Order adjustment method and device, equipment and storage medium
Through preset price adjustment prediction, the middle platform uses historical order information and preset supply and demand prediction models to conduct supply and demand prediction and core indicator prediction, which solves the problem of inaccurate configuration of operation personnel during peak periods, and achieves the accuracy of supply and demand prediction and order adjustment. Timely, reducing the risk of artificial errors.
Patent Information
- Application Number
- CN202411253986.3
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-09-09
- Publication Date
- 2024-12-17
AI Technical Summary
During the period of peak business, holidays or bad weather, operating personnel find it difficult to adjust order configuration in a timely manner, resulting in tight supply and demand problems, and the configuration is inaccurate, making it impossible to effectively deal with emergencies.
Through the preset price adjustment prediction middle platform, use historical order information and preset supply and demand prediction models to make supply and demand prediction, obtain order response rate prediction results, and combine project operation configuration information to estimate core indicators, provide configuration suggestions, and reduce manual experience dependence. Improve the accuracy and timeliness of supply and demand forecasts and order adjustments.
It has achieved the accuracy and timeliness of supply and demand forecasting, core indicator forecasting and order adjustment while reducing manual experience dependence, avoiding the adverse effects of manual adjustments, and being able to timely perceive supply and demand changes and optimize configuration.
Smart Images
Figure CN119151574A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the field of price adjustment forecasting, and in particular to an order adjustment method, device, equipment and storage medium. Background Art
[0002] Taking the Huolala platform as an example, the basic pricing model for platform orders is mileage segment pricing, but during peak business hours, holidays or bad weather, which are prone to cause supply and demand tensions, it is necessary to further optimize the pricing method to improve the supply and demand situation. Figure 1 The algorithm shown in the figure adjusts orders based on the business experience of operators. However, due to limited manpower and lack of generalization of manual experience, it has the following problems: 1. Operations lag in sensing changes in supply and demand. Operations will be slow to detect a drop in response rate due to weather and other factors, making it impossible to make timely adjustments to deal with emergencies.
[0003] 2. Inaccurate configuration. Since operators cannot predict the changes in related indicators (such as the number of orders, platform revenue, etc.) after configuration, and adjustments based on experience alone will result in not daring to adjust or adjusting too much, it is impossible to perform accurate configuration. Summary of the invention
[0004] In view of this, the purpose of the present invention is to provide an order adjustment method, device, equipment and storage medium, which can effectively improve the accuracy and timeliness of supply and demand forecasting, core indicator forecasting and order adjustment under the condition of reducing reliance on manual experience, thereby avoiding the adverse effects of manual adjustment. The specific scheme is as follows: In a first aspect, the present application provides an order adjustment method, which is applied to a preset price adjustment prediction platform, comprising: Obtain corresponding historical order information, and perform supply and demand forecasting operations at corresponding forecasting granularity based on the historical order information and the corresponding preset supply and demand forecasting model to obtain corresponding order response rate forecasting results; Perform corresponding preset core indicator estimation operations according to the project operation configuration information to obtain corresponding indicator estimation results; wherein the preset core indicators include the impact of the strategy taking effect, the increase in matching orders, and the increase in the total matching transaction amount; When a configuration suggestion request is received from the preset real-time query operating system, the corresponding supply and demand forecasting operation and the preset core indicator estimation operation are triggered through the configuration suggestion request to obtain corresponding configuration suggestion information, and complete corresponding order adjustments and operational configuration optimization based on the configuration suggestion information.
[0005] Optionally, the obtaining of corresponding historical order information, and performing supply and demand forecasting operations at corresponding forecasting granularity based on the historical order information and a corresponding preset supply and demand forecasting model, includes: Obtain corresponding historical order information; wherein the historical order information includes corresponding historical order response rate information, historical order growth information, historical online capacity, and corresponding historical weather information, city information, vehicle model information, and holiday information; Obtaining a target prediction step corresponding to the historical order information, and determining a corresponding target preset supply and demand prediction model based on the target prediction step; Based on the target preset supply and demand forecasting model and the historical order information, a supply and demand forecasting operation at a corresponding forecasting granularity is performed to obtain a corresponding order response rate forecasting result.
[0006] Optionally, after obtaining the corresponding order response rate prediction result, the method further includes: A visualization dashboard corresponding to the order response rate prediction result is obtained based on Tableau, so as to perform corresponding result visualization based on the visualization dashboard.
[0007] Optionally, the performing supply and demand forecasting operation at a corresponding forecasting granularity based on the target preset supply and demand forecasting model and the historical order information includes: When the target prediction step is not less than the corresponding preset step threshold, a multi-step prediction is performed using the Prophet model and the historical order information to complete the supply and demand forecasting operation at the corresponding prediction granularity.
[0008] Optionally, the method further includes: When an indicator query request sent by the preset real-time query operating system is received, a corresponding indicator estimation result search operation is performed based on the city information and time period information in the indicator query request, and the obtained target indicator estimation result is returned to the preset real-time query operating system.
[0009] Optionally, performing a corresponding preset core indicator estimation operation according to the project operation configuration information to obtain a corresponding indicator estimation result includes: Based on the multivariate nonlinear regression model and the historical response rate, operation configuration information, proportion of orders triggering the strategy, and proportion of sub-scenario response rate in the project operation configuration information, the corresponding strategy effectiveness impact estimation results are obtained; Based on the preset pairing prediction model and the project operation configuration information, the probability of users placing orders and orders being matched under different price change ranges is estimated, and the obtained probability estimation results are used to determine the corresponding paired order increase range estimation results and the corresponding paired total transaction amount increase range estimation results.
[0010] Optionally, when receiving a configuration suggestion request sent by the preset real-time query operating system, triggering the corresponding supply and demand forecasting operation and the preset core indicator estimation operation through the configuration suggestion request includes: When receiving a configuration suggestion request sent by a preset real-time query operating system, triggering the corresponding supply and demand forecasting operation and the preset core indicator estimation operation based on the target project operation area and target time information in the configuration suggestion request to obtain corresponding configuration suggestion information; By returning the corresponding configuration suggestion information as the corresponding request response result to the preset interactive interface, so that the preset real-time query operating system can complete the corresponding order adjustment and operation configuration optimization based on the preset interactive interface.
[0011] In a second aspect, the present application provides an order adjustment device, which is applied to a preset price adjustment prediction platform, including: A supply and demand forecasting module is used to obtain corresponding historical order information, and perform supply and demand forecasting operations at corresponding forecasting granularity based on the historical order information and the corresponding preset supply and demand forecasting model to obtain corresponding order response rate forecasting results; The indicator estimation module is used to perform corresponding preset core indicator estimation operations according to the project operation configuration information to obtain corresponding indicator estimation results; wherein the preset core indicators include the impact of the strategy taking effect, the increase in matching orders, and the increase in the total matching transaction amount; The order adjustment module is used to trigger the corresponding supply and demand forecasting operation and the preset core indicator estimation operation through the configuration suggestion request when receiving the configuration suggestion request sent by the preset real-time query operating system, so as to obtain the corresponding configuration suggestion information, and complete the corresponding order adjustment and operation configuration optimization based on the configuration suggestion information.
[0012] In a third aspect, the present application provides an electronic device, including: Memory, used to store computer programs; A processor is used to execute the computer program to implement the steps of the aforementioned order adjustment method.
[0013] In a fourth aspect, the present application provides a computer-readable storage medium for storing a computer program, which implements the steps of the aforementioned order adjustment method when executed by a processor.
[0014] It can be seen that in this application, the corresponding historical order information is obtained, and the supply and demand forecasting operation under the corresponding forecasting granularity is performed based on the historical order information and the corresponding preset supply and demand forecasting model to obtain the corresponding order response rate forecast result; the corresponding preset core indicator estimation operation is performed according to the project operation configuration information to obtain the corresponding indicator estimation result; wherein the preset core indicators include the impact of the strategy taking effect, the increase in the matching order, and the increase in the matching total transaction amount; when the configuration suggestion request sent by the preset real-time query operating system is received, the corresponding supply and demand forecasting operation and the preset core indicator estimation operation are triggered by the configuration suggestion request to obtain the corresponding configuration suggestion information, and the corresponding order adjustment and operation configuration optimization are completed based on the configuration suggestion information. That is, in this application, the corresponding order response rate prediction result is obtained based on the preset supply and demand forecasting model of different forecasting granularities and the corresponding historical order information, and the core indicator estimation is performed. Then, when the corresponding configuration suggestion request is received, the corresponding supply and demand forecast and indicator estimation can be triggered to provide decision-making direction for operation, and when assisting operation decision-making, both the corresponding configuration suggestion and the estimated value of the core indicator can be provided to assist operation in selecting a better configuration strategy for optimization. In this way, the preset price adjustment forecasting platform plays an auxiliary role in all stages of operational decision-making. It can effectively improve the accuracy and timeliness of supply and demand forecasts, core indicator forecasts, and order adjustments while reducing reliance on manual experience, thereby avoiding the adverse effects of manual adjustments. BRIEF DESCRIPTION OF THE DRAWINGS
[0015] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying creative work.
[0016] Figure 1 A schematic diagram of an existing price adjustment process provided for this application; Figure 2 A flow chart of an order adjustment method provided for this application; Figure 3 A specific module interaction flow diagram provided for this application; Figure 4 A specific flowchart of price adjustment prediction provided for this application; Figure 5 A schematic diagram of configuring one-click online access provided for this application; Figure 6 A schematic diagram of a configuration-based estimation indicator provided for this application; Figure 7A schematic diagram of the structure of an order adjustment device provided in this application; Figure 8 A structural diagram of an electronic device provided for this application. DETAILED DESCRIPTION
[0017] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.
[0018] Currently, it is mainly through operators and using Figure 1 The algorithm shown adjusts orders based on the business experience of operators. However, due to limited manpower and lack of generalization of manual experience, the following problems exist: 1. Operations lag in perceiving changes in supply and demand. Operations will be delayed in noticing a drop in response rate due to weather, etc., making it impossible to make timely adjustments to deal with emergencies. 2. Inaccurate configuration. Since operators cannot predict changes in relevant indicators (such as the number of orders, platform revenue, etc.) after configuration, and adjustments based solely on experience will result in not daring to adjust or adjusting too much, accurate configuration cannot be performed. To this end, the present application provides a price adjustment prediction scheme that can effectively improve the accuracy and timeliness of supply and demand forecasts, core indicator forecasts, and order adjustments while reducing reliance on manual experience, thereby avoiding the adverse effects of manual adjustments.
[0019] See also Figure 2 As shown, an embodiment of the present invention discloses an order adjustment method, which is applied to a preset price adjustment prediction platform, including: Step S11: Obtain corresponding historical order information, and perform supply and demand forecasting operations at corresponding forecasting granularity based on the historical order information and the corresponding preset supply and demand forecasting model to obtain corresponding order response rate forecasting results.
[0020] It should be understood that, in this embodiment, the acquisition of corresponding historical order information and the performance of supply and demand forecasting operations at corresponding forecasting granularity based on the historical order information and the corresponding preset supply and demand forecasting model include: acquiring corresponding historical order information; wherein the historical order information includes corresponding historical order response rate information, historical order growth rate information, historical online capacity, and corresponding historical weather information, city information, vehicle type information, and holiday information; acquiring the target forecasting step corresponding to the historical order information, and determining the corresponding target preset supply and demand forecasting model based on the target forecasting step; performing supply and demand forecasting operations at corresponding forecasting granularity based on the target preset supply and demand forecasting model and the historical order information to obtain corresponding order response rate forecasting results. Specifically, in combination with Figure 3 , Figure 4 As shown, it can be seen that the order response rate prediction result will be transmitted to the corresponding preset real-time query operating system for operators to view. It should be understood that the response rate prediction results at different prediction granularities are provided in this embodiment. The response rate refers to the number of responded orders / number of orders at different granularities, such as the response rate of a certain area on a certain day = the number of responded orders in the area on a certain day / the total number of orders in the area. The response rate prediction problem belongs to the time series prediction problem. It is necessary to predict the future based on the historical response rate. The response rate is generally arranged by day or hour. The prediction granularity ranges from coarse to fine. Examples are as follows: 1) Daily response rate forecast for the region t+14 (next 14 days), that is, the target prediction step is 14 days.
[0021] 2) t+7-day response rate prediction for each vehicle type and city, that is, the target prediction step is 7 days.
[0022] 3) Hourly response rate prediction of city h+6 (next 6 hours), that is, the target prediction step is 6 hours.
[0023] Under a certain error tolerance, the prediction granularity will be as fine as possible. The error tolerance value is usually MAPE (Mean Absolute Percentage Error) < 8% (it can be set to other values during the specific implementation process, and can also be dynamically adjusted based on needs), MAPE = (predicted value - true value) / true value.
[0024] It is further necessary to understand that, in this embodiment, for problems with a large target prediction step (for example, >= 7 days), the Prophet model is used, and the input features are the historical daily response rates, special period dates such as holidays, and the number of days affected. That is, the supply and demand forecasting operation at the corresponding forecast granularity is performed based on the target preset supply and demand forecasting model and the historical order information, including: when the target prediction step is not less than the corresponding preset step threshold, multi-step forecasting is performed using the Prophet model and the historical order information to complete the supply and demand forecasting operation at the corresponding forecast granularity. In this process, the trend, cycle, and holidays of the time series are modeled and fitted separately, and the final result is summed up. Since the Prophet model is a Transformer model for multi-step forecasting tasks, under this model, the input features can be divided into the following two types: 1) Static variables (variables that do not change over time), such as inherent attributes such as city, vehicle type, and geographic location.
[0025] 2) Time-varying variables (variables that change over time). Time-varying variables are divided into two categories: those that are known in the past but unknown in the future (such as order growth) and those that are known in the past and future (such as holidays, weather, etc.). Designing the network for different data sources will improve the model.
[0026] It is further necessary to understand that in this embodiment, real-time warning will be carried out by visualizing the results, that is, after obtaining the corresponding order response rate prediction results, it also includes: obtaining a visualization dashboard corresponding to the order response rate prediction results based on Tableau, and visualizing the corresponding results based on the visualization dashboard.
[0027] Step S12: perform corresponding preset core indicator estimation operations according to the project operation configuration information to obtain corresponding indicator estimation results; wherein the preset core indicators include the impact of the strategy taking effect, the increase in matching orders, and the increase in the total matching transaction amount.
[0028] It should be understood that in this embodiment, the corresponding preset core indicator estimation operation is performed according to the project operation configuration information to obtain the corresponding indicator estimation result, including: estimating based on the historical response rate, operation configuration information, proportion of the number of orders triggering the strategy, and proportion of the sub-scenario response rate in the multivariate nonlinear regression model and the project operation configuration information, and obtaining the corresponding strategy effectiveness impact estimation result; estimating the probability of the user placing an order and the order being paired under different price change ranges based on the preset pairing prediction model and the project operation configuration information, and using the obtained probability estimation result to determine the corresponding pairing order increase range estimation result and the corresponding pairing total transaction amount increase range estimation result. Specifically, in the problem of estimating the increase range of the pairing order and the increase range of the pairing order GTV (also known as Gross Transaction Vaks, total transaction amount), by constructing the corresponding demand pairing rate model, and using the S-learner method in causal inference to build a prediction model (the base model is XGBoost, Extreme Gradient Boosting is also called distributed gradient enhancement library), based on similar date data, the probability of the user placing an order and the order being paired under different price change ranges is estimated. The overall matching probability is summarized as the estimated value of the matching order after the price adjustment, so as to obtain the increase in the matching order. At the same time, the estimated value of the matching order * the average price of the matching order * (1 + the price change range) is the estimated value of the total transaction amount of the matching order. (Estimated value - actual value) / actual value is the relative increase (i.e. the increase in the total matching transaction amount).
[0029] Step S13: When a configuration suggestion request is received from the preset real-time query operating system, the corresponding supply and demand forecasting operation and the preset core indicator estimation operation are triggered through the configuration suggestion request to obtain corresponding configuration suggestion information, and complete corresponding order adjustments and operation configuration optimization based on the configuration suggestion information.
[0030] Specifically, in this embodiment, when the configuration suggestion request sent by the preset real-time query operating system is received, the corresponding supply and demand forecasting operation and the preset core indicator estimation operation are triggered by the configuration suggestion request, including: when the configuration suggestion request sent by the preset real-time query operating system is received, the corresponding supply and demand forecasting operation and the preset core indicator estimation operation are triggered based on the target project operation area and target time information in the configuration suggestion request to obtain the corresponding configuration suggestion information; by returning the obtained corresponding configuration suggestion information as the corresponding request response result to the preset interactive interface, so that the preset real-time query operating system can complete the corresponding order adjustment and operation configuration optimization based on the preset interactive interface. In addition, the method also includes: when the indicator query request sent by the preset real-time query operating system is received, the corresponding indicator estimation result search operation is performed based on the city information and time period information in the indicator query request, and the obtained target indicator estimation result is returned to the preset real-time query operating system. That is, in this embodiment, two functions, recommended configuration and indicator query, can be provided. The operator can set conditions by presetting the real-time query operating system to filter information such as the target project operating area to obtain the corresponding request, and then return the corresponding response rate prediction results, as well as the recommended configuration or indicator prediction results under different goals.
[0031] It should be understood that in this embodiment, the real-time query operating system makes corresponding policy modifications to the received request response results to obtain the corresponding target configuration strategy to complete the corresponding order adjustment and configuration optimization. That is, in this embodiment, after receiving the configuration suggestion information and / or the indicator estimation results, the operator can choose to go online with one click or go online by customizing the process (that is, go online after modification). Taking the smart surcharge project as an example, the input and output are in the form of a table as follows: Figure 5 And, combined with Figure 6 As shown in the figure, after optimizing the configuration, the core indicators can be estimated based on the optimized configuration information. In this way, through one-click online, it is possible to avoid the situation where the risk of manual errors caused by frequent configuration adjustments during periods of large supply and demand fluctuations can be greatly increased due to the current offline import of configuration tables for operational configuration online.
[0032] In summary, in this embodiment, the preset price adjustment prediction center can instantly perceive changes in supply and demand, and multi-step predictions can be used to assist operations in preparing strategic plans in advance, and time-sharing predictions can be used to assist operations in instantly adjusting online configurations. Moreover, the optimal configuration recommendations can be used to increase the matching order quantity and matching GTV revenue while increasing supply. Taking the smart surcharge project as an example, during a certain peak demand period, going online according to the optimal configuration can increase higher revenue. In addition, the prediction capabilities and business operations are executed simultaneously on the same platform to reduce the risk of manual errors. Therefore, the preset price adjustment prediction center can play an auxiliary role in the early, mid-term and late stages of operational decision-making.
[0033] It can be seen that in the embodiment of the present application, the corresponding historical order information is obtained, and the supply and demand forecasting operation under the corresponding forecasting granularity is performed based on the historical order information and the corresponding preset supply and demand forecasting model to obtain the corresponding order response rate forecast result; the corresponding preset core indicator estimation operation is performed according to the project operation configuration information to obtain the corresponding indicator estimation result; wherein the preset core indicators include the impact of the strategy taking effect, the increase in the matching order, and the increase in the matching total transaction amount; when the configuration suggestion request sent by the preset real-time query operating system is received, the corresponding supply and demand forecasting operation and the preset core indicator estimation operation are triggered by the configuration suggestion request to obtain the corresponding configuration suggestion information, and the corresponding order adjustment and operation configuration optimization are completed based on the configuration suggestion information. That is, in this application, the corresponding order response rate prediction result is obtained based on the preset supply and demand forecasting model of different forecasting granularities and the corresponding historical order information, and the core indicator estimation is performed. Then, when the corresponding configuration suggestion request is received, the corresponding supply and demand forecast and indicator estimation can be triggered to provide decision-making direction for operation, and when assisting operation decision-making, both the corresponding configuration suggestion and the estimated value of the core indicator can be provided to assist operation in selecting a better configuration strategy for optimization. In this way, the preset price adjustment forecasting platform plays an auxiliary role in all stages of operational decision-making. It can effectively improve the accuracy and timeliness of supply and demand forecasts, core indicator forecasts, and order adjustments while reducing reliance on manual experience, thereby avoiding the adverse effects of manual adjustments.
[0034] See also Figure 7 As shown, the embodiment of the present application also discloses an order adjustment device, which is applied to a preset price adjustment prediction platform, including: The supply and demand forecasting module 11 is used to obtain corresponding historical order information, and perform supply and demand forecasting operations at corresponding forecasting granularity based on the historical order information and the corresponding preset supply and demand forecasting model to obtain corresponding order response rate forecasting results; The indicator estimation module 12 is used to perform corresponding preset core indicator estimation operations according to the project operation configuration information to obtain corresponding indicator estimation results; wherein the preset core indicators include the impact of the strategy taking effect, the increase in matching orders, and the increase in the total matching transaction amount; The order adjustment module 13 is used to trigger the corresponding supply and demand forecasting operation and the preset core indicator estimation operation through the configuration suggestion request when receiving the configuration suggestion request sent by the preset real-time query operating system, so as to obtain the corresponding configuration suggestion information, and complete the corresponding order adjustment and operation configuration optimization based on the configuration suggestion information.
[0035] Among them, for more specific working processes of the above-mentioned modules, please refer to the corresponding contents disclosed in the aforementioned embodiments, which will not be repeated here.
[0036] It can be seen that in this application, the preset supply and demand forecasting models based on different forecast granularities and the corresponding historical order information are used to obtain the corresponding order response rate forecast results, and the core indicators are estimated. Then, when the corresponding configuration suggestion request is received, the corresponding supply and demand forecast and indicator estimation can be triggered to provide decision-making direction for the operation, and when assisting the operation decision, it can provide both the corresponding configuration suggestions and the estimated values of the core indicators to assist the operation in selecting a better configuration strategy for optimization. In this way, the preset price adjustment forecasting platform plays an auxiliary role in all stages of the operation decision-making, and can effectively improve the accuracy and timeliness of supply and demand forecasts, core indicator forecasts, and order adjustments while reducing reliance on manual experience, thereby avoiding the adverse effects of manual adjustments.
[0037] In a specific embodiment, the supply and demand forecasting module 11 may specifically include: An information acquisition unit, used to acquire corresponding historical order information; wherein the historical order information includes corresponding historical order response rate information, historical order increase information, historical online capacity, and corresponding historical weather information, city information, vehicle model information, and holiday information; A model acquisition unit, used to acquire a target prediction step corresponding to the historical order information, and determine a corresponding target preset supply and demand prediction model based on the target prediction step; The supply and demand forecasting unit is used to perform supply and demand forecasting operations at corresponding forecasting granularity based on the target preset supply and demand forecasting model and the historical order information to obtain corresponding order response rate forecasting results.
[0038] In a specific embodiment, the order adjustment device can also be used to: obtain a visualization dashboard corresponding to the order response rate prediction result based on Tableau, and perform corresponding result visualization based on the visualization dashboard.
[0039] In a specific embodiment, the supply and demand forecasting unit can be specifically used to: when the target prediction step is not less than the corresponding preset step threshold, use the Prophet model and the historical order information to perform multi-step forecasting to complete the supply and demand forecasting operation at the corresponding forecast granularity.
[0040] In a specific embodiment, the order adjustment device can also be used to: when receiving an indicator query request sent by the preset real-time query operating system, perform a corresponding indicator estimation result search operation based on the city information and time period information in the indicator query request, and return the obtained target indicator estimation result to the preset real-time query operating system.
[0041] In a specific embodiment, the indicator estimation module 12 can be specifically used to: make an estimate based on a multivariate nonlinear regression model and the historical response rate, operation configuration information, the proportion of triggered strategy orders, and the proportion of sub-scenario response rate intervals in the project operation configuration information to obtain a corresponding strategy effectiveness impact estimation result; estimate the probability of a user placing an order and the order being paired under different price change ranges based on a preset pairing prediction model and the project operation configuration information, and use the obtained probability estimation results to determine the corresponding paired order increase estimation results and the corresponding paired total transaction amount increase estimation results.
[0042] In a specific embodiment, the order adjustment module 13 can be specifically used for: when receiving a configuration suggestion request sent by a preset real-time query operating system, triggering the corresponding supply and demand forecasting operation and the preset core indicator estimation operation based on the target project operating area and target time information in the configuration suggestion request to obtain corresponding configuration suggestion information; by returning the obtained corresponding configuration suggestion information as a corresponding request response result to the preset interactive interface, so that the preset real-time query operating system can complete the corresponding order adjustment and operation configuration optimization based on the preset interactive interface.
[0043] Furthermore, the present application also discloses an electronic device. Figure 8 This is a structural diagram of an electronic device 20 according to an exemplary embodiment. The content in the diagram cannot be regarded as any limitation on the scope of use of the present application.
[0044] Figure 8The present invention provides a schematic diagram of the structure of an electronic device 20 provided in an embodiment of the present application. The electronic device 20 may specifically include: at least one processor 21, at least one memory 22, a power supply 23, a communication interface 24, an input / output interface 25, and a communication bus 26. The memory 22 is used to store a computer program, which is loaded and executed by the processor 21 to implement the relevant steps in the order adjustment method disclosed in any of the aforementioned embodiments. In addition, the electronic device 20 in this embodiment may specifically be an electronic computer.
[0045] In this embodiment, the power supply 23 is used to provide working voltage for each hardware device on the electronic device 20; the communication interface 24 can create a data transmission channel between the electronic device 20 and the external device, and the communication protocol it follows is any communication protocol that can be applied to the technical solution of the present application, and is not specifically limited here; the input and output interface 25 is used to obtain external input data or output data to the outside world, and its specific interface type can be selected according to specific application needs and is not specifically limited here.
[0046] In addition, the memory 22, as a carrier for storing resources, can be a read-only memory, a random access memory, a disk or an optical disk, etc. The resources stored thereon can include an operating system 221, a computer program 222, etc., and the storage method can be temporary storage or permanent storage.
[0047] The operating system 221 is used to manage and control the hardware devices and computer program 222 on the electronic device 20, which can be Windows Server, Netware, Unix, Linux, etc. In addition to including a computer program that can be used to complete the order adjustment method performed by the electronic device 20 disclosed in any of the aforementioned embodiments, the computer program 222 can further include a computer program that can be used to complete other specific tasks.
[0048] Furthermore, the present application also discloses a computer-readable storage medium for storing a computer program; wherein the computer program, when executed by a processor, implements the order adjustment method disclosed above. The specific steps of the method can refer to the corresponding contents disclosed in the above embodiments, and will not be repeated here.
[0049] In this specification, each embodiment is described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the embodiments can be referred to each other. For the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part.
[0050] Professionals may further appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in the above description according to function. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians may use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.
[0051] The steps of the method or algorithm described in conjunction with the embodiments disclosed herein may be implemented directly using hardware, a software module executed by a processor, or a combination of the two. The software module may be placed in a random access memory (RAM), a memory, a read-only memory (ROM), an electrically programmable ROM, an electrically erasable programmable ROM, a register, a hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.
[0052] Finally, it should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the presence of other identical elements in the process, method, article or device including the elements.
[0053] The technical solution provided by the present application is introduced in detail above. Specific examples are used in this article to illustrate the principles and implementation methods of the present application. The description of the above embodiments is only used to help understand the method of the present application and its core idea. At the same time, for general technicians in this field, according to the idea of the present application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as a limitation on the present application.
Claims
1. An order adjustment method, It is characterized in that Applied to the preset price adjustment forecasting platform, including: Obtain corresponding historical order information, and perform supply and demand forecasting operations at corresponding forecasting granularity based on the historical order information and the corresponding preset supply and demand forecasting model to obtain corresponding order response rate forecasting results; Perform corresponding preset core indicator estimation operations according to the project operation configuration information to obtain corresponding indicator estimation results; wherein the preset core indicators include the impact of the strategy taking effect, the increase in matching orders, and the increase in the total matching transaction amount; When a configuration suggestion request is received from the preset real-time query operating system, the corresponding supply and demand forecasting operation and the preset core indicator estimation operation are triggered through the configuration suggestion request to obtain corresponding configuration suggestion information, and complete corresponding order adjustments and operational configuration optimization based on the configuration suggestion information.
2. The order adjustment method according to claim 1, It is characterized in that The obtaining of corresponding historical order information, and performing supply and demand forecasting operations at corresponding forecasting granularity based on the historical order information and the corresponding preset supply and demand forecasting model, includes: Obtain corresponding historical order information; wherein the historical order information includes corresponding historical order response rate information, historical order growth information, historical online capacity, and corresponding historical weather information, city information, vehicle model information, and holiday information; Obtaining a target prediction step corresponding to the historical order information, and determining a corresponding target preset supply and demand prediction model based on the target prediction step; Based on the target preset supply and demand forecasting model and the historical order information, a supply and demand forecasting operation at a corresponding forecasting granularity is performed to obtain a corresponding order response rate forecasting result.
3. The order adjustment method according to claim 2, It is characterized in that After obtaining the corresponding order response rate prediction result, the method further includes: A visualization dashboard corresponding to the order response rate prediction result is obtained based on Tableau, so as to perform corresponding result visualization based on the visualization dashboard.
4. The order adjustment method according to claim 3, It is characterized in that The supply and demand forecasting operation at a corresponding forecasting granularity based on the target preset supply and demand forecasting model and the historical order information includes: When the target prediction step is not less than the corresponding preset step threshold, a multi-step prediction is performed using the Prophet model and the historical order information to complete the supply and demand forecasting operation at the corresponding prediction granularity.
5. The order adjustment method according to claim 1, It is characterized in that Also includes: When an indicator query request sent by the preset real-time query operating system is received, a corresponding indicator estimation result search operation is performed based on the city information and time period information in the indicator query request, and the obtained target indicator estimation result is returned to the preset real-time query operating system.
6. The order adjustment method according to claim 1, It is characterized in that The corresponding preset core indicator estimation operation is performed according to the project operation configuration information to obtain the corresponding indicator estimation result, including: Based on the multivariate nonlinear regression model and the historical response rate, operation configuration information, proportion of orders triggering the strategy, and proportion of sub-scenario response rate in the project operation configuration information, the corresponding strategy effectiveness impact estimation results are obtained; Based on the preset pairing prediction model and the project operation configuration information, the probability of users placing orders and orders being matched under different price change ranges is estimated, and the obtained probability estimation results are used to determine the corresponding paired order increase range estimation results and the corresponding paired total transaction amount increase range estimation results.
7. The order adjustment method according to any one of claims 1 to 6, It is characterized in that When receiving a configuration suggestion request sent by the preset real-time query operating system, triggering the corresponding supply and demand forecasting operation and the preset core indicator estimation operation through the configuration suggestion request includes: When receiving a configuration suggestion request sent by a preset real-time query operating system, triggering the corresponding supply and demand forecasting operation and the preset core indicator estimation operation based on the target project operation area and target time information in the configuration suggestion request to obtain corresponding configuration suggestion information; By returning the corresponding configuration suggestion information as the corresponding request response result to the preset interactive interface, so that the preset real-time query operating system can complete the corresponding order adjustment and operation configuration optimization based on the preset interactive interface.
8. An order adjustment device, It is characterized in that Applied to the preset price adjustment forecasting platform, including: A supply and demand forecasting module is used to obtain corresponding historical order information, and perform supply and demand forecasting operations at corresponding forecasting granularity based on the historical order information and the corresponding preset supply and demand forecasting model to obtain corresponding order response rate forecasting results; The indicator estimation module is used to perform corresponding preset core indicator estimation operations according to the project operation configuration information to obtain corresponding indicator estimation results; wherein the preset core indicators include the impact of the strategy taking effect, the increase in matching orders, and the increase in the total matching transaction amount; The order adjustment module is used to trigger the corresponding supply and demand forecasting operation and the preset core indicator estimation operation through the configuration suggestion request when receiving the configuration suggestion request sent by the preset real-time query operating system, so as to obtain the corresponding configuration suggestion information, and complete the corresponding order adjustment and operation configuration optimization based on the configuration suggestion information.
9. An electronic device, It is characterized in that include: Memory, used to store computer programs; A processor, configured to execute the computer program to implement the order adjustment method according to any one of claims 1 to 7.
10. A computer-readable storage medium, It is characterized in that Used to store a computer program, which, when executed by a processor, implements the order adjustment method according to any one of claims 1 to 7.