than threshold 1, incrementally increase the initial price adjustment coefficient by a preset optimization adjustment value;

[0022] If the idle rate of the driver is greater than threshold 1 and less than threshold 2, the initial price adjustment coefficient remains unchanged;

[0023] If the idle rate of the driver is greater than threshold 2, decrementally decrease the initial price adjustment coefficient by a preset optimization adjustment value, and both threshold 1 and threshold 2 are obtained through statistical analysis of historical record data.

[0024] An embodiment of the present invention aims to provide a dynamic price adjustment system in the scenario of explosive orders for online car-hailing, including:

[0025] A cycle splitting module, configured to establish a rated evaluation time cycle and set data query nodes at intervals after the initial time node of each said evaluation time cycle;

[0026] An initial price adjustment module, configured to obtain the order volume, response rate, and year-on-year increase rate of order volume in the region within the previous evaluation time cycle at the data query time node, and perform a comparison and determination of price adjustment thresholds based on multiple parameters to obtain an initial price adjustment coefficient;

[0027] An optimized price adjustment module, configured to evaluate the driver's idle rate after executing the initial price adjustment coefficient, and optimize the price adjustment coefficient based on the idle rate. The current price adjustment coefficient optimization adopts a price adjustment management with a low price adjustment span and a high price adjustment frequency.

[0028] As a further solution of the present invention: it further includes a region management module, specifically including:

[0029] A region division unit, configured to set the size of the honeycomb grid region according to management requirements, divide the urban management region through the honeycomb grid size, and obtain an urban management region composed of several closely connected honeycomb grids;

[0030] A data collection unit, configured to perform big data real-time collection and management on several orders and drivers in each said honeycomb grid, and record the order volume, response rate, driver idle rate, and year-on-year increase rate of order volume in the corresponding honeycomb grid in the evaluation time cycle.

[0031] As a further solution of the present invention: the initial price adjustment coefficient includes multiple step coefficients, respectively used to narrow the range of price adjustment requirements, so as to facilitate the rapid completion of the optimization adjustment of the initial price adjustment coefficient during the coefficient optimization process;

[0032] The price adjustment coefficient also includes a rated maximum value, used to limit the maximum price increase range.

[0033] As a further solution of the present invention: in the initial price adjustment module, there is a mapping relationship between multiple parameters and the initial price adjustment coefficient:

[0034] If the order volume is greater than 50, the response rate is less than 10%, and the year-on-year increase rate of order volume is greater than 200%, the initial price adjustment coefficient is 10%;

[0035] If the order volume is greater than 100, the response rate is less than 10%, and the year-on-year increase rate of order volume is greater than 300%, the initial price adjustment coefficient is 15%;

[0036] If the order volume is greater than 200, the response rate is less than 10%, and the year-on-year increase in the order volume is greater than 500%, the initial price adjustment coefficient is 20%.

[0037] As a further solution of the present invention: the optimized price adjustment module includes:

[0038] An upward floating judgment unit, configured to incrementally float the initial price adjustment coefficient by a preset optimized adjustment value if the idle rate of the driver is less than threshold 1;

[0039] A maintenance judgment unit, configured to maintain the initial price adjustment coefficient if the idle rate of the driver is greater than threshold 1 and less than threshold 2;

[0040] A downward floating judgment unit, configured to decrementally float the initial price adjustment coefficient by a preset optimized adjustment value if the idle rate of the driver is greater than threshold 2, and both threshold 1 and threshold 2 are obtained by statistical analysis of historical record data.

[0041] Compared with the prior art, the beneficial effects of the present invention are: calculating the price increase coefficient through indicators statistically analyzed by real-time big data, refined to the honeycomb dimension, solving the problem of insufficient refinement of city-wide price increases, and dynamically adjusting the price increase coefficient according to the busy and idle status of drivers, effectively solving the problem of unreasonable manual setting of the price adjustment coefficient. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 It is a flowchart block diagram of a dynamic price adjustment method in a scenario of a large number of orders for online car-hailing services.

[0043] Figure 2 It is a flowchart logic diagram of a dynamic price adjustment method in a scenario of a large number of orders for online car-hailing services.

[0044] Figure 3 It is a block diagram of the composition of a dynamic price adjustment system in a scenario of a large number of orders for online car-hailing services. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0045] In order to make the objectives, technical solutions and advantages of the present invention clearer and more understandable, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0046] The following describes in detail the specific implementation manners of the present invention with reference to specific embodiments.

[0047] As Figure 1 、 Figure 2 described, a dynamic price adjustment method in a scenario of a large number of orders for online car-hailing services provided by an embodiment of the present invention includes the following steps:

[0048] S10. Establish a rated evaluation time period, and set data query nodes at intervals after the initial time node of each said evaluation time period;

[0049] S20. At the said data query time node, obtain the order volume, response rate, and year-on-year increase rate of order volume in the region within the previous evaluation time period, and perform a comparison and determination of the price adjustment threshold based on multiple parameters to obtain an initial price adjustment coefficient;

[0050] S30. After executing the said initial price adjustment coefficient, evaluate the idle rate of the driver, and optimize the price adjustment coefficient based on the idle rate. The current price adjustment coefficient optimization adopts a price adjustment management with a low price adjustment span and a high price adjustment frequency.

[0051] In this embodiment, a dynamic price adjustment method in the scenario of a large number of orders in online car-hailing is given. The price increase coefficient is calculated through indicators statistically analyzed by real-time big data, refined to the honeycomb dimension, which solves the problem that the price increase across the whole city is not fine enough. At the same time, the price increase coefficient is dynamically adjusted according to the busy and idle degree of the driver, effectively solving the problem of unreasonable manual setting of the price adjustment coefficient. Specifically, it solves the problems of refinement and automation of the price increase plan in the scenario of a large number of orders in online car-hailing, and can adjust the price increase coefficient according to the busy and idle situation of the driver. The algorithm will query the order volume, response rate, and year-on-year increase rate of order volume of each honeycomb in the large database in the previous 10 minutes at the 2nd minute of every fixed 10 minutes (i.e., after the big data statistics are completed), and judge whether these indicators meet the set thresholds. If they meet, an initial price adjustment coefficient will be obtained. However, since the initial price adjustment coefficient is a value determined by manual estimation, after adopting this coefficient, it may not necessarily be able to better adjust the supply and demand. Therefore, after obtaining the initial price adjustment coefficient, further judge the driver idle rate, and further refine the price adjustment coefficient according to the driver idle rate.

[0052] As another preferred embodiment of the present invention, it further includes a regionalized management division step, specifically including:

[0053] Set the size of the honeycomb grid area according to management requirements, divide the urban management area through the said honeycomb grid size, and obtain an urban management area composed of several closely connected honeycomb grids;

[0054] Perform real-time big data collection and management on several orders and drivers in each said honeycomb grid, and record the order volume, response rate, driver idle rate, and year-on-year increase rate of order volume in the corresponding honeycomb grid with an evaluation time period.

[0055] Furthermore, the said initial price adjustment coefficient includes multiple step coefficients, which are respectively used to narrow the scope of price adjustment requirements, facilitating the rapid completion of the optimization adjustment of the said initial price adjustment coefficient during the coefficient optimization process;

[0056] The price adjustment coefficient also includes a rated maximum value for limiting the maximum price increase range.

[0057] Further, the step of comparing and determining the price adjustment threshold based on multiple parameters to obtain the initial price adjustment coefficient specifically includes:

[0058] If the order volume is greater than 50, the response rate is less than 10%, and the year-on-year increase rate of the order volume is greater than 200%, the initial price adjustment coefficient is 10%;

[0059] If the order volume is greater than 100, the response rate is less than 10%, and the year-on-year increase rate of the order volume is greater than 300%, the initial price adjustment coefficient is 15%;

[0060] If the order volume is greater than 200, the response rate is less than 10%, and the year-on-year increase rate of the order volume is greater than 500%, the initial price adjustment coefficient is 20%.

[0061] In this embodiment, in order to give different price increase coefficients under different order explosion levels, the thresholds are divided into different levels here, different gears are calculated according to the indicators, and different initial price adjustment coefficients are configured for each gear. If no gear is matched, the honeycomb in this time slice will not increase the price; at the same time, there is also a rated maximum value within the corresponding area, and the price increase result does not exceed the price increase limit.

[0062] As another preferred embodiment of the present invention, the step of optimizing the price adjustment coefficient based on the idle rate, and currently optimizing the price adjustment coefficient by using a price adjustment management with a low price adjustment span and a high price adjustment frequency specifically includes:

[0063] If the idle rate of the driver is less than threshold 1, the initial price adjustment coefficient is incrementally increased by a preset optimization adjustment value;

[0064] If the idle rate of the driver is greater than threshold 1 and less than threshold 2, the initial price adjustment coefficient remains unchanged;

[0065] If the idle rate of the driver is greater than threshold 2, the initial price adjustment coefficient is decrementally decreased by a preset optimization adjustment value, and both threshold 1 and threshold 2 are obtained by statistically analyzing historical record data.

[0066] In this embodiment, since the initial price adjustment coefficient is a value determined by manual estimation, after adopting this coefficient, it may not necessarily be able to better adjust the supply and demand. Therefore, after obtaining the initial price adjustment coefficient, further judge the driver's idle rate. If the driver's idle rate is very low (the driver's idle rate is less than threshold 1), it means that the price increase is insufficient, so it is appropriately increased on the basis of the initial coefficient. If the driver's idle rate is very high (the driver's idle rate is greater than threshold 2), it is appropriately decreased on the basis of the initial coefficient. If the driver's idle situation is moderate (the driver's idle rate is between threshold 1 and threshold 2), the initial coefficient is maintained.

[0067] Such asFigure 3 As shown in the figure, the present invention also provides a dynamic price adjustment system in the scenario of explosive orders for online car-hailing, which includes:

[0068] A cycle splitting module 100, configured to establish a rated evaluation time cycle, and set data query nodes at intervals after the initial time node of each said evaluation time cycle;

[0069] An initial price adjustment module 200, configured to obtain the order volume, response rate, and year-on-year increase rate of order volume in the region within the previous evaluation time cycle at the said data query time node, and perform a comparison and determination of price adjustment thresholds based on multiple parameters to obtain an initial price adjustment coefficient;

[0070] An optimized price adjustment module 300, configured to evaluate the driver's idle rate after executing the said initial price adjustment coefficient, and optimize the price adjustment coefficient based on the idle rate. The current price adjustment coefficient optimization adopts a price adjustment management with a low price adjustment span and a high price adjustment frequency.

[0071] As another preferred embodiment of the present invention, it further includes a regional management module, specifically including:

[0072] A regional division unit, configured to set the size of the honeycomb grid area according to management requirements, divide the urban management area through the said honeycomb grid size, and obtain an urban management area composed of several closely connected honeycomb grids;

[0073] A data collection unit, configured to perform big data real-time collection and management on several orders and drivers in each said honeycomb grid, and record the order volume, response rate, driver idle rate, and year-on-year increase rate of order volume in the corresponding honeycomb grid in the evaluation time cycle.

[0074] As another preferred embodiment of the present invention, the said initial price adjustment coefficient includes multiple step coefficients, respectively used to narrow the price adjustment demand range, facilitating the rapid completion of the optimization adjustment of the said initial price adjustment coefficient during the coefficient optimization process;

[0075] The said price adjustment coefficient also includes a rated maximum value, used to limit the maximum price increase range.

[0076] As another preferred embodiment of the present invention, in the said initial price adjustment module, there is a mapping relationship between multiple parameters and the initial price adjustment coefficient:

[0077] If the order volume is greater than 50, the response rate is less than 10%, and the year-on-year increase rate of order volume is greater than 200%, then the initial price adjustment coefficient is 10%;

[0078] If the order volume is greater than 100, the response rate is less than 10%, and the year-on-year increase rate of order volume is greater than 300%, then the initial price adjustment coefficient is 15%;

[0079] If the order quantity is greater than 200, the response rate is less than 10%, and the year-on-year increase rate of the order quantity is greater than 500%, the initial price adjustment coefficient is 20%.

[0080] As another preferred embodiment of the present invention, the optimized price adjustment module includes:

[0081] A floating judgment unit, configured to incrementally float the initial price adjustment coefficient by a preset optimized adjustment value if the idle rate of the driver is less than threshold 1;

[0082] A maintenance judgment unit, configured to maintain the initial price adjustment coefficient if the idle rate of the driver is greater than threshold 1 and less than threshold 2;

[0083] A downward floating judgment unit, configured to decrementally float the initial price adjustment coefficient by a preset optimized adjustment value if the idle rate of the driver is greater than threshold 2, and both threshold 1 and threshold 2 are obtained by statistically analyzing historical record data.

[0084] Those of ordinary skill in the art can understand that all or part of the processes of implementing the methods in the above embodiments can be completed by instructing relevant hardware through a computer program. The program can be stored in a non-volatile computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to a memory, storage, database, or other medium used in the various embodiments provided in the present application can include non-volatile and / or volatile memories. Non-volatile memories can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memories can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0085] The description of the above service device is only an example and does not constitute a limitation on the terminal device. It may include more or fewer components than the above description, or combine certain components, or different components. For example, it may include input / output devices, network access devices, buses, etc.

[0086] The so-called processor may be a Central Processing Unit (CPU), or may also be other general-purpose processors, Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field-Programmable Gate Arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc. The above-mentioned processor is the control center of the above-mentioned terminal device, and uses various interfaces and lines to connect all parts of the entire user terminal.

[0087] The above-mentioned memory can be used to store computer programs and / or modules. The above-mentioned processor realizes various functions of the above-mentioned terminal device by running or executing the computer programs and / or modules stored in the memory, and by calling the data stored in the memory. The memory mainly includes a program storage area and a data storage area. Among them, the program storage area can store an operating system, application programs required for at least one function (such as information collection template display function, product information release function, etc.); the data storage area can store data created according to the use of the berth status display system (such as product information collection templates corresponding to different product types, product information to be released by different product providers, etc.). In addition, the memory may include high-speed random access memory, and may also include non-volatile memory, such as hard disks, memory, plug-in hard disks, Smart Media Cards (SMCs), Secure Digital (SD) cards, Flash Cards, at least one magnetic disk storage device, flash memory device, or other volatile solid-state storage devices.

[0088] If the modules / units integrated in the terminal device are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on such understanding, all or part of the modules / units in the above-mentioned embodiment system of the present invention can also be completed by instructing relevant hardware through a computer program. The above computer program can be stored in a computer-readable storage medium. When the computer program is executed by a processor, the functions of the above-mentioned various system embodiments can be realized. Among them, the computer program includes computer program code, and the computer program code can be in the form of source code, object code, executable file or some intermediate form, etc. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disc, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electrical carrier signal, telecommunication signal, and software distribution medium, etc.

[0089] It should be noted that in this article, the term "including", "comprising" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements not only includes those elements, but also includes other elements not explicitly listed, or further includes elements inherent to such process, method, article or device. Without further limitation, an element defined by the statement "including one..." does not exclude the existence of another identical element in the process, method, article or device including the element.

[0090] The above are only the preferred embodiments of the present invention, and do not limit the patent scope of the present invention accordingly. Any equivalent structure or equivalent process transformation made by using the content of the specification and drawings of the present invention, or directly or indirectly applied in other related technical fields, shall be equally included in the patent protection scope of the present invention.

Claims

1. A dynamic pricing method for online car-hailing service in a scenario of explosive orders, characterized in that: Include: Establishing a rated evaluation time period, and setting a data query node at intervals after an initial time node of each evaluation time period; At the data query time node, the order volume, response rate, and year-on-year increase in order volume in the region within the previous evaluation time period are obtained, and a price adjustment threshold comparison and determination is performed based on multiple parameters to obtain an initial price adjustment coefficient; After executing the initial price adjustment coefficient, the idle rate of the driver is evaluated, and the price adjustment coefficient is optimized based on the idle rate. The current price adjustment coefficient optimization adopts price adjustment management with low price span and high price adjustment frequency.

2. According to claim 1, a dynamic price adjustment method in the scenario of explosive online car-hailing orders, characterized in that: It also includes the steps of regional management division, including: Setting the size of the honeycomb grid area according to management requirements, dividing the urban management area according to the size of the honeycomb grid, and obtaining the urban management area closely connected by several honeycomb grids; The big data of several orders and drivers in each honeycomb grid is collected and managed in real time, and the order quantity, response rate, driver idle rate and year-on-year increase in order quantity in the corresponding honeycomb grid are recorded in an evaluation time period.

3. According to claim 2, a dynamic price adjustment method in the scenario of explosive online car-hailing orders, characterized in that: The initial price adjustment coefficient includes a plurality of step coefficients, which are respectively used to narrow the price adjustment demand range, so as to facilitate the adjustment coefficient optimization process to quickly complete the optimization adjustment of the initial price adjustment coefficient; The price adjustment coefficient also includes a rated maximum value to limit the maximum price increase range.

4. According to claim 3, a dynamic price adjustment method in the scenario of explosive online car-hailing orders is characterized in that: The step of comparing and determining the price adjustment threshold based on multiple parameters to obtain the initial price adjustment coefficient specifically includes: If the order volume is greater than 50, the response rate is less than 10%, and the year-on-year increase in order volume is greater than 200%, the initial price adjustment coefficient is 10%; If the order volume is greater than 100, the response rate is less than 10%, and the year-on-year increase in order volume is greater than 300%, the initial price adjustment coefficient is 15%; If the order volume is greater than 200, the response rate is less than 10%, and the year-on-year increase in order volume is greater than 500%, the initial price adjustment coefficient is 20%.

5. According to claim 4, a dynamic price adjustment method in the scenario of explosive online car-hailing orders, characterized in that: The steps of optimizing the price adjustment coefficient based on the idle rate and adopting the price adjustment management with low price span and high price adjustment frequency for the current price adjustment coefficient optimization specifically include: If the idle rate of the driver is less than the threshold value 1, the initial price adjustment coefficient is increased incrementally by a preset optimized adjustment value; If the idle rate of the driver is greater than threshold 1 and less than threshold 2, the initial price adjustment coefficient is maintained; If the idle rate of the driver is greater than threshold 2, the initial price adjustment coefficient is reduced by a preset optimization adjustment value, and threshold 1 and threshold 2 are both obtained through historical record data statistics.

6. A dynamic pricing system for online car-hailing service in the scenario of explosive orders, characterized in that: Include: A cycle splitting module, used to establish a rated evaluation time cycle, and to set a data query node at intervals after an initial time node of each evaluation time cycle; An initial price adjustment module is used to obtain the order volume, response rate and year-on-year increase of the order volume in the region within the previous evaluation time period at the data query time node, and compare and determine the price adjustment threshold based on multiple parameters to obtain an initial price adjustment coefficient; The optimization price adjustment module is used to evaluate the idle rate of the driver after executing the initial price adjustment coefficient, and optimize the price adjustment coefficient based on the idle rate. The current price adjustment coefficient optimization adopts price adjustment management with low price span and high price adjustment frequency.

7. A dynamic price adjustment system for online car-hailing service in the scenario of explosive orders according to claim 6, characterized in that: It also includes a regional management module, including: A region division unit, used to set the honeycomb grid region size according to management requirements, divide the urban management region according to the honeycomb grid size, and obtain an urban management region closely connected by a plurality of honeycomb grids; The data collection unit is used to perform real-time collection and management of big data of a plurality of orders and drivers in each of the honeycomb grids, and to record the order quantity, response rate, driver idle rate and year-on-year increase in the order quantity in the corresponding honeycomb grid in an evaluation time period.

8. A dynamic price adjustment system for online car-hailing service in the scenario of explosive orders according to claim 7, characterized in that: The initial price adjustment coefficient includes a plurality of step coefficients, which are respectively used to narrow the price adjustment demand range, so as to facilitate the adjustment coefficient optimization process to quickly complete the optimization adjustment of the initial price adjustment coefficient; The price adjustment coefficient also includes a rated maximum value to limit the maximum price increase range.

9. A dynamic price adjustment system for online car-hailing service in the scenario of explosive orders according to claim 8, characterized in that: In the initial price adjustment module, multiple parameters and initial price adjustment coefficients include a mapping relationship: If the order volume is greater than 50, the response rate is less than 10%, and the year-on-year increase in order volume is greater than 200%, the initial price adjustment coefficient is 10%; If the order volume is greater than 100, the response rate is less than 10%, and the year-on-year increase in order volume is greater than 300%, the initial price adjustment coefficient is 15%; If the order volume is greater than 200, the response rate is less than 10%, and the year-on-year increase in order volume is greater than 500%, the initial price adjustment coefficient is 20%.

10. A dynamic price adjustment system for online car-hailing service in the scenario of explosive orders according to claim 9, characterized in that: The optimization price adjustment module includes: An upward determination unit, configured to increase the initial price adjustment coefficient by a preset optimized adjustment value if the idle rate of the driver is less than a threshold value 1; A maintenance judgment unit, configured to maintain the initial price adjustment coefficient if the idle rate of the driver is greater than a threshold 1 and less than a threshold 2; The downward floating judgment unit is used to reduce the initial price adjustment coefficient by a preset optimized adjustment value if the idle rate of the driver is greater than threshold 2, and threshold 1 and threshold 2 are both obtained through historical record data statistics.

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