Data push methods, devices, electronic devices and storage media

Abstract

This disclosure provides a data push method, apparatus, electronic device, and storage medium, relating to the field of computers, and particularly to the field of intelligent transportation. The specific implementation scheme is as follows: acquiring order data of a ride-hailing order to be initiated by a passenger; determining a first acceptance level for the ride-hailing order based on the order data, wherein the first acceptance level characterizes the ease with which the passenger can receive the ride-hailing order; responding to the first acceptance level meeting a first threshold, determining target travel data based on the public transportation route corresponding to the order data, wherein the target travel data represents the passenger's travel strategy; and pushing the target travel data to the passenger.

Description

Technical Field

[0001] This disclosure relates to the field of computers, and more particularly to data push methods, devices, electronic devices, and storage media in the field of intelligent transportation. Background Technology

[0002] Currently, when hailing a ride through an app, the system typically recommends travel options based solely on order data. Summary of the Invention

[0003] This disclosure provides a data push method, apparatus, electronic device, and storage medium.

[0004] According to one aspect of this disclosure, a data push method is provided. The method includes: acquiring order data of a ride-hailing order to be initiated by a passenger; determining a first reception level for the ride-hailing order based on the order data, wherein the first reception level characterizes the ease with which the passenger can receive the ride-hailing order; in response to the first reception level satisfying a first threshold, determining target travel data based on the public transportation route corresponding to the order data, wherein the target travel data represents the passenger's travel strategy; and pushing the target travel data to the passenger.

[0005] According to another aspect of this disclosure, a data push device is also provided. The device includes: an acquisition unit for acquiring order data of a ride-hailing order to be initiated by a passenger; a first determination unit for determining a first reception level of the ride-hailing order based on the order data, wherein the first reception level characterizes the ease with which the passenger can receive the ride-hailing order; a second determination unit for determining target travel data based on the public transportation route corresponding to the order data in response to the first reception level satisfying a first threshold, wherein the target travel data represents the passenger's travel strategy; and a push unit for pushing the target travel data to the passenger.

[0006] According to another aspect of this disclosure, an electronic device is also provided. The electronic device may include: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform the data push method of the embodiments of this disclosure.

[0007] According to another aspect of this disclosure, a non-transitory computer-readable storage medium storing computer instructions is also provided, wherein the computer instructions are used to cause a computer to execute the data push method of the embodiments of this disclosure.

[0008] According to another aspect of this disclosure, a computer program product is also provided, which may include a computer program that, when executed by a processor, implements the data push method of the embodiments of this disclosure.

[0009] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of this disclosure, nor is it intended to limit the scope of this disclosure. Other features of this disclosure will become readily apparent from the following description. Attached Figure Description

[0010] The accompanying drawings are provided to better understand this solution and do not constitute a limitation of this disclosure. Wherein:

[0011] Figure 1 This is a flowchart of a data push method according to an embodiment of the present disclosure;

[0012] Figure 2 This is a flowchart of a route recommendation method according to an embodiment of the present disclosure;

[0013] Figure 3 This is a flowchart illustrating a method for identifying the difficulty level of a route according to an embodiment of this disclosure;

[0014] Figure 4 This is a schematic diagram of a data push device according to an embodiment of the present disclosure;

[0015] Figure 5 This is a block diagram of an electronic device according to an embodiment of the present disclosure of a data push method. Detailed Implementation

[0016] The exemplary embodiments of this disclosure are described below with reference to the accompanying drawings, including various details of the embodiments to aid understanding, and should be considered merely exemplary. Therefore, those skilled in the art will recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of this disclosure. Similarly, for clarity and brevity, descriptions of well-known functions and structures are omitted in the following description.

[0017] Figure 1 This is a flowchart of a data push method according to an embodiment of this disclosure. Figure 1 As shown, the method may include the following steps:

[0018] Step S102: Obtain the order data of the ride-hailing orders to be initiated by the passenger.

[0019] In the technical solution provided in step S102 of this disclosure, the passenger selects the origin and destination on the client and obtains the order data of the ride-hailing order to be initiated by the passenger. The order data may include the origin point, destination and selected time period of the ride-hailing order to be initiated.

[0020] Optionally, data such as the origin point, destination, and selected time period of the ride-hailing order to be initiated by the passenger can be obtained. The passenger can be the account or client used by the user when taking the ride, which is associated with the user. For example, it can be the user's ride-hailing software.

[0021] For example, the order data in this embodiment can be the user's input of the origin and destination of a ride-hailing order in the ride-hailing app; and the data obtained from the ride-hailing app, such as the origin and destination of the ride-hailing order to be initiated and the selected time period.

[0022] Step S104: Determine the first acceptance level of the ride-hailing order based on the order data, wherein the first acceptance level is used to characterize the ease with which the ride-hailing order is received by the vehicle dispatcher.

[0023] In the technical solution provided by step S104 of this disclosure, a first acceptance level of the ride-hailing order is determined based on the order data of the acquired ride-hailing order. The first acceptance level can be used to indicate the difficulty of the vehicle dispatcher accepting the ride-hailing order, and can be extremely difficult, difficult, easy, etc. The vehicle dispatcher can be a taxi, a private car, etc., without specific restrictions here.

[0024] Optionally, based on previous order data and according to the time dimension in the previous order data, combined with data such as order placement-completion rate and order placement-cancellation rate, the starting point of the order can be determined by using a geohash method to encode the location block and the time period of the order placement. The difficulty of hailing a ride can be extremely difficult, difficult, average, or easy.

[0025] Optionally, before a passenger places an order, they can input the origin and destination, obtain the order data of the ride-hailing orders that the passenger is about to initiate, and when the passenger is ready to place an order, they can determine the first acceptance level (ride-hailing difficulty) based on the ride-hailing difficulty of each location block and each time period that has been determined.

[0026] Step S106: In response to the first reception level meeting the first threshold, target travel data is determined based on the public transportation route corresponding to the order data, wherein the target travel data is used to represent the travel strategy of the passenger.

[0027] In the technical solution provided by step S106 of this disclosure, it is determined whether the first reception level meets the first threshold. In response to the first reception level meeting the first threshold, a public transportation route is determined based on the origin-to-destination data of the order data terminal. Based on the public transportation route, each waypoint of the public transportation route is traversed, and a point with moderate or easy taxi-hailing difficulty is selected for connection to determine the target travel data. The first threshold can be a value set in advance according to the actual situation, and can be used to represent the degree of difficulty in hailing a taxi. For example, it can be a threshold value for the degree of extreme difficulty in hailing a taxi or a threshold value for the degree of easy taxi-hailing. The public transportation route can be a route arranged based on public transportation tools such as bus or subway routes.

[0028] Optionally, satisfying the first threshold can be greater than the first threshold or less than the first threshold, etc. There is no specific restriction here. For example, when the first reception level is easy, satisfying the first threshold can be that the difficulty of hailing a ride is greater than the first threshold; when the first reception level is difficult, satisfying the first threshold can be that the difficulty of hailing a ride is less than the first threshold, etc.

[0029] Optionally, the first acceptance level of the ride-hailing order (e.g., extremely difficult, difficult, easy, etc.) can be judged. If the first acceptance level is greater than the first threshold corresponding to the easy difficulty of the ride-hailing, then based on the origin-to-destination data of the order data terminal, a public transportation route is determined. Based on the public transportation route, each waypoint of the public transportation route is traversed, and a point with an easy ride-hailing difficulty is selected for connection to determine the target travel data. Here, the connection point can be the point where the bus is changed to a ride-hailing.

[0030] Optionally, the first acceptance level of the ride-hailing order (e.g., extremely difficult, difficult, easy, etc.) can be judged. If the first acceptance level is less than the first threshold corresponding to the difficulty of the ride-hailing order, then based on the origin-to-destination data of the order data terminal, a public transportation route can be determined. Based on the public transportation route, each waypoint of the public transportation route can be traversed, and a point with an easy ride-hailing difficulty can be selected for connection to determine the target travel data.

[0031] Step S108: Push the target travel data to the passenger.

[0032] In the technical solution provided by step S108 of this disclosure, target travel data is determined based on the public transportation route corresponding to the order data, and suitable target travel data is pushed to the passenger.

[0033] Through steps S102 to S108, order data of the ride-hailing order to be initiated by the passenger is obtained; a first acceptance level of the ride-hailing order is determined based on the order data, wherein the first acceptance level is used to characterize the ease with which the ride-hailing order is received by the passenger; in response to the first acceptance level meeting a first threshold, target travel data is determined based on the public transportation route corresponding to the order data, wherein the target travel data is used to represent the passenger's travel strategy; and the target travel data is pushed to the passenger. In other words, this disclosure achieves the technical effect of effectively determining travel strategies by selecting a point where it is easy to hail a ride from the order data and determining the target travel data based on the public transportation route on the route corresponding to the order data, thereby solving the technical problem of the inability to effectively determine travel strategies.

[0034] The method described in this embodiment will now be described in further detail.

[0035] As an optional implementation, step S106, determining the target travel data based on the public transportation route corresponding to the order data, includes: determining the departure location and destination location of the passenger in the order data; determining the public transportation route between the departure location and the destination location; determining multiple locations on the public transportation route, wherein the locations are used to allow the passenger to hail a taxi; and determining the target travel data based on the passenger's taxi hailing data at each location, wherein the taxi hailing data is used to characterize the factors that affect the target travel data through each corresponding location.

[0036] In this embodiment, the departure and destination locations of passengers are determined from the order data, the public transportation routes between the departure and destination locations are determined, and multiple locations on the public transportation routes where passengers are allowed to hail a taxi are determined. Based on the taxi-hailing data of passengers at each location, the target travel data is determined. Here, the multiple locations can be multiple waypoints, and each location can be obtained by iterating through each waypoint. The taxi-hailing data can be used to characterize the factors that affect the target travel data through each corresponding location.

[0037] Optionally, the departure and destination locations of passengers are determined in the order data. Based on the departure and destination locations of passengers, public transportation routes from the departure and destination locations are obtained. Multiple locations on the public transportation routes where passengers are allowed to hail a taxi are determined. The multiple locations where passengers can hail a taxi are traversed. Based on the passenger's taxi hailing data at each location, the target travel data is determined. By determining the difficulty of hailing a taxi at each of the multiple locations, a point with moderate or easy taxi hailing difficulty is selected for connection, thereby achieving the technical effect of determining the target travel data.

[0038] Optionally, for each selected route, each waypoint has a latitude and longitude coordinate. By iterating through each waypoint, a waypoint with moderate or easy difficulty in hailing a ride can be selected for connection to determine the target travel data. If there are too many moderate or easy waypoints, a location can be randomly selected.

[0039] As an optional implementation, the ride-hailing data for each location includes the ride-hailing price of the starting location of the ride-hailing vehicle with each location as the passenger. Based on the ride-hailing data of the passenger at each location, determining the target travel data includes: determining at least one target ride-hailing price that satisfies a second threshold from multiple ride-hailing prices corresponding to multiple locations; and determining the target travel data based on at least one first location corresponding to at least one target ride-hailing price, wherein the multiple locations include at least one first location.

[0040] In this embodiment, the ride-hailing data for each location includes the ride-hailing price from the starting point of the ride-hailing trip for each location. From the multiple ride-hailing prices corresponding to multiple locations, at least one target ride-hailing price that meets a second threshold is determined. Based on at least one first location corresponding to the at least one target ride-hailing price, target travel data is determined. The second threshold can be a value set in advance according to the actual situation, or it can be a threshold for the consumption level. It can be used to characterize the amount of the ride-hailing fee and to filter out location points that are higher than the consumption level of the ride-hailing target.

[0041] Optionally, past order data can be calculated, and the user's consumption level can be statistically analyzed by selecting one of the following dimensions: device number (Called User IDentification number, abbreviated as cuid), user address (User Identification, abbreviated as uid), user age, and city of residence. A second threshold can be determined based on the user's consumption level. For example, the second threshold can be set as the maximum value of the consumption level, and the taxi fare below the second threshold can be determined as the target taxi fare; or the minimum value of the passenger's consumption level can be set as the second threshold, and the taxi fare above the second threshold can be determined as the target taxi fare.

[0042] Optionally, from multiple ride-hailing prices corresponding to multiple locations, at least one target ride-hailing price that meets the second threshold is determined. Based on at least one first location corresponding to the at least one target ride-hailing price, location points that are higher than the consumption level of the passenger are filtered out, and travel data that matches the passenger is retained, so as to achieve the purpose of determining target travel data.

[0043] Optionally, the ride-hailing data for each location includes the ride-hailing price from the starting point of the ride-hailing trip for each location. From the multiple ride-hailing prices corresponding to multiple locations, connections that are higher than the traveler's spending level are filtered out. By retaining the options that meet the traveler's spending level, the target travel data is determined.

[0044] This embodiment filters out prices that do not meet the criteria based on taxi fares, thereby achieving the technical effect of retaining prices that meet the consumption conditions of the passengers.

[0045] As an optional implementation, the ride-hailing data for each location includes multiple types of ride-hailing data for each first location. Determining the target travel data based on at least one first location corresponding to at least one target ride-hailing price includes: determining a target score corresponding to each first location based on the multiple types of ride-hailing data for each first location, wherein the target score is used to represent the probability of determining each first location corresponding to the multiple types of ride-hailing data as the initial ride-hailing location for the passenger; and determining the target travel data based on the target score.

[0046] In this embodiment, based on multiple types of ride-hailing data for each first location, a target score is determined for each first location, and target travel data is determined based on the target score. The ride-hailing data types may include ride-hailing price, overall price, ride-hailing duration, overall duration, and whether to prioritize ride-hailing or public transportation. The target score can be used to represent the probability of determining each first location corresponding to multiple types of ride-hailing data as the initial ride-hailing location for the passenger.

[0047] Optionally, the ride-hailing data for each location includes multiple types of ride-hailing data for each primary location, which may include ride-hailing price, overall price, ride-hailing duration, overall duration, whether to prioritize ride-hailing or public transportation, etc. Based on the multiple types of ride-hailing data for each primary location, a target score is determined for each primary location, and target travel data is selected and recommended based on the target score.

[0048] Optionally, the target score can be calculated using a weighted approach. This involves setting weights for factors such as ride-hailing price, overall price, ride-hailing duration, overall duration, and whether to prioritize ride-hailing or public transportation. The travel data with the highest target score is then selected as the target travel data to achieve the technical effect of obtaining suitable target travel data.

[0049] As an optional implementation, determining target travel data based on target scores includes: determining the highest target score among at least one target score corresponding to at least one first position; determining the second position corresponding to the highest target score among at least one first position; and determining target travel data based on the departure position, destination position, and second position.

[0050] In this embodiment, the highest target score is determined among at least one target score corresponding to at least one first position, and the second position corresponding to the highest target score is determined among at least one first position. Target travel data is determined based on the departure position, destination position, and second position, wherein the second position can be a connecting position.

[0051] Optionally, the highest target score is determined from at least one target score corresponding to at least one first position, the second position is determined based on the highest target score, and the target travel data is determined based on the departure position, the destination position, and the second position.

[0052] This embodiment selects the second position with the highest target score from multiple first positions based on the target score, in order to achieve the technical effect of determining the connection position.

[0053] As an optional implementation, determining the target travel data based on the departure location, destination location, and second location includes: determining public travel data between the departure location and the second location, wherein the public travel data is used to represent passengers using public transportation to travel from the departure location to the second location; determining taxi travel data between the second location and the destination location, wherein the taxi travel data is used to represent passengers using a vehicle to travel from the second location to the destination location; and determining the public travel data and taxi travel data as the target travel data.

[0054] In this embodiment, the public transportation data of the passenger from the departure location to the second location is used to determine the ride-hailing data from the second location to the destination location. The public transportation data and the ride-hailing data are then used to determine the target travel data.

[0055] Optionally, the highest target score is determined among at least one target score corresponding to at least one first position, the second position is determined based on the highest target score, the vehicle object is determined using public transportation data from the departure position to the second position and ride-hailing data from the second position to the destination position, and the target travel data is determined based on the public transportation data and the ride-hailing data, that is, the target travel data includes public transportation data and ride-hailing data.

[0056] This embodiment uses the second location, departure location, and destination location to confirm public transportation data and ride-hailing data, thereby achieving the technical effect of confirming target travel data.

[0057] As an optional implementation method, determining the target score corresponding to each first location based on multiple types of ride-hailing data for each first location includes: weighting the multiple types of ride-hailing data for each first location to obtain the target score corresponding to each first location.

[0058] In this embodiment, multiple types of ride-hailing data for each first location are processed in a weighted manner to obtain a target score corresponding to each first location, which can be used to determine the comprehensive score obtained after weighted processing for each first location.

[0059] Optionally, according to a pre-designed proportion, various types of ride-hailing data, such as ride-hailing price, overall price, ride-hailing duration, overall duration, and whether to prioritize ride-hailing or public transportation, are comprehensively scored using a weighted method to obtain the target score corresponding to each first position.

[0060] This embodiment processes various types of ride-hailing data for each first location using a weighted approach. The proportion of each weight can be set according to actual needs, thereby eliminating irrelevant interference and obtaining an accurate target score.

[0061] As an optional implementation, the various types of ride-hailing data include at least one of the following: the ride-hailing price based on the starting location of each first location; the travel price of the travel strategy based on the starting location of each first location; the ride-hailing duration based on the starting location of each first location; the travel duration of the travel strategy based on the starting location of each first location; and the priority of the starting location of each first location in the corresponding travel strategy.

[0062] In this embodiment, various types of ride-hailing data can be price data from each first location to the target location. For example, it can be the ride-hailing price of the starting location of each first location as the passenger; or it can be the travel price (overall price of the ride) of the travel strategy of the starting location of each first location as the passenger.

[0063] Optionally, various types of ride-hailing data can be time data from each first location to the destination location. For example, it can be the ride-hailing duration from the starting point of each first location (the driving time from each first location to the destination location); or it can be the travel duration of the travel strategy from the starting point of each first location (the total time from each first location to the destination location).

[0064] Optionally, various types of ride-hailing data can be used to prioritize the starting location of each ride-hailing destination in the corresponding travel strategy, such as prioritizing ride-hailing or prioritizing public transportation.

[0065] This embodiment is based on multiple types of ride-hailing data to achieve a comprehensive score for the selected routes, thereby achieving the technical effect of obtaining an accurate score.

[0066] As an optional implementation, determining at least one target taxi price below a second threshold from multiple taxi prices corresponding to multiple locations includes: taking each location as the starting location of the passenger's taxi ride, determining second order data for the passenger's second taxi order, resulting in multiple second order data; determining a second acceptance level for the second taxi order based on each second order data, resulting in multiple second acceptance levels, wherein the second acceptance level is used to characterize the ease with which the second taxi order is accepted by the passenger; determining at least one target acceptance level that meets a third threshold from the multiple second acceptance levels; determining at least one third location corresponding to the at least one target acceptance level from the multiple locations; and determining at least one target taxi price from at least one taxi price corresponding to the at least one third location.

[0067] In this embodiment, a location with moderate or easy taxi-hailing difficulty (e.g., a bus stop or subway station) can be selected for pick-up. Each location is used as the starting point for the passenger's taxi hailing. Second order data for the passenger's second taxi-hailing order is determined, resulting in multiple second order data sets. Based on each second order data set, a second acceptance level for the second taxi-hailing order is determined, resulting in multiple second acceptance levels. The second acceptance levels are then evaluated, and at least one target acceptance level that meets a third threshold is determined from among the multiple second acceptance levels. At least one third location corresponding to the at least one target acceptance level is determined from among the at least one taxi-hailing price corresponding to the at least one third location. Here, the second acceptance level characterizes the ease with which the passenger accepts the second taxi-hailing order; the third threshold can be a value set in advance based on actual conditions, and can be used to represent a critical value indicating the degree of difficulty in hailing a taxi, such as extremely difficult or easy to hail a taxi; the public transportation route can be a bus or subway route, or other routes arranged based on public transportation; and the target taxi-hailing price can be the passenger's consumption level.

[0068] Optionally, satisfying the third threshold can be greater than the third threshold or less than the third threshold, etc. There is no specific restriction here. For example, when the second reception level is easy, satisfying the third threshold can be that the level of ease is greater than the third threshold; when the second reception level is difficult, satisfying the third threshold can be that the level of difficulty is less than the third threshold, etc.

[0069] Optionally, a location with moderate or easy ride-hailing difficulty (e.g., bus stop, subway station) can be selected for pick-up. Each location is used as the starting point for the passenger's ride-hailing. Second order data for the passenger's second ride-hailing order is determined, resulting in multiple second order data. Based on each second order data, the ease of acceptance by the passenger in the second ride-hailing order is determined, resulting in multiple ease of acceptance by the passenger. The ease of acceptance by the passenger is judged to determine at least one target acceptance level that meets a third threshold. At least one third location corresponding to the at least one target acceptance level is determined among the multiple locations. At least one ride-hailing price corresponding to the third location is determined. From the at least one ride-hailing price corresponding to the at least one third location, at least one target ride-hailing price package lower than the second threshold is determined.

[0070] Optionally, based on the ease with which the vehicle dispatcher can receive the ride, the position where the vehicle dispatcher's acceptance level meets the third threshold is determined, so as to achieve the technical effect of determining an appropriate target ride price.

[0071] As an optional implementation, the order data includes the passenger's departure location, destination location, and ride-hailing time. Determining the first acceptance level of a ride-hailing order based on the order data includes: identifying historical order data in the target database that has the same departure location as the passenger's departure location, the same destination location as the passenger's destination location, and the same ride-hailing time as the passenger's ride-hailing time; and determining the first acceptance level based on the historical order data.

[0072] In this embodiment, the order data includes the passenger's departure location, destination location, and ride time. The passenger's departure location, destination location, and ride time are obtained from the order data. Historical order data with the same departure location, destination location, and ride time as the passenger are selected from the target database. Based on the acceptance level corresponding to the historical data, a first acceptance level is determined. The target database can be a database that stores historical order data.

[0073] Optionally, the data in the target database is labeled with difficulty levels, including: obtaining historical data from the historical database, processing the order origin offline using a geographic coordinate encoding algorithm according to the time dimension and combining the order placement-completion rate and order placement-cancellation rate, calculating a string from the latitude and longitude of the data to represent a specific rectangle, with all coordinates within the rectangle sharing this string, calculating the ride-hailing difficulty for each location block and each time period, such as extremely difficult, difficult, moderate, and easy, determining the acceptance level of historical orders, and selecting historical order data from the target database that have the same departure location, destination location, and ride-hailing time as the passenger, to achieve the technical effect of determining the first acceptance level of ride-hailing orders based on historical order data with already determined levels.

[0074] In this embodiment, order data of a ride-hailing order to be initiated by a passenger is obtained; a first acceptance level for the ride-hailing order is determined based on the order data, wherein the first acceptance level is used to characterize the ease with which the passenger can accept the ride-hailing order; in response to the first acceptance level meeting a first threshold, target travel data is determined based on the public transportation route corresponding to the order data, wherein the target travel data is used to represent the passenger's travel strategy; and the target travel data is pushed to the passenger. In other words, this disclosure achieves the technical effect of effectively determining travel strategies by selecting a point where it is easy to hail a ride from the order data and determining the target travel data based on the public transportation route corresponding to the order data, thereby solving the technical problem of the inability to effectively determine travel strategies.

[0075] The above technical solutions of the present disclosure will be further illustrated below with reference to preferred embodiments.

[0076] In related technologies, when hailing a ride through a client, the system typically recommends ride-hailing options based solely on order data, without recommending options such as public transportation or cars. This results in a low order acceptance rate during peak travel periods, failing to meet user travel needs and presenting a technical problem of difficulty in effectively determining travel strategies.

[0077] Therefore, to solve the above problems, this embodiment of the disclosure traverses each waypoint, selects points with moderate or easy difficulty in hailing a ride and connects them, calculates the estimated price and time from the starting point to each waypoint, retains paths that match the user's spending level based on the user's spending level, and comprehensively scores the retained paths, recommending the path with the highest score, thereby solving the technical problem of low efficiency in recommending suitable paths and achieving the technical effect of improving the efficiency of recommending suitable paths.

[0078] Figure 2 This is a flowchart of a route recommendation method according to an embodiment of the present disclosure, such as... Figure 2 As shown, the steps of this embodiment are as follows.

[0079] Step S201: Determine if the passenger has difficulty hailing a taxi.

[0080] Before placing an order, the user enters the origin and destination and the order preparation time in the client. The server receives the origin and destination and the order preparation time. Based on the origin and destination, the server determines the difficulty of hailing a ride during that time period. If the difficulty is extremely difficult or very difficult, then step S202 is executed.

[0081] Step S202: Retrieve the public transportation routes from the origin to the destination from the database.

[0082] Based on the obtained origin and destination, the server retrieves public transportation routes from the database, which can be bus or subway routes.

[0083] Step S203: Loop through and obtain the difficulty and cost of hailing a taxi for each point.

[0084] Iterate through each waypoint and retrieve the difficulty and cost of hailing a taxi for each point from the online database.

[0085] Optionally, calculate the estimated price (according to the billing standard) and time taken from the origin to each waypoint.

[0086] Optionally, the difficulty of hailing a ride at each point in the online database is identified, and the difficulty of hailing a ride at each point is retrieved from the online database in a loop. Figure 3 This is a flowchart illustrating a method for identifying the difficulty level of a route according to an embodiment of this disclosure, such as... Figure 3 As shown, identifying the difficulty of hailing a ride at each point in the online database can include the following steps:

[0087] Step S301: Retrieve data from the order database.

[0088] Data is retrieved from the order database based on order placement records for a specific time period in the past. The time period can be the past year, two years, etc., and there are no specific restrictions here.

[0089] Step S302: Process the starting point according to the geographic coordinate encoding algorithm based on the time dimension.

[0090] Based on the time dimension, combined with the order placement-completion rate and order placement-cancellation rate, the system can process the order placement origin using a geographic coordinate encoding algorithm in an offline state to calculate the ride-hailing difficulty for each location block and each time period. The ride-hailing difficulty can be categorized as extremely difficult, difficult, moderate, or easy.

[0091] Optionally, the time dimension can be 30 minutes, one hour, etc., which can be set according to the actual situation.

[0092] Step S303: For each location block and each time period, add 1 for a completed ride and subtract 1 for a cancelled ride.

[0093] Optionally, the ride-hailing situation for each location block and each time period can be calculated. If a ride is completed in a certain location block during a certain time period, the number of rides is increased by 1, and if a ride is canceled, the number of rides is decreased by 1, in order to determine the difficulty of hailing a ride for each location block and each time period.

[0094] Step S304: Obtain the median of all position blocks and label the difficulty level of all blocks.

[0095] Based on the determined difficulty of hailing a ride for each location block and each time period, the median of all location blocks is obtained. Based on the median of all location blocks, the difficulty level of all location blocks is obtained, and the difficulty level of all location blocks is marked.

[0096] Step S305: Write the difficulty level of the location block into the online database.

[0097] Write the location blocks marked with difficulty levels into the online database.

[0098] Step S204: Obtain the user's consumption level from the online data source.

[0099] Alternatively, a user's spending level can be determined in the following way: pull an offline data source, process past order data offline, and statistically analyze the user's spending level by device number. The user can be divided into three categories: ordinary (order amount below 20 yuan), medium (20-40 yuan), and advanced (above 40 yuan). The spending level corresponding to the device number is written into an online data source, and the user's spending level can be obtained from the online data source based on the device number.

[0100] Optionally, statistics can also be compiled based on user address, user age, city of residence, etc., without specific restrictions.

[0101] Optionally, due to the large amount of data, past order data is currently calculated at the hourly level. As computing power increases, it can be achieved in near real-time, that is, online calculation of past order data at the minute level.

[0102] Step S205: Filter out connections that exceed the user's spending level.

[0103] Based on spending levels, retain user-matched solutions and filter out connections that exceed the user's spending level.

[0104] Step S206: Determine if there is still a connection.

[0105] After filtering out connections that exceed the user's spending level, determine if there are still connections available. Select a location with moderate or easy ride-hailing difficulty to make a connection. If there are, proceed to step S207; otherwise, proceed to step S208. By default, all connections are retained.

[0106] Step S207: Iterate through each connection scheme and calculate the weighted score.

[0107] If there is a connection, the connection options are cycled through and scored comprehensively using a weighted method, including factors such as "taxi price, overall price, ride time, overall duration, and whether to prioritize taxi or public transportation".

[0108] Step S209: Recommend the one with the highest score.

[0109] Based on the scoring, the solution with the highest score is recommended. If no solution is available, the recall fails and no solution is recommended.

[0110] This disclosed embodiment is based on the starting point, and determines whether it is during peak travel time by considering the location and travel time. It then selects a point where it is easy to hail a taxi, such as a bus stop or subway station, from the public transportation options along the entire route and makes a taxi recommendation. If there are multiple options, it assesses the user's spending level and selects a suitable travel combination to recommend.

[0111] This embodiment of the disclosure supplements the single ride-hailing option for price-sensitive users by combining map capabilities with other travel options, primarily using ride-hailing, thus solving the problems of high prices for single ride-hailing options and the inconvenience and long travel times of single public transportation options. By dispatching users to "off-peak areas" to hail rides, it solves the problem of regional difficulty in hailing rides during peak hours. It achieves the technical effect of adding a mixed system within the map's vehicle-vehicle list function for different groups of people, different times, and different locations in specific scenarios, and providing different travel options for different groups of people.

[0112] This disclosure also provides an embodiment for performing Figure 1 The data push device of the data push method in the illustrated embodiment.

[0113] Figure 4 This is a schematic diagram of a data push device according to an embodiment of the present disclosure. Figure 4 As shown, the data push device 40 may include: an acquisition unit 41, a first determination unit 42, a second determination unit 43, and a push unit 44.

[0114] The acquisition unit 41 is used to acquire the order data of the ride-hailing order to be initiated by the passenger.

[0115] The first determining unit 42 is used to determine the first acceptance level of a ride-hailing order based on the order data, wherein the first acceptance level is used to characterize the ease with which the ride-hailing order is received by the vehicle dispatcher.

[0116] The second determining unit 43 is used to determine target travel data based on the public transportation route corresponding to the order data in response to the first reception level meeting the first threshold, wherein the target travel data is used to represent the travel strategy of the passenger.

[0117] Push unit 44 is used to push target travel data to passengers.

[0118] Optionally, the push unit 44 includes: a determination module, used to determine the departure location and destination location of the passenger in the order data; determine the public transportation route between the departure location and the destination location; determine multiple locations on the public transportation route, wherein the locations are used to identify locations where the passenger is allowed to hail a taxi; and determine target travel data based on the passenger's taxi hailing data at each location, wherein the taxi hailing data is used to characterize the factors that influence the target travel data through each corresponding location.

[0119] Optionally, the determining module includes: a first determining submodule, configured to determine at least one target ride-hailing price that satisfies a second threshold from multiple ride-hailing prices corresponding to multiple locations; and to determine target travel data based on at least one first location corresponding to the at least one target ride-hailing price, wherein the multiple locations include at least one first location.

[0120] Optionally, the first determining submodule determines the target travel data based on at least one first location corresponding to at least one target ride-hailing price through the following steps: determining a target score corresponding to each first location based on multiple types of ride-hailing data for each first location, wherein the target score is used to represent the probability of determining each first location corresponding to multiple types of ride-hailing data as the initial ride-hailing location of the passenger; and determining the target travel data based on the target score.

[0121] Optionally, the determining submodule determines the target travel data based on the target score through the following steps: determining the highest target score among at least one target score corresponding to at least one first position; determining the second position corresponding to the highest target score among at least one first position; and determining the target travel data based on the departure position, destination position, and second position.

[0122] Optionally, the determination submodule determines the target travel data based on the departure location, destination location, and second location through the following steps: determining public travel data between the departure location and the second location, wherein the public travel data is used to represent passengers using public transportation to travel from the departure location to the second location; determining taxi travel data between the second location and the destination location, wherein the taxi travel data is used to represent passengers using a vehicle to travel from the second location to the destination location; and determining the public travel data and taxi travel data as the target travel data.

[0123] Optionally, the determining submodule determines the target score corresponding to each first location based on the multiple types of ride-hailing data for each first location through the following steps: weighting the multiple types of ride-hailing data for each first location to obtain the target score corresponding to each first location.

[0124] Optionally, the various types of ride-hailing data include at least one of the following: the ride-hailing price based on the starting location of each first location; the travel price of the travel strategy based on the starting location of each first location; the ride-hailing duration based on the starting location of each first location; the travel duration of the travel strategy based on the starting location of each first location; and the priority of the starting location of each first location in the corresponding travel strategy.

[0125] Optionally, the determining submodule determines at least one target ride-hailing price lower than a second threshold from multiple ride-hailing prices corresponding to multiple locations through the following steps: taking each location as the starting location of the ride-hailing passenger, determining the second order data of the second ride-hailing order of the passenger, obtaining multiple second order data; determining the second acceptance level of the second ride-hailing order based on each second order data, obtaining multiple second acceptance levels, wherein the second acceptance level is used to characterize the ease with which the second ride-hailing order is accepted by the passenger; determining at least one target acceptance level that meets a third threshold from the multiple second acceptance levels; determining at least one third location corresponding to the at least one target acceptance level from the multiple locations; and determining at least one target ride-hailing price from at least one ride-hailing price corresponding to the at least one third location.

[0126] Optionally, the order data includes the passenger's departure location, destination location, and ride-hailing time. Determining the first acceptance level of a ride-hailing order based on the order data includes: identifying historical order data in the target database that has the same departure location as the passenger's departure location, the same destination location as the passenger's destination location, and the same ride-hailing time as the passenger's ride-hailing time; and determining the first acceptance level based on the historical order data.

[0127] In the apparatus of this disclosed embodiment, an acquisition unit acquires order data of a ride-hailing order to be initiated by a passenger; a first determination unit determines a first acceptance level of the ride-hailing order based on the order data, wherein the first acceptance level characterizes the ease with which the passenger can accept the ride-hailing order; a second determination unit, in response to the first acceptance level satisfying a first threshold, determines target travel data based on the public transportation route corresponding to the order data, wherein the target travel data represents the passenger's travel strategy; and a push unit pushes the target travel data to the passenger. In other words, this disclosure achieves the technical effect of effectively determining travel strategies by selecting a point where it is easy to hail a ride from the order data and determining the target travel data based on the public transportation route on the route corresponding to the order data, thereby solving the technical problem of the inability to effectively determine travel strategies.

[0128] The acquisition, storage, and application of user personal information involved in the technical solution disclosed herein comply with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0129] According to embodiments of this disclosure, this disclosure also provides an electronic device, a readable storage medium, and a computer program product.

[0130] Embodiments of this disclosure provide an electronic device that may include: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform the data push method of the embodiments of this disclosure.

[0131] Optionally, the electronic device may further include a transmission device and an input / output device, wherein the transmission device is connected to the processor and the input / output device is connected to the processor.

[0132] According to embodiments of this disclosure, this disclosure also provides a non-transitory computer-readable storage medium storing computer instructions, wherein the computer instructions are used to cause a computer to execute the data push method of embodiments of this disclosure.

[0133] Optionally, in this embodiment, the non-volatile storage medium described above can be configured to store a computer program for performing the following steps:

[0134] S1, retrieve the order data of the ride-hailing orders to be initiated by the passenger;

[0135] S2, determine the first acceptance level of the ride-hailing order based on the order data, wherein the first acceptance level is used to characterize the ease with which the ride-hailing order is accepted by the vehicle dispatcher;

[0136] S3, in response to the first reception level meeting the first threshold, the target travel data is determined based on the public transportation route corresponding to the order data, wherein the target travel data is used to represent the travel strategy of the passenger;

[0137] S4 pushes target travel data to passengers.

[0138] Optionally, in this embodiment, the aforementioned non-transitory computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or equipment, or any suitable combination of the foregoing. More specific examples of readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0139] According to embodiments of this disclosure, this disclosure also provides a computer program product, including a computer program that, when executed by a processor, performs the following steps:

[0140] S1, retrieve the order data of the ride-hailing orders to be initiated by the passenger;

[0141] S2, determine the first acceptance level of the ride-hailing order based on the order data, wherein the first acceptance level is used to characterize the ease with which the ride-hailing order is accepted by the vehicle dispatcher;

[0142] S3, in response to the first reception level meeting the first threshold, the target travel data is determined based on the public transportation route corresponding to the order data, wherein the target travel data is used to represent the travel strategy of the passenger;

[0143] S4 pushes target travel data to passengers.

[0144] Figure 5 This is a block diagram of an electronic device for a data push method according to an embodiment of the present disclosure. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the present disclosure described and / or claimed herein.

[0145] like Figure 5As shown, device 500 includes a computing unit 501, which can perform various appropriate actions and processes based on a computer program stored in read-only memory (ROM) 502 or a computer program loaded from storage unit 508 into random access memory (RAM) 503. RAM 503 may also store various programs and data required for the operation of device 500. The computing unit 501, ROM 502, and RAM 503 are interconnected via bus 504. Input / output (I / O) interface 505 is also connected to bus 504.

[0146] Multiple components in device 500 are connected to I / O interface 505, including: input unit 506, such as keyboard, mouse, etc.; output unit 507, such as various types of monitors, speakers, etc.; storage unit 508, such as disk, optical disk, etc.; and communication unit 509, such as network card, modem, wireless transceiver, etc. Communication unit 509 allows device 500 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0147] The computing unit 501 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 501 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 501 performs the various methods and processes described above, such as the method data push method. For example, in some embodiments, the method data push method can be implemented as a computer software program tangibly contained in a machine-readable medium, such as storage unit 508. In some embodiments, part or all of the computer program can be loaded and / or installed on device 500 via ROM 502 and / or communication unit 509. When the computer program is loaded into RAM 503 and executed by the computing unit 501, one or more steps of the data push method described above can be performed. Alternatively, in other embodiments, the computing unit 501 can be configured to perform the data push method by any other suitable means (e.g., by means of firmware).

[0148] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0149] The program code used to implement the methods of this disclosure may be written in any combination of one or more programming languages. This program code may be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data delivery device, such that when executed by the processor or controller, the program code causes the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The program code may be executed entirely on a machine, partially on a machine, as a standalone software package partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0150] In the context of this disclosure, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can be, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0151] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device for displaying information to the user (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor); and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the computer. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0152] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as a data server), or computing systems that include middleware components (e.g., an application server), or computing systems that include frontend components (e.g., a user computer with a graphical user interface or web browser through which a user can interact with embodiments of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., a communication network). Examples of communication networks include local area networks (LANs), wide area networks (WANs), and the Internet.

[0153] Computer systems can include clients and servers. Clients and servers are generally located far apart and typically interact via communication networks. Client-server relationships are created by computer programs running on the respective computers and having a client-server relationship with each other. Servers can be cloud servers, servers in distributed systems, or servers incorporating blockchain technology.

[0154] It should be understood that the various forms of processes shown above can be used to rearrange, add, or delete steps. For example, the steps described in this disclosure can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution disclosed in this disclosure can be achieved, and this is not limited herein.

[0155] The specific embodiments described above do not constitute a limitation on the scope of protection of this disclosure. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this disclosure should be included within the scope of protection of this disclosure.

Claims

1. A data push method, comprising: Retrieve order data for ride-hailing orders that a passenger is waiting to initiate; Based on the order data, a first acceptance level for the ride-hailing order is determined, wherein the first acceptance level is used to characterize the ease with which the ride-hailing order is received by the vehicle dispatcher; In response to the first reception level meeting the first threshold, target travel data is determined based on the public transportation route corresponding to the order data, wherein the first threshold is used to represent a critical value for the difficulty of hailing a taxi, and the target travel data is used to represent the travel strategy of the passenger. The target travel data is pushed to the passenger. The determination of target travel data based on the public transportation route corresponding to the order data includes: determining the public transportation route corresponding to the order data; traversing each waypoint of the public transportation route to obtain multiple locations, where each location is a location where the passenger is allowed to hail a taxi; obtaining the passenger's taxi fare data at each location, the taxi fare data including the taxi fare starting from each location, the taxi fare data being used to characterize the factors affecting the target travel data through each corresponding location; determining at least one target taxi fare below a second threshold from the multiple taxi fares corresponding to the multiple locations, the second threshold being a threshold for the passenger's consumption level; the multiple locations including at least one first location corresponding to at least one target taxi fare, the taxi fare data for each location including multiple types of taxi fare data for at least one first location, determining a target score corresponding to each first location based on the multiple types of taxi fare data for each first location, the target score being used to represent the probability of determining the first location as the passenger's initial taxi fare location; and determining the target travel data based on the target score.

2. The method according to claim 1, wherein, Determining the public transportation route corresponding to the order data includes: determining the departure location and destination location of the passenger in the order data; Determine the public transportation route between the departure location and the destination location.

3. The method according to claim 2, wherein, Determining the target travel data based on the target score includes: Determine the highest target score from at least one of the target scores corresponding to at least one of the first positions; Determine the second position corresponding to the highest target score from among the at least one first position; The target travel data is determined based on the departure location, the destination location, and the second location.

4. The method according to claim 3, wherein, Based on the departure location, the destination location, and the second location, determining the target travel data includes: Determine public travel data between the departure location and the second location, wherein the public travel data is used to characterize the passenger using public transportation from the departure location to the second location; Determine ride-hailing data between the second location and the destination location, wherein the ride-hailing data is used to characterize the passenger using the vehicle dispatcher to travel from the second location to the destination location; The public transportation data and the ride-hailing data are identified as the target travel data.

5. The method according to claim 1, wherein, The target score for each of the first locations is determined based on multiple types of ride-hailing data for each first location, including: The various types of ride-hailing data for each first location are weighted to obtain the target score corresponding to each first location.

6. The method according to claim 1, wherein, The various types of ride-hailing data include at least one of the following: The taxi fare for each passenger whose starting point for taking the taxi is the first location; The travel price for a travel strategy that uses each of the first locations as the starting point of the ride-hailing trip for the passenger. The duration of the ride, with each of the first locations as the starting point of the ride for the passenger; The travel duration of the travel strategy with each of the first locations as the starting point of the ride-hailing for the passenger; The first location is used as the priority of the ride-hailing starting location of the passenger in the corresponding travel strategy.

7. The method according to claim 1, wherein, Determining at least one target ride-hailing price lower than the second threshold from multiple ride-hailing prices corresponding to multiple locations includes: Using each of the aforementioned locations as the starting point for the ride-hailing of the passenger, the second order data of the second ride-hailing order of the passenger is determined, resulting in multiple sets of second order data. Based on each second order data, a second acceptance level for the second ride-hailing order is determined, resulting in multiple second acceptance levels. The second acceptance level is used to characterize the ease with which the second ride-hailing order is received by the vehicle dispatcher. Among a plurality of second reception levels, at least one target reception level that satisfies a third threshold is determined; Determine at least one third location among the plurality of locations corresponding to the at least one target reception level; The at least one target ride-hailing price is determined from at least one ride-hailing price corresponding to at least one of the third positions.

8. The method according to any one of claims 1 to 7, wherein, The order data includes the passenger's departure location, destination location, and time of booking. Determining the first acceptance level of the ride-hailing order based on the order data includes: In the target database, identify historical order data where the departure location is the same as the passenger's departure location, the destination location is the same as the passenger's destination location, and the ride-hailing time is the same as the passenger's ride-hailing time. The first acceptance level is determined based on the historical order data.

9. A data push device, comprising: The acquisition unit is used to acquire order data for ride-hailing orders that the passenger is about to initiate. The first determining unit is used to determine a first acceptance level of the ride-hailing order based on the order data, wherein the first acceptance level is used to characterize the ease with which the ride-hailing order is received by the vehicle dispatcher; The second determining unit is configured to determine target travel data based on the public transportation route corresponding to the order data in response to the first reception level meeting the first threshold, wherein the first threshold is used to represent a critical value for the difficulty of hailing a taxi, and the target travel data is used to represent the travel strategy of the passenger. The push unit is used to push the target travel data to the passenger. The second determining unit is configured to determine target travel data based on the public transportation route corresponding to the order data by performing the following steps: determining the public transportation route corresponding to the order data; traversing each waypoint of the public transportation route to obtain multiple locations, the locations being locations where the passenger is allowed to hail a taxi; obtaining the passenger's taxi-hailing data at each of the multiple locations, the taxi-hailing data including the taxi price with each location as the passenger's starting taxi-hailing location, the taxi-hailing data being used to characterize the factors affecting the target travel data through each corresponding location; determining at least one target taxi-hailing price lower than a second threshold from the multiple taxi-hailing prices corresponding to the multiple locations, the second threshold being a threshold for the passenger's consumption level; the multiple locations including at least one first location corresponding to at least one target taxi-hailing price, the taxi-hailing data for each location including multiple types of taxi-hailing data for at least one first location, determining a target score corresponding to each first location based on the multiple types of taxi-hailing data for each first location, the target score being used to represent the probability of determining the first location as the passenger's initial taxi-hailing location; and determining the target travel data based on the target score.

10. An electronic device, comprising: At least one processor; as well as A memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-8.

11. A non-transitory computer-readable storage medium storing computer instructions, wherein, The computer instructions are used to cause the computer to perform the method according to any one of claims 1-8.

12. A computer program product comprising a computer program that, when executed by a processor, implements the method according to any one of claims 1-8.

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