Logistics monitoring board

Abstract

The invention discloses a logistics monitoring board, which is used for monitoring a logistics track and comprises a track acquisition module for acquiring the logistics track, a condition setting module, a node control module, a monitoring analysis module and an information display module, and the condition setting module is used for setting a to-be-analyzed logistics provider, a logistics state and an analysis date; the node control module is used for presetting multiple standard reaching times and generating corresponding monitoring nodes based on the multiple standard reaching times and the analysis date, the monitoring analysis module is used for calculating the track coincidence rate of each monitoring node based on the track delay time and the standard reaching time, and the more logistics tracks with the track delay time smaller than the standard reaching time are, the more logistics tracks with the track delay time smaller than the standard reaching time are. The track coincidence rate of the corresponding monitoring node is higher; and the information display module is used for aggregating and displaying the track coincidence rate, the analysis date and the up-to-standard time information of each monitoring node. The monitoring board can enable simple logistics track information to clearly reflect the service quality of a logistics provider and the tracking efficiency of a logistics tracking system.

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] The existing logistics tracking system is mainly used for logistics order number query, which processes the logistics track information obtained from different logistics providers' systems and outputs it to users, so that users can check the progress of logistics packages in time. In this conventional logistics tracking system, users can only view the logistics track information of the corresponding logistics order number.

[0003] However, there are thousands of logistics companies around the world, and the logistics information of each logistics company system is very different in terms of data quality, operating specifications, and implementation standards, which greatly increases the probability of abnormalities in logistics queries, thereby affecting the efficiency of logistics tracking. When the logistics query results are abnormal (especially hidden abnormalities such as logistics time delays), it is difficult for users to find the abnormality based on the logistics track information, resulting in users being unable to formulate corresponding countermeasures for abnormal conditions.

[0004] In addition, when outputting query results, the existing logistics tracking system only outputs the track time, track location and track description information of the logistics track 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 described 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 main purpose of this application is to provide a logistics tracking efficiency analysis method and system, which can conduct in-depth analysis of logistics query results, and judge the logistics time delay of the logistics track by the fluctuation of the track compliance rate, so as to provide 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 track information, so that simple logistics track 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, the present application proposes a logistics tracking efficiency analysis method, which includes: Step S1: Obtain the logistics track and record the track upload time t1 and track query time t2 of each logistics track; Step S2: Determine the logistics provider, logistics status and analysis date to be analyzed, retrieve the corresponding logistics trajectory according to 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, N3≥2, generate corresponding monitoring nodes on each reference day according to the size of each target-reaching 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, and 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; Step S5: Calculate the trajectory compliance rate of each monitoring node, where the trajectory compliance rate is the ratio of the trajectory compliance number of the corresponding monitoring node to the number of target logistics trajectories in the corresponding reference day; Step S6: Aggregate the trajectory compliance rate, reference day, and target-reaching time information corresponding to each monitoring node, and organize, output, or display the aggregated information.

[0008] 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: Since the logistics information provided by the logistics provider system is to inform the logistics package (transported goods) of the transportation progress, the logistics tracking system obtains very limited logistics information from the logistics provider system. Through the logistics tracking system, only the track time, track location and track description information of the corresponding logistics track can be viewed. However, it is difficult for users to judge whether there is any abnormality in the logistics timeliness of the logistics provider or logistics order number based on this logistics track information alone.

[0009] When evaluating the logistics time efficiency of logistics providers, users usually compare the logistics provider's declared logistics time with the actual logistics time of the logistics package. If the time from the origin to the destination of the logistics package is within the logistics provider's declared logistics time range, the logistics package's time efficiency is normal; otherwise, the logistics time efficiency is abnormal. This method of judging logistics time efficiency is based on the logistics provider's declared logistics time to judge the actual logistics time efficiency delay. Its disadvantage is that it can only judge the time efficiency delay for the entire logistics transportation stage, and cannot judge the time efficiency delay for a single logistics transportation stage (such as collection status, in transit, arrival for pickup, delivery and dispatch, etc.). In addition, when judging the time efficiency delay of each logistics transportation stage, due to the lack of benchmark time for each logistics stage, it is difficult to judge the time efficiency delay of each stage.

[0010] 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, it is impossible to make a timeliness delay judgment; (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 states of different logistics providers with different origins and destinations is calculated, which will greatly increase the cost of data calculation and storage.

[0011] The applicant also found that the existing logistics tracking system basically tracks logistics order numbers at fixed times. Usually, the query interval time (query cycle) of each logistics order number is queried every 4, 6, 12 or 24 hours, with a fixed logistics track query cycle. When there is a delay in logistics timeliness, the upload time of the corresponding logistics track will also be delayed. Since the query time of the logistics order number is fixed, theoretically, the difference between the query time of the logistics track and the upload time of the logistics track can reflect the delay of logistics timeliness to a certain extent (such as the delay scenario 1 and delay scenario 2 described later). The time when the logistics provider uploads the logistics track to the logistics provider system is defined as the track upload time t1 (that is, the track generation time or the Internet access time), and the query time when the logistics tracking system first queries and obtains the corresponding logistics track is the track query time t2 of the logistics track. The difference between the track query time and the track upload time is the track delay time T, T=t2-t1.

[0012] For example, in case A, the logistics tracking system has a query cycle of 12 hours for a logistics order number. The first logistics query time for the logistics order number is 18:00 on February 1st. Then, starting from the first query time, the logistics order number will be automatically queried every 12 hours. If the logistics status of the logistics package is in transit, and if 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 time), then the logistics tracking system will first obtain the logistics track arriving at the distribution center from the logistics provider system at 6:00 on February 2nd.

[0013] Delay scenario 1: If the logistics package arrives at the distribution center at 2:00 on February 2, that is, the logistics delay time T1 = 1 hour, then the logistics tracking system will first obtain the logistics track information at 6:00 on February 2, and the track delay time T = 4 hours.

[0014] Delay scenario 2: If the logistics package arrives at the distribution center at 4:00 on February 2, that is, the logistics delay time T1 = 3 hours, then the logistics tracking system will first obtain the logistics track information at 6:00 on February 2, and the track delay time T = 2 hours.

[0015] Delay scenario 3: If the logistics package arrives at the distribution center at 17:00 on February 2, that is, the logistics delay time T1 = 16 hours, then the logistics tracking system will first obtain the logistics track information at 18:00 on February 2, and the track delay time T = 1 hour.

[0016] In the above delay scenarios, delay scenario 1 and delay scenario 2 are in the same query cycle (the time interval from 18:00 on February 1 to 6:00 on February 2). The larger the trajectory delay time T, the smaller the logistics delay time T1. The trajectory delay time can reflect the delay in logistics timeliness. However, delay scenario 1 and delay scenario 3 are in different query cycles. Although the trajectory delay time of delay scenario 1 is smaller than that of delay scenario 3, the logistics delay time of delay scenario 1 is larger. It can be seen that although in theory the trajectory delay time can reflect the delay in logistics timeliness to a certain extent, in reality, since the logistics delay time may span multiple logistics trajectory query cycles and it is impossible to determine in which logistics trajectory query cycle the logistics delay is located, it is difficult for the trajectory delay time to directly reflect the logistics delay time.

[0017] 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, and 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 at each target time, then the trajectory compliance rate corresponding to each target time under the conditions of the corresponding logistics provider, logistics status and analysis date can be calculated, and then judge whether each logistics trajectory within the corresponding analysis date is abnormal based on the fluctuation range of these trajectory compliance rates.

[0018] For example, in Case B, when analyzing whether the logistics track of a logistics company from February 1 to 4 is abnormal, the analysis can be performed one by one according to the logistics status. Table 1 is the logistics data after the logistics tracking efficiency analysis of the logistics track with the logistics status of "in transit". The main operation steps are as follows.

[0019] 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.

[0020] Retrieve the historical logistics tracks that belong to the logistics provider and are "in transit" as the target logistics tracks for analysis. Split the analysis date of February 1-4 into 4 reference days, and calculate the number N2 of target logistics tracks in each reference day, as well as the track delay time T of each target logistics track.

[0021] Furthermore, a total of 7 target times are preset, namely 2H, 4H, 6H, 10H, 12H, 18H, and 24H, so that these target times can include the query cycle of each logistics trajectory to be analyzed, and each reference day uses these 7 target times as monitoring nodes. Then, it is determined whether the target logistics trajectory of each reference day is consistent with each monitoring node, and the trajectory compliance number N4 of 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 a 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 a target time corresponding to a monitoring node, then the target logistics trajectory is inconsistent with the monitoring node.

[0022] For example, the number of target logistics trajectories on the reference day February 1 is N2 = 100, of which 60 have a trajectory delay time of ≤2 hours. Then the number of trajectories that meet the requirements of the monitoring node corresponding to the target time of 2H on February 1 is N4 = 60, and the trajectory compliance rate of this monitoring node = N4 / N2 = 60%. Similarly, the trajectory compliance rates of other monitoring nodes are calculated to form the deep logistics data shown in Table 1.

[0023] According to the analysis data in Table 1, the four reference days and the seven target 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 2H target time on February 4 is 30%. The trajectory compliance rate of this monitoring node is significantly lower than the trajectory compliance rate of the monitoring node at the 2H target time on other reference days. Therefore, it can be judged that there is an abnormality in the logistics trajectory on February 4.

[0024] 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% February 3 trajectory compliance rate 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% Table 1 This logistics tracking efficiency analysis method can calculate the logistics tracking capability of the logistics tracking system for each logistics track, and conduct in-depth analysis of the logistics query results, discover logistics timeliness anomalies in advance (including logistics timeliness delays, untimely logistics tracking, delayed uploading of logistics track information, and other hidden anomalies), so that the query service provided by the logistics tracking system can be timely and transparently known to users, providing data support for users to select logistics providers and improve logistics tracking methods. This method is simple to operate, and does not require complex logistics timeliness analysis for the origin and destination of logistics packages, and can also clearly present the delay of logistics timeliness; and even if there is only one target logistics track, the analysis results can be output normally.

[0025] In addition, the present application also provides a logistics monitoring dashboard for monitoring logistics tracks, including: Track acquisition module, used to obtain logistics tracks; 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; A node control module, used to preset multiple target-reaching times and generate corresponding monitoring nodes based on the 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 the target time. If there are more logistics trajectories whose trajectory delay time is less than the target time, the trajectory compliance rate of the corresponding monitoring node will be greater; and The information display module aggregates and displays the trajectory compliance rate, analysis date and compliance time information of each monitoring node.

[0026] The logistics monitoring dashboard can be used to perform various operations of the above-mentioned logistics tracking efficiency analysis method, and provide an interactive interface for convenient operation, which reduces the user's understanding cost of logistics tracking efficiency analysis, and makes simple logistics track information clearly reflect the service quality of the logistics provider and the tracking efficiency of the logistics tracking system. In addition, the logistics monitoring dashboard can be used as a part of the logistics tracking system, which is convenient for users to query the logistics track and conduct logistics tracking efficiency analysis on the corresponding logistics track in a timely manner.

[0027] Furthermore, the present application also includes systems corresponding to various methods, the system including the functional modules involved in the present application, executing the operating instructions of the corresponding functional modules or the corresponding methods, and outputting the relevant data information to the system front-end interface. The system is stored in a server and / or computer device including a processor, and the processor is used to execute the operating instructions of the system.

[0028] Disclaimer: The functional modules of the present application can be integrated with each other, or can exist independently, or one functional module can be a submodule of another functional module; step numbers such as S1, S2 do not limit the sequence of the corresponding operation steps.

[0029] The description of the relevant nouns in this application is as follows (the letters of English words are not case sensitive).

[0030] (1) Logistics track refers to the logistics route 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; multiple logistics track information with the same logistics order number is used to present the transportation process of the package.

[0031] (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

[0032] 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 belong to the protection scope of the present application.

[0033] Figure 1 This is a schematic diagram of the logistics tracking efficiency analysis principle in one embodiment of the present application.

[0034] Figure 2 Schematic diagram of the flow of logistics tracking efficiency analysis method in one embodiment of the present application.

[0035] Figure 3 This is a diagram of the interactive interface of the logistics monitoring dashboard in one embodiment of the present application.

[0036] Figure 4 This is a schematic diagram of the logistics monitoring dashboard structure in one embodiment of the present application. DETAILED DESCRIPTION

[0037] In order to make the purpose, technical solution 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.

[0038] like Figure 1-Figure 2 , the present application proposes a logistics tracking efficiency analysis method, the method also includes steps S1-S6, as follows.

[0039] Step S1: Obtain the logistics track 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 the historical logistics track.

[0040] Step S2: Determine the logistics provider, logistics status and analysis date to be analyzed, retrieve the corresponding logistics track according to the logistics provider, logistics status and analysis date, define the retrieved logistics track as the target logistics track, and calculate the track delay time T of each target logistics track, where T=t2-t1. In this application, retrieval means screening and calling.

[0041] Step S3: Split the analysis date into N1 reference days, N1≥1, and calculate the number N2 of target logistics tracks in each reference day. In other embodiments, the reference day can be changed to a reference time of other time lengths, such as every half day or every week as a reference time.

[0042] 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, the trajectory compliance number 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.

[0043] Step S5: Calculate the trajectory compliance rate of each monitoring node, where the trajectory compliance rate is the ratio of the trajectory compliance number 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.

[0044] Step S6: Aggregate the trajectory compliance rate, reference day, and target-reaching time information corresponding to each monitoring node, and organize, output, or display the aggregated information.

[0045] In one implementation, when N3 target times are preset, the shortest target time can be ≤2 hours and the longest target time can be ≥20 hours, so as to take into account most of the logistics track query cycle and improve the analysis accuracy of logistics tracking efficiency. In addition, 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, and the time unit adopts the UTC or GMT time measurement standard.

[0046] In one embodiment, the logistics time delay of the logistics trajectory can also be evaluated by the trajectory non-conformity rate. In step S5, the trajectory non-conformity rate is calculated, and the trajectory non-conformity rate = 1-trajectory conformity rate; then in step S6, the trajectory non-conformity rate, reference day, and standard-reaching time information corresponding to each monitoring node are aggregated, and the aggregated information is organized, output or displayed. In step S4, the trajectory non-conformity number N5 of each monitoring node can also be directly calculated, and the trajectory non-conformity rate = N5 / N2.

[0047] In one embodiment, 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. That is, the analysis date is the date corresponding to the track upload time or the date corresponding to the track query time.

[0048] 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; 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.

[0049] 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 respectively, 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.

[0050] In a related embodiment, the "track compliance rate" may be referred to as the "update timeliness rate" or the "query timeliness rate" etc., so as to help users better understand the relationship between the logistics track delay time and each target achievement time.

[0051] In one embodiment, the logistics track within the monitoring date can also be automatically monitored based on the above-mentioned logistics tracking efficiency analysis method, such as steps S71-S73.

[0052] Step S71: preset the monitoring date, determine the monitored logistics status and the monitored compliance time of the monitored logistics provider, and set the benchmark trajectory compliance rate based on each monitored logistics status and each monitored compliance time of the monitored logistics provider. The monitoring date can be the same day or the previous day when the logistics tracking efficiency analysis is performed, or it can be the most recent week or other time. After setting the monitoring date, the logistics tracking system automatically calculates the trajectory compliance rate of the monitoring date and monitors the abnormality of the logistics trajectory.

[0053] Step S72: taking the logistics trajectory belonging to the monitored logistics state within the monitoring date as the target logistics trajectory, and generating the corresponding monitoring node on the monitoring date according to the size of the monitored target achievement time; calculating the monitoring trajectory compliance rate of each monitoring node 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.

[0054] 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.

[0055] For example, in case C, when monitoring whether the logistics trajectory of the logistics provider USPS in the past week (monitoring date) is abnormal, the benchmark trajectory compliance rate of USPS (monitored logistics provider) in the collection state (monitored logistics state) and 10-hour target time (monitored target time) can be set to 80%. At this time, the historical logistics trajectory of the USPS collection state in the past week is the target logistics trajectory, and the monitoring node A is generated corresponding to the collection state and 10-hour target time. Monitoring node A can also be regarded as the coordinate position of (collection state, 10-hour target time). Then calculate the number of target logistics trajectories in the past week (N2), and calculate the number of target logistics trajectories that match the corresponding monitoring node in the past week (N4), and then use the value of N4 / N2 as the monitoring trajectory compliance rate. When the monitoring trajectory compliance rate is less than the benchmark trajectory compliance rate, generate warning information for monitoring node A (collection state, 10-hour target time) to prompt users to pay attention to the abnormal logistics trajectory of the 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.

[0056] In other embodiments, you can first set the screening conditions including but not limited to one or more of the analysis date, logistics provider, logistics status and target time, and then retrieve the corresponding logistics track as the target logistics track according to the screening conditions, and then determine whether the retrieved target logistics track is consistent with the target time, and take the proportion of the number of consistent target logistics tracks in the total number of retrieved target logistics tracks as the track compliance rate, and then aggregate and output the track compliance rate information corresponding to each target time. In this way, users can flexibly set the screening conditions, and the system automatically outputs the corresponding track compliance rate information, so that the timeliness information of the logistics track under each condition can be quickly and completely analyzed.

[0057] In one embodiment, when obtaining the logistics track, the query cycle of each logistics track is recorded, the duration of the monitoring date is used as the monitoring cycle, and the number of monitoring nodes whose monitoring track compliance rate is less than the benchmark track compliance rate is defined as the warning number (N6). If the warning number under a certain monitored logistics state is lower than the preset warning number (the first adjustment condition), the query cycle under the monitored logistics state is reduced in the next monitoring cycle (that is, the first adjustment strategy is executed on the query cycle) to ensure that the logistics tracking system obtains the logistics track in time. Among them, the query cycle is adjusted within a predetermined time range, such as the minimum query cycle is 2 hours and the maximum query cycle is 48 hours. In a related embodiment, the preset warning number can be set according to the number of target time N3: when N3≤7, the preset warning number is 3; when N3≥8, the preset warning number is taken as the integer part of 0.5*N3.

[0058] 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 cycle under the monitored logistics status 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 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 the corresponding monitoring trajectory compliance rate is greater than or equal to (≥) the benchmark trajectory compliance rate of the benchmark compliance time.

[0059] For example, referring to Table 1, the monitoring dates are February 1st to 4th, and the monitoring cycle is 4 days. If each target time is used as the monitoring target time, the benchmark trajectory compliance rate set for the 10-hour target time is 80%, and the 10-hour monitored target time is selected as the benchmark target time, and the number of warnings is lower than the preset number of warnings, then since the trajectory compliance rate of the 6-hour target 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).

[0060] In one embodiment, the abnormal reason of the logistics track can be determined based on the query cycle and track compliance rate of the logistics track. Specifically, the query cycle of the logistics track can be used as the first target time, and the first target time can be the target time with the longest time length; then the track compliance rate corresponding to the first target time within the monitoring date is calculated (defined as the first track compliance rate). If the first track compliance rate is less than 100%, it is determined that the reason for the abnormal logistics track is that the logistics tracking system queries the logistics track abnormally, and a corresponding prompt message is generated.

[0061] For example, in case D, referring to Table 1, if the query cycle of the logistics tracking system is 24 hours, then the 24-hour target time is used as the first target time. At this time, if February 4 is used as the monitoring time, the first track compliance rate is 99%, which means that there are still 1% of logistics tracks with track delays exceeding 24 hours. The reason for the track abnormality of these logistics tracks with track delays exceeding 24 hours is that they occur in the tracking link of the logistics track (such as logistics order number recognition errors, missed query cycles, logistics track data processing errors, and other abnormalities on the logistics tracking system side), rather than logistics delays or logistics track information upload delays by logistics providers. Therefore, the reason for the abnormality of the logistics track can be judged by the query cycle and the first track compliance rate, and the corresponding prompt information can be generated to feedback the corresponding track abnormality to the user to improve the efficiency of logistics tracking.

[0062] In addition, the present application also provides a logistics monitoring dashboard for monitoring logistics trajectories, including a trajectory acquisition module for acquiring logistics trajectories, a condition setting module, a node control module, a monitoring and analysis module, and an information display module.

[0063] Among them, the condition setting module is used to set the logistics provider to be analyzed, the logistics status and the analysis date (that is, the conditions to be analyzed), and retrieve the corresponding logistics trajectory as the monitoring object (that is, the target logistics trajectory) according to the logistics provider to be analyzed, the logistics status and the analysis date. The node control module is used to preset multiple target times and generate corresponding monitoring nodes based on the multiple target 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 the target time. The more logistics trajectories whose trajectory delay time is less than the target 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 time information of each monitoring node.

[0064] 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 step S4 and step S72) through a chart and / or a table. The table may be in the form of Table 1 above.

[0065] When the information display module includes the chart, the chart uses the target time as the horizontal axis and the trajectory compliance rate as the vertical axis to display the coordinate points of the corresponding monitoring nodes, splits the analysis date into multiple reference days, and marks the coordinate points of each monitoring node on the same reference day with the same color, and marks the coordinate points of each monitoring node on different reference days with different colors, and connects the coordinate points of each monitoring node on the same reference day into a curve in the order of the target time, 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 changes in trajectory compliance rate can be clearly presented, so that users can quickly find abnormal logistics trajectory data.

[0066] In addition, each reference day option can be displayed in different colors in the chart, and the color of each reference day option is the same as the coordinate point color of the corresponding monitoring node; if the corresponding reference day is selected, the trajectory compliance rate curve of the corresponding reference day is displayed, otherwise, the trajectory compliance rate curve of the corresponding reference day is not displayed. In this way, the trajectory compliance rates of different reference days can be compared and analyzed in a targeted manner.

[0067] In one embodiment, in the condition setting module, the logistics provider to be analyzed, the logistics status and the 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.

[0068] like Figure 3 In the "Set trajectory compliance rate" control key setting interface, the node control module can also be used to set the baseline trajectory compliance rate. The node control module sets the baseline trajectory compliance rate corresponding to the monitored logistics provider, the monitored logistics status and the monitored compliance time, determines the monitoring date, generates the corresponding second monitoring node based on the monitoring date and the monitored compliance time, and uses the trajectory compliance rate of the second monitoring node as the monitoring trajectory compliance rate; when the monitoring trajectory compliance rate is less than the baseline trajectory compliance rate, generates an early warning message 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.

[0069] 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.

[0070] 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 the corresponding monitoring trajectory compliance rate is greater than or equal to the benchmark trajectory compliance rate of the benchmark compliance time.

[0071] 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 abnormal analysis module. Among them, the account management module is used to manage the user account information of the logistics monitoring dashboard (logistics tracking system). After the user logs in to the account, only the logistics track queried through the account is retrieved as the monitoring object. The log push module is used to push the monitoring log to the user, such as by email, system message, mobile phone text message, etc., to push the monitoring log within the corresponding analysis date to the user; the content of the monitoring log can be the logistics track information that the system automatically monitors regularly, or it can be the warning information when the track is abnormal. The logistics provider monitoring module is used to monitor the logistics track data of each logistics provider and manage the logistics track monitoring data of each logistics provider in different time periods. The abnormal analysis module is used to analyze the cause of the abnormal track. After the warning information of the abnormal logistics track is generated, the abnormal information is analyzed and an abnormal analysis report is generated; for example, in the above case D, the cause of the abnormal logistics track is recorded through the abnormal analysis report.

[0072] 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 by 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 monitoring dashboard, used to monitor logistics tracks, characterized in that: include: Track acquisition module, used to obtain logistics tracks; 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; A node control module, used to preset multiple target-reaching times and generate corresponding monitoring nodes based on the 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 the target time. If there are more logistics trajectories whose trajectory delay time is less than the target time, the trajectory compliance rate of the corresponding monitoring node will be greater. as well as The information display module aggregates and displays the trajectory compliance rate, analysis date and compliance time information of each monitoring node.

2. The monitoring board according to claim 1, characterized in that: The information display module displays the trajectory compliance rate, analysis date and target-reaching time information corresponding to each monitoring node through charts and / or tables.

3. The monitoring board according to claim 2, characterized in that: When the information display module includes the chart, the chart displays the coordinate points of the corresponding monitoring nodes with the target time as the horizontal axis and the trajectory compliance rate as the vertical axis, and 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 sequence into a curve in the order of the target time, and the curve is used as the trajectory compliance rate curve.

4. The monitoring board according to claim 3, characterized in that: In the chart, each reference day option is displayed in a different color, and the color of each reference day option is the same as the coordinate point color of the corresponding monitoring node; if the corresponding reference day is selected, the trajectory compliance curve of the corresponding reference day is displayed, otherwise, the trajectory compliance curve of the corresponding reference day is not displayed.

5. The monitoring board according to claim 1, characterized in that: In the condition setting module, the logistics provider to be analyzed, logistics status and analysis date are set through the interactive method of the filter box. After calculating the logistics track of each monitoring node, the trajectory compliance rate, analysis date and compliance time information of each monitoring node are generated into a monitoring file through the export control key; And / or, the analysis date is a date corresponding to the trajectory upload time or a date corresponding to the trajectory query time.

6. The monitoring board according to claim 1, characterized in that: The node control module is used to set a baseline trajectory compliance rate corresponding to the monitored logistics provider, the monitored logistics status and the monitored target time, determine the monitoring date, generate a corresponding second monitoring node based on the monitoring date and the monitored target time, and use the trajectory compliance rate of the second monitoring node as the monitoring trajectory compliance rate; when the monitoring trajectory compliance rate is less than the baseline trajectory compliance rate, generate early warning information based on the monitored logistics status and the monitored target time.

7. The monitoring board according to claim 6, characterized in that: 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.

8. The monitoring board according to claim 7, characterized in that: From multiple monitored compliance times corresponding to the monitored logistics status, one monitored compliance time is selected 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 the corresponding monitoring trajectory compliance rate is greater than or equal to the benchmark trajectory compliance rate of the benchmark compliance time.

9. The monitoring board according to claim 1, characterized in that: The query period of the logistics trajectory is taken as the first target time, and the first target time is taken as the target time with the longest time length; the trajectory compliance rate corresponding to the first target time within the monitoring date is defined as the first trajectory compliance rate. If the first trajectory compliance rate is less than 100%, it is determined that the cause of the abnormal logistics trajectory is the abnormal query of the logistics tracking system.

10. The monitoring board according to claim 1, characterized in that: The monitoring dashboard also includes one or more of an account management module, a log push module, a logistics provider monitoring module, and an abnormality analysis module; The account management module is used to manage the user account information of the logistics monitoring dashboard; The log push module is used to push monitoring logs to users; The logistics provider monitoring module is used to monitor the logistics track data of each logistics provider; The abnormality analysis module is used to analyze the cause of trajectory abnormality.

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