Information processing method, system and device, storage medium and program product

Abstract

The embodiment of the invention provides an information processing method, system and device, a storage medium and a program product, and relates to the technical field of computers, the method comprises the following steps: receiving traffic event information and trajectory data sent by a client, the traffic event information being used for describing an occurred traffic event, and the trajectory data being used for representing an action trajectory of the client; obtaining each target trajectory point meeting a predetermined spatial distribution condition in the target time period from the trajectory data; taking the central point position of each target track point as the stay point position of the client, wherein the central point position is the central position of an area formed by connecting the track points; determining an event occurrence position according to the stay point position of the client; and according to the event occurrence positions corresponding to the plurality of clients within the predetermined time length, the event multi-occurrence position is determined, and the number of traffic events occurring at the event multi-occurrence position within the predetermined time length is greater than a predetermined event number threshold. According to the method, the traffic incident high-incidence area can be accurately identified, so that the travel safety of the user can be guaranteed.

Description

Technical Field

[0001] This application relates to the field of computer technology, and in particular to an information processing method, system, device, storage medium, and program product. Background Technology

[0002] In real-world scenarios, when encountering traffic incidents such as accidents, road obstructions, or sudden weather events, users can report traffic incident information to the designated processing platform through the traffic incident reporting system. Based on the textual description of the incident's location included in the traffic incident information, the platform can use geocoding to convert the textual description of the incident's location into corresponding geographic coordinates, which are then used to determine the location coordinates of the incident.

[0003] However, the location of the incident reported by the user may be an address fragment, such as a school gate or a construction site, rather than a complete address. Incomplete address information can prevent the precise latitude and longitude from being obtained through geocoding, or even if the corresponding latitude and longitude are obtained, there may be significant errors. This results in the inability to accurately determine the location of the incident through geocoding, making it impossible to identify areas with a high incidence of traffic incidents and impacting user travel safety. Summary of the Invention

[0004] This application provides an information processing method, system, device, storage medium, and program product to alleviate or solve one or more technical problems existing in the prior art.

[0005] In a first aspect, embodiments of this application provide an information processing method applied to a server. The method includes: receiving traffic event information and trajectory data sent by a client, wherein the traffic event information describes a traffic event and the trajectory data characterizes the client's movement trajectory; obtaining target trajectory points from the trajectory data that satisfy predetermined spatial distribution conditions within a target time period, wherein the target time period is determined based on the event time in the traffic event information; wherein the interval between the trajectory point with the earliest passing time and the trajectory point with the latest passing time is greater than a first time period threshold, and the predetermined spatial distribution conditions are used to indicate the positional relationship between the target trajectory points; using the center point position of each target trajectory point as the client's stop point position, wherein the center point position is the center position of the area formed by connecting the trajectory points; determining the event occurrence location based on the client's stop point position; and determining the event-frequent location based on the event occurrence locations corresponding to multiple clients within a predetermined time period, wherein the number of traffic events occurring at the event-frequent location within the predetermined time period is greater than a predetermined event number threshold.

[0006] Secondly, embodiments of this application provide an information processing method applied to a client. The method includes: receiving traffic event information input by a user, the traffic event information describing a traffic event; sending the traffic event information and collected trajectory data of the user to a server, so that the server obtains target trajectory points that meet predetermined spatial distribution conditions within a target time period from the trajectory data, uses the center point position of each target trajectory point as the client's dwell point position, determines the event occurrence location based on the client's dwell point position, and determines the event-frequent location based on the event occurrence locations corresponding to multiple clients within a predetermined time period; the target... The time period is determined based on the event time in the traffic event information; the interval between the earliest and latest passing trajectory points among the target trajectory points is greater than a first duration threshold; the predetermined spatial distribution conditions are used to indicate the positional relationship between the target trajectory points; the center point is the center of the area formed by the connected trajectory points; the number of traffic events occurring at the event-prone location within the predetermined duration is greater than a predetermined event number threshold; if the client's current delivery route passes through the event-prone location, a first prompt message is received from the server, which prompts the client to change the delivery route.

[0007] Thirdly, embodiments of this application provide an information processing system, including a client and a server; the client is used to receive traffic event information input by a user and send the traffic event information and collected trajectory data of the user to the server; the traffic event information is used to describe the traffic event that occurred, and the trajectory data is used to characterize the client's movement trajectory; the server is used to obtain target trajectory points that meet predetermined spatial distribution conditions within a target time period from the trajectory data, the target time period being a time period determined based on the event time in the traffic event information; the interval between the trajectory point with the earliest passing time and the trajectory point with the latest passing time among the target trajectory points is greater than a first time period threshold, and the predetermined spatial distribution conditions are used to indicate the positional relationship between the target trajectory points; the center point position of each target trajectory point is used as the client's stop point position, the center point position being the center position of the area formed by connecting the trajectory points; the event occurrence position is determined based on the client's stop point position; the event frequently occurring position is determined based on the event occurrence positions corresponding to multiple clients within a predetermined time period, the number of traffic events occurring in the event frequently occurring position within the predetermined time period being greater than a predetermined event number threshold.

[0008] Fourthly, embodiments of this application provide an electronic device, including a memory, a processor, and a computer program stored in the memory, wherein the processor implements any of the methods of embodiments of this application when executing the computer program.

[0009] Fifthly, embodiments of this application provide a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the method of any one of the embodiments of this application.

[0010] Sixthly, embodiments of this application provide a computer program product, including a computer program that, when executed by a processor, implements any of the methods described in the embodiments of this application.

[0011] According to the method of this application embodiment, the server can receive traffic incident information and trajectory data sent by the client, filter out target trajectory points from the trajectory data within a target time period, and determine the location of frequent incidents based on the incident locations corresponding to multiple clients within a predetermined time period. Compared with related technologies that use geocoding to parse traffic incident information to determine the incident location, which may be inaccurate, the information processing method of this application embodiment determines the incident location by combining traffic incident information and trajectory data. Considering that trajectory data usually has sequentiality in both time and spatial dimensions, it can provide accurate time and spatial information when traffic incident information is inaccurate or incident location is incomplete, thereby facilitating more accurate location of incidents. Based on the accuracy of the incident location, the location of frequent incidents determined based on the incident locations corresponding to multiple clients within a predetermined time period is also more accurate, thereby facilitating the accurate identification of high-risk areas where frequent traffic incidents occur and ensuring user travel safety.

[0012] The above description is only an overview of the technical solution of this application. In order to better understand the technical means of this application, it can be implemented according to the contents of the specification. In order to make the above and other objects, features and advantages of this application more obvious and understandable, specific embodiments of this application are given below. Attached Figure Description

[0013] In the accompanying drawings, unless otherwise specified, the same reference numerals throughout the various drawings denote the same or similar parts or elements. These drawings are not necessarily drawn to scale. It should be understood that these drawings depict only some embodiments according to this application and should not be construed as limiting the scope of this application.

[0014] Figure 1 A flowchart of an information processing method according to an embodiment of this application is shown; Figure 2 A flowchart illustrating an exemplary embodiment of the information processing method of this application; Figure 3 This is a schematic diagram illustrating a scenario in which the location of an event is selected from the locations where the client stops, according to an exemplary embodiment of this application. Figure 4 This diagram illustrates a clustering of event locations according to an embodiment of this application. Figure 5 A schematic diagram illustrating the clustering of frequently occurring events in an embodiment of this application; Figure 6 A flowchart illustrating an embodiment of the information processing method of this application is shown; Figure 7 This invention provides a schematic diagram of the structure of an information processing apparatus according to an embodiment of the present application. Figure 8 This invention provides a schematic diagram of the structure of an information processing apparatus according to an embodiment of the present application. Figure 9 This invention provides a schematic diagram of the structure of an information processing system according to an embodiment of the present application. Figure 10 A block diagram of an electronic device provided in an embodiment of this application is shown. Detailed Implementation

[0015] In the following description, only certain exemplary embodiments are briefly described. As those skilled in the art will recognize, the described embodiments can be modified in various ways without departing from the concept or scope of this application. Therefore, the drawings and description are considered to be exemplary in nature and not restrictive.

[0016] To facilitate understanding of the technical solutions of the embodiments of this application, the relevant technologies of the embodiments of this application are described below. The following relevant technologies are optional solutions and can be combined with the technical solutions of the embodiments of this application in any way, and all of them fall within the protection scope of the embodiments of this application.

[0017] In real-world scenarios, with the rapid development of the express delivery industry, the scale and complexity of delivery networks continue to grow. Daily route planning, traffic safety, and emergency handling for delivery personnel have become crucial aspects of ensuring service efficiency and customer satisfaction. In practical applications, when delivery personnel encounter traffic incidents such as accidents, road obstacles, or sudden weather conditions during their deliveries, they can report the incidents to the designated processing platform through a traffic incident reporting system. The reported traffic incident information is typically recorded in unstructured text format (such as free-format work orders reported by riders, user feedback, etc.), and this data contains a wealth of potential road condition risk information related to geographical location.

[0018] In related technologies, platforms can use geocoding to convert textual descriptions of an event's location into corresponding geographic coordinates, which are then used as the location coordinates of the event. Geocoding is the process of converting natural language addresses (such as "No. xx, xx Road, xx District, xx City") into geographic coordinates (such as latitude and longitude).

[0019] In some scenarios, the location information in user-reported traffic incidents may not contain a complete address with all necessary details; it might only include the street name or landmark where the incident occurred. Therefore, the probability of not being able to accurately pinpoint the incident's location is relatively high. Consequently, the coordinates of the incident location derived from geocoding and text parsing may be inaccurate. Inaccurate incident locations hinder the accurate identification of high-risk areas, impacting user travel safety.

[0020] If a user is unable to report a traffic incident in a timely manner due to loss of mobility, when they report it later, their memory of the location and time of the incident may be vague, affecting the accuracy of these two pieces of information (location and time of the incident) in the proactively reported traffic incident information.

[0021] Existing traffic incident information processing platforms have accumulated a large amount of user-reported traffic incident data. These platforms employ traditional data processing methods (such as manual classification and statistical report analysis) to analyze this information. Manual classification is labor-intensive and inefficient. Statistical report analysis often focuses on a single dimension, such as analyzing only the event type or the location of the traffic incident. These methods have limitations in uncovering deep spatial correlations. For example, taking traffic accidents as an example, analyzing only the text description "a traffic accident occurred at the entrance of a certain residential area" in a user-reported traffic incident cannot determine whether traffic accidents frequently occur at that location or in a specific area. Accurately identifying high-risk areas with frequent traffic incidents is difficult, hindering the dynamic adjustment of delivery routes or the prevention of potential dangers. When the user is a delivery person, this affects their safety, increasing the probability of encountering traffic congestion, road obstacles, or traffic accidents in high-risk areas, leading to longer delivery times, impacting delivery efficiency and their income.

[0022] It should be noted that the application scenarios or examples provided in the embodiments of this application are for ease of understanding, and the embodiments of this application do not specifically limit the application of the technical solutions. In addition, the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of related data must comply with the relevant laws, regulations and standards of relevant countries and regions, and corresponding operation entry points are provided for users to choose to authorize or refuse.

[0023] The technical solution of this application and how it solves the aforementioned technical problems are described in detail below with specific embodiments. The listed specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments. The embodiments of this application will be described in detail below with reference to the accompanying drawings.

[0024] Figure 1 A flowchart illustrating an embodiment of the information processing method of this application is shown. This method can be applied to a server. For example... Figure 1 As shown, the method may include steps S101 to S105.

[0025] Step S101: Receive traffic event information and trajectory data sent by the client. The traffic event information is used to describe the traffic event that occurred, and the trajectory data is used to characterize the client's movement trajectory.

[0026] Step S102: Obtain from the trajectory data each target trajectory point that meets the predetermined spatial distribution conditions within the target time period. The target time period is the time period determined based on the event time in the traffic event information. The interval between the trajectory point with the earliest passing time and the trajectory point with the latest passing time is greater than the first time duration threshold. The predetermined spatial distribution conditions are used to indicate the positional relationship between each target trajectory point.

[0027] Step S103: The center point of each target trajectory point is used as the client's stopping point. The center point is the center of the area formed by connecting the trajectory points.

[0028] Step S104: Determine the location where the event occurred based on the client's dwell point location.

[0029] Step S105: Based on the event occurrence locations corresponding to multiple clients within a predetermined time period, determine the event-frequent locations where the number of traffic events occurring at the event-frequent locations within the predetermined time period exceeds a predetermined event number threshold.

[0030] According to the information processing method of this application embodiment, the server can receive traffic event information and trajectory data sent by the client, filter out target trajectory points from the trajectory data within a target time period, and determine the location of frequent incidents based on the event locations corresponding to multiple clients within a predetermined time period. Compared with related technologies that use geocoding to parse traffic event information to determine the event location, which may be inaccurate, the information processing method of this application embodiment determines the event location by combining traffic event information and trajectory data. Considering that trajectory data usually has sequentiality in both time and spatial dimensions, it can provide accurate time and spatial information when traffic event information is inaccurate or event location is incomplete, thereby facilitating more accurate location of the event. Based on the accuracy of the event location, the location of frequent incidents determined based on the event locations corresponding to multiple clients within a predetermined time period is also more accurate, thereby facilitating the accurate identification of high-risk areas where frequent traffic events occur and ensuring user travel safety.

[0031] In some scenarios, when the client user is a delivery person, this information processing method can accurately locate the locations where incidents frequently occur and identify high-risk areas where traffic incidents frequently occur. This can effectively ensure the safety of delivery personnel on the delivery map, thereby reducing the probability of delivery personnel encountering traffic congestion, road obstacles or traffic accidents in high-risk areas, reducing the probability of delivery time delays caused by traffic incidents, improving delivery efficiency and increasing their income.

[0032] In step S101 above, the client can be a user terminal. The user terminal can include, but is not limited to, smartphones, tablets, personal digital assistants, etc. The server can include, but is not limited to, any of the following: a standalone physical server, a server cluster consisting of multiple servers, or a cloud server capable of cloud computing.

[0033] As an example, the server receives traffic event information and trajectory data from the client through a communication connection. This communication connection includes, but is not limited to, at least one of the following: a communication connection based on a mobile data network, or a communication connection based on wireless local area network technology.

[0034] As an example, traffic incident information includes, but is not limited to, at least one of the following information items: the user ID of the client, the time of information reporting, the time description of the incident, the location description of the incident, and the process description of the incident.

[0035] As an example, trajectory data may include: latitude and longitude coordinates collected by a positioning system carried on the client, along with the corresponding collection time. Each latitude and longitude coordinate (longitude and latitude) provides specific geographic coordinates. The collection time characterizes the point in time when the corresponding latitude and longitude coordinates were recorded. The server can receive trajectory data sent by the client in real time, or it can receive trajectory data collected by the client at predetermined intervals. The specific sending frequency can be customized according to actual needs; this embodiment does not impose specific limitations.

[0036] As an example, a pre-defined application runs on the client, which includes an event reporting module. A display page for the event reporting module within the pre-defined application is shown on the client. This page receives traffic event information input by the user. The user can input traffic event information via a touchscreen keyboard, an external keyboard, voice input, etc. The display page includes interactive elements. These interactive elements trigger the transmission of the received traffic event information to the server. Interactive elements can be options or buttons. In response to an action command on an interactive element, the client sends the received traffic event information to the server. For example, the action command on an interactive element can be triggered by an action such as clicking or selecting.

[0037] In step S101 above, the server receives traffic incident information and trajectory data sent by the client, providing more reliable data support for determining the location of the incident and the location where the incident occurs frequently.

[0038] In step S102 above, the traffic incident information includes the incident time, and the target time period can include the incident time and a predetermined time period before and after it. This predetermined time period can be, for example, 1 hour, 30 minutes, 10 minutes, or 5 minutes. As a specific example, assuming the incident time is 10:00:00 on the day the server receives the traffic incident information, the target time period can be from 09:30:00 to 10:30:00 on the same day. As an example, the target time period can include the incident time and a predetermined time period before it, or the incident time and a predetermined time period after it. It should be understood that the specific value of the predetermined time period can be customized according to actual needs, and this application embodiment does not impose specific limitations. The incident time is the moment the incident occurs, that is, the moment the incident is recorded or the moment the incident is reported.

[0039] As an example, the trajectory points within the target time period can be represented as follows: ,in, Let be the longitude and latitude coordinates of the i-th trajectory point. Let N be the timestamp of the i-th trajectory point. N is an integer greater than or equal to 1.

[0040] Represent the trajectory points within a time window W as follows: Let be a continuous subsequence of trajectory T, where 1 ≤ i < j ≤ N, containing a total of m trajectory points, where m = j - i + 1. The time interval between the trajectory point with the earliest arrival time and the trajectory point with the latest arrival time among all target trajectory points is denoted as the time window W. This interval can also be called the time span. Starting from the arrival time of the i-th trajectory point, the arrival time of the j-th trajectory point is obtained after passing through a time window W. If the trajectory point with the earliest arrival time among all the target trajectory points in the time window... The trajectory point with the latest travel time The interval between them is longer than the first duration threshold. If the trajectory points satisfy a predetermined positional relationship, then the trajectory points within the time window W are determined as target trajectory points. The first duration threshold is used to characterize the preset minimum time span of the time window W. The trajectory points included within the time window W satisfy the positional relationships contained in the predetermined spatial distribution conditions.

[0041] For example, the positional relationship includes, but is not limited to, at least one of the following: the distance between the trajectory point with the earliest arrival time and the trajectory point with the latest arrival time is less than a predetermined distance threshold (denoted as...). (also known as distance span), the dispersion of each trajectory point from the center point is less than a predetermined offset threshold, and the maximum distance from each trajectory point to the center point is less than a predetermined distance upper limit.

[0042] For example, the center point location is the center of the region formed by connecting all the trajectory points. For instance, the center point is the geometric center of the region formed by connecting all the trajectory points, obtained by calculating the average of the position coordinates of each trajectory point. As another example, the center point is a location where, among all the position points contained in the region formed by connecting the trajectory points, the sum of the distances from all the trajectory points in the target trajectory to that location point is minimized.

[0043] In this embodiment, target trajectory points that meet certain conditions are selected from the trajectory data within the target time period. These conditions include the time interval between the earliest and latest trajectory points and the positional relationship between the target trajectory points. The center point of these target trajectory points is used as the stop point, thereby accurately determining the location of the event.

[0044] In some embodiments, step S102 above, which involves obtaining target trajectory points that meet predetermined spatial distribution conditions within a target time period from trajectory data, includes: obtaining trajectory points whose travel times are within the target time period from trajectory data; determining the distance between the trajectory point with the earliest travel time and the trajectory point with the latest travel time; determining the center point position based on each trajectory point, wherein the center point position is the center position of the area formed by connecting the trajectory points; and determining that each trajectory point meets the predetermined spatial distribution conditions based on the distance being less than a predetermined distance threshold and the offset degree between each trajectory point and the center point position being less than a predetermined offset degree threshold, and using each trajectory point as a target trajectory point.

[0045] For example, the distance between the trajectory point with the earliest passing time and the trajectory point with the latest passing time is less than a predetermined distance threshold, which can be expressed as the following expression (1): (1) in, The longitude of the trajectory point that is visited earliest among all trajectory points. Let be the longitude of the trajectory point that is visited latest among all trajectory points. Let be the latitude of the trajectory point that is visited earliest among all trajectory points. It represents the latitude of the trajectory point that is visited latest among all trajectory points. This is a predetermined distance threshold.

[0046] For example, the degree of offset between each trajectory point and the center point can be calculated as follows. First, the center point position of each trajectory point is calculated using the following expression (2): , (2) In the above expression (2), i is the index of the trajectory point with the earliest passing time, j is the index of the trajectory point with the latest passing time, j-i+1 is the total number of trajectory points, and k is the index of the k-th trajectory point. Let k be the longitude of the kth trajectory point. Let k be the latitude of the k-th trajectory point, where k is an integer greater than or equal to i and less than or equal to j. The average longitude of all trajectory points is used as the longitude of the center point. The latitude of each trajectory point is represented as the latitude of the center point. In this embodiment, the sequence number can also be an index value.

[0047] Next, the distance from any trajectory point to the center point is calculated using the following expression (3).

[0048] (3) In the above expression (3), The distance from the kth trajectory point to the center point is given by expression (3). The same labels in expression (2) have the same meaning, which will not be repeated here.

[0049] Next, the variance of the distance from each trajectory point to the center point is calculated using the following expression (4).

[0050] (4) In the above expression (4), This represents the average distance from each trajectory point to the center point, i.e., the average distance. Let $\mathbf{k}$ be the difference between the distance from the k-th trajectory point to the center point and the average distance. The variance of the distances from each trajectory point to the center point is also given. Equal to: the average of the sum of the squares of the distance differences corresponding to each trajectory point.

[0051] For example, if each trajectory point in the time window W within the target time period simultaneously meets the above conditions, then each trajectory point in the time window W is considered as a target trajectory point, and the center point of each target estimated point is taken as the corresponding client's dwell point position.

[0052] In this embodiment, variance is used to measure the degree of deviation of each trajectory point from the center point. A larger variance indicates a more dispersed distribution of trajectory points; a smaller variance indicates a more concentrated distribution. By limiting the deviation of each trajectory point from the center point to less than a predetermined deviation threshold—which defines the maximum allowable deviation distance from the center point—any trajectory point whose distance from the center point exceeds this threshold is considered abnormal. This ensures that the application moves within a predetermined trajectory area (the area formed by connecting the target trajectory points), avoiding situations where the application leaves midway and then returns, thus improving the accuracy of the determined target trajectory points.

[0053] As an example, the following pseudocode can be used to filter out the client's stop location from the trajectory point data.

[0054] enter: , , ,

[0055] Output: Initialization: i=1 Loop: while i≤N do max_end=i Valid_window=None for j = i to N do if <ΔT continue if break window =

[0056] calculate and

[0057] if < valid_window = window, max_end = j If valid_window exists: Add a stop point ( , , ) to S i = max_end+1 else: i+=1 In the input of the above pseudocode, These are the trajectory points within the target time period. The first duration threshold, For a predetermined distance threshold, This is the offset threshold, also known as the variance threshold.

[0058] In the output of the pseudocode above, This represents the set of client stop locations. This set of stop locations corresponds to a time window M, where m is the index of any stop location within time window M. The start time of this set of stop locations is... The end time is The duration is - .

[0059] In the loop of the pseudocode above, index i is initialized to 1, and the trajectory points are iterated until i ≤ N, where N is the total number of trajectory points in the trajectory data within the target time period. max_end = i is set to initialize the stop point position to empty. For each j (values ​​from i to N, from the first trajectory point to the last trajectory point within the target time period), if... If <ΔT, the loop continues. Then the loop will exit, and the set of stopping point locations will be set to... It includes trajectory points that satisfy the following two conditions: Condition 1: The time interval between the earliest and latest trajectory points is greater than the first time threshold. And condition 2: The distance between the trajectory point with the earliest passing time and the trajectory point with the latest passing time is less than a predetermined distance threshold. The set of trajectory points.

[0060] Calculate the coordinates of the center point within the window ( , ) and variance If the variance is less than the variance threshold, set valid_window=window and max_end=j. This indicates that the trajectory points within the window have relatively low dispersion. Set the current window as the valid window and record the end index j of the current window as max_end. This indicates that a valid stop point window has been found, where all trajectory points are close to a common center point and their distribution is relatively concentrated. Then update index i. If a valid window (valid_window) exists, add the corresponding stop point to the stop point location set S. Then update index i again: if a valid window exists, i=max_end+1; otherwise, i=i+1.

[0061] In this pseudocode, the same reference numerals as those in the above embodiments have the same or equivalent meanings, which will not be repeated in this application embodiment.

[0062] In some embodiments, in step S104 above, determining the location of the event based on the client's stop location includes the following steps: obtaining the target location from the client's order data within the target time period, the target location including the start and destination locations of order delivery; and filtering the location of the event from the client's stop locations based on the target location.

[0063] For example, the server can retrieve the order data of a user with that user identifier from the orders of multiple users within a target time period, and use this data as the client's order data for that target time period. The starting location in the target location includes the pickup point location (e.g., the merchant location), and the destination location in the target location includes the delivery point location (e.g., the order delivery location).

[0064] For example, based on the target location, the location of the event is filtered from the client's stop locations, including: filtering out the starting location and the destination location from the stop locations. Alternatively, stop locations within a predetermined area of ​​the starting location and stop locations within a predetermined area of ​​the destination location are filtered out from the stop locations. As an example, the predetermined area may include an area where the straight-line distance from the target location and / or the driving distance on the navigation route is less than a first distance. The first distance may be, for example, 200 meters, 100 meters, or 50 meters. As a specific example, stop locations within a 100-meter radius of the merchant's location (where the straight-line distance from the merchant's location is less than 100 meters) and stop locations within a 100-meter radius of the order delivery location (where the straight-line distance from the order delivery location is less than 100 meters) are filtered out from each stop location. It should be understood that the first distance can be customized according to actual needs, and this application embodiment does not impose specific limitations.

[0065] For example, among the remaining stops after filtering out the aforementioned location points, at least one of the following filtering methods can also be applied. For instance, location points within a preset safe area (such as gas stations and service areas) can be filtered out, stops with a duration less than the minimum stay threshold (second duration threshold) can be filtered out, and stops with a time interval greater than the interval threshold contained in the traffic event information can be filtered out.

[0066] According to the method in the embodiments of this application, by analyzing the start and destination locations of order delivery in the order data, and filtering out the stop points related to the probability of the event from the stop points of the client based on these locations, it is helpful to determine the specific location where the event occurs.

[0067] In this embodiment of the application, the location of the event is filtered from the client's dwell point location. In addition to the filtering method described above, there are other implementation methods, which will be described below through specific embodiments.

[0068] In some embodiments, the method of filtering the event location from the client's stop locations based on the target location includes: filtering a first stop location from the client's stop locations, wherein the stop duration corresponding to the first stop location is greater than a second duration threshold; filtering out stop locations located within a target area from the first stop locations to obtain a second stop location, wherein the target area is an area within a preset distance range centered on the target location; and selecting a target stop location from the second stop locations as the event location, wherein the interval between the travel time of the target stop location and the event time in the traffic event information is the shortest, and the interval is less than or equal to a third duration threshold. It should be understood that the second duration threshold can be customized according to actual needs, and this embodiment does not impose specific limitations.

[0069] As an example, the first selected stop location is the stop location where the time window is greater than the second duration threshold. In real-world scenarios, such as waiting at a traffic light, stops are generally considered normal stops rather than stops caused by traffic events. The second duration threshold can be the duration of a normal stop window preset based on experience or experimentation. As an example, the second duration threshold could be 1 minute, 2 minutes, 3 minutes, etc.

[0070] As an example, the locations of stops within the target area include, but are not limited to, at least one of the following: the start and destination locations of order delivery contained in the order data within the target time period, and known normal locations of stops (locations of stops in areas such as gas stations and service areas).

[0071] As an example, among the stop locations filtered above, the stop location with the shortest interval between the time of the event and the time of the traffic incident contained in the traffic incident information is retained and used as the target stop location. This interval can be, for example, 15 minutes, 20 minutes, 25 minutes, etc.

[0072] As a specific example, stop locations with a stay duration greater than 2 or 3 minutes are selected from the client's stop locations and designated as the first stop location. From the first stop location, stop locations within 100 meters of the start location and within 100 meters of the destination location in the order data of the client's user (e.g., delivery personnel) within the target time period are filtered out (the meaning of "nearby" is described in the above embodiment), resulting in the second stop location. From the second stop location, the stop location with the shortest (closest) interval to the event time in the traffic event information, and not exceeding 20 or 25 minutes, is selected as the event occurrence location.

[0073] For example, the location points within the aforementioned target area and safe area (such as gas stations and service areas) can be determined by obtaining road network information and the locations of pre-defined points of interest (POIs). Road network information includes, but is not limited to, one or more of road databases, map databases, and real-time traffic databases.

[0074] It should be understood that the interval length can be customized according to actual needs, and this application embodiment does not impose specific limitations.

[0075] In this embodiment, users, such as delivery personnel, may also stop when picking up or delivering food or waiting at a red light. This type of stop is considered normal. Therefore, the locations of the stops of candidate clients can be filtered to determine the location of the traffic incident. This can reduce false alarms and improve the accuracy of the determined incident location.

[0076] In some embodiments, the step of determining the event-frequent location based on the event occurrence locations corresponding to multiple clients within a predetermined time period may specifically include: clustering the event occurrence locations corresponding to multiple clients within a predetermined time period to obtain an event-frequent region; the area of ​​the event-frequent region is less than a preset area threshold and the number of event occurrence locations located within the event-frequent region is greater than or equal to a predetermined event number threshold; obtaining the center point location of the event-frequent region as the event-frequent location.

[0077] As an example, clustering algorithms, such as Density-Based Spatial Clustering of Applications with Noise (DBSCAN), can be used to cluster the locations of events occurring across multiple clients within a predetermined time period. The parameters in this clustering algorithm include: neighborhood radius (eps) and a predetermined event count threshold (minpts). A neighborhood radius smaller than the predetermined threshold indicates that the area of ​​the frequently occurring event region is smaller than a preset area threshold. The neighborhood radius characterizes the distance to the center point of the frequently occurring event region.

[0078] As a specific example, considering the inherent error (drift error) in the trajectory itself, in the process of clustering the event occurrence locations corresponding to multiple clients within a predetermined time period to obtain the event-prone area, it is necessary to limit the spatial range (area smaller than a preset area threshold) and event occurrence frequency (the number of event occurrence locations within the predetermined time period, used to characterize the number of events) of the event-prone area in order to improve the accuracy of the determined event-prone locations.

[0079] As an example, the output of this clustering algorithm includes: the identifier of each event occurrence location in the event occurrence locations corresponding to multiple clients within a predetermined time period, which is assigned to a cluster (including event occurrence locations that are divided into the same class), and the location coordinates of the center point of each cluster are extracted as the location coordinates of the event-frequent area points.

[0080] In this embodiment, by clustering analysis of the location of events occurring on multiple clients within a predetermined time period, it is possible to identify areas where events frequently occur. This is beneficial for conducting security warnings and formulating security measures based on the identified areas where events frequently occur, thereby ensuring the safety of users' travel.

[0081] In some embodiments, after determining the location of the frequent incidents, the method further includes: obtaining the current delivery route of the client's current order; and sending a first notification message to the client if the current delivery route passes through the location of the frequent incidents, the first notification message being used to prompt a change of delivery route.

[0082] As an example, the server can send a delivery route query request to the logistics server, which includes the order number of the client's current order. The server then receives the delivery route information corresponding to that order number from the logistics server to obtain the current delivery route.

[0083] As an example, the first alert message might be: "Your current delivery route will pass through an area prone to incidents. Please confirm whether you wish to change your delivery route." This is used to send an early warning notification to the client.

[0084] In this embodiment, the safety of the delivery process is improved by reminding users to pay attention to areas where incidents frequently occur and suggesting that they change their routes.

[0085] In some embodiments, the method further includes: receiving a feedback message in response to the first prompt message, the feedback message indicating agreement to change the delivery route; generating a new delivery route based on the current delivery route and the location of the incident, the new delivery route not including the location of the incident; and sending the new delivery route to the client.

[0086] For example, if the user agrees to change the delivery route, the server can generate a new delivery route, or the server can send a delivery route change request to the logistics server and receive a new delivery route returned by the logistics server in response to the delivery route change request.

[0087] As an example, a delivery route change request includes the current delivery route and the location of the most frequent event. The logistics server responds to the delivery route change request by generating and returning a new delivery route. Alternatively, the delivery route change request includes the order number of the current order and the location of the most frequent event. In response to the delivery route change request, the logistics server retrieves the generated current delivery route by the order number and then generates a new delivery route based on the current delivery route and the location of the most frequent event.

[0088] In this embodiment, generating new delivery routes helps to bypass incident-prone locations, thereby improving the safety of users such as delivery personnel and other road users by avoiding passing through incident-prone locations on the delivery route.

[0089] In some embodiments, after determining the location of the frequent event, the method further includes the following steps: based on the trajectory data reported by the client, determining that the client's trajectory will pass through the location of the frequent event and that the distance between the client's current location and the location of the frequent event is less than a preset distance threshold; sending a second prompt message to the client, the second prompt message being used to provide a message warning for the location of the frequent event.

[0090] For example, if the distance between the client's current location and the location where the event frequently occurs is less than a preset distance threshold, it indicates that there is a high probability that a new delivery route cannot be changed in time. In this case, a second notification message can be used to issue a warning to the user to whom the client belongs.

[0091] The second warning message may include, but is not limited to, at least one of the following: "You are about to enter an area prone to traffic incidents. Please slow down and be more vigilant," or "The road ahead is a section prone to traffic incidents. Please drive carefully."

[0092] In this embodiment, after determining the locations of frequent incidents, a second alert message is sent to these locations to provide timely risk warnings, improve emergency response capabilities, and enhance the travel safety of delivery personnel and other road users.

[0093] According to the method of this application embodiment, the server can receive traffic incident information and trajectory data sent by the client, filter out target trajectory points from the trajectory data within a target time period, and determine the location of frequent incidents based on the incident locations corresponding to multiple clients within a predetermined time period. Compared with related technologies that use geocoding to parse traffic incident information to determine the incident location, which may be inaccurate, the information processing method of this application embodiment determines the incident location by combining traffic incident information and trajectory data. Considering that trajectory data usually has sequentiality in both time and spatial dimensions, it can provide accurate time and spatial information when traffic incident information is inaccurate or incident location is incomplete, thereby facilitating more accurate location of incidents. Based on the accuracy of the incident location, the location of frequent incidents determined based on the incident locations corresponding to multiple clients within a predetermined time period is also more accurate, thereby facilitating the accurate identification of high-risk areas where frequent traffic incidents occur and ensuring user travel safety.

[0094] Figure 2 A flowchart illustrating an exemplary embodiment of the information processing method of this application is provided. The method includes the following steps.

[0095] S201, receives traffic incident information.

[0096] Specifically, it receives traffic incident information sent by the client.

[0097] S202, the trajectory before and after the associated event.

[0098] Specifically, target trajectory points that meet predetermined spatial distribution conditions within the target time period are obtained from the trajectory data. The target time period includes the event time and the predetermined time periods before and after it.

[0099] S203, Dwell point detection.

[0100] Specifically, the detected stop points include target trajectory points that meet the following conditions: within the corresponding time window, the time interval between the trajectory point with the earliest passing time and the trajectory point with the latest passing time is greater than a first time duration threshold; and within the corresponding time window, the distance between the trajectory point with the earliest passing time and the trajectory point with the latest passing time is less than a predetermined distance threshold; and the degree of offset between each trajectory point and the center point is less than a predetermined offset degree threshold.

[0101] S204, related event: waybill pickup and delivery points before and after the event.

[0102] Specifically, the system obtains the client's order data within the target time period. The order data includes the target location, namely the starting and destination locations of the order delivery.

[0103] S205, determine whether the dwell time exceeds the second duration threshold.

[0104] Specifically, for any stop location, if the corresponding time window is greater than the second duration threshold, then proceed to step S206. In some embodiments, if the corresponding time window is less than or equal to the second duration threshold, then the next stop location can be detected.

[0105] S206, Determine whether the stop point is near the waybill pickup / delivery point.

[0106] As an example, if the stop point is not within the predetermined area of ​​the destination location, then proceed to step S207. In some embodiments, if the stop point is within the predetermined area of ​​the destination location, then the detection of the next stop point can continue.

[0107] S207, take the stop point closest to the event time.

[0108] Specifically, after filtering the stops in S205 and S206, the stop with the shortest interval between the time of the event contained in the traffic event information and the interval is less than or equal to the third duration threshold is selected.

[0109] S208, Determine the location of the event.

[0110] Specifically, the stopping point determined in S207 is taken as the event location point, that is, the location where the event occurs.

[0111] S209, identify high-incidence areas of the incident.

[0112] Specifically, the locations of events occurring across multiple clients within a predetermined timeframe are clustered to identify high-incidence areas. The clustering method can be found in the description of the above embodiments and will not be repeated here.

[0113] In steps S201-S209 above, the location of a traffic incident can be determined by the traffic incident information and trajectory data sent by the client. Furthermore, based on the incident locations corresponding to multiple clients within a predetermined time period, the location of frequent incidents can be identified. This can reduce the probability of delivery personnel encountering traffic congestion, road obstacles, or traffic accidents in high-risk areas, effectively ensuring the safety of delivery personnel on the delivery route.

[0114] Figure 3 This is a schematic diagram illustrating a scenario in which the location of an event is selected from the client's dwell points, according to an exemplary embodiment of this application. Figure 3 The lines in the diagram represent the driving route, and the triangle icons represent the various stops on the client's map. The square icons represent the target locations (including the start and destination locations for order delivery). The circle icons represent the locations where events occurred, selected from the client's various stops.

[0115] For example, in Figure 3 In the process, among the various stop points on the client side, those within a preset distance range centered on the target location (represented by the box icon indicating the start and destination locations of order delivery) will be filtered out (corresponding to...). Figure 3 (The first filtered dwell point in the middle).

[0116] Furthermore, locations where the time window is less than or equal to the second duration threshold will be filtered out (corresponding to...). Figure 3 (The second filtered stop point). The second duration threshold, such as 90 seconds, is used to represent the normal stop points of delivery personnel (red, green, etc.), and these normal stop points need to be filtered out.

[0117] Furthermore, locations where the interval between the traffic incident information and the event time exceeds the third duration threshold will be filtered out (corresponding to...). Figure 3 (The "third filtered dwell point" in the middle).

[0118] exist Figure 3 Among the unfiltered stop points, the stop point with the shortest interval between the travel time and the event time in the traffic event information is selected as the target stop point location, i.e., the selected event occurrence location (corresponding to...). Figure 3 The circle icon indicates the location where the event occurred.

[0119] Figure 4 This diagram illustrates a clustering of event locations according to an embodiment of this application. Figure 5 This diagram illustrates the clustering of frequently occurring events according to an embodiment of this application. Figure 4The diagram shows the locations of events occurring in a specific region, such as a city, over the past year, with each "circle" indicating an event location. Clustering these event locations yields... Figure 5 The image shows multiple locations where events frequently occur.

[0120] In this embodiment of the application, after determining the locations where incidents frequently occur, the probability of delivery personnel encountering traffic congestion, road obstacles, or traffic accidents in high-risk areas can be reduced, effectively ensuring the travel safety of delivery personnel on the delivery map.

[0121] Figure 6 A flowchart illustrating an information processing method according to an embodiment of this application is provided. This method can be applied to a client. In some embodiments, the method includes the following steps.

[0122] S601 receives traffic event information input by the user, which describes the traffic event that occurred.

[0123] S602, the traffic incident information and the collected user trajectory data are sent to the server so that the server can obtain each target trajectory point that meets the predetermined spatial distribution conditions within the target time period from the trajectory data, take the center point of each target trajectory point as the client's stop point position, determine the incident location based on the client's stop point position, and determine the incident multiple occurrence location based on the incident occurrence locations corresponding to multiple clients within the predetermined time period.

[0124] Among them, the target time period is the time period determined based on the event time in the traffic event information; the interval between the earliest and latest passing time of each target trajectory point is greater than the first time duration threshold; the predetermined spatial distribution conditions are used to indicate the positional relationship between each target trajectory point; the center point position is the center position of the area formed by connecting each trajectory point; and the number of traffic events occurring in the event-prone location within the predetermined time period is greater than the predetermined event number threshold.

[0125] S603, when the client's current delivery route passes through a location with frequent incidents, receives a first prompt message from the server. The first prompt message is used to prompt the client to change the delivery route.

[0126] According to the method in this application embodiment, the client sends traffic incident information and trajectory data to the server. The server can then filter target trajectory points from the trajectory data within a target time period and determine the locations of frequent incidents based on the locations of multiple clients within a predetermined time period. If the client's current delivery route passes through a location of frequent incidents, the client receives a first notification message from the server prompting it to change its delivery route. Because the server accurately locates the incident locations, the client can promptly identify areas where frequent traffic incidents occur, which facilitates taking preventative measures based on the notification message, thereby helping to ensure user travel safety.

[0127] In some embodiments, after receiving the first prompt message sent by the server in step S603 above, the method further includes: sending a feedback message to the server in response to the first prompt message, the feedback message indicating agreement to change the delivery route; receiving a new delivery route sent by the server, the new delivery route not containing the location of the incident; and displaying the new delivery route.

[0128] In this embodiment, the new delivery route helps to avoid locations prone to incidents, reduces the risk of traffic incidents during delivery, and protects the safety of road users, including delivery personnel.

[0129] In some embodiments, the information processing method further includes: receiving a second alert message sent by the server when the client's current delivery route passes through a location prone to incidents, the second alert message being used to issue a warning for the location prone to incidents; and issuing a voice message to issue a warning for the location prone to incidents based on the second alert message.

[0130] In this embodiment, the client can receive a second notification message from the server. Based on the second notification message, the client can quickly learn about the locations where incidents are likely to occur and take preventive measures to effectively improve safety during delivery.

[0131] Corresponding to the application scenarios and methods of the information processing methods provided in the embodiments of this application, the embodiments of this application also provide an information processing device.

[0132] Figure 7 This diagram illustrates the structure of an information processing apparatus according to an embodiment of this application. The information processing apparatus is used to execute the information processing method provided in any of the embodiments applied to a server, such as... Figure 7 As shown, the information processing device includes: The data receiving module 710 is used to receive traffic event information and trajectory data sent by the client. The traffic event information is used to describe the traffic event that occurred, and the trajectory data is used to characterize the client's movement trajectory.

[0133] The acquisition module 720 is used to acquire each target trajectory point that meets the predetermined spatial distribution conditions within the target time period from the trajectory data. The target time period is the time period determined according to the event time in the traffic event information. The interval between the trajectory point with the earliest passing time and the trajectory point with the latest passing time is greater than the first time duration threshold. The predetermined spatial distribution conditions are used to indicate the positional relationship between each target trajectory point.

[0134] The first determining module 730 is used to take the center point position of each target trajectory point as the client's stopping point position, where the center point position is the center position of the area formed by connecting the trajectory points; and to determine the location where the event occurs based on the client's stopping point position. The second determining module 740 is used to determine the event-frequent location based on the event occurrence locations corresponding to multiple clients within a predetermined time period, wherein the number of traffic events occurring at the event-frequent location within the predetermined time period is greater than a predetermined event number threshold.

[0135] In some embodiments, when the acquisition module 720 acquires each target trajectory point that meets the predetermined spatial distribution conditions within the target time period from the trajectory data, it is specifically used to: acquire each trajectory point whose passing time is within the target time period from the trajectory data; determine the distance between the trajectory point with the earliest passing time and the trajectory point with the latest passing time; and determine that each trajectory point meets the predetermined spatial distribution conditions based on the distance being less than a predetermined distance threshold and the degree of offset between each trajectory point and the center point being less than a predetermined offset degree threshold, and use each trajectory point as each target trajectory point.

[0136] In some embodiments, when the first determining module 730 determines the location of the event based on the client's stop location, it is specifically used to: obtain the target location from the client's order data within the target time period, the target location including the start location and destination location of order delivery; and filter out the location of the event from the client's stop locations based on the target location.

[0137] In some embodiments, when the first determining module 730 is used to filter out the event occurrence location from the client's stop locations based on the target location, it is specifically used to: filter out a first stop location from the client's stop locations, where the stop duration corresponding to the first stop location is greater than a second duration threshold; filter out stop locations located within a target area from the first stop locations to obtain a second stop location, where the target area is an area within a preset distance range centered on the target location; select a target stop location from the second stop locations as the event occurrence location, where the interval between the travel time of the target stop location and the event time in the traffic event information is the shortest, and the interval is less than or equal to a third duration threshold.

[0138] In some embodiments, the second determining module 740 is specifically used to: cluster the event occurrence locations corresponding to multiple clients within a predetermined time period to obtain an event-frequent region; the area of ​​the event-frequent region is less than a preset area threshold and the number of event occurrence locations located within the event-frequent region is greater than or equal to a predetermined event number threshold; and obtain the center point location of the event-frequent region as the event-frequent location.

[0139] In some embodiments, the information processing apparatus further includes: a sending module, configured to, after determining the location of the event-prone area, obtain the current delivery route of the client's current order; and, if the current delivery route passes through the location of the event-prone area, send a first prompt message to the client, the first prompt message being used to prompt a change of delivery route.

[0140] In some embodiments, the information processing apparatus further includes: a delivery route update module, configured to receive a feedback message in response to the first prompt message, the feedback message indicating agreement to change the delivery route; generate a new delivery route based on the current delivery route and the location of the incident, the new delivery route not including the location of the incident; and send the new delivery route to the client.

[0141] In some embodiments, the sending module is further configured to, after determining the location of the event-prone area, determine, based on the trajectory data reported by the client, that the client's trajectory will pass through the location of the event-prone area and that the distance between the client's current location and the location of the event-prone area is less than a preset distance threshold; and send a second prompt message to the client, the second prompt message being used to provide a message warning for the location of the event-prone area.

[0142] The functions of each module in each device in the embodiments of this application can be found in the corresponding description in the above method, and they have corresponding beneficial effects, which will not be repeated here.

[0143] Figure 8 This diagram illustrates the structure of an information processing apparatus according to an embodiment of this application. The information processing apparatus is used to execute the information processing method provided in any of the embodiments applied to a client described above, such as... Figure 8 As shown, the information processing device includes: The information receiving module 810 is used to receive traffic event information input by the user. The traffic event information is used to describe the traffic event that has occurred, and the trajectory data is used to characterize the client's movement trajectory.

[0144] The sending module 820 is used to send traffic incident information and collected user trajectory data to the server. This allows the server to obtain target trajectory points that meet predetermined spatial distribution conditions within a target time period from the trajectory data. The center point of each target trajectory point is used as the client's dwell point location. Based on the client's dwell point location, the incident location is determined, and multiple incident locations are determined based on the incident locations corresponding to multiple clients within a predetermined time period. The target time period is determined based on the incident time in the traffic incident information. The time interval between the earliest and latest passing trajectory points among the target trajectory points is greater than a first time period threshold. The predetermined spatial distribution conditions are used to indicate the positional relationship between the target trajectory points. The center point location is the center of the area formed by connecting the trajectory points. The multiple incident locations have a higher number of traffic incidents within a predetermined time period than a predetermined incident number threshold.

[0145] The message receiving module 830 is used to receive a first prompt message sent by the server when the client's current delivery route passes through a location with frequent incidents. The first prompt message is used to prompt the client to change the delivery route.

[0146] In some embodiments, the sending module 820 is further configured to send a feedback message to the server after receiving the first prompt message sent by the server, the feedback message being used to indicate agreement to change the delivery route; the information processing device further includes: a route receiving module, configured to receive a new delivery route sent by the server, the new delivery route not containing locations with frequent incidents; and a display module, configured to display the new delivery route.

[0147] In some embodiments, the information processing device further includes: an early warning module, configured to receive a second alert message sent by the server when the client's current delivery route passes through a location prone to incidents, the second alert message being used to issue an early warning for the location prone to incidents; and based on the second alert message, to issue a voice message to issue an early warning for the location prone to incidents.

[0148] The functions of each module in each device in the embodiments of this application can be found in the corresponding description in the above method, and they have corresponding beneficial effects, which will not be repeated here.

[0149] Figure 9 A schematic diagram of the structure of an information processing system according to an embodiment of this application is shown. Figure 9As shown, the information processing system includes a client 910 and a server 920. The client 910 receives traffic event information input by the user and sends the traffic event information and collected user trajectory data to the server 920. The traffic event information describes the traffic event, and the trajectory data represents the movement trajectory of the client 910. The server 920 obtains target trajectory points from the trajectory data that meet predetermined spatial distribution conditions within a target time period. The target time period is determined based on the event time in the traffic event information. The interval between the earliest and latest passing trajectory points among the target trajectory points is greater than a first time period threshold. The predetermined spatial distribution conditions indicate the positional relationship between the target trajectory points. The center point of each target trajectory point is used as the stopping point of the client 910, where the center point is the center of the area formed by connecting the trajectory points. The location of the event is determined based on the stopping point of the client 910. Based on the event locations corresponding to multiple clients 910 within a predetermined time period, a frequently occurring event location is determined, where the number of traffic events occurring at the frequently occurring event location within the predetermined time period is greater than a predetermined event number threshold.

[0150] The functions of the client and server in each device in the embodiments of this application can be found in the corresponding descriptions in the above methods, and they have corresponding beneficial effects, which will not be repeated here.

[0151] Figure 10 This is a block diagram of an electronic device used to implement embodiments of this application. For example... Figure 10 As shown, the electronic device includes a memory 1001 and a processor 1002. The memory 1001 stores a computer program that can run on the processor 1002. When the processor 1002 executes the computer program, it implements the method described in the above embodiments. The number of memories 1001 and processors 1002 can be one or more. In a specific implementation, the electronic device may also include a communication interface 1003 for communicating with external devices and performing data exchange and transmission.

[0152] In practical implementation, if the memory 1001, processor 1002, and communication interface 1003 are implemented independently, they can be interconnected via a bus to communicate with each other. This bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. This bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 10 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0153] Optionally, in a specific implementation, if the memory 1001, processor 1002 and communication interface 1003 are integrated on a single chip, the memory 1001, processor 1002 and communication interface 1003 can communicate with each other through an internal interface.

[0154] This application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the information processing method provided in this application.

[0155] This application provides a computer program product, including a computer program that, when executed by a processor, implements the information processing method provided in this application.

[0156] This application also provides a chip, which includes a processor for calling and executing instructions stored in a memory, causing a communication device equipped with the chip to perform the information processing method provided in this application.

[0157] This application also provides a chip, including: an input interface, an output interface, a processor, and a memory. The input interface, output interface, processor, and memory are connected through an internal connection path. The processor is used to execute code in the memory. When the code is executed, the processor is used to execute the information processing method provided in the application embodiment.

[0158] It should be understood that the aforementioned processor can be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. General-purpose processors can be microprocessors or any conventional processor. It is worth noting that the processor can be a processor supporting Advanced Reduced Instruction Set Machines (ARM) architecture.

[0159] Further, optionally, the aforementioned memory may include read-only memory and random access memory. The memory may be volatile memory or non-volatile memory, or may include both. Non-volatile memory may include read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. Volatile memory may include random access memory (RAM), which serves as an external cache. By way of example, but not limitation, many forms of RAM are available. Examples include Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), Synchronous DRAM (SDRAM), Double Data Rate SDRAM (DDR SDRAM), Enhanced Synchronous DRAM (ESDRAM), Sync Link DRAM (SLDRAM), and Direct Rambus RAM (DR RAM).

[0160] In the above embodiments, implementation can be achieved, in whole or in part, through software, hardware, firmware, or any combination thereof. When implemented in software, it can be implemented, in whole or in part, as a computer program product. A computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions according to this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transferred from one computer-readable storage medium to another.

[0161] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of those different embodiments or examples.

[0162] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this application, "a plurality of" means two or more, unless otherwise explicitly specified.

[0163] Any process or method described in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or more executable instructions for implementing a particular logical function or process. Furthermore, the scope of the preferred embodiments of this application includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functionality involved.

[0164] The logic and / or steps described in the flowchart or otherwise herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus or device (such as a computer-based system, a processor-included system or other system that can fetch and execute instructions from, an instruction execution system, apparatus or device).

[0165] It should be understood that various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. All or part of the steps of the methods in the above embodiments can be implemented by a program instructing related hardware, the program being stored in a computer-readable storage medium, which, when executed, includes one or a combination of the steps of the method embodiments.

[0166] Furthermore, the functional units in the various embodiments of this application can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into a module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. This storage medium can be a read-only memory, a disk, or an optical disk, etc.

[0167] The above are merely exemplary embodiments of this application, but the scope of protection of this application is not limited thereto. Any person skilled in the art can easily conceive of various variations or substitutions within the technical scope described in this application, and these should all be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. An information processing method characterized by comprising: Applied to a server, the method comprises: receiving traffic event information and trajectory data sent by a client, the traffic event information being used to describe a traffic event, and the trajectory data being used to represent an action trajectory of the client; from the trajectory data, obtaining each target trajectory point in a target period that meets a predetermined spatial distribution condition, the target period being a period determined according to an event time in the traffic event information; an interval time length between a trajectory point with the earliest passing time and a trajectory point with the latest passing time in the each target trajectory point is greater than a first time length threshold, and the predetermined spatial distribution condition is used to indicate a positional relationship between the each target trajectory point; taking a center point position of the each target trajectory point as a stay point position of the client, the center point position being a center position of a region formed by the each trajectory point; determining an event occurrence position according to the stay point position of the client; determining an event high-occurrence position according to event occurrence positions corresponding to a plurality of clients in a predetermined time length, the event high-occurrence position having a number of traffic events occurring in the predetermined time length greater than a predetermined event number threshold.

2. The method of claim 1, wherein, The method comprises: obtaining each trajectory point with a passing time in the target period from the trajectory data; determining a distance between the trajectory point with the earliest passing time and the trajectory point with the latest passing time in the each trajectory point; based on the distance being less than a predetermined distance threshold and an offset degree of the each trajectory point from the center point position being less than a predetermined offset degree threshold, determining that the each trajectory point meets the predetermined spatial distribution condition, and taking the each trajectory point as the each target trajectory point.

3. The method of claim 1, wherein, The method comprises: obtaining order data of the client in the target period; obtaining a starting position and a destination position of order delivery from the order data; taking the starting position and the destination position of the order delivery as a target position; based on the target position, screening an event occurrence position from the stay point position of the client.

4. The method of claim 3, wherein, The method comprises: screening a first stay point position from the stay point position of the client, the first stay point position corresponding to a stay time length greater than a second time length threshold; from the first stay point position, filtering out a stay point position located in a target area to obtain a second stay point position, the target area being an area within a preset distance range centered on the target position; selecting a target stay point position as the event occurrence position from the second stay point position, the target stay point position having a shortest interval time length between a passing time and an event time in the traffic event information, and the interval time length being less than or equal to a third time length threshold.

5. The method according to any one of claims 1 to 4, characterized in that, The method comprises: cluster the event occurrence positions corresponding to the plurality of clients in the predetermined time length to obtain an event high-occurrence area; the event high-occurrence area has an area less than a preset area threshold and a number of event occurrence positions located in the event high-occurrence area is greater than or equal to the predetermined event number threshold; obtain a center point position of the event high-occurrence area as the event high-occurrence position.

6. The method according to any one of claims 1 to 4, characterized in that, After the event high-occurrence position is determined, the method further includes: obtaining a current delivery route of a current order of the client; in a case where the current delivery route passes through the event high-occurrence position, sending a first prompt message to the client, the first prompt message being used to prompt to change the delivery route.

7. The method of claim 6, wherein, The method further includes: receiving a feedback message for the first prompt message, the feedback message being used to indicate to agree to change the delivery route; generating a new delivery route based on the current delivery route and the event high-occurrence position, the new delivery route not containing the event high-occurrence position; sending the new delivery route to the client.

8. The method according to any one of claims 1 to 4, characterized in that, After the event high-occurrence position is determined, the method further includes: determining, based on the trajectory data reported by the client, that a trajectory of the client will pass through the event high-occurrence position and a distance between a current position of the client and the event high-occurrence position is less than a preset distance threshold; sending a second prompt message to the client, the second prompt message being used to message warn the event high-occurrence position.

9. An information processing method characterized by comprising: Applied to a client, the method includes: receiving traffic event information input by a user, the traffic event information being used to describe a traffic event occurring; sending the traffic event information and collected trajectory data of the user to a server, so that the server obtains each target trajectory point meeting a predetermined spatial distribution condition in a target period from the trajectory data, takes a center point position of each target trajectory point as a stay point position of the client, determines an event occurrence position according to the stay point position of the client, and determines an event high-occurrence position based on event occurrence positions corresponding to a plurality of clients in a predetermined time length; the target period is a period determined according to an event time in the traffic event information; an interval time between a trajectory point with the earliest passing time and a trajectory point with the latest passing time in the each target trajectory point is greater than a first time threshold; the predetermined spatial distribution condition is used to indicate a positional relationship between the each target trajectory point; the center point position is a center position of a region formed by the each trajectory point; the event high-occurrence position has a number of traffic events occurring in the predetermined time length greater than a predetermined event number threshold; in a case where a current delivery route of the client passes through the event high-occurrence position, receiving a first prompt message sent by the server, the first prompt message being used to prompt to change the delivery route.

10. The method of claim 9, wherein, After the first prompt message sent by the server is received, the method further includes: sending a feedback message for the first prompt message to the server, the feedback message being used to indicate to agree to change the delivery route; receiving a new delivery route sent by the server, the new delivery route not containing the event high-occurrence position; display the new delivery route.

11. The method of claim 9, wherein, The method further comprises: receiving a second prompt message sent by the server, in a case where the current delivery route of the client passes through the event multi-occurrence location, the second prompt message being used for message warning of the event multi-occurrence location; based on the second prompt message, issuing voice information for warning of the event multi-occurrence location.

12. An information processing system, characterized by comprising: The system comprises a client and a server; The client is configured to receive traffic event information input by a user, and send the traffic event information and collected trajectory data of the user to the server; the traffic event information is used to describe a traffic event, and the trajectory data is used to represent an action trajectory of the client. The server is configured to obtain, from the trajectory data, each target trajectory point that satisfies a predetermined spatial distribution condition in a target period, the target period being a period determined according to an event time in the traffic event information; an interval time length between a trajectory point with the earliest passing time and a trajectory point with the latest passing time in the each target trajectory point is greater than a first time length threshold; the predetermined spatial distribution condition is used to indicate a positional relationship between the each target trajectory point; a center point position of the each target trajectory point is taken as a stay point position of the client, the center point position being a center position of a region formed by the each trajectory point; an event occurrence location is determined according to the stay point position of the client; and an event multi-occurrence location is determined according to event occurrence locations corresponding to a plurality of clients in a predetermined time length, the event multi-occurrence location having a number of traffic events greater than a predetermined event number threshold in the predetermined time length. 13.An electronic device comprising a memory, a processor, and a computer program stored in the memory, wherein the processor, when executing the computer program, implements the method of any one of claims 1 to 8 or any one of claims 9-11. 14.A computer readable storage medium having a computer program stored therein, wherein the computer program, when executed by a processor, implements the method of any one of claims 1 to 8 or any one of claims 9-11. 15.A computer program product comprising a computer program, wherein the computer program, when executed by a processor, implements the method of any one of claims 1 to 8 or any one of claims 9-11.

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