Logistics tracking efficiency analysis method and system

Abstract

The present application discloses a logistics tracking efficiency analysis method and system, belonging to the field of logistics trajectory information technology. The method comprises: obtaining a logistics trajectory and recording the trajectory upload time and trajectory query time; retrieving the corresponding target logistics trajectory based on the logistics provider to be analyzed, the logistics status, and the analysis date, and calculating the trajectory delay time of each target logistics trajectory; splitting the analysis date into multiple reference days and calculating the number of target logistics trajectories on each reference day; presetting multiple target completion times, generating corresponding monitoring nodes based on each target completion time and reference day, determining whether the target logistics trajectory is consistent with the monitoring node, and calculating the number of trajectory compliances for each monitoring node; calculating the trajectory compliance rate for each monitoring node; and aggregating the trajectory compliance rate, reference date, and target completion time information corresponding to each monitoring node, and organizing them for output or display. The method and system can determine the time delay of the logistics trajectory by the fluctuation amplitude of the trajectory compliance rate.

Description

Technical Field

[0001] The present application relates to the field of logistics track information technology, and in particular to a logistics tracking efficiency analysis method and system, and a logistics monitoring dashboard. Background Art

[0002] Existing logistics tracking systems are primarily used to query shipping order numbers. They process and output shipping tracking information obtained from various logistics providers' systems, allowing users to track the progress of their packages. However, with these conventional systems, users can only view shipping tracking information for the corresponding shipping order number.

[0003] However, there are thousands of logistics providers worldwide, and the logistics information in each system varies significantly in terms of data quality, operational specifications, and implementation standards. This significantly increases the probability of anomalies in logistics queries, which in turn affects logistics tracking efficiency. When anomalies in logistics query results are found (especially hidden anomalies such as delays in delivery times), users find it difficult to detect the anomaly based on the logistics tracking information, making it difficult for users to formulate appropriate countermeasures.

[0004] In addition, when outputting query results, the existing logistics tracking system only outputs the trajectory time, trajectory location and trajectory description information of the logistics trajectory to users for reference, which cannot reflect the service quality of the logistics provider and the tracking efficiency of the logistics tracking system.

[0005] Other technical issues related to this application will be further elaborated later. The above content is only used to assist in understanding the technical solution of this application and does not mean that all of the above content is prior art. Summary of the Invention

[0006] The primary purpose of this application is to provide a logistics tracking efficiency analysis method and system that can conduct in-depth analysis of logistics query results and determine the logistics time delay of the logistics trajectory by changes in the fluctuation range of the trajectory compliance rate, providing data support for users to select logistics providers and improve logistics tracking methods. In addition, this application also provides a logistics monitoring dashboard for monitoring logistics trajectory information, so that simple logistics trajectory information can clearly reflect the service quality of the logistics provider and the tracking efficiency of the logistics tracking system.

[0007] To achieve the above objectives, this application proposes a logistics tracking efficiency analysis method, which includes:

[0008] Step S1: Obtain logistics tracks and record the track upload time t1 and track query time t2 of each logistics track;

[0009] Step S2: Determine the logistics provider, logistics status, and analysis date to be analyzed, retrieve the corresponding logistics trajectory based on the logistics provider, logistics status, and analysis date, define the retrieved logistics trajectory as the target logistics trajectory, and calculate the trajectory delay time T of each target logistics trajectory, where T=t2-t1;

[0010] Step S3: Split the analysis date into N1 reference days, N1 ≥ 1, and calculate the number N2 of target logistics trajectories in each reference day;

[0011] Step S4: Preset N3 target-reaching times, where N3 ≥ 2. Generate corresponding monitoring nodes for each reference day based on the size of each target-reaching time. Determine whether the target logistics trajectory for each reference day is consistent with each monitoring node. Calculate the trajectory compliance number N4 for each monitoring node. The trajectory compliance number refers to the number of target logistics trajectories that can be consistent with the corresponding monitoring node on the corresponding reference day.

[0012] Step S5: Calculate the trajectory compliance rate of each monitoring node, where the trajectory compliance rate is the ratio of the number of trajectory compliances of the corresponding monitoring node to the number of target logistics trajectories on the corresponding reference day;

[0013] Step S6: Aggregate the trajectory compliance rate, reference day, and compliance time information corresponding to each monitoring node, and organize the aggregated information for output or display.

[0014] Other technical features and technical effects of this application are described in the following part of the specification. The technical problem-solving ideas and related product design solutions of this application are as follows:

[0015] Because logistics providers provide logistics information to inform the progress of packages (shipments), the logistics tracking system receives very limited information from these providers. The tracking system can only display the time, location, and description of the corresponding logistics track. However, based solely on this logistics track information, it is difficult for users to determine whether there are any anomalies in the logistics provider's or the logistics tracking number's delivery timeliness.

[0016] When users evaluate a logistics provider's delivery time, they typically compare the provider's stated delivery time with the actual delivery time of the package. If the package's journey from origin to destination falls within the provider's stated delivery time, the package's delivery time is considered normal; otherwise, the delivery time is abnormal. This method of determining delivery time, which uses the provider's stated delivery time to determine actual delivery time delays, has the disadvantage of only being able to determine delivery time delays across the entire logistics transport phase, not for individual stages (such as collection, in transit, arrival for pickup, delivery, and so on). Furthermore, the lack of a baseline time for each stage makes it difficult to determine delivery time delays for each stage.

[0017] If the average time and deviation range of each logistics provider in each logistics transportation stage are calculated based on the historical logistics trajectory data of the logistics tracking system, then the benchmark time of each logistics stage can be determined based on these average time and deviation range data, and then the logistics timeliness of the logistics trajectory can be judged. Although this timeliness judgment method can determine the benchmark time of each logistics stage, it has the following defects: (1) A large amount of historical logistics data is required to determine the benchmark time of each logistics provider in each logistics stage, which makes this method overly dependent on historical logistics data. If historical logistics data is missing, timeliness delay judgment cannot be made; (2) For multiple logistics packages of the same logistics provider, since the origin and destination cannot be exactly the same, the transportation time of each logistics stage will be very different, which will make the logistics timeliness judgment of each logistics stage inaccurate; (3) Based on a large amount of historical logistics trajectory information, the benchmark time of different logistics statuses of different logistics providers with different origins and destinations will be calculated, which will greatly increase the cost of data calculation and storage.

[0018] The applicant also discovered that existing logistics tracking systems generally track logistics order numbers at fixed times. Typically, the query interval (query cycle) for each logistics order number is every 4, 6, 12, or 24 hours, with a fixed logistics track query cycle. When a logistics time delay occurs, the upload time of the corresponding logistics track will also be delayed. Since the query time for the logistics order number is fixed, theoretically, the difference between the logistics track query time and the logistics track upload time can, to a certain extent, reflect the logistics time delay (such as Delay Scenario 1 and Delay Scenario 2 described below). The time when the logistics provider uploads the logistics track to the logistics provider system is defined as the track upload time t1 (also known as the track generation time or Internet access time). The time when the logistics tracking system first queries and obtains the corresponding logistics track is defined as the track query time t2 for that logistics track. The difference between the track query time and the track upload time is the track delay time T, where T = t2 - t1.

[0019] For example, in Case A, the logistics tracking system has a 12-hour query cycle for a particular logistics tracking number. The first logistics query for this logistics tracking number is at 18:00 on February 1st. From that time on, the logistics tracking number will be automatically queried every 12 hours. If the logistics status of the logistics package is "in transit" and the transportation is normal, the logistics package will arrive at the distribution center at 1:00 on February 2nd (assuming that the logistics track can be generated immediately after the package arrives and there is no lag in the online connection time). The logistics tracking system will first obtain the logistics track of the arrival at the distribution center from the logistics provider's system at 6:00 on February 2nd.

[0020] Delay scenario 1: If the package arrives at the distribution center at 2:00 a.m. on February 2, with a logistics delay of T1 = 1 hour, the tracking system will first obtain the package's trajectory information at 6:00 a.m. on February 2, with a trajectory delay of T = 4 hours.

[0021] Delay scenario 2: If the package arrives at the distribution center at 4:00 a.m. on February 2, with a logistics delay of T1 = 3 hours, the tracking system will first obtain the package's trajectory information at 6:00 a.m. on February 2, with a trajectory delay of T = 2 hours.

[0022] Delay scenario 3: If the package arrives at the distribution center at 17:00 on February 2, with a logistics delay of T1 = 16 hours, the tracking system will first obtain the package's trajectory information at 18:00 on February 2, with a trajectory delay of T = 1 hour.

[0023] In the delay scenarios described above, Delay Scenario 1 and Delay Scenario 2 fall within the same query period (the time interval from 18:00 on February 1st to 6:00 on February 2nd). The greater the trajectory delay time T, the smaller the logistics delay time T1. Therefore, the trajectory delay time can reflect the logistics delay. However, Delay Scenario 1 and Delay Scenario 3 fall within different query periods. Although the trajectory delay time for Delay Scenario 1 is smaller than that for Delay Scenario 3, the logistics delay time for Delay Scenario 1 is actually larger. This shows that while trajectory delay time can theoretically reflect logistics delays to a certain extent, in practice, because logistics delay time can span multiple logistics trajectory query periods and it is impossible to determine in which logistics trajectory query period the logistics delay occurs, trajectory delay time cannot directly reflect logistics delay time.

[0024] The applicant also found that if the logistics status corresponding to each logistics transportation stage is taken as the analysis object, and multiple target times are pre-set for each logistics status, so that these target times can be distributed in different logistics trajectory query cycles, then when judging the logistics timeliness of the logistics trajectory, first determine the logistics status of the logistics trajectory to be analyzed, and then retrieve several historical logistics trajectories (target logistics trajectories) under the logistics status, and further judge whether these target logistics trajectories are consistent with each target time (monitoring node), and calculate the trajectory compliance rate under each target time, then the trajectory compliance rate corresponding to each target time under the corresponding logistics provider, logistics status and analysis date conditions can be calculated, and then based on the fluctuation range of these trajectory compliance rates, it can be judged whether each logistics trajectory within the corresponding analysis date is abnormal.

[0025] For example, in Case B, when analyzing whether a logistics provider's logistics trajectory from February 1 to 4 is abnormal, we can analyze each one according to the logistics status. Table 1 shows the logistics data after the logistics tracking efficiency analysis of the logistics trajectory with the logistics status of "in transit". The main operation steps are as follows.

[0026] When obtaining the logistics track, the track upload time t1 and track query time t2 of each logistics track are recorded, and the logistics status of each logistics track, as well as the logistics provider and user (the user who queries the logistics track) to which it belongs are determined.

[0027] Retrieve historical logistics tracks belonging to the logistics provider and in the "in-transit" state as the target logistics tracks for analysis. Split the analysis period from February 1st to 4th into four reference days, and calculate the number N2 of target logistics tracks and the track delay T of each target logistics track for each reference day.

[0028] Furthermore, seven target times are preset: 2 hours, 4 hours, 6 hours, 10 hours, 12 hours, 18 hours, and 24 hours. These target times encompass the query period of each logistics trajectory to be analyzed. Each reference day uses these seven target times as monitoring nodes. Next, the target logistics trajectory for each reference day is determined to be consistent with each monitoring node, and the trajectory consistency number N4 for each monitoring node is calculated: if the trajectory delay time of a target logistics trajectory is less than (or less than or equal to) a target time or the target time corresponding to a monitoring node, then the target logistics trajectory is consistent with the monitoring node; if the trajectory delay time of a target logistics trajectory is greater than a target time or the target time corresponding to a monitoring node, then the target logistics trajectory is inconsistent with the monitoring node.

[0029] For example, the number of target logistics trajectories on the reference day, February 1, is N2 = 100, of which 60 have a trajectory delay of ≤ 2 hours. Therefore, the number of trajectories that meet the target on February 1, corresponding to the monitoring node with a target time of 2 hours, is N4 = 60, and the trajectory compliance rate of this monitoring node is N4 / N2 = 60%. Similarly, the trajectory compliance rates of other monitoring nodes are calculated to form the deep logistics data shown in Table 1.

[0030] According to the analysis data in Table 1, the four reference days and the seven compliance times constitute a total of 28 monitoring nodes. From the fluctuation range of the trajectory compliance rate of each monitoring node, it can be seen that the trajectory compliance rate of the monitoring node at the 2-hour compliance time on February 4th is 30%. The trajectory compliance rate of this monitoring node is significantly lower than the trajectory compliance rate of the monitoring nodes at the 2-hour compliance time on other reference days. Therefore, it can be judged that there is an anomaly in the logistics trajectory on February 4th.

[0031] Time to reach the target 2H 4H 6H 10H 12H 18H 24H February 1 trajectory compliance rate 60.0% 70.0% 80.0% 85.0% 90.0% 96.0% 100.0% February 2 trajectory compliance rate 70.0% 80.0% 85.0% 90.0% 91.0% 95.0% 100.0% Trajectory compliance rate on February 3 65.0% 75.0% 80.0% 90.0% 95.0% 98.0% 100.0% February 4th trajectory compliance rate 30.0% 45.0% 80.0% 85.0% 90.0% 95.0% 99.0%

[0032] Table 1

[0033] This logistics tracking efficiency analysis method calculates the logistics tracking system's tracking capabilities for each logistics track and conducts in-depth analysis of logistics query results, proactively identifying hidden anomalies in logistics timeliness (including delays, untimely tracking, and delayed uploads of logistics track information). This method ensures timely and transparent user awareness of the logistics tracking system's query services, providing data support for selecting logistics providers and improving logistics tracking methods. This method is simple to use and eliminates the need for complex logistics timeliness analysis specific to the origin and destination of a package. It can also clearly display logistics timeliness delays. Furthermore, analysis results can be output even for a single target logistics track.

[0034] In addition, this application also provides a logistics monitoring dashboard for monitoring logistics tracks, including:

[0035] Track acquisition module, used to obtain logistics tracks;

[0036] The condition setting module is used to set the logistics provider to be analyzed, the logistics status and the analysis date, and retrieve the corresponding logistics track as the monitoring object according to the logistics provider to be analyzed, the logistics status and the analysis date;

[0037] A node control module is used to preset multiple target-reaching times and generate corresponding monitoring nodes based on the multiple target-reaching times and analysis dates;

[0038] The monitoring and analysis module calculates the trajectory compliance rate of each monitoring node based on the trajectory delay time and the target time. The more logistics trajectories with trajectory delay time shorter than the target time, the greater the trajectory compliance rate of the corresponding monitoring node; and

[0039] The information display module aggregates and displays the trajectory compliance rate, analysis date and compliance time of each monitoring node.

[0040] The logistics monitoring dashboard allows users to perform various actions in the aforementioned logistics tracking efficiency analysis method, providing a user-friendly interactive interface. This reduces the user's understanding of the logistics tracking efficiency analysis, allowing simple logistics track information to clearly reflect the logistics provider's service quality and the tracking efficiency of the logistics tracking system. Furthermore, the logistics monitoring dashboard can be used as part of the logistics tracking system, allowing users to query logistics tracks and conduct timely logistics tracking efficiency analysis on the corresponding logistics tracks.

[0041] Furthermore, the present application also includes systems corresponding to various methods, wherein the systems include the functional modules involved in the present application, execute the operating instructions of the corresponding functional modules or the corresponding methods, and output relevant data information to the system front-end interface. The systems are stored in a server and / or computer device including a processor, and the processor is used to execute the operating instructions of the system.

[0042] Disclaimer: The functional modules of this application can be integrated with each other, or can exist independently, or a functional module can serve as a submodule of another functional module; step numbers such as S1, S2 do not limit the order of the corresponding operation steps.

[0043] The following are the descriptions of the relevant nouns in this application (the letters of English words are not case sensitive).

[0044] (1) Logistics track refers to the logistics routing information of a package (goods) from shipment to receipt, including the track time, track location, and track description (such as package status, etc.) of the package in the corresponding logistics link; the transportation process of the package is presented through multiple logistics track information with the same logistics order number.

[0045] (2) UTC (Coordinated Universal Time) is a time measurement standard based on the length of the atomic second, while GMT (Greenwich Mean Time) is a time measurement standard based on the Earth's 0 degrees longitude. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] The accompanying drawings are used to provide further understanding of the present application and do not constitute a limitation of the present application; the contents shown in the accompanying drawings may be real data of the embodiments and fall within the scope of protection of the present application.

[0047] Figure 1 This is a schematic diagram of the logistics tracking efficiency analysis principle in one embodiment of the present application.

[0048] Figure 2 This is a flow chart of a logistics tracking efficiency analysis method in one embodiment of the present application.

[0049] Figure 3 This is a diagram of the interactive interface of the logistics monitoring dashboard in one embodiment of the present application.

[0050] Figure 4 This is a schematic diagram of the logistics monitoring dashboard structure in one embodiment of the present application. DETAILED DESCRIPTION

[0051] In order to make the purpose, technical solutions and advantages of this application more clear, the following is a further detailed description of the embodiments of this application through specific implementation methods combined with the accompanying drawings. It should be understood that the specific embodiments described here are only used to explain this application and are not used to limit this application.

[0052] like Figure 1-Figure 2 ,This application proposes a logistics tracking efficiency analysis method, which also includes steps S1-S6, as follows.

[0053] Step S1: Obtain logistics tracks from the logistics provider system, record the track upload time t1 and track query time t2 of each logistics track, and store the information of the corresponding logistics track in the database of the logistics tracking system. The obtained logistics track can be called a historical logistics track.

[0054] Step S2: Determine the logistics provider, logistics status, and analysis date to be analyzed. Based on the logistics provider, logistics status, and analysis date, retrieve the corresponding logistics trajectory. Define the retrieved logistics trajectory as the target logistics trajectory. Calculate the trajectory delay time T for each target logistics trajectory, where T = t2 - t1. In this application, "retrieve" means to filter or call.

[0055] Step S3: Split the analysis date into N1 reference days, where N1 ≥ 1, and calculate the number N2 of target logistics trajectories in each reference day. In other embodiments, the reference day can be changed to a reference time of other lengths, such as every half day or every week.

[0056] Step S4: Preset N3 target times, N3 ≥ 2, generate corresponding monitoring nodes on each reference day according to the size of each target time, and judge whether the target logistics trajectory of each reference day is consistent with each monitoring node; calculate the trajectory compliance number N4 of each monitoring node, which refers to the number of target logistics trajectories that can be consistent with the corresponding monitoring node (or the target time corresponding to the monitoring node) on the corresponding reference day.

[0057] Step S5: Calculate the trajectory compliance rate of each monitoring node. The trajectory compliance rate is the ratio of the number of trajectory compliances of the corresponding monitoring node to the number of target logistics trajectories in the corresponding reference day; that is, the trajectory compliance rate = N4 / N2.

[0058] Step S6: Aggregate the trajectory compliance rate, reference day, and compliance time information corresponding to each monitoring node, and organize the aggregated information for output or display.

[0059] In one implementation, when presetting N3 target times, the shortest target time can be set to ≤ 2 hours and the longest target time to ≥ 20 hours. This allows for the majority of logistics track query cycles and improves the accuracy of logistics tracking efficiency analysis. Furthermore, before calculating the track delay time T, the track upload time t1 and the track query time t2 are converted to the same time unit, using the UTC or GMT time unit.

[0060] In one embodiment, the trajectory non-compliance rate can also be used to assess the logistics time delay of the logistics trajectory. In step S5, the trajectory non-compliance rate is calculated as 1-trajectory compliance rate. Then, in step S6, the trajectory non-compliance rate, reference date, and compliance time information corresponding to each monitoring node are aggregated and organized for output or display. Alternatively, in step S4, the trajectory non-compliance number N5 for each monitoring node can be directly calculated as trajectory non-compliance rate = N5 / N2.

[0061] In one embodiment, when retrieving a target logistics track based on an analysis date, the track upload time or track query time is the same as the analysis date. In other words, the analysis date is the date corresponding to the track upload time or the date corresponding to the track query time.

[0062] In one embodiment, in step S2, the logistics provider to be analyzed, the logistics status, and the analysis date are defined as the conditions to be analyzed, and the corresponding target logistics trajectory is retrieved according to the conditions to be analyzed;

[0063] When the conditions to be analyzed include multiple logistics states, the trajectory compliance rate of each logistics state at the monitoring node is calculated respectively. In response to a specified logistics state, the trajectory compliance rate of each corresponding monitoring node under the specified logistics state is output or displayed in step S6, thereby facilitating targeted review of the logistics trajectory under the logistics state.

[0064] In addition, when the conditions to be analyzed include multiple logistics providers, the trajectory compliance rate of each logistics status of each logistics provider at the monitoring node is calculated separately, and in response to the specified logistics status, reference date and target time, the sorting information of each logistics provider according to the trajectory compliance rate under the specified logistics status, reference date and target time is output or displayed in step S6, so as to screen logistics providers with different degrees of trajectory delay, rank these logistics providers, and provide data support for users to select or replace logistics providers.

[0065] In relevant embodiments, the "track compliance rate" can be referred to as the "update timeliness rate" or the "query timeliness rate" to help users better understand the relationship between the logistics track delay time and each target time.

[0066] In one embodiment, the logistics track within the monitoring date may be automatically monitored based on the above-mentioned logistics tracking efficiency analysis method, such as steps S71-S73.

[0067] Step S71: Preset a monitoring date, determine the monitored logistics status and monitored compliance time for the monitored logistics provider, and set a baseline trajectory compliance rate based on each monitored logistics status and each monitored compliance time for each monitored logistics provider. The monitoring date can be the same day or the previous day of the logistics tracking efficiency analysis, or it can be within the past week or other time period. After the monitoring date is set, the logistics tracking system automatically calculates the trajectory compliance rate for that monitoring date and monitors the logistics trajectory for any anomalies.

[0068] Step S72: The logistics trajectory belonging to the monitored logistics state within the monitoring date is taken as the target logistics trajectory, and the corresponding monitoring node is generated on the monitoring date according to the size of the monitored compliance time; the monitoring trajectory compliance rate of each monitoring node is calculated based on the number of target logistics trajectories within the monitoring date, and the monitoring trajectory compliance rate is equal to the ratio of the number of target logistics trajectories (N4) that are consistent with the corresponding monitoring node on the monitoring date to the number of target logistics trajectories (N2) on the monitoring date.

[0069] Step S73: When the monitoring trajectory compliance rate is less than (or less than or equal to) the reference trajectory compliance rate, an early warning message is generated based on the monitored logistics status and the monitored target-reaching time.

[0070] For example, in Case C, when monitoring the USPS logistics provider's logistics trajectory for the past week (monitoring date) for anomalies, the baseline trajectory compliance rate for the USPS (the monitored logistics provider) in terms of pickup status (monitored logistics status) and 10-hour compliance time (monitored compliance time) can be set to 80%. In this case, the historical logistics trajectory of the USPS in the past week that occurred during the pickup status is used as the target logistics trajectory. Monitoring node A is generated for the pickup status and 10-hour compliance time. Monitoring node A can also be considered to be located at the coordinates of (pickup status, 10-hour compliance time). The number of target logistics trajectories in the past week (N2) and the number of target logistics trajectories that match the corresponding monitoring node in the past week (N4) are then calculated. The monitoring trajectory compliance rate is then calculated as N4 / N2. If the monitoring trajectory compliance rate is lower than the baseline trajectory compliance rate, an alert is generated for monitoring node A (pickup status, 10-hour compliance time) to alert the user to the anomaly in the logistics trajectory of this monitoring node. Similarly, the benchmark trajectory compliance rate can be set based on other monitoring dates, other monitoring logistics providers, other monitoring logistics status and other monitoring compliance times to generate more monitoring nodes, thereby automatically, flexibly and completely monitoring logistics trajectory information and improving the efficiency and quality of logistics tracking.

[0071] In other embodiments, filtering conditions can be set, including but not limited to one or more of the following: analysis date, logistics provider, logistics status, and target time. Based on these filtering conditions, the corresponding logistics trajectory is retrieved as the target logistics trajectory. The system then determines whether the retrieved target logistics trajectory matches the target time. The percentage of matching target logistics trajectories to the total number of retrieved target logistics trajectories is used as the trajectory matching rate. The system then aggregates and outputs trajectory matching rate information corresponding to each target time. This allows users to flexibly set filtering conditions, and the system automatically outputs the corresponding trajectory matching rate information, enabling a quick and complete analysis of the timeliness of logistics trajectories under various conditions.

[0072] In one embodiment, when acquiring logistics trajectories, the query cycle for each logistics trajectory is recorded. The monitoring cycle is defined as the duration of the monitoring date. The number of monitoring nodes whose monitored trajectory compliance rate is less than the baseline trajectory compliance rate is defined as the warning number (N6). If the warning number for a monitored logistics state falls below a preset warning number (a first adjustment condition), the query cycle for that monitored logistics state is reduced in the next monitoring cycle (i.e., the first adjustment strategy is implemented for the query cycle) to ensure that the logistics tracking system obtains logistics trajectories in a timely manner. The query cycle is adjusted within a predetermined time range, for example, with a minimum query cycle of 2 hours and a maximum query cycle of 48 hours. In a related embodiment, the preset warning number can be set based on the number of target time periods, N3: when N3 ≤ 7, the preset warning number is 3; when N3 ≥ 8, the preset warning number is the integer portion of 0.5*N3.

[0073] Furthermore, a monitored compliance time is selected from multiple monitored compliance times corresponding to the monitored logistics state as the benchmark compliance time. When the second adjustment condition is met, the query cycle of the monitored logistics state is increased in the next monitoring cycle (that is, the second adjustment strategy is executed on the query cycle). The second adjustment condition includes: the number of warnings under the monitored logistics state is lower than the preset number of warnings, there is at least one compliance time that is less than the benchmark compliance time, and the corresponding monitoring trajectory compliance rate is greater than or equal to (≥) the benchmark trajectory compliance rate of the benchmark compliance time.

[0074] For example, referring to Table 1, the monitoring dates are February 1st to 4th, and the monitoring cycle is 4 days. If each compliance time is used as the monitoring compliance time, the benchmark trajectory compliance rate set for the 10-hour compliance time is 80%, and the 10-hour monitored compliance time is selected as the benchmark compliance time, and the number of warnings is lower than the preset number of warnings, then since the trajectory compliance rate of the 6-hour compliance time is ≥ the benchmark trajectory compliance rate of 80%, the second condition is met, and the query cycle can be increased in the next monitoring cycle (February 5th to 8th).

[0075] In one embodiment, the cause of a logistics trajectory anomaly can be determined based on the logistics trajectory query period and trajectory compliance rate. Specifically, the logistics trajectory query period can be used as the first target time, which can be the longest target time. The trajectory compliance rate corresponding to the first target time within the monitoring date is then calculated (defined as the first trajectory compliance rate). If the first trajectory compliance rate is less than 100%, the cause of the logistics trajectory anomaly is determined to be an abnormality in the logistics tracking system's query of the logistics trajectory, and a corresponding prompt message is generated.

[0076] For example, in Case D, referring to Table 1, if the logistics tracking system's query cycle is 24 hours, then the 24-hour target time is used as the first target time. In this case, if February 4th is used as the monitoring period, the first track compliance rate is 99%, indicating that 1% of the logistics tracks have a track delay exceeding 24 hours. These track delays exceeding 24 hours are caused by track anomalies within the tracking process (such as errors in logistics order number recognition, missed query cycles, errors in track data processing, and other anomalies on the logistics tracking system side), rather than delays in logistics delivery or delayed track information upload by the logistics provider. Therefore, the query cycle and the first track compliance rate can be used to determine the cause of the track anomaly and generate corresponding prompts to inform users of the track anomaly, thereby improving logistics tracking efficiency.

[0077] In addition, the present application also provides a logistics monitoring dashboard for monitoring logistics trajectories, including a trajectory acquisition module for obtaining logistics trajectories, a condition setting module, a node control module, a monitoring and analysis module, and an information display module.

[0078] The condition setting module is used to set the logistics provider to be analyzed, the logistics status, and the analysis date (i.e., the analysis conditions). Based on the logistics provider, logistics status, and analysis date, the corresponding logistics trajectory is retrieved as the monitoring object (i.e., the target logistics trajectory). The node control module is used to preset multiple target-reaching times and generate corresponding monitoring nodes based on these multiple target-reaching times and analysis dates. The monitoring and analysis module calculates the trajectory compliance rate of each monitoring node based on the trajectory delay time and target-reaching time. The more logistics trajectories with trajectory delay times shorter than the target-reaching time, the greater the trajectory compliance rate of the corresponding monitoring node. The information display module is used to aggregate and display the trajectory compliance rate, analysis date, and target-reaching time information of each monitoring node.

[0079] In one embodiment, the information display module displays the trajectory compliance rate, analysis date, and compliance time information corresponding to each monitoring node (including the second monitoring node and the monitoring nodes described in steps S4 and S72) via a chart and / or table. The table may be in the form of Table 1 above.

[0080] When the information display module includes the chart, the chart displays the coordinate points of the corresponding monitoring nodes with the time of compliance as the horizontal axis and the trajectory compliance rate as the vertical axis. The analysis date is divided into multiple reference days. The coordinate points of each monitoring node on the same reference day are marked with the same color, and the coordinate points of each monitoring node on different reference days are marked with different colors. The coordinate points of each monitoring node on the same reference day are connected in order of the size of the compliance time to form a curve, and the curve is used as the trajectory compliance rate curve. In this way, the logistics trajectory information of three dimensions can be displayed in a two-dimensional chart, and the monitoring nodes with large fluctuations in the trajectory compliance rate are clearly presented, making it easier for users to quickly find abnormal logistics trajectory data.

[0081] In addition, the chart can display each reference day option in a different color. The color of each reference day option is the same as the coordinate color of the corresponding monitoring node. If the corresponding reference day is selected, the trajectory compliance curve for the corresponding reference day will be displayed. Otherwise, the trajectory compliance curve for the corresponding reference day will not be displayed. This allows for targeted comparative analysis of the trajectory compliance rates of different reference days.

[0082] In one embodiment, in the condition setting module, the logistics provider to be analyzed, logistics status and analysis date are set through the interaction of the filter box. After the logistics trajectory of each monitoring node is calculated, the trajectory compliance rate, analysis date and compliance time information of each monitoring node are exported to generate a monitoring file.

[0083] like Figure 3 In the "Set track compliance rate" control key setting interface, the node control module can also be used to set the baseline track compliance rate. The node control module sets the baseline track compliance rate corresponding to the monitored logistics provider, the monitored logistics status and the monitored compliance time, determines the monitoring date, and generates the corresponding second monitoring node based on the monitoring date and the monitored compliance time. The track compliance rate of the second monitoring node is used as the monitoring track compliance rate; when the monitoring track compliance rate is less than the baseline track compliance rate, an early warning message is generated based on the monitored logistics status and the monitored compliance time. That is, the operations of steps S71-S73 are executed, wherein the monitoring date can be the analysis date or other reset time.

[0084] In one embodiment, the monitoring dashboard also includes a query optimization module, which records the query cycle of each logistics trajectory through the trajectory acquisition module, takes the duration of the monitoring date as the monitoring cycle, and defines the number of monitoring nodes whose monitoring trajectory compliance rate is less than the benchmark trajectory compliance rate as the warning number. When the warning number under the monitored logistics state is lower than the preset warning number, the query optimization module executes a first adjustment strategy for the query cycle, and the first adjustment strategy includes reducing the query cycle under the monitored logistics state in the next monitoring cycle.

[0085] Furthermore, a monitored compliance time is selected from multiple monitored compliance times corresponding to the monitored logistics status as the benchmark compliance time. When the second adjustment condition is met, the query optimization module executes a second adjustment strategy for the query cycle. The second adjustment strategy includes increasing the query cycle under the monitored logistics status in the next monitoring cycle. The second adjustment condition includes: the number of warnings under the monitored logistics status is lower than the preset number of warnings, there is at least one compliance time that is less than the benchmark compliance time, and its corresponding monitoring trajectory compliance rate is greater than or equal to the benchmark trajectory compliance rate of the benchmark compliance time.

[0086] In addition, the monitoring dashboard may also include one or more of an account management module, a log push module, a logistics provider monitoring module, and an anomaly analysis module. The account management module manages user account information for the logistics monitoring dashboard (logistics tracking system). Once a user logs in, only logistics tracks queried through that account are retrieved as monitored items. The log push module pushes monitoring logs to users, for example, via email, system messages, or text messages, for the specified analysis date. The monitoring logs may contain information about logistics tracks that the system automatically monitors regularly or alerts when track anomalies occur. The logistics provider monitoring module monitors the logistics track data of each logistics provider and manages the logistics track monitoring data for each provider over different time periods. The anomaly analysis module analyzes the causes of track anomalies. Once an alert is generated regarding a logistics track anomaly, the module analyzes the anomaly information and generates an anomaly analysis report. For example, in the aforementioned Example D, the anomaly analysis report records the causes of the anomaly.

[0087] The above description is only a preferred embodiment of the present application and does not limit the patent scope of the present application. All equivalent transformations made using the contents of the present application specification and drawings under the inventive concept of the present application, or direct / indirect application in other related technical fields are included in the patent protection scope of the present application.

Claims

1. A logistics tracking efficiency analysis method, characterized in that: The method comprises: Step S1: Obtain logistics tracks and record the track upload time t1 and track query time t2 of each logistics track. The track upload time t1 is the time when the logistics provider uploads the corresponding logistics track to the logistics provider system, and the track query time t2 is the time when the logistics tracking system first obtains the corresponding logistics track from the logistics provider system; Step S2: Determine the logistics provider, logistics status, and analysis date to be analyzed, retrieve the corresponding logistics trajectory based on the logistics provider, logistics status, and analysis date, define the retrieved logistics trajectory as the target logistics trajectory, and calculate the trajectory delay time T of each target logistics trajectory, where T=t2-t1; Step S3: Split the analysis date into N1 reference days, N1 ≥ 1, and calculate the number N2 of target logistics trajectories in each reference day; Step S4: Preset N3 target-reaching times, where N3 ≥ 2. Generate corresponding monitoring nodes for each reference day based on the size of each target-reaching time. Determine whether the target logistics trajectory for each reference day is consistent with each monitoring node. Calculate the trajectory compliance number N4 for each monitoring node. The trajectory compliance number refers to the number of target logistics trajectories that can be consistent with the corresponding monitoring node on the corresponding reference day. The target-reaching time is used to compare the size of the trajectory delay time. Step S5: Calculate the trajectory compliance rate of each monitoring node, where the trajectory compliance rate is the ratio of the number of trajectory compliances of the corresponding monitoring node to the number of target logistics trajectories on the corresponding reference day; Step S6: Aggregate the trajectory compliance rate, reference day, and compliance time information corresponding to each monitoring node, and organize the aggregated information for output or display.

2. The logistics tracking efficiency analysis method according to claim 1, characterized in that: In step S4, if the trajectory delay time of a target logistics trajectory is less than the target time corresponding to a monitoring node, then the target logistics trajectory is consistent with the monitoring node; If the trajectory delay time of a target logistics trajectory is greater than the target time corresponding to a monitoring node, the target logistics trajectory does not match the monitoring node.

3. The logistics tracking efficiency analysis method according to claim 1, characterized in that: When N3 target time periods are preset, the shortest target time is ≤ 2 hours and the longest target time is ≥ 20 hours. and / or, before calculating the trajectory delay time T, converting the trajectory upload time t1 and the trajectory query time t2 into the same time unit, wherein the time unit adopts the UTC or GMT time measurement standard; And / or, in step S5, the trajectory non-compliance rate is calculated, where the trajectory non-compliance rate = 1 - trajectory compliance rate; in step S6, the trajectory non-compliance rate, reference day, and compliance time information corresponding to each monitoring node are aggregated, and the aggregated information is organized, output, or displayed.

4. The logistics tracking efficiency analysis method according to claim 1, wherein: When retrieving the target logistics track according to the analysis date, the logistics track whose track upload time or track query time is the same as the analysis date is retrieved.

5. The logistics tracking efficiency analysis method according to claim 1, wherein: In step S2, the logistics provider to be analyzed, the logistics status and the analysis date are defined as the conditions to be analyzed, and the corresponding target logistics trajectory is retrieved according to the conditions to be analyzed; When the conditions to be analyzed include multiple logistics states, the trajectory compliance rate of each logistics state at the monitoring node is calculated respectively, and in response to a specified logistics state, the trajectory compliance rate of each corresponding monitoring node under the specified logistics state is output or displayed in step S6.

6. The logistics tracking efficiency analysis method according to claim 5, characterized in that: When the conditions to be analyzed include multiple logistics providers, the trajectory compliance rate of each logistics status of each logistics provider at the monitoring node is calculated respectively. In response to the specified logistics status, reference date and target time, the sorting information of each logistics provider according to the trajectory compliance rate under the specified logistics status, reference date and target time is output or displayed in step S6.

7. The logistics tracking efficiency analysis method according to claim 1, characterized in that: The method further comprises: Step S71: Preset a monitoring date, determine the monitored logistics status and the monitored compliance time of the monitored logistics provider, and set a benchmark trajectory compliance rate based on each monitored logistics status and each monitored compliance time of the monitored logistics provider; Step S72: The logistics trajectory belonging to the monitored logistics state during the monitoring date is used as the target logistics trajectory, and corresponding monitoring nodes are generated on the monitoring date according to the time to reach the monitored target; the monitoring trajectory compliance rate of each monitoring node is calculated based on the number of target logistics trajectories during the monitoring date, and the monitoring trajectory compliance rate is equal to the ratio of the number of target logistics trajectories that comply with the corresponding monitoring node during the monitoring date to the number of target logistics trajectories during the monitoring date; Step S73: When the monitoring trajectory compliance rate is less than the reference trajectory compliance rate, an early warning message is generated based on the monitored logistics status and the monitored target-reaching time.

8. The logistics tracking efficiency analysis method according to claim 7, characterized in that: When obtaining the logistics trajectory, the query cycle of each logistics trajectory is recorded, the duration of the monitoring date is used as the monitoring cycle, and the number of monitoring nodes whose monitoring trajectory compliance rate is less than the benchmark trajectory compliance rate is defined as the warning number. If the warning number under a monitored logistics state is lower than the preset warning number, the query cycle under the monitored logistics state will be reduced in the next monitoring cycle.

9. The logistics tracking efficiency analysis method according to claim 8, characterized in that: From a plurality of monitored compliance times corresponding to the monitored logistics status, one monitored compliance time is selected as a benchmark compliance time. When a second adjustment condition is met, the query period of the monitored logistics status is increased in the next monitoring period. The second adjustment condition includes: The number of warnings under the monitored logistics state is lower than the preset number of warnings, there is at least one compliance time that is less than the benchmark compliance time, and its corresponding monitoring trajectory compliance rate is greater than or equal to the benchmark trajectory compliance rate of the benchmark compliance time.

10. A logistics tracking system, characterized in that: The system executes the operating instructions contained in the logistics tracking efficiency analysis method according to any one of claims 1 to 9.

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