A method and apparatus for processing route data

Abstract

The application discloses a route data processing method and device, and relates to the technical field of digital marketing. The specific implementation of the method comprises the following steps: acquiring historical query data corresponding to a plurality of routes; performing continuous processing on the historical query data corresponding to the plurality of routes respectively, and determining continuous processing results; determining one or more target routes and a target coverage time period corresponding to each target route according to a preset coverage time period and the continuous processing results; and acquiring route data corresponding to each target route according to the target coverage time period corresponding to each target route, and storing the route data in a cache. According to the actual route resource use scene, the implementation can maximize the use of the cost of consideration, determine an accurate and continuous route resource coverage range, process special time periods, respond to users in time to improve user experience, prevent interference of abnormal data, and improve operation and management efficiency.

Description

Technical Field

[0001] This invention relates to the field of digital marketing technology, and in particular to a method and apparatus for processing route data. Background Technology

[0002] A route refers to the path taken by transportation such as airplanes, trains, buses, and ships from point A to point B, for example, the Beijing-Shanghai route. A route corresponds to one or more train services, and the set of remaining seats on each service is called route resources. E-commerce platforms need to acquire route resources from various transportation agencies to meet users' travel needs.

[0003] To respond promptly to user queries, the platform needs to acquire and cache route resources for future times in advance. Since each data acquisition incurs a cost, the coverage of route resources is typically determined based on user query volume, thus constrained by these costs.

[0004] However, the existing coverage is not continuous, which makes it difficult to respond to users in a timely manner, resulting in a poor user experience; it also cannot meet the query needs of users during certain special periods (such as holidays); especially when affected by malicious crawlers, the coverage becomes detached from reality, making the route resources obtained from it unusable and wasting the cost. Summary of the Invention

[0005] In view of this, embodiments of the present invention provide a method and apparatus for processing route data, which can maximize the utilization of cost based on actual route resource usage scenarios, determine accurate and continuous route resource coverage, process special time periods, respond to users in a timely manner to improve user experience, prevent interference from abnormal data, and improve operational management efficiency.

[0006] To achieve the above objectives, according to one aspect of the present invention, a method for processing route data is provided, comprising:

[0007] Obtain historical query data for multiple routes from users;

[0008] The historical query data corresponding to the multiple routes are processed continuously to determine the continuous processing result;

[0009] Based on the preset coverage time period and the continuous processing result, one or more target routes and the target coverage time period corresponding to each target route are determined; wherein, the total target coverage time corresponding to the one or more target routes is not greater than the preset coverage time period;

[0010] Based on the target coverage time period corresponding to each target route, obtain the route data corresponding to the target route and store the route data in the cache.

[0011] Optionally, the historical query data includes the historical query volume corresponding to multiple time periods under multiple routes;

[0012] For each of the routes, the historical query volume corresponding to the multiple time periods is used as the first sequence corresponding to the route;

[0013] Based on each of the first sequences, the historical query volumes are segmented and processed continuously to determine the result of the continuous processing.

[0014] Optionally, the step of performing segmented continuous processing on multiple historical query volumes based on each of the first sequences, and determining the continuous processing result, includes:

[0015] Determine whether the first sequence is a decreasing sequence;

[0016] If the first sequence is not a decreasing sequence, determine one or more inflection points in the first sequence; wherein the inflection points are determined based on the changing trend of the first sequence;

[0017] Based on one or more inflection points, the historical query volume is processed continuously.

[0018] Optionally, the step of performing continuous processing on the historical query volume based on the one or more inflection points includes:

[0019] Determine the first inflection point among the one or more inflection points; wherein, the first inflection point is the first historical query that makes the first sequence not a decreasing sequence;

[0020] The historical query volume at the first inflection point and the first historical query volume before the first inflection point are used as the target segments;

[0021] A1: Determine the segmental average of the historical query volume for the target segment;

[0022] A2: Compare the average value of the target segment with the first historical query volume before the target segment;

[0023] A3: Determine whether the average value of the segments is greater than the first historical query volume before the target segment;

[0024] A4: If the average value of the segments is greater than the first historical query volume before the target segment, add the first historical query volume before the target segment to the target segment;

[0025] Repeat steps A1-A4 until the average value of the segments is not greater than the first historical query volume before the target segment.

[0026] Optionally, the step of performing continuous processing on the historical query volume based on the one or more inflection points includes:

[0027] Determine the first inflection point among the one or more inflection points; wherein the first inflection point is the first of the peaks or troughs corresponding to the changing trend of the first sequence;

[0028] B1: Starting from the first historical query volume of the first sequence, up to the historical query volume corresponding to the first inflection point, all the historical query volumes are taken as the target segment.

[0029] B2: Determine the segmental average of the historical query volume for the target segment;

[0030] B3: Compare the average value of the target segment with the first historical query volume after the first inflection point;

[0031] B4: Determine whether the average value of the segments is less than the first historical query volume after the first inflection point;

[0032] B5: If the average value of the segments is less than the first historical query volume after the first inflection point, the first historical query volume after the first inflection point shall be taken as the first inflection point.

[0033] Repeat steps B1-B5 until the average value of the segments is not less than the first historical query volume after the first inflection point.

[0034] Optionally, if the average segment value is not greater than the first historical query volume before the target segment, or if the average segment value is not less than the first historical query volume after the first inflection point, the method further includes:

[0035] Update the segment average value to the historical query volume of each time period of the target segment;

[0036] The second sequence is determined based on the updated historical query volume.

[0037] Optionally, the step of performing continuous processing on the historical query volume based on the one or more inflection points includes:

[0038] Based on the one or more inflection points, determine multiple target segments corresponding to the first sequence;

[0039] The historical query volumes corresponding to the multiple target segments are processed continuously.

[0040] Optionally, determining multiple target segments corresponding to the first sequence based on the one or more inflection points includes:

[0041] Determine the first inflection point among the one or more inflection points. Starting from the first historical query volume of the first sequence, up to the historical query volume corresponding to the first inflection point, take all the historical query volumes as the target segment; wherein, the first inflection point is the first of the peaks or troughs corresponding to the change trend of the first sequence.

[0042] And / or,

[0043] The historical query volume between any two adjacent inflection points and the historical query volume corresponding to the next inflection point among the two adjacent inflection points are taken as a target segment.

[0044] Optionally, the historical query volumes corresponding to the multiple target segments are processed continuously, including:

[0045] The historical query volume corresponding to the target segment is averaged.

[0046] Based on the mean processing result, a second sequence corresponding to the route is determined; wherein, the second sequence is a decreasing sequence.

[0047] Optionally, the mean processing involves determining the average of historical query volumes included in the target segment; determining the second sequence corresponding to the route based on the mean processing result includes:

[0048] The average values ​​of each segment of the target segment are compared one by one;

[0049] Determine whether the average value of each target segment of the first sequence corresponding to the route is a decreasing sequence. If so, determine the second sequence based on the average value of each target segment of the first sequence corresponding to the route.

[0050] Optionally, if the average value of each target segment in the first sequence corresponding to the route is not a decreasing sequence, the method further includes:

[0051] Determine whether there are any peaks or troughs after the first inflection point. If yes, determine the second peak or trough corresponding to the change trend of the first sequence as the first inflection point; if no, determine the last historical query volume of the first sequence as the first inflection point.

[0052] Optionally, determining one or more target routes and the target coverage time period corresponding to each target route based on the preset coverage time period and the continuous processing result includes:

[0053] The multiple second sequences corresponding to the multiple routes are compared, and a preset number of second sequence values ​​are determined from the multiple second sequences; the preset number corresponds to the preset coverage time period, and the second sequence value is not less than the other sequence values ​​in the second sequence;

[0054] The route corresponding to the second sequence containing the second sequence value is determined to be the target route, and the time period corresponding to the second sequence value is determined to be the target coverage time period corresponding to the target route.

[0055] Optionally, it also includes:

[0056] Determine the high-frequency query time;

[0057] Obtain the route data corresponding to the high-frequency query time, and store the route data corresponding to the high-frequency query time in the cache.

[0058] Optionally, it also includes:

[0059] The terminal receives a query request; the query request indicates the query route, data timestamp, and target query time.

[0060] Determine whether the target query time belongs to the high-frequency query time;

[0061] If the target query time does not fall within the high-frequency query time, the query time period is determined based on the data timestamp and the target query time.

[0062] Determine whether the query route and the query time period belong to the target route and the target coverage time period. If so, retrieve the route data corresponding to the query time period of the query route from the cached route data and send it to the terminal.

[0063] Optionally, if the target query time falls within the high-frequency query time, the method further includes:

[0064] The route data corresponding to the target query time is obtained from the route data corresponding to the high-frequency query time in the cache and sent to the terminal.

[0065] Optionally, it also includes:

[0066] Based on a preset filtering period, determine whether the historical query volume within the preset filtering period is abnormal;

[0067] In the event of an abnormal historical query volume, the abnormal historical query volume is corrected based on other historical query volumes within the preset filtering period.

[0068] Optionally, determining whether the historical query volume within the preset filtering period is abnormal includes:

[0069] Compare each historical query volume with the average of other historical query volumes within the preset filtering period;

[0070] Determine whether the ratio of each historical query volume to the average of the other historical query volumes exceeds the preset multiple threshold.

[0071] If the ratio of the historical query volume to the average of the other historical query volumes exceeds the preset multiple threshold, the historical query volume is determined to be abnormal.

[0072] Optionally, correcting abnormal historical query volumes based on other historical query volumes within the preset filtering period includes:

[0073] The abnormal historical query volume will be corrected to the average of other historical query volumes within the corresponding preset filtering period.

[0074] According to another aspect of the present invention, a route data processing apparatus is provided, comprising:

[0075] The acquisition module is used to acquire historical query data for multiple routes from the user;

[0076] The data processing module is used to perform continuous processing on the historical query data corresponding to the multiple routes respectively, and determine the continuous processing result;

[0077] The time determination module is used to determine one or more target routes and the target coverage time period corresponding to each target route based on the preset coverage time period and the continuous processing result; wherein the target coverage time period corresponding to the one or more target routes is not greater than the preset coverage time period;

[0078] The storage module is used to obtain the route data corresponding to each target route according to the target coverage time period, and store the route data in the cache.

[0079] According to another aspect of the present invention, an electronic device for processing route data is provided, comprising:

[0080] One or more processors;

[0081] Storage device for storing one or more programs.

[0082] When the one or more programs are executed by the one or more processors, the one or more processors implement the route data processing method provided by the present invention.

[0083] According to another aspect of the present invention, a computer-readable medium is provided having a computer program stored thereon, which, when executed by a processor, implements the route data processing method provided by the present invention.

[0084] One embodiment of the above invention has the following advantages or beneficial effects: Because it employs continuous processing of query data to determine the target route and its target coverage time period based on a preset coverage time period within a limited economic cost, and caches the corresponding route data, it overcomes the existing problems of inconsistent coverage, inability to respond to users promptly leading to poor user experience, inability to meet user query needs during special time periods (such as holidays), and, especially, coverage deviating from reality when encountering malicious crawlers, resulting in unusable route resources and wasted costs. This achieves the technical effect of maximizing the utilization of cost based on actual route resource usage scenarios, determining accurate and continuous route resource coverage, processing special time periods, responding to users promptly to improve user experience, preventing interference from abnormal data, and improving operational management efficiency.

[0085] The further effects of the aforementioned unconventional alternative methods will be explained below in conjunction with specific implementation methods. Attached Figure Description

[0086] The accompanying drawings are provided to better understand the invention and are not intended to unduly limit the scope of the invention. Wherein:

[0087] Figure 1 This is a schematic diagram of the main flow of the route data processing method according to an embodiment of the present invention;

[0088] Figure 2 This is a schematic diagram of the main flow of the abnormal data processing method according to an embodiment of the present invention;

[0089] Figure 3(a) is a schematic diagram of abnormal data processing according to an embodiment of the present invention. Figure 1 ;

[0090] Figure 3(b) is a schematic diagram of abnormal data processing according to an embodiment of the present invention. Figure 2 ;

[0091] Figure 3(c) is a schematic diagram of abnormal data processing according to an embodiment of the present invention;

[0092] Figure 4 This is a schematic diagram of the main flow of the continuous processing method for query data according to the first embodiment of the present invention;

[0093] Figure 5This is a schematic diagram of the main flow of the continuous processing method for query data according to the second embodiment of the present invention;

[0094] Figure 6 This is a schematic diagram of the main flow of the continuous processing method for query data according to the third embodiment of the present invention;

[0095] Figure 7 This is a schematic diagram of the main flow of the method for determining the target route and the target coverage time period according to an embodiment of the present invention;

[0096] Figure 8 This is a schematic diagram of the main flow of a high-frequency query scenario processing method according to an embodiment of the present invention;

[0097] Figure 9 This is a schematic diagram of the main flow of a query request processing method according to an embodiment of the present invention;

[0098] Figure 10 This is a schematic diagram of the main modules of a route data processing device according to an embodiment of the present invention;

[0099] Figure 11 An exemplary system architecture diagram is shown, which is suitable for a method or apparatus for processing route data applied to embodiments of the present invention.

[0100] Figure 12 This is a schematic diagram of the structure of a computer system suitable for implementing terminal devices or servers of the present invention. Detailed Implementation

[0101] The following description, in conjunction with the accompanying drawings, illustrates exemplary embodiments of the present invention, including various details to aid understanding. These details 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 the invention. Similarly, for clarity and brevity, descriptions of well-known functions and structures are omitted in the following description.

[0102] Time period: Tn, also known as T(n), represents the distance N days between the user's query date and the departure date of the scheduled bus. For example, querying today's bus is T0, querying tomorrow's bus is T1, querying the day after tomorrow's bus is T2, and so on.

[0103] Coverage Period: This indicates the time period for caching route resources. As shown in Table 1, the coverage period for Beijing-Shanghai is T0-T19, meaning that the remaining seat information for Beijing-Shanghai routes for the next 19 days is cached. In other words, when a user queries routes for Beijing-Shanghai for any day within the next 19 days, the remaining seat information is retrieved from the cache.

[0104] Table 1

[0105] Set off arrive time Beijing Shanghai T0-T19 Shanghai Beijing T0-T1 Beijing Yichun T0-T19

[0106] Furthermore, if users inquire about available seats for flights from Beijing to Shanghai more than 20 days in the next 20 days, or flights from Shanghai to Beijing more than 2 days in the next 20 days, or flights from Beijing to Yichun more than 20 days in the next 20 days, they must directly pay the transportation agency to obtain the corresponding information. Obtaining the information directly from the transportation agency will result in a slow response time for the platform to user query requests, leading to a poor user experience.

[0107] Figure 1 This is a schematic diagram of the main flow of the route data processing method according to an embodiment of the present invention, as shown below. Figure 1 As shown, the route data processing method of the present invention includes the following steps:

[0108] Acquiring route resources incurs costs, including time and economic costs. Time cost corresponds to the response speed of resource acquisition, while economic cost corresponds to the direct price paid for acquiring the resources. In the process of acquiring route resources, economic costs are usually limited. Therefore, given a fixed economic cost, route resources are typically cached to improve their response speed to users and ensure reasonable time costs.

[0109] Limited economic resources cannot cover all route resources. Therefore, constrained by economic costs, the route data processing method of this invention can determine the optimal coverage period for acquiring and caching route resources while maximizing the use of budgeted funds. After determining the optimal coverage period for route resources, the route resources can be updated according to the required update frequency. Factors affecting the update frequency include price expiration, schedule changes, and policy adjustments. The route data processing method of this invention mainly focuses on the optimal coverage period for acquiring and caching route resources before updating.

[0110] The route data processing method of the present invention is applicable to the processing of route data of various means of transportation such as airplanes, trains, buses, and ships. Taking airplanes as an example, the processing method of route data of other means of transportation such as trains, buses, and ships is the same as that of airplanes.

[0111] Taking an airplane as an example, the path from point A to point B is called a flight route, such as the Beijing-Shanghai route. A flight route usually corresponds to one or more flights, and the set of remaining seats on each flight under a flight route is called the route resource. For airplanes, the route resource is also called the AV resource. The flight route determines the origin, destination, and stopover points of the airplane's flight, and indicates the specific direction of the airplane's flight. In order to maintain air traffic order and ensure flight safety, the width and altitude of the flight route are usually restricted.

[0112] On the one hand, currently, the determination of the route data coverage period is usually based on the statistical results of historical query volume. The statistical results of historical query volume are shown in Table 2:

[0113] Table 2

[0114] Set off arrive time query volume Beijing Yichun T1 300 Beijing Hangzhou T8 200 Beijing Shanghai T5 200 Beijing Yichun T2 199

[0115] Given that the economic cost can only cover 2 days of route resources (i.e., the preset coverage period is 2 days), based on the statistical results in Table 1, and following the principle of prioritizing routes with higher historical query volume, the coverage period for route data is determined to be: T1 for Beijing-Yichun and T8 for Beijing-Hangzhou. Since T8 for Beijing-Hangzhou and T5 for Beijing-Shanghai have the same historical query volume, one of them is usually chosen. Accordingly, the coverage period for route data can also be T1 for Beijing-Yichun and T5 for Beijing-Shanghai.

[0116] However, the historical query volume for T2 of Beijing-Yichun and T8 of Beijing-Hangzhou is almost the same, and users often prioritize flights with nearby dates when querying. Therefore, when acquiring and caching route resources, continuous coverage of the time periods of T1 and T2 of Beijing-Yichun can better serve users, respond to user queries in a timely manner, and the corresponding time cost is the most reasonable.

[0117] Therefore, the existing method of determining the coverage period of route data simply by sorting according to historical query volume results in discontinuous coverage, leading to a poor user experience due to untimely responses. Moreover, if the nearest dates for each route are mechanically determined as the coverage period, the limited economic cost may only cover routes with no historical query volume, thus wasting the cost.

[0118] On the other hand, currently, since Tn is determined based on historical query volume over relative time, certain situations arise during special time periods (such as holidays), as shown in Table 3:

[0119] Table 3

[0120] Set off arrive time query volume Beijing Yichun T9 1000 Beijing Yichun T10 800 Beijing Yichun T2 199

[0121] Table 3 shows the user query volume for September 21, 2021. T9 is September 30, 2021, and T10 is October 1, 2021 (National Day holiday). If we follow the principle of prioritizing routes with higher historical query volume, the determined coverage time periods are T9 and T10 for Beijing-Yichun. However, when retrieving and caching route resources based on these coverage time periods, the corresponding routes are T9 and T10 for Beijing-Yichun. When a user queries on September 23, 2021, the cached information is for routes 2021-1002 and 2021-1003, causing them to miss the optimal travel time. Therefore, the coverage time periods determined by prioritizing routes with higher historical query volume are extremely inaccurate, resulting in route resources cached based on these coverage time periods failing to meet user requirements and leading to a poor user experience.

[0122] On the other hand, due to the prevalence of malicious web scraping technology, the platform is affected by malicious web scraping and various abnormal data appears, as shown in Table 4:

[0123] Table 4

[0124] Set off arrive time query volume Beijing Alashan T9 1000000 Beijing Yichun T10 800 Beijing Yichun T2 199

[0125] Table 4 shows 1,000,000 queries for the Beijing-Alashan T9 route. However, such a massive amount of user travel data is unlikely to exist for the Beijing-Alashan route in reality, and air travel is not the only travel option for users. Following the existing principle of prioritizing routes with higher historical query volumes, using malicious data to cover specific time periods would waste limited economic resources and fail to cover the routes users need, resulting in a very poor user experience.

[0126] Step S101: Obtain the user's historical query data for multiple routes.

[0127] In this embodiment of the invention, historical query data is obtained through statistical tracking. Before analyzing the historical query data, abnormal query data is removed to determine a more accurate target coverage time period.

[0128] In embodiments of the present invention, such as Figure 2 As shown, the abnormal data processing method of the present invention includes the following steps:

[0129] Step S201: Determine whether the historical query volume within the preset filtering period is abnormal, based on the preset filtering period.

[0130] In this embodiment of the invention, the preset screening period can be set as needed. For example, as shown in Figure 3(a), the preset screening period is 7 days, including T2 to T8.

[0131] Step S2011: Compare each historical query volume with the average of other historical query volumes within the preset filtering period.

[0132] In this embodiment of the invention, the historical query volume of T2 is compared with the average historical query volume of (T3-T8). The historical query volume of T2 is 90 times, and the average historical query volume of (T3-T8) is 220 times. The historical query volume of T5 is compared with the average historical query volume of (T2-T4, T6-T8). The historical query volume of T5 is 1000 times, and the average historical query volume of (T2-T4, T6-T8) is 68 times.

[0133] Furthermore, the comparison method for historical query volumes of T3, T4, T6 to T8 is the same as the comparison method for historical query volumes of T2 and T5.

[0134] Step S2012: Determine whether the ratio of each of the historical query volumes to the average of the other historical query volumes exceeds the preset multiple threshold. If yes, proceed to step S2013; otherwise, proceed to step S2014.

[0135] In this embodiment of the invention, the preset multiple threshold can be set as needed, for example, the preset multiple threshold is 8 times.

[0136] In this embodiment of the invention, the ratio of the historical query volume of T2 to the average historical query volume of (T3-T8) is 0.41, which does not exceed the preset multiple threshold of 8 times; the ratio of the historical query volume of T5 to the average historical query volume of (T2-T4, T6-T8) is 14.7, which exceeds the preset multiple threshold of 8 times.

[0137] Step S2013: Determine that the historical query volume is abnormal.

[0138] In this embodiment of the invention, the historical query volume of T5 is abnormal.

[0139] Step S2014: Determine that the historical query volume is normal.

[0140] In this embodiment of the invention, the historical query volume of T2 is normal.

[0141] Step S202: In the event of abnormal historical query volume, the abnormal historical query volume is corrected based on other historical query volumes within the preset filtering period.

[0142] Step S2021: Correct the abnormal historical query volume to the average of other historical query volumes within the corresponding preset filtering period.

[0143] In this embodiment of the invention, as shown in Figure 3(b), the abnormal historical query volume of 1000 times for T5 is corrected to the average historical query volume of 68 times within the corresponding preset filtering period (T2-T4, T6-T8). That is, as shown in Figure 3(c), the historical query volume of T5 is 68.

[0144] In this embodiment of the invention, the abnormal data processing method of the present invention can detect abnormal data in historical query volume before analyzing the historical query volume for continuous processing, and correct the abnormal query volume so that the historical query volume is all normal data. Then, the target coverage time period is determined based on the normal data, making the determination of the target coverage time period more accurate.

[0145] Step S102: Perform continuous processing on the historical query data corresponding to the multiple routes respectively, and determine the continuous processing result.

[0146] In this embodiment of the invention, the historical query data includes multiple time periods, and each time period for each route corresponds to one historical query volume. The historical query data is shown in Table 5:

[0147] Table 5

[0148] Set off arrive T0 T1 T2 T3 T4 Beijing Yichun 200 100 500 100 4 Beijing Shanghai 200 100 100 50 150 Shanghai Beijing 204 188 100 50 1

[0149] As shown in the table above, there are multiple time periods including T0, T1, T2, T3, and T4; among them, the historical query volume for T2 (Beijing-Yichun) is 500 times, the historical query volume for T0 (Beijing-Shanghai) is 200 times, and the historical query volume for T1 (Shanghai-Beijing) is 188 times.

[0150] In embodiments of the present invention, such as Figure 4 As shown, the continuous processing method for query data in the first embodiment of the present invention includes the following steps:

[0151] Step S401: For each route, the historical query volume corresponding to the multiple time periods is used as the first sequence corresponding to the route.

[0152] In this embodiment of the invention, for the Beijing-Yichun route, the historical query volumes corresponding to multiple time periods T0, T1, T2, T3, and T4, respectively, are 200, 100, 500, 100, and 4, respectively, as the first sequence [200, 100, 500, 100, 4] corresponding to the Beijing-Yichun route; for the Beijing-Shanghai route, the historical query volumes corresponding to multiple time periods T0, T1, T2, T3, and T4, respectively, are 200, 100, 100, 50, and 150, respectively, as the first sequence [200, 100, 100, 50, 150] corresponding to the Beijing-Shanghai route; for the Shanghai-Beijing route, the historical query volumes corresponding to multiple time periods T0, T1, T2, T3, and T4, respectively, are 204, 188, 100, 50, and 1, respectively, as the first sequence [204, 188, 100, 50, 1] corresponding to the Beijing-Shanghai route.

[0153] Step S402: Based on each of the first sequences, perform segmented continuous processing on the multiple historical query volumes, and determine the continuous processing result.

[0154] Step S4021: Determine whether the first sequence is a decreasing sequence. If yes, proceed to step S4022; otherwise, proceed to step S4023.

[0155] In this embodiment of the invention, the first sequence corresponding to the Shanghai-Beijing route is a decreasing sequence; the first sequence corresponding to the Beijing-Yichun and Beijing-Shanghai routes is not a decreasing sequence.

[0156] Step S4022: Determine the second sequence based on the first sequence, and proceed to step S103.

[0157] In this embodiment of the invention, based on the first sequence [204, 188, 100, 50, 1] corresponding to the Shanghai-Beijing route, the second sequence corresponding to the Shanghai-Beijing route is determined to be [204, 188, 100, 50, 1].

[0158] In this embodiment of the invention, for the Beijing-Yichun route, based on the new first sequence [266, 266, 266, 100, 4], the second sequence corresponding to the Beijing-Yichun route is determined to be [266, 266, 266, 100, 4]; for the Beijing-Shanghai route, based on the new first sequence [200, 100, 100, 100, 100], the second sequence corresponding to the Beijing-Shanghai route is determined to be [200, 100, 100, 100, 100].

[0159] Step S4023: If the first sequence is not a decreasing sequence, determine one or more inflection points in the first sequence; wherein the inflection points are determined based on the changing trend of the first sequence.

[0160] In this embodiment of the invention, line graphs of the historical query volumes of the first sequence can be drawn to determine each inflection point. For example, an inflection point can be the historical query volume that makes the first sequence not a decreasing sequence, or an inflection point can be a peak or trough of the line graph.

[0161] Step S4024: Perform continuous processing on the historical query volume based on the one or more inflection points.

[0162] Step S40241: Determine the first inflection point among the one or more inflection points; wherein, the first inflection point is the first historical query that makes the first sequence not a decreasing sequence.

[0163] In this embodiment of the invention, the first inflection point of the first sequence [200, 100, 500, 100, 4] corresponding to the Beijing-Yichun route is T2; the first inflection point of the first sequence [200, 100, 100, 50, 150] corresponding to the Beijing-Shanghai route is T4.

[0164] Step S40242: Take the historical query volume of the first inflection point and the first historical query volume before the first inflection point as the target segment.

[0165] In this embodiment of the invention, for the Beijing-Yichun route, the historical query volume of 500 at the first inflection point T2 and the first historical query volume of 100 before the first inflection point T2 are used as the target segment; for the Beijing-Shanghai route, the historical query volume of 150 at the first inflection point T4 and the first historical query volume of 50 before the first inflection point T4 are used as the target segment.

[0166] Step S40243: Determine the average value of the historical query volume of the target segment.

[0167] In this embodiment of the invention, for the Beijing-Yichun route, the average historical query volume of the target segment is determined to be (500+100) / 2=300 times; for the Beijing-Shanghai route, the average historical query volume of the target segment is determined to be (150+50) / 2=100 times.

[0168] Step S40244: Determine whether there are historical query volumes before the target segment. If yes, proceed to step S40245; if no, proceed to step S40248.

[0169] In this embodiment of the invention, for the Beijing-Yichun route, historical query volume is also included before determining the target segment; for the Beijing-Shanghai route, historical query volume is also included before determining the target segment.

[0170] Step S40245: Compare the average value of the target segment with the first historical query volume before the target segment.

[0171] In this embodiment of the invention, for the Beijing-Yichun route, the average segment value of the target segment (300) is compared with the first historical query volume (200) before the target segment; for the Beijing-Shanghai route, the average segment value of the target segment (100) is compared with the first historical query volume (100) before the target segment.

[0172] Step S40246: Determine whether the average value of the segments is greater than the first historical query volume before the target segment. If yes, proceed to step S40247; otherwise, proceed to step S40248.

[0173] In this embodiment of the invention, for the Beijing-Yichun route, the average segment value of the target segment (300) is determined to be greater than the first historical query volume (200) before the target segment; for the Beijing-Shanghai route, the average segment value of the target segment (100) is determined to be equal to (i.e., not greater than) the first historical query volume (100) before the target segment.

[0174] Step S40247: Add the first historical query volume before the target segment to the target segment, and proceed to step S40243.

[0175] In this embodiment of the invention, for the Beijing-Yichun route, the first historical query volume of 200 before the target segment is added to the target segment, i.e., the target segments are 200, 100, and 500, and the calculation continues as follows:

[0176] The average historical query volume for the target segment is determined to be (500+100+200) / 3 = 266 times;

[0177] Historical query volume is not included before determining the target segment; proceed to step S40248.

[0178] Step S40248: Update the segment average value to the historical query volume of each time period of the target segment.

[0179] In this embodiment of the invention, for the Beijing-Yichun route, the segment average value of 266 is updated to the historical query volume of each time period T0, T1, and T2 of the target segment, and correspondingly, the historical query volume of each time period of the target segment is 266 times; for the Beijing-Shanghai route, the segment average value of 100 is updated to the historical query volume of each time period T3 and T4 of the target segment, and correspondingly, the historical query volume of each time period of the target segment is 100 times.

[0180] Step S40249: Determine a new first sequence based on the updated historical query volume, and proceed to step S4021.

[0181] In this embodiment of the invention, for the Beijing-Yichun route, the new first sequence is determined as [266, 266, 266, 100, 4] based on the updated historical query volume; for the Beijing-Shanghai route, the new first sequence is determined as [200, 100, 100, 100, 100] based on the updated historical query volume.

[0182] In embodiments of the present invention, such as Figure 5 As shown, the continuous processing method for query data according to the second embodiment of the present invention includes the following steps:

[0183] Step S501: For each route, the historical query volume corresponding to the multiple time periods is used as the first sequence corresponding to the route.

[0184] In this embodiment of the invention, for the Beijing-Yichun route, the historical query volumes corresponding to multiple time periods T0, T1, T2, T3, and T4, respectively, are 200, 100, 500, 100, and 4, respectively, as the first sequence [200, 100, 500, 100, 4] corresponding to the Beijing-Yichun route; for the Beijing-Shanghai route, the historical query volumes corresponding to multiple time periods T0, T1, T2, T3, and T4, respectively, are 200, 100, 100, 50, and 150, respectively, as the first sequence [200, 100, 100, 50, 150] corresponding to the Beijing-Shanghai route; for the Shanghai-Beijing route, the historical query volumes corresponding to multiple time periods T0, T1, T2, T3, and T4, respectively, are 204, 188, 100, 50, and 1, respectively, as the first sequence [204, 188, 100, 50, 1] corresponding to the Beijing-Shanghai route.

[0185] Step S502: Based on each of the first sequences, perform segmented continuous processing on the multiple historical query volumes, and determine the continuous processing result.

[0186] Step S5021: Determine whether the first sequence is a decreasing sequence. If yes, proceed to step S5022; otherwise, proceed to step S5023.

[0187] In this embodiment of the invention, the first sequence corresponding to the Shanghai-Beijing route is a decreasing sequence; the first sequence corresponding to the Beijing-Yichun and Beijing-Shanghai routes is not a decreasing sequence.

[0188] Step S5022: Determine the second sequence based on the first sequence, and proceed to step S103.

[0189] In this embodiment of the invention, a second sequence [204, 188, 100, 50, 1] corresponding to the Shanghai-Beijing route is determined based on the first sequence [204, 188, 100, 50, 1] corresponding to the Shanghai-Beijing route.

[0190] In this embodiment of the invention, the second sequence corresponding to the Beijing-Yichun route is determined to be [266, 266, 266, 100, 4]; the second sequence corresponding to the Beijing-Shanghai route is determined to be [120, 120, 120, 120, 120].

[0191] Step S5023: If the first sequence is not a decreasing sequence, determine one or more inflection points in the first sequence; wherein the inflection points are determined based on the changing trend of the first sequence.

[0192] In this embodiment of the invention, line graphs of the historical query volumes of the first sequence can be drawn to determine each inflection point. For example, an inflection point can be the historical query volume that makes the first sequence not a decreasing sequence, or an inflection point can be a peak or trough of the line graph.

[0193] Step S5024: Perform continuous processing on the historical query volume based on the one or more inflection points.

[0194] Step S50241: Determine the first inflection point among the one or more inflection points; wherein the first inflection point is the first of the peaks or troughs corresponding to the change trend of the first sequence.

[0195] In this embodiment of the invention, the first inflection point of the first sequence [200, 100, 500, 100, 4] corresponding to the Beijing-Yichun route is T1; the first inflection point of the first sequence [200, 100, 100, 50, 150] corresponding to the Beijing-Shanghai route is T3.

[0196] Step S50242: Starting from the first historical query volume of the first sequence and ending at the historical query volume corresponding to the first inflection point, all the historical query volumes are taken as the target segment.

[0197] In this embodiment of the invention, starting from the first historical query volume 200 of the first sequence [200, 100, 500, 100, 4] corresponding to the Beijing-Yichun route, up to the historical query volume 100 corresponding to the first inflection point T1, all historical query volumes 200 and 100 are taken as a target segment; starting from the first historical query volume 200 of the first sequence [200, 100, 100, 50, 150] corresponding to the Beijing-Shanghai route, up to the historical query volume 50 corresponding to the first inflection point T3, all historical query volumes 200, 100, 100, and 50 are taken as a target segment.

[0198] Step S50243: Determine the average value of the historical query volume of the target segment.

[0199] In this embodiment of the invention, the average historical query volume of target segments 200 and 100 corresponding to the Beijing-Yichun route is determined to be 150; the average historical query volume of target segments 200, 100, 100 and 50 corresponding to the Beijing-Shanghai route is determined to be 112.5.

[0200] Step S50244: Determine whether the target segment includes historical query volume. If yes, proceed to step S50245; otherwise, proceed to step S50248.

[0201] In this embodiment of the invention, for the Beijing-Yichun route, historical query volume is also included after determining the target segment; for the Beijing-Shanghai route, historical query volume is also included after determining the target segment.

[0202] Step S50245: Compare the average value of the target segment with the first historical query volume after the first inflection point.

[0203] In this embodiment of the invention, for the Beijing-Yichun route, the average segment value of the target segment, 150, is compared with the first historical query volume, 500, after the first inflection point T1; for the Beijing-Shanghai route, the average segment value of the target segment, 112.5, is compared with the first historical query volume, 150, after the first inflection point T3.

[0204] Step S50246: Determine whether the average value of the segments is less than the first historical query volume after the first inflection point. If yes, proceed to step S50247; if no, proceed to step S50248.

[0205] In this embodiment of the invention, for the Beijing-Yichun route, the average segment value of 150 is determined to be less than the first historical query volume of 500 after the first inflection point T1; for the Beijing-Shanghai route, the average segment value of 112.5 is determined to be greater than the first historical query volume of 150 after the first inflection point T1.

[0206] Step S50247: Take the first historical query volume after the first inflection point as the new first inflection point, and proceed to step S50242.

[0207] In this embodiment of the invention, for the Beijing-Yichun route, the first historical query volume of 500 after the first inflection point T1 is taken as the new first inflection point, and correspondingly, the first inflection point is T2. The calculation continues as follows:

[0208] Starting from the first historical query volume of 200 in the first sequence [200, 100, 500, 100, 4], and ending at the historical query volume of 500 corresponding to the first inflection point T2, all historical query volumes of 200, 100, and 500 are treated as a target segment.

[0209] The average historical query volume for the target segments 200, 100, and 500 was determined to be 266.

[0210] Compare the average segment value of the target segment, 266, with the first historical query volume of 100 after the first inflection point T2;

[0211] Once the average value of the segments, 266, is determined to be greater than the first historical query volume, 100, after the first inflection point T2, proceed to step S50248.

[0212] In this embodiment of the invention, for the Beijing-Shanghai route, the first historical query volume of 150 after the first inflection point T3 is taken as the new first inflection point, and correspondingly, the first inflection point is T4. The calculation continues as follows:

[0213] Starting from the first historical query volume of 200 in the first sequence [200, 100, 100, 50, 150], and ending at the historical query volume of 150 corresponding to the first inflection point T4, all historical query volumes 200, 100, 100, 50, and 150 are treated as a target segment.

[0214] The average historical query volume for the target segments 200, 100, 100, 50, and 150 is determined to be 120.

[0215] After determining the target segment, excluding historical query volume, proceed to step S50248.

[0216] Step S50248: Update the segment average value to the historical query volume of each time period of the target segment.

[0217] In this embodiment of the invention, for the Beijing-Yichun route, the segment average value of 266 is updated to the historical query volume of each time period T0, T1, and T2 of the target segment, and correspondingly, the historical query volume of each time period of the target segment is 266 times; for the Beijing-Shanghai route, the segment average value of 120 is updated to the historical query volume of each time period T0, T1, T2, T3, and T4 of the target segment, and correspondingly, the historical query volume of each time period of the target segment is 120 times.

[0218] Step S50249: Determine a new first sequence based on the updated historical query volume, and proceed to step S5021.

[0219] In this embodiment of the invention, for the Beijing-Yichun route, the new first sequence is determined as [266, 266, 266, 100, 4] based on the updated historical query volume; for the Beijing-Shanghai route, the new first sequence is determined as [120, 120, 120, 120, 120] based on the updated historical query volume.

[0220] In this embodiment of the invention, in contrast to the implementation of steps S5021 to S50248, the last peak or trough can also be taken as the first inflection point, and the judgment can be made backward from the first inflection point.

[0221] Furthermore, historical query volumes that differ significantly from other historical query volumes in the first sequence can be removed before the query data is processed to be continuous. Taking the first sequence [200, 100, 500, 100, 4] corresponding to the Beijing-Yichun route as an example, the historical query volume 4 of T4 is removed before continuous processing is performed.

[0222] In embodiments of the present invention, such as Figure 6 As shown, the continuous processing method for query data according to the third embodiment of the present invention includes the following steps:

[0223] Step S601: For each route, the historical query volume corresponding to the multiple time periods is used as the first sequence corresponding to the route.

[0224] In this embodiment of the invention, for the Beijing-Yichun route, the historical query volumes corresponding to multiple time periods T0, T1, T2, T3, and T4, respectively, are 200, 100, 500, 100, and 4, respectively, as the first sequence [200, 100, 500, 100, 4] corresponding to the Beijing-Yichun route; for the Beijing-Shanghai route, the historical query volumes corresponding to multiple time periods T0, T1, T2, T3, and T4, respectively, are 200, 100, 100, 50, and 150, respectively, as the first sequence [200, 100, 100, 50, 150] corresponding to the Beijing-Shanghai route; for the Shanghai-Beijing route, the historical query volumes corresponding to multiple time periods T0, T1, T2, T3, and T4, respectively, are 204, 188, 100, 50, and 1, respectively, as the first sequence [204, 188, 100, 50, 1] corresponding to the Beijing-Shanghai route.

[0225] Step S602: Based on each of the first sequences, perform segmented continuous processing on the multiple historical query volumes, and determine the continuous processing result.

[0226] Step S6021: Determine whether the first sequence is a decreasing sequence. If yes, proceed to step S6022; otherwise, proceed to step S6023.

[0227] In this embodiment of the invention, the first sequence corresponding to the Shanghai-Beijing route is a decreasing sequence; the first sequence corresponding to the Beijing-Yichun and Beijing-Shanghai routes is not a decreasing sequence.

[0228] Step S6022: Determine the second sequence based on the first sequence, and proceed to step S103.

[0229] In this embodiment of the invention, a second sequence [204, 188, 100, 50, 1] corresponding to the Shanghai-Beijing route is determined based on the first sequence [204, 188, 100, 50, 1] corresponding to the Shanghai-Beijing route.

[0230] In this embodiment of the invention, the second sequence corresponding to the Beijing-Yichun route is determined to be [266, 266, 266, 52, 52]; the second sequence corresponding to the Beijing-Shanghai route is determined to be [120, 120, 120, 120, 120].

[0231] Step S6023: If the first sequence is not a decreasing sequence, determine one or more inflection points in the first sequence; wherein the inflection points are determined based on the changing trend of the first sequence.

[0232] In this embodiment of the invention, line graphs of the historical query volumes of the first sequence can be drawn to determine each inflection point. For example, an inflection point can be the historical query volume that makes the first sequence not a decreasing sequence, or an inflection point can be a peak or trough of the line graph.

[0233] Step S6024: Perform continuous processing on the historical query volume based on the one or more inflection points.

[0234] Step S60241: Determine multiple target segments corresponding to the first sequence based on the one or more inflection points.

[0235] Step S602411: Determine the first inflection point among the one or more inflection points. Starting from the first historical query volume of the first sequence, up to the historical query volume corresponding to the first inflection point, take all the historical query volumes as the target segment; wherein, the first inflection point is the first of the peaks or troughs corresponding to the change trend of the first sequence.

[0236] In this embodiment of the invention, the first inflection point of the first sequence [200, 100, 500, 100, 4] corresponding to the Beijing-Yichun route is T1; the first inflection point of the first sequence [200, 100, 100, 50, 150] corresponding to the Beijing-Shanghai route is T3.

[0237] In this embodiment of the invention, starting from the first historical query volume 200 of the first sequence [200, 100, 500, 100, 4] corresponding to the Beijing-Yichun route, up to the historical query volume 100 corresponding to the first inflection point T1, all historical query volumes 200 and 100 are taken as a target segment; starting from the first historical query volume 200 of the first sequence [200, 100, 100, 50, 150] corresponding to the Beijing-Shanghai route, up to the historical query volume 50 corresponding to the first inflection point T3, all historical query volumes 200, 100, 100, and 50 are taken as a target segment.

[0238] Step S602412: Take the historical query volume between any two adjacent inflection points and the historical query volume corresponding to the next inflection point among the two adjacent inflection points as a target segment.

[0239] In this embodiment of the invention, the historical query volume 500 corresponding to the latter inflection point T2 of two adjacent inflection points T1 and T2 in the first sequence corresponding to the Beijing-Yichun route is taken as a target segment; wherein, the historical query volume between two adjacent inflection points T1 and T2 is empty.

[0240] Step S602413: Take the historical query volume after the last inflection point as a target segment.

[0241] In this embodiment of the invention, the historical query volume of 100 and 4 after the last inflection point T2 of the first sequence corresponding to the Beijing-Yichun route is taken as a target segment.

[0242] The historical query volume of 150 after the last inflection point T3 of the first sequence corresponding to the Beijing-Shanghai route is taken as a target segment.

[0243] Step S60242: Perform continuous processing on the historical query volume corresponding to the multiple target segments respectively.

[0244] Step S602421: Average the historical query volume corresponding to the target segment.

[0245] In this embodiment of the invention, the mean processing can be to take the average of the historical query volume of the target segment.

[0246] In this embodiment of the invention, the average value of the target segments 200 and 100 of the first sequence corresponding to the Beijing-Yichun route is 150;

[0247] The average segment value of the first sequence corresponding to the Beijing-Yichun route is 500;

[0248] The average value of the target segments 100 and 4 in the first sequence corresponding to the Beijing-Yichun route is 52.

[0249] The average value of the target segments 200, 100, 100, and 50 in the first sequence corresponding to the Beijing-Shanghai route is 112.5.

[0250] The average segment value of the target segment 150 corresponding to the Beijing-Shanghai route is 150.

[0251] Step S602422: Determine the second sequence corresponding to the route based on the mean processing result.

[0252] In this embodiment of the invention, the average values ​​of each target segment are compared one by one to determine that the average values ​​of each target segment in the first sequence corresponding to the route are not a decreasing sequence. If they are, the second sequence is determined based on the average values ​​of each target segment.

[0253] Furthermore, if the average value of each target segment in the first sequence is not a decreasing sequence, determine whether there are peaks or troughs after the first inflection point. If yes, take the second peak or trough corresponding to the change trend of the first sequence as the first inflection point and proceed to step S602411; if no, take the last historical query volume of the first sequence as the first inflection point and proceed to step S602411.

[0254] In this embodiment of the invention, the average segment values ​​of the target segments of the first sequence corresponding to the Beijing-Yichun route are compared one by one to determine that the average segment values ​​of each target segment of the first sequence corresponding to the Beijing-Yichun route are not a decreasing sequence.

[0255] After determining the first inflection point of the first sequence corresponding to the Beijing-Yichun route, there are also peaks or troughs. The second peak or trough corresponding to the trend of the first sequence is taken as the first inflection point. Accordingly, the first inflection point of the first sequence [200, 100, 500, 100, 4] corresponding to the Beijing-Yichun route is T2. The calculation continues as follows:

[0256] Starting from the first historical query volume of 200 in the first sequence [200, 100, 500, 100, 4], and ending at the historical query volume of 500 corresponding to the first inflection point T2, all historical query volumes of 200, 100, and 500 are treated as a target segment.

[0257] The historical query volume of 100 and 4 after the last inflection point T2 of the first sequence is taken as a target segment;

[0258] The average value of the target segments 200, 100, and 500 in the first sequence is 266.

[0259] The average value of the target segments 100 and 4 in the first sequence is 52;

[0260] The average segment value of each target segment in the first sequence corresponding to the Beijing-Yichun route is determined to be a decreasing sequence. Based on the average segment value of each target segment, the second sequence corresponding to the Beijing-Yichun route is determined to be [266, 266, 266, 52, 52].

[0261] In this embodiment of the invention, the average segment values ​​of the target segments of the first sequence corresponding to the Beijing-Shanghai route are compared one by one to determine that the average segment values ​​of each target segment of the first sequence corresponding to the Beijing-Shanghai route are not a decreasing sequence.

[0262] After determining the first inflection point of the first sequence corresponding to the Beijing-Shanghai route, excluding peaks or troughs, the last historical query volume of the first sequence corresponding to the Beijing-Shanghai route is taken as the first inflection point. Accordingly, the first inflection point of the first sequence [200, 100, 100, 50, 150] corresponding to the Beijing-Shanghai route is T4, and the calculation continues as follows:

[0263] Starting from the first historical query volume of 200 in the first sequence [200, 100, 100, 50, 150], and ending at the historical query volume of 150 corresponding to the first inflection point T4, all historical query volumes 200, 100, 100, 50, and 150 are treated as a target segment.

[0264] The average value of the target segments 200, 100, 100, 50, and 150 in the first sequence is 120.

[0265] The average segment value of the target segment corresponding to the Beijing-Shanghai route is determined to be a decreasing sequence (where the historical query volume of the first sequence is equal, it is also considered a decreasing sequence). Based on the average segment value of the target segment, the second sequence corresponding to the Beijing-Shanghai route is determined to be [120, 120, 120, 120, 120].

[0266] In this embodiment of the invention, the continuous processing method for query data of the present invention can continuously process the historical query data corresponding to multiple user routes to obtain a second sequence of historical query volume for each route. The second sequence is a decreasing sequence. Thus, when determining the target coverage time period in the future, the optimal route and its coverage time period can be determined based on the limited preset coverage time period (corresponding to limited economic cost), so as to respond to users in a timely manner and improve user experience.

[0267] Step S103: Based on the preset coverage time period and the continuous processing result, determine one or more target routes and the target coverage time period corresponding to each target route; wherein the total target coverage time corresponding to the one or more target routes is not greater than the preset coverage time period.

[0268] In this embodiment of the invention, the preset coverage time period can be determined according to the actual economic cost. For example, the preset coverage time period is 3 (that is, 3 days). Correspondingly, the target route may include 1, 2 or 3 routes, and the target coverage time period may be 1, 2 or 3 of the multiple time periods in the historical query data corresponding to the target route.

[0269] Furthermore, the total target coverage time is the sum of the target coverage time periods of the target route, which can be one, two, or three time periods.

[0270] In embodiments of the present invention, such as Figure 7 As shown, the method for determining the target route and the target coverage time period of the present invention includes the following steps:

[0271] Step S701: Compare the multiple second sequences corresponding to the multiple routes, and determine a preset number of second sequence values ​​from the multiple second sequences; the preset number corresponds to the preset coverage time period, and the second sequence value is not less than the other sequence values ​​in the second sequence.

[0272] In this embodiment of the invention, the preset quantity is 3.

[0273] In this embodiment of the invention, the continuous processing method for query data according to the first embodiment of the invention includes multiple second sequences corresponding to multiple routes, comprising:

[0274] The second sequence corresponding to the Beijing-Yichun route is [266, 266, 266, 100, 4];

[0275] The second sequence corresponding to the Beijing-Shanghai route is [200, 100, 100, 100, 100];

[0276] The second sequence corresponding to the Shanghai-Beijing route is [204, 188, 100, 50, 1];

[0277] The three second sequences corresponding to the three routes are compared, and the three largest second sequence values ​​(that is, the top three second sequence values) are determined, namely 266, 266, and 266.

[0278] In this embodiment of the invention, the continuous processing method for query data according to the second embodiment of the invention includes multiple second sequences corresponding to multiple routes, including:

[0279] The second sequence corresponding to the Beijing-Yichun route is [266, 266, 266, 100, 4];

[0280] The second sequence corresponding to the Beijing-Shanghai route is [120, 120, 120, 120, 120];

[0281] The second sequence corresponding to the Shanghai-Beijing route is [204, 188, 100, 50, 1];

[0282] The three second sequences corresponding to the three routes were compared, and the three largest second sequence values ​​were determined, namely 266, 266, and 266.

[0283] In this embodiment of the invention, the continuous processing method for query data according to the third embodiment of the invention includes multiple second sequences corresponding to multiple routes, comprising:

[0284] The second sequence corresponding to the Beijing-Yichun route is [266, 266, 266, 52, 52];

[0285] The second sequence corresponding to the Beijing-Shanghai route is [120, 120, 120, 120, 120];

[0286] The second sequence corresponding to the Shanghai-Beijing route is [204, 188, 100, 50, 1];

[0287] The three second sequences corresponding to the three routes were compared, and the three largest second sequence values ​​were determined, namely 266, 266, and 266.

[0288] Step S702: Determine the route corresponding to the second sequence containing the second sequence value as the target route, and determine the time period corresponding to the second sequence value as the target coverage time period corresponding to the target route.

[0289] In this embodiment of the invention, the Beijing-Yichun route corresponding to the second sequence containing 266, 266, and 266 is determined as the target route, and the time periods corresponding to 266, 266, and 266 are determined as T0, T1, and T2, which are the target coverage time periods corresponding to the Beijing-Yichun route.

[0290] In this embodiment of the invention, the method for determining the target route and the target coverage time period of the present invention can compare the decreasing second sequence corresponding to multiple routes, and determine the optimal target route and the target coverage time period corresponding to the target route based on the limited economic cost, i.e. the preset coverage time period. This maximizes the utilization of cost, determines an accurate and continuous route resource coverage range, and responds to users in a timely manner to improve user experience.

[0291] Step S104: Based on the target coverage time period corresponding to each target route, obtain the route data corresponding to the target route, and store the route data in the cache.

[0292] In this embodiment of the invention, route data corresponding to the target route can be obtained based on the target route determined in step S103 and the target coverage time period corresponding to the target route, and the route data can be stored in the cache. This can greatly improve the response speed and enhance user satisfaction while accurately meeting the user's query needs during subsequent user queries.

[0293] In this embodiment of the invention, some high-frequency query scenarios are handled separately, such as... Figure 8 As shown, the method for handling high-frequency query scenarios in this invention includes the following steps:

[0294] Step S801: Determine the high-frequency query time.

[0295] In this embodiment of the invention, high-frequency query scenarios typically correspond to popular travel periods, and high-frequency query times can be special time periods, such as statutory holidays, as shown in Table 6:

[0296] Table 6

[0297]

[0298] High-frequency query periods can also include traditional family reunion festivals such as the Spring Festival and the Mid-Autumn Festival, which can be determined by the corresponding Gregorian calendar dates for each year.

[0299] Step S802: Obtain the route data corresponding to the high-frequency query time, and store the route data corresponding to the high-frequency query time in the cache.

[0300] In this embodiment of the invention, since there are not many popular travel periods each year, route data corresponding to high-frequency query times can be obtained in advance and stored in the cache to facilitate user queries and improve response speed.

[0301] In this embodiment of the invention, the high-frequency query scenario processing method of the present invention can cache route data for popular time periods in advance, thereby facilitating subsequent user queries and responding to users in a timely manner to improve user experience.

[0302] In embodiments of the present invention, such as Figure 9 As shown, the query request processing method of the present invention includes the following steps:

[0303] Step S901: Receive a query request sent by the terminal; the query request indicates the query route, data timestamp, and target query time.

[0304] In this embodiment of the invention, the target query time can be achieved by embedding data points in the user data of the terminal.

[0305] In an embodiment of the present invention, for example, the query route is Beijing-Yichun, the data timestamp is 0920, and the target query time is 0927 and 0921.

[0306] Step S902: Determine whether the target query time belongs to the high-frequency query time. If yes, proceed to step S903; if no, proceed to step S904.

[0307] In this embodiment of the invention, the target query time 0927 is determined to be a high-frequency query time during the National Day holiday (0923-1014); the target query time 0921 is determined not to be a high-frequency query time.

[0308] Step S903: Obtain the route data corresponding to the target query time from the route data corresponding to the high-frequency query time in the cache and send it to the terminal.

[0309] In this embodiment of the invention, when the target query time 0927 is a high-frequency query time, the route data corresponding to the target query time 0927 is directly obtained from the route data corresponding to the high-frequency query time in the cache and sent to the terminal.

[0310] Step S904: Determine the query time period based on the data timestamp and the target query time.

[0311] In this embodiment of the invention, if the target query time 0921 is not a high-frequency query time, the query time period is determined to be T1 based on 0920 and 0921.

[0312] Step S905: Determine whether the query route and the query time period belong to the target route and the target coverage time period, and proceed to step S906; if not, proceed to step S907.

[0313] In this embodiment of the invention, the query route Beijing-Yichun is determined to be the target route, and the query time period T1 is determined to be the target coverage time period.

[0314] Step S906: Obtain the route data corresponding to the query time period from the cached route data and send it to the terminal.

[0315] In this embodiment of the invention, when the query route Beijing-Yichun is the target route and the query time period T1 is the target coverage time period, the route data corresponding to the query time period of the query route is directly obtained from the cached route data and sent to the terminal.

[0316] Step S907: Send a request to a third party to obtain the time period corresponding to the query route.

[0317] In this embodiment of the invention, for example, the third party could be an airline corresponding to a flight route, a railway department corresponding to a train, or a passenger transport department corresponding to a bus. For data exceeding the cache's coverage period, it needs to be obtained directly from the third party.

[0318] Step S908: Based on the acquisition results returned by the third party, determine the route data corresponding to the query time period of the query route.

[0319] Step S909: Send the route data corresponding to the query time period of the queried route to the terminal.

[0320] In this embodiment of the invention, the query request processing method of the present invention can determine whether it is a popular time period based on the target query time after receiving the user's query request, and make a judgment in advance, so as to respond to users faster for popular time periods and improve user experience.

[0321] In this embodiment of the invention, by acquiring historical query data of users for multiple routes; performing continuous processing on the historical query data corresponding to the multiple routes respectively to determine the continuous processing result; determining one or more target routes and the target coverage time period corresponding to each target route according to the preset coverage time period and the continuous processing result; wherein the total target coverage time corresponding to the one or more target routes is not greater than the preset coverage time period; acquiring route data corresponding to each target route according to the target coverage time period corresponding to each target route, and storing the route data in the cache, etc., the invention can maximize the utilization of cost based on the actual route resource usage scenario, determine an accurate and continuous route resource coverage range, process special time periods, respond to users in a timely manner to improve user experience, prevent interference from abnormal data, and improve operational management efficiency.

[0322] Figure 10 This is a schematic diagram of the main modules of a route data processing device according to an embodiment of the present invention, such as... Figure 10 As shown, the route data processing apparatus 1000 of the present invention includes:

[0323] The acquisition module 1001 is used to acquire historical query data for multiple routes by the user.

[0324] In this embodiment of the invention, the historical query data is obtained by the acquisition module 1001 through statistical tracking. Before analyzing the historical query data, abnormal query data is removed to determine a more accurate target coverage time period.

[0325] The data processing module 1002 is used to perform continuous processing on the historical query data corresponding to the multiple routes respectively, and determine the continuous processing result.

[0326] In this embodiment of the invention, the historical query data includes multiple time periods, and each time period of each route corresponds to one historical query volume. The data processing module 1002 is used to perform continuous processing on the historical query data corresponding to multiple routes respectively, and determine the continuous processing result.

[0327] The time determination module 1003 is used to determine one or more target routes and the target coverage time period corresponding to each target route based on the preset coverage time period and the continuous processing result; wherein the total target coverage time corresponding to the one or more target routes is not greater than the preset coverage time period.

[0328] In this embodiment of the invention, the preset coverage time period can be determined based on actual economic costs. For example, the preset coverage time period can be three (i.e., three days). Correspondingly, the target routes can include one, two, or three routes, and the target coverage time period can be one, two, or three of the multiple time periods in the historical query data corresponding to the target routes. The time determination module 1003 is used to determine one or more target routes and the target coverage time period corresponding to each target route based on the preset coverage time period and the continuous processing results.

[0329] The storage module 1004 is used to obtain the route data corresponding to the target route according to the target coverage time period corresponding to each target route, and store the route data in the cache.

[0330] In this embodiment of the invention, the storage module 1004 can obtain the route data corresponding to the target route based on the target route determined by the time determination module 1003 and the target coverage time period corresponding to the target route, and store the route data in the cache. This can greatly improve the response speed and enhance user satisfaction while accurately meeting the user's query needs during subsequent user queries.

[0331] In this embodiment of the invention, by using modules such as an acquisition module, a data processing module, a time determination module, and a storage module, the system can maximize the utilization of cost based on the actual route resource usage scenario, determine an accurate and continuous route resource coverage area, process special time periods, respond to users in a timely manner to improve user experience, prevent interference from abnormal data, and improve operational management efficiency.

[0332] Figure 11 An exemplary system architecture diagram is shown, which is suitable for a route data processing method or route data processing apparatus applied to embodiments of the present invention, such as... Figure 11 As shown, an exemplary system architecture for a route data processing method or route data processing apparatus according to embodiments of the present invention includes:

[0333] like Figure 11 As shown, system architecture 1100 may include terminal devices 1101, 1102, and 1103, network 1104, and server 1105. Network 1104 is used as a medium to provide communication links between terminal devices 1101, 1102, and 1103 and server 105. Network 1104 may include various connection types, such as wired or wireless communication links or fiber optic cables, etc.

[0334] Users can use terminal devices 1101, 1102, and 1103 to interact with server 1105 via network 1104 to receive or send messages, etc. Various communication client applications can be installed on terminal devices 1101, 1102, and 1103, such as ticketing applications, shopping applications, web browser applications, search applications, instant messaging tools, email clients, and social media platform software.

[0335] Terminal devices 1101, 1102, and 1103 can be various electronic devices with displays and web browsing capabilities, including but not limited to smartphones, tablets, laptops, and desktop computers.

[0336] Server 1105 can be a server that provides various services, such as a backend management server that supports ticketing websites browsed by users using terminal devices 1101, 1102, and 1103. The backend management server can analyze and process data such as received query requests, and feed back the processing results (such as target route data) to terminal devices 1101, 1102, and 1103.

[0337] It should be noted that the route data processing method provided in this embodiment of the invention is generally executed by server 1105, and correspondingly, the route data processing device is generally located in server 1105.

[0338] It should be understood that Figure 11The number of terminal devices, networks, and servers shown is merely illustrative. Depending on implementation needs, any number of terminal devices, networks, and servers can be included.

[0339] Figure 12 This is a schematic diagram of the structure of a computer system suitable for implementing terminal devices or servers of the present invention, such as... Figure 12 As shown, the computer system 1200 of the terminal device or server in this embodiment of the invention includes:

[0340] The central processing unit (CPU) 1201 can perform various appropriate actions and processes based on programs stored in read-only memory (ROM) 1202 or programs loaded from storage section 1208 into random access memory (RAM) 1203. RAM 1203 also stores various programs and data required for the operation of system 1200. CPU 1201, ROM 1202, and RAM 1203 are interconnected via bus 1204. Input / output (I / O) interface 1205 is also connected to bus 1204.

[0341] The following components are connected to I / O interface 1205: an input section 1206 including a keyboard, mouse, etc.; an output section 1207 including a cathode ray tube (CRT), liquid crystal display (LCD), etc., and speakers, etc.; a storage section 1208 including a hard disk, etc.; and a communication section 1209 including a network interface card such as a LAN card, modem, etc. The communication section 1209 performs communication processing via a network such as the Internet. A drive 1210 is also connected to I / O interface 1205 as needed. Removable media 1211, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., are installed on drive 1210 as needed so that computer programs read from them can be installed into storage section 1208 as needed.

[0342] In particular, according to embodiments disclosed in this invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments disclosed in this invention include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication section 1209, and / or installed from removable medium 1211. When the computer program is executed by central processing unit (CPU) 1201, it performs the functions defined above in the system of this invention.

[0343] It should be noted that the computer-readable medium shown in this invention can be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. A computer-readable storage medium can be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, 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 device, magnetic storage device, or any suitable combination thereof. In this invention, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In this invention, a computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. Computer-readable signal media can also be any computer-readable medium other than computer-readable storage media, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to: wireless, wire, optical fiber, RF, etc., or any suitable combination thereof.

[0344] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram or flowchart, and combinations of blocks in a block diagram or flowchart, may be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0345] The modules described in the embodiments of the present invention can be implemented in software or hardware. The described modules can also be housed in a processor; for example, a processor can be described as including an acquisition module, a data processing module, a time determination module, and a storage module. The names of these modules do not necessarily limit the module itself; for example, the time determination module can also be described as "a module that determines one or more target routes and the target coverage time period corresponding to each target route based on a preset coverage time period and continuous processing results."

[0346] In another aspect, the present invention also provides a computer-readable medium, which may be included in the device described in the above embodiments; or it may exist independently and not assembled into the device. The computer-readable medium carries one or more programs, which, when executed by the device, cause the device to include: acquiring historical query data corresponding to multiple routes; performing continuous processing on the historical query data corresponding to the multiple routes respectively, and determining the continuous processing result; determining one or more target routes and a target coverage time period corresponding to each target route based on a preset coverage time period and the continuous processing result; wherein the total target coverage time corresponding to the one or more target routes is not greater than the preset coverage time period; acquiring route data corresponding to each target route based on the target coverage time period corresponding to each target route, and storing the route data in a cache.

[0347] According to the technical solution of the present invention, abnormal traffic problems such as sudden traffic surges are solved by removing abnormal data; separate processing is performed for popular time periods (such as holidays); and continuous processing of query data is performed to obtain a more continuous cache coverage range, thereby determining a more accurate and continuous route coverage range, which can improve the response speed of the terminal and thus enhance the user experience.

[0348] According to the technical solutions of the present invention, the cost can be maximized based on the actual route resource usage scenario, the accurate and continuous route resource coverage can be determined, special time periods can be processed, users can be responded to in a timely manner to improve user experience, abnormal data can be prevented from interfering, and operation and management efficiency can be improved.

[0349] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can occur depending on design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A method for processing route data, characterized in that, include: Obtain historical query data for multiple routes, including the historical query volume for multiple time periods under the multiple routes; The historical query data corresponding to the multiple routes are processed continuously to determine the continuous processing result; Based on the preset coverage time period and the continuous processing result, one or more target routes and the target coverage time period corresponding to each target route are determined; wherein, the total target coverage time corresponding to the one or more target routes is not greater than the preset coverage time period; Based on the target coverage time period corresponding to each target route, obtain the route data corresponding to the target route and store the route data in the cache.

2. The method according to claim 1, characterized in that, For each of the routes, the historical query volume corresponding to the multiple time periods is used as the first sequence corresponding to the route; Based on each of the first sequences, the historical query volumes are segmented and processed continuously to determine the result of the continuous processing.

3. The method according to claim 2, characterized in that, The step of segmenting and continuously processing multiple historical query volumes based on each of the first sequences, and determining the continuous processing result, includes: Determine whether the first sequence is a decreasing sequence; If the first sequence is not a decreasing sequence, determine one or more inflection points in the first sequence; wherein the inflection points are determined based on the changing trend of the first sequence; Based on one or more inflection points, the historical query volume is processed continuously.

4. The method according to claim 3, characterized in that, The step of performing continuous processing on the historical query volume based on the one or more inflection points includes: Determine the first inflection point among the one or more inflection points; wherein, the first inflection point is the first historical query that makes the first sequence not a decreasing sequence; The historical query volume at the first inflection point and the first historical query volume before the first inflection point are used as the target segments; A1: Determine the segmental average of the historical query volume for the target segment; A2: Compare the average value of the target segment with the first historical query volume before the target segment; A3: Determine whether the average value of the segments is greater than the first historical query volume before the target segment; A4: If the average value of the segments is greater than the first historical query volume before the target segment, add the first historical query volume before the target segment to the target segment; Repeat steps A1-A4 until the average value of the segments is not greater than the first historical query volume before the target segment.

5. The method according to claim 3, characterized in that, The step of performing continuous processing on the historical query volume based on the one or more inflection points includes: Determine the first inflection point among the one or more inflection points; wherein the first inflection point is the first of the peaks or troughs corresponding to the changing trend of the first sequence; B1: Starting from the first historical query volume of the first sequence, up to the historical query volume corresponding to the first inflection point, all the historical query volumes are taken as the target segment. B2: Determine the segmental average of the historical query volume for the target segment; B3: Compare the average value of the target segment with the first historical query volume after the first inflection point; B4: Determine whether the average value of the segments is less than the first historical query volume after the first inflection point; B5: If the average value of the segments is less than the first historical query volume after the first inflection point, the first historical query volume after the first inflection point shall be taken as the first inflection point. Repeat steps B1-B5 until the average value of the segments is not less than the first historical query volume after the first inflection point.

6. The method according to claim 4 or 5, characterized in that, If the average value of the segments is not greater than the first historical query volume before the target segment, or if the average value of the segments is not less than the first historical query volume after the first inflection point, the method further includes: Update the segment average value to the historical query volume of each time period of the target segment; The second sequence is determined based on the updated historical query volume.

7. The method according to claim 3, characterized in that, The step of performing continuous processing on the historical query volume based on the one or more inflection points includes: Based on the one or more inflection points, determine multiple target segments corresponding to the first sequence; The historical query volumes corresponding to the multiple target segments are processed continuously.

8. The method according to claim 7, characterized in that, The step of determining multiple target segments corresponding to the first sequence based on the one or more inflection points includes: Determine the first inflection point among the one or more inflection points. Starting from the first historical query volume of the first sequence, up to the historical query volume corresponding to the first inflection point, take all the historical query volumes as the target segment; wherein, the first inflection point is the first of the peaks or troughs corresponding to the change trend of the first sequence. And / or, The historical query volume between any two adjacent inflection points and the historical query volume corresponding to the next inflection point among the two adjacent inflection points are taken as a target segment.

9. The method according to claim 8, characterized in that, The historical query volumes corresponding to the multiple target segments are processed continuously, including: The historical query volume corresponding to the target segment is averaged. Based on the mean processing result, a second sequence corresponding to the route is determined; wherein, the second sequence is a decreasing sequence.

10. The method according to claim 9, characterized in that, The mean processing involves determining the average of historical query volumes included in the target segment; determining the second sequence corresponding to the route based on the mean processing result includes: The average values ​​of each segment of the target segment are compared one by one; Determine whether the average value of each target segment in the first sequence corresponding to the route is a decreasing sequence. If so, determine the second sequence based on the average value of each target segment in the first sequence corresponding to the route.

11. The method according to claim 10, characterized in that, If the average value of each target segment in the first sequence corresponding to the route is not a decreasing sequence, the method further includes: If it is determined whether there are peaks or troughs after the first inflection point, then the second peak or trough corresponding to the change trend of the first sequence is determined as the first inflection point; otherwise, the last historical query volume of the first sequence is determined as the first inflection point.

12. The method according to claim 6, characterized in that, The step of determining one or more target routes and the target coverage time period corresponding to each target route based on the preset coverage time period and the continuous processing result includes: The multiple second sequences corresponding to the multiple routes are compared, and a preset number of second sequence values ​​are determined from the multiple second sequences; the preset number corresponds to the preset coverage time period, and the second sequence value is not less than the other sequence values ​​in the second sequence; The route corresponding to the second sequence value is determined to be the target route, and the time period corresponding to the second sequence value is determined to be the target coverage time period corresponding to the target route.

13. The method according to claim 1, characterized in that, Also includes: Determine the high-frequency query time; Obtain the route data corresponding to the high-frequency query time, and store the route data corresponding to the high-frequency query time in the cache.

14. The method according to claim 13, characterized in that, Also includes: Receive query requests sent by the terminal; The query request specifies the query route, data timestamp, and target query time; Determine whether the target query time belongs to the high-frequency query time; If the target query time does not fall within the high-frequency query time, the query time period is determined based on the data timestamp and the target query time. Determine whether the query route and the query time period belong to the target route and the target coverage time period. If so, retrieve the route data corresponding to the query time period of the query route from the cached route data and send it to the terminal.

15. The method according to claim 14, characterized in that, If the target query time falls within the high-frequency query time, the method further includes: The route data corresponding to the target query time is obtained from the route data corresponding to the high-frequency query time in the cache and sent to the terminal.

16. The method according to claim 1, characterized in that, Also includes: Based on a preset filtering period, determine whether the historical query volume within the preset filtering period is abnormal; In the event of an abnormal historical query volume, the abnormal historical query volume is corrected based on other historical query volumes within the preset filtering period.

17. The method according to claim 16, characterized in that, Determining whether the historical query volume within the preset filtering period is abnormal includes: Compare each historical query volume with the average of other historical query volumes within the preset filtering period; Determine whether the ratio of each historical query volume to the average of the other historical query volumes exceeds the preset multiple threshold. If the ratio of the historical query volume to the average of the other historical query volumes exceeds the preset multiple threshold, the historical query volume is determined to be abnormal.

18. The method according to claim 16, characterized in that, The step of correcting abnormal historical query volumes based on other historical query volumes within the preset filtering period includes: The abnormal historical query volume will be corrected to the average of other historical query volumes within the corresponding preset filtering period.

19. A route data processing apparatus, characterized in that, include: The acquisition module is used to acquire historical query data of users for multiple routes, wherein the historical query data includes the historical query volume corresponding to multiple time periods under the multiple routes; The data processing module is used to perform continuous processing on the historical query data corresponding to the multiple routes respectively, and determine the continuous processing result; The time determination module is used to determine one or more target routes and the target coverage time period corresponding to each target route based on the preset coverage time period and the continuous processing result; wherein the target coverage time period corresponding to the one or more target routes is not greater than the preset coverage time period; The storage module is used to obtain the route data corresponding to each target route according to the target coverage time period, and store the route data in the cache.

20. An electronic device for processing route data, characterized in that, include: One or more processors; Storage device for storing one or more programs. When the one or more programs are executed by the one or more processors, the one or more processors implement the method as described in any one of claims 1-18.

21. A computer-readable medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the method as described in any one of claims 1-18.

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