Logistics data early warning and optimization method, device and equipment

Abstract

The invention discloses a logistics data early warning and optimization method, device and equipment, the method is applied to the technical field of logistics, and the method comprises the steps: obtaining target logistics data of a target logistics order in a target logistics channel; inputting the target logistics data into the logistics information prediction model to obtain a target logistics state updating frequency and a target expected time window; detecting real-time updating information of the target logistics order based on the target logistics state updating frequency; if the update time corresponding to the real-time update information lags behind the target expected time window, triggering an early warning signal; and performing data analysis based on the early warning signal and historical logistics data corresponding to the target logistics order, identifying a bottleneck area and a high-risk time period in the logistics network, and performing optimization processing on a logistics path corresponding to the target logistics order. According to the invention, the resource allocation strategy is optimized, and the processing efficiency of the logistics order is improved.

Description

Technical Field

[0001] The present invention relates to the field of computer technology, and in particular to a logistics data early warning and optimization method, device and equipment. Background Art

[0002] Amid the rapid growth of modern e-commerce and the express delivery industry, logistics, as the bridge connecting merchants and consumers, has become a crucial measure of service quality, with its efficiency and transparency becoming crucial. Timely updates of logistics information are not only crucial for accurately tracking every step of the way goods travel from warehouse to consumer, but are also crucial for ensuring cargo safety, reducing the risk of lost items, and improving customer satisfaction. As consumers increasingly demand a more personalized shopping experience, they expect real-time access to the logistics status of their orders, enabling them to plan their delivery times and reduce wait times. However, the problem of logistics outages—the sudden cessation or delay of logistics information at any given point in the process—often hinders this realization. The causes of logistics outages are diverse, including but not limited to technical failures, human error, weather conditions, traffic congestion, insufficient warehouse processing capacity, and poor information connectivity between carriers. These issues not only raise consumer concerns about the reliability and efficiency of logistics services, undermine brand trust, but can also lead to complaints and product returns. Summary of the Invention

[0003] The present invention provides a logistics data early warning and optimization method, device and equipment, which can optimize resource allocation strategies, effectively alleviate the pressure during logistics peak periods, and reduce customer complaints caused by logistics interruptions.

[0004] In one aspect, the present invention provides a logistics data early warning and optimization method, the method comprising:

[0005] Obtain target logistics data of target logistics orders in target logistics channels;

[0006] Inputting the target logistics data into the logistics information prediction model to perform logistics status update frequency prediction processing and expected time window prediction processing to obtain the target logistics status update frequency and the target expected time window;

[0007] Based on the target logistics status update frequency, detecting the real-time update information of the target logistics order;

[0008] If the update time corresponding to the real-time update information lags behind the target expected time window, triggering an early warning signal;

[0009] Based on the warning signal and the historical logistics data corresponding to the target logistics order, data analysis is performed to identify bottleneck areas and high-risk periods in the logistics network;

[0010] Based on the bottleneck area and high-risk period, the logistics path corresponding to the target logistics order is optimized.

[0011] Optionally, the method further includes:

[0012] determining a time difference between the update time and the target expected time window;

[0013] determining a current warning level according to the time difference, and generating warning information according to the warning signal and the current warning level;

[0014] Push the warning information to the terminal corresponding to the target logistics order.

[0015] Optionally, the method further includes:

[0016] Constructing an early warning processing strategy, wherein the early warning processing strategy includes early warning strategies corresponding to multiple early warning levels;

[0017] Determining a target warning strategy corresponding to the current warning level according to the warning processing strategy;

[0018] If the current warning level is a high warning level, a warning strategy of urgently dispatching a spare vehicle or contacting a manual customer for intervention is executed.

[0019] Optionally, the optimizing the logistics route corresponding to the target logistics order based on the bottleneck area and the high-risk period includes:

[0020] Filtering the target paths corresponding to the bottleneck areas and high-risk periods from the logistics paths corresponding to the target logistics orders;

[0021] Deleting the target path from the logistics path and updating the logistics path of the target logistics order;

[0022] Adjust the allocation strategy of transportation resources based on the historical logistics data corresponding to the target logistics order and the current real-time traffic conditions; the allocation strategy includes increasing the transportation frequency and the number of temporary sorting points for the bottleneck area.

[0023] Optionally, the training method of the logistics information prediction model includes:

[0024] Obtain a sample logistics data set corresponding to a plurality of sample logistics channels; each sample logistics data in the sample logistics data set is annotated with a sample logistics status update frequency label and a sample expected time window label;

[0025] Input the sample logistics data set into a preset machine learning model to perform logistics status update frequency prediction processing and expected time window prediction processing to obtain sample logistics status update frequency results and sample expected time window results;

[0026] determining first loss data according to a difference between the sample logistics status update frequency result and the sample logistics status update frequency label;

[0027] determining second loss data based on a difference between the sample expected time window result and the sample expected time window label;

[0028] determining target loss data according to the first loss data and the second loss data;

[0029] The parameters of the preset machine learning model are adjusted according to the target loss data until the training end conditions are met to obtain the logistics information prediction model.

[0030] Optionally, the method further includes:

[0031] At every preset time interval, a sample updated logistics data set corresponding to multiple sample logistics channels is obtained;

[0032] Updating and training the logistics information prediction model based on the sample updated logistics data set to obtain an updated logistics information prediction model;

[0033] The step of inputting the target logistics data into a logistics information prediction model to perform logistics status update frequency prediction processing and expected time window prediction processing to obtain a target logistics status update frequency and a target expected time window includes:

[0034] The target logistics data is input into the updated logistics information prediction model to perform logistics status update frequency prediction processing and expected time window prediction processing to obtain the target logistics status update frequency and the target expected time window.

[0035] Optionally, the method further includes:

[0036] Obtaining sample warning signals and sample historical logistics data of sample logistics orders; the sample logistics orders are marked with sample bottleneck area labels and sample high-risk time period labels;

[0037] Input the sample warning signal and sample historical logistics data into the preset model for prediction processing to obtain the sample bottleneck area results and sample high-risk period results;

[0038] According to the difference between the sample bottleneck area result and the sample bottleneck area label, and the difference between the sample high-risk period result and the sample high-risk period label, the preset model is trained to obtain an early warning model;

[0039] The performing of data analysis based on the early warning signal and the historical logistics data corresponding to the target logistics order to identify bottleneck areas and high-risk periods in the logistics network includes:

[0040] The early warning signal and the historical logistics data corresponding to the target logistics order are input into the early warning model for data analysis to identify bottleneck areas and high-risk periods in the logistics network.

[0041] On the other hand, a logistics data early warning and optimization device is provided, the device comprising:

[0042] A target logistics data acquisition module is used to acquire target logistics data of target logistics orders in target logistics channels;

[0043] A target time update module is used to input the target logistics data into a logistics information prediction model to perform logistics status update frequency prediction processing and expected time window prediction processing to obtain a target logistics status update frequency and a target expected time window;

[0044] An information detection module, configured to detect real-time update information of the target logistics order based on the target logistics status update frequency;

[0045] An early warning signal triggering module is used to trigger an early warning signal if the update time corresponding to the real-time update information lags behind the target expected time window;

[0046] A data analysis module, configured to perform data analysis based on the warning signal and the historical logistics data corresponding to the target logistics order, and identify bottleneck areas and high-risk periods in the logistics network;

[0047] The path optimization module is used to optimize the logistics path corresponding to the target logistics order based on the bottleneck area and high-risk period.

[0048] Optionally, the device further includes:

[0049] a time difference determination module, configured to determine a time difference between the update time and the target expected time window;

[0050] an early warning information generating module, configured to determine a current early warning level according to the time difference, and generate early warning information according to the early warning signal and the current early warning level;

[0051] The warning information sending module is used to push the warning information to the terminal corresponding to the target logistics order.

[0052] Optionally, the device further includes:

[0053] A strategy building module is used to build an early warning processing strategy, wherein the early warning processing strategy includes early warning strategies corresponding to multiple early warning levels;

[0054] An early warning strategy determination module is used to determine a target early warning strategy corresponding to the current early warning level according to the early warning processing strategy;

[0055] The strategy execution module is used to execute the warning strategy of emergency dispatching spare vehicles or contacting manual customer intervention if the current warning level is a high warning level.

[0056] Optionally, the path optimization module includes:

[0057] a target path screening unit, configured to screen out target paths corresponding to the bottleneck area and the high-risk period from the logistics paths corresponding to the target logistics orders;

[0058] a path updating unit, configured to delete the target path from the logistics path and update the logistics path of the target logistics order;

[0059] A strategy adjustment unit is used to adjust the allocation strategy of transportation resources based on the historical logistics data corresponding to the target logistics order and the current real-time traffic conditions; the allocation strategy includes increasing the transportation frequency and the number of temporary sorting points for the bottleneck area.

[0060] Optionally, the device further includes:

[0061] A sample data acquisition module is used to obtain a sample logistics data set corresponding to multiple sample logistics channels; each sample logistics data in the sample logistics data set is marked with a sample logistics status update frequency label and a sample expected time window label;

[0062] A result determination module is used to input the sample logistics data set into a preset machine learning model to perform logistics status update frequency prediction processing and expected time window prediction processing to obtain the sample logistics status update frequency result and the sample expected time window result;

[0063] a first loss determination module, configured to determine first loss data according to a difference between the sample logistics state update frequency result and the sample logistics state update frequency label;

[0064] a second loss determination module, configured to determine second loss data based on a difference between the sample expected time window result and the sample expected time window label;

[0065] a target loss determination module, configured to determine target loss data based on the first loss data and the second loss data;

[0066] A model training module is used to adjust the parameters of the preset machine learning model according to the target loss data until the training end conditions are met to obtain the logistics information prediction model.

[0067] Optionally, the device further includes:

[0068] A data update module is used to obtain sample updated logistics data sets corresponding to multiple sample logistics channels at every preset time interval;

[0069] A model updating module, configured to update and train the logistics information prediction model based on the sample updated logistics data set to obtain an updated logistics information prediction model;

[0070] The target time update module is also used to input the target logistics data into the updated logistics information prediction model to perform logistics status update frequency prediction processing and expected time window prediction processing to obtain the target logistics status update frequency and the target expected time window.

[0071] Optionally, the device further includes:

[0072] A sample data acquisition module is used to obtain sample warning signals and sample historical logistics data of sample logistics orders; the sample logistics orders are marked with sample bottleneck area labels and sample high-risk time period labels;

[0073] A data input module is used to input the sample warning signal and sample historical logistics data into a preset model for prediction processing to obtain sample bottleneck area results and sample high-risk period results;

[0074] An early warning model training module is used to train the preset model based on the difference between the sample bottleneck area result and the sample bottleneck area label, and the difference between the sample high-risk period result and the sample high-risk period label to obtain an early warning model;

[0075] The data analysis module is further used to input the early warning signal and the historical logistics data corresponding to the target logistics order into the early warning model for data analysis to identify bottleneck areas and high-risk periods in the logistics network.

[0076] On the other hand, an electronic device is provided, which includes a processor and a memory, wherein the memory stores at least one instruction or at least one program, and the at least one instruction or the at least one program is loaded and executed by the processor to implement the logistics data warning and optimization method as described above.

[0077] On the other hand, a computer storage medium is provided, which stores at least one instruction or at least one program, and the at least one instruction or at least one program is loaded and executed by a processor to implement the logistics data warning and optimization method as described above.

[0078] Another aspect provides a computer program product or computer program, comprising computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to implement the logistics data early warning and optimization method described above.

[0079] The logistics data early warning and optimization method, device and equipment provided by the present invention have the following technical effects:

[0080] The present invention obtains target logistics data of target logistics orders in target logistics channels; inputs the target logistics data into a logistics information prediction model to perform logistics status update frequency prediction processing and expected time window prediction processing to obtain a target logistics status update frequency and a target expected time window; based on the target logistics status update frequency, detects real-time update information of the target logistics order; if the update time corresponding to the real-time update information lags behind the target expected time window, triggers an early warning signal; based on the early warning signal and the historical logistics data corresponding to the target logistics order, performs data analysis to identify bottleneck areas and high-risk periods in the logistics network; based on the bottleneck areas and high-risk periods, optimizes the logistics path corresponding to the target logistics order. The present invention optimizes resource allocation strategies, effectively alleviates the pressure during peak logistics periods, and reduces customer complaints caused by logistics interruptions. BRIEF DESCRIPTION OF THE DRAWINGS

[0081] In order to more clearly illustrate the technical solutions and advantages of the embodiments of this specification or the prior art, the following is a brief introduction to the drawings required for use in the embodiments or the prior art descriptions. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0082] Figure 1 This is a schematic diagram of a logistics data early warning and optimization system provided by an embodiment of this specification;

[0083] Figure 2 This is a flow chart of a logistics data early warning and optimization method provided in an embodiment of this specification;

[0084] Figure 3 This is a flow chart of a method for training a logistics information prediction model provided in an embodiment of this specification;

[0085] Figure 4 This is a flowchart of a training method for an early warning model provided in an embodiment of this specification;

[0086] Figure 5 This is a flowchart of an early warning method provided in an embodiment of this specification;

[0087] Figure 6 This is a structural diagram of a logistics data early warning and optimization device provided in an embodiment of this specification;

[0088] Figure 7 This is a structural diagram of a server provided in an embodiment of this specification. DETAILED DESCRIPTION

[0089] The following will be combined with the drawings in the embodiments of this specification to clearly and completely describe the technical solutions in the embodiments of this specification. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making any creative efforts shall fall within the scope of protection of the present invention.

[0090] It should be noted that the terms "first", "second", etc. in the description and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that the numbers used in this way are interchangeable where appropriate so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or server that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0091] See also Figure 1 , Figure 1 This is a schematic diagram of a logistics data early warning and optimization system provided by an embodiment of this specification. Figure 1 As shown, the logistics data early warning and optimization system can include at least a server 01 and a client 02.

[0092] Specifically, in the embodiments of this specification, the server 01 may include an independently operated server, or a distributed server, or a server cluster composed of multiple servers. It may also be a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN (Content Delivery Network), and big data and artificial intelligence platforms. Server 01 may include a network communication unit, a processor, a memory, and the like. Specifically, the server 01 can be used to identify bottleneck areas and high-risk periods in the logistics network based on target logistics data.

[0093] Specifically, in the embodiments of this specification, the client 02 may include physical devices such as smartphones, desktop computers, tablet computers, laptops, digital assistants, smart wearable devices, smart speakers, in-vehicle terminals, and smart TVs. It may also include software running on physical devices, such as web pages provided by service providers to users, or applications provided by these service providers to users. Specifically, the client 02 may be used to display bottleneck areas and high-risk periods in the logistics network.

[0094] The following describes a logistics data early warning and optimization method of the present invention. Figure 2 It is a flow chart of a logistics data warning and optimization method provided in the embodiment of this specification. This specification provides the method operation steps as described in the embodiment or flow chart, but it may include more or fewer operation steps based on conventional or non-creative labor. The order of steps listed in the embodiment is only one way of executing the steps among many orders, and does not represent the only order of execution. When the actual system or server product is executed, it can be executed in sequence or in parallel (for example, in a parallel processor or multi-threaded processing environment) according to the method shown in the embodiment or the accompanying drawings. Specifically, Figure 2 As shown, the method may include:

[0095] S201: Acquire target logistics data of a target logistics order in a target logistics channel.

[0096] In the embodiments of this specification, the target logistics channel can be any one of multiple existing logistics channels, and the target logistics data may include but is not limited to express delivery number, location information (such as GPS coordinates), status update time, transportation node stay time, weather conditions, traffic congestion index, etc. This data will be stored in a distributed database to ensure high concurrent processing capabilities and data security.

[0097] S203: Input the target logistics data into the logistics information prediction model to perform logistics status update frequency prediction processing and expected time window prediction processing to obtain the target logistics status update frequency and the target expected time window.

[0098] In the embodiments of this specification, in order to build an efficient and accurate logistics information monitoring platform, firstly, advanced big data processing technology and machine learning algorithms are integrated. The core of the platform is to collect and integrate massive data from multiple logistics channels in real time, including but not limited to express order number, location information (such as GPS coordinates), status update time, transportation node residence time, weather conditions, traffic congestion index, etc. These data will be stored in a distributed database to ensure high concurrent processing capability and data security. At the data processing level, stream processing technology (such as Apache Kafka, SparkStreaming) is used to pre-process and clean the real-time data stream, remove noise and outliers, and improve data quality. Subsequently, machine learning algorithms (such as time series analysis, LSTM neural network, random forest, etc.) are used to deeply mine these data to predict the normal frequency and expected time window of logistics status updates. These prediction models will be trained based on historical data and dynamically adjusted and optimized as new data is continuously added to improve the accuracy of the prediction.

[0099] In the embodiments of this specification, Figure 3 As shown, Figure 3 This is a flowchart of a method for training a logistics information prediction model, including:

[0100] S301: Acquire a sample logistics data set corresponding to a plurality of sample logistics channels; each sample logistics data in the sample logistics data set is annotated with a sample logistics status update frequency label and a sample expected time window label;

[0101] S303: Input the sample logistics data set into a preset machine learning model to perform logistics status update frequency prediction processing and expected time window prediction processing to obtain sample logistics status update frequency results and sample expected time window results;

[0102] S305: Determine first loss data according to a difference between the sample logistics status update frequency result and the sample logistics status update frequency label;

[0103] S307: Determine second loss data according to a difference between the sample expected time window result and the sample expected time window label;

[0104] S309: Determine target loss data according to the first loss data and the second loss data;

[0105] S3011: Adjust the parameters of the preset machine learning model according to the target loss data until the training end conditions are met to obtain the logistics information prediction model.

[0106] In the embodiments of this specification, the sample logistics data set can be preprocessed and cleaned to remove noise and outliers, and then input into the model for training to improve data quality. The preset machine learning model may include but is not limited to models such as time series analysis, LSTM neural network, and random forest algorithm. The preset machine learning model can be used to perform logistics status update frequency prediction processing and expected time window prediction processing to obtain sample logistics status update frequency results and sample expected time window results; then, based on the difference between the sample logistics status update frequency result and the sample logistics status update frequency label, the first loss data is determined; based on the difference between the sample expected time window result and the sample expected time window label, the second loss data is determined; then, the sum of the first loss data and the second loss data or the weighted sum of the two is calculated to obtain the target loss data; finally, the parameters of the preset machine learning model are adjusted according to the target loss data until the training end condition is met to obtain the logistics information prediction model.

[0107] This embodiment inputs the target logistics data into the logistics information prediction model to perform logistics status update frequency prediction processing and expected time window prediction processing, so as to quickly and accurately obtain the target logistics status update frequency and target expected time window.

[0108] In the embodiment of this specification, the method further includes:

[0109] At every preset time interval, a sample updated logistics data set corresponding to multiple sample logistics channels is obtained;

[0110] Updating and training the logistics information prediction model based on the sample updated logistics data set to obtain an updated logistics information prediction model;

[0111] The step of inputting the target logistics data into a logistics information prediction model to perform logistics status update frequency prediction processing and expected time window prediction processing to obtain a target logistics status update frequency and a target expected time window includes:

[0112] The target logistics data is input into the updated logistics information prediction model to perform logistics status update frequency prediction processing and expected time window prediction processing to obtain the target logistics status update frequency and the target expected time window.

[0113] In the embodiments of this specification, a feedback mechanism for logistics information updates can be established to collect multi-source data such as consumer feedback and logistics execution results. The accuracy and response efficiency of the early warning system can be regularly evaluated, and the algorithm model and early warning strategy can be continuously iterated and optimized. The model can be dynamically adjusted and optimized as new data is continuously added to improve prediction accuracy.

[0114] S205: Based on the target logistics status update frequency, detect the real-time update information of the target logistics order.

[0115] In the embodiment of this specification, the real-time update information of the target logistics order can be detected based on the predicted target logistics status update frequency, so as to further determine the early warning strategy.

[0116] S207: If the update time corresponding to the real-time update information lags behind the target expected time window, triggering a warning signal.

[0117] In the embodiments of this specification, in the intelligent monitoring system, the setting of early warning rules is the key to ensuring timely response to the problem of logistics outages. According to the prediction results in step one, a series of personalized early warning rules can be set. These rules will take into account the influence of different logistics channels, transportation methods, seasonal changes, holidays and other factors. When the system detects that the update of a certain logistics information lags behind the expected time window, an early warning signal is triggered immediately. The triggering conditions of the early warning signal can be flexibly set according to the actual situation, such as the outage duration exceeds a certain threshold, multiple consecutive status updates are delayed, etc. The early warning signal will be promptly notified to logistics service providers, managers and consumers through multiple channels (such as SMS, email, APP push, etc.) to ensure the rapid transmission of information.

[0118] S209: Based on the early warning signal and the historical logistics data corresponding to the target logistics order, perform data analysis to identify bottleneck areas and high-risk periods in the logistics network.

[0119] In the embodiments of this specification, Figure 4 As shown, Figure 4 The flowchart of a training method for an early warning model includes:

[0120] S401: Obtaining a sample warning signal and sample historical logistics data of a sample logistics order; the sample logistics order is marked with a sample bottleneck area label and a sample high-risk period label;

[0121] S403: Inputting the sample warning signal and sample historical logistics data into a preset model for prediction processing to obtain sample bottleneck area results and sample high-risk period results;

[0122] S405: According to the difference between the sample bottleneck area result and the sample bottleneck area label, and the difference between the sample high-risk period result and the sample high-risk period label, the preset model is trained to obtain an early warning model.

[0123] In an embodiment of the present specification, sample data can be constructed using historical logistics data, and an early warning model can be trained. Based on the difference between the sample bottleneck area result and the sample bottleneck area label, as well as the difference between the sample high-risk period result and the sample high-risk period label, training loss data can be determined, and the model parameters of the preset model can be adjusted based on the training loss data until the training end conditions are met, and the preset model at the end of training is determined as the early warning model.

[0124] In the embodiment of this specification, the data analysis based on the early warning signal and the historical logistics data corresponding to the target logistics order to identify bottleneck areas and high-risk periods in the logistics network includes:

[0125] The early warning signal and the historical logistics data corresponding to the target logistics order are input into the early warning model for data analysis to identify bottleneck areas and high-risk periods in the logistics network.

[0126] In the embodiments of this specification, early warning signals are combined with historical data analysis to identify bottleneck areas and high-risk periods in the logistics network. After receiving the early warning signal, historical data analysis is combined to identify bottleneck areas and high-risk periods in the logistics network. This involves deep mining and correlation analysis of large amounts of logistics data to discover potential transportation bottlenecks and risk factors.

[0127] S2011: Based on the bottleneck area and high-risk period, the logistics path corresponding to the target logistics order is optimized.

[0128] In the embodiment of this specification, the optimization process of the logistics path corresponding to the target logistics order based on the bottleneck area and the high-risk period includes:

[0129] Filtering target routes corresponding to the bottleneck areas and high-risk periods from the logistics routes corresponding to the target logistics orders;

[0130] Deleting the target path from the logistics path and updating the logistics path of the target logistics order;

[0131] Adjust the allocation strategy of transportation resources based on the historical logistics data corresponding to the target logistics order and the current real-time traffic conditions; the allocation strategy includes increasing the transportation frequency and the number of temporary sorting points for the bottleneck area.

[0132] In the embodiments of this specification, algorithmic optimization of delivery routes can reduce unnecessary detours and waiting time, thereby improving transportation efficiency. Simultaneously, based on historical data and real-time traffic conditions, the allocation strategy for transportation resources can be adjusted, such as increasing transportation frequency in hotspots and deploying more temporary sorting points, to cope with sudden demand and reduce the probability of logistics interruptions. Furthermore, machine learning algorithms can be used to continuously optimize the logistics network, improving its flexibility and responsiveness through continuous learning and adjustment of algorithm parameters.

[0133] In the embodiment of this specification, the method further includes:

[0134] determining a time difference between the update time and the target expected time window;

[0135] determining a current warning level according to the time difference, and generating warning information according to the warning signal and the current warning level;

[0136] Push the warning information to the terminal corresponding to the target logistics order.

[0137] In the embodiments of this specification, when the system detects that a particular logistics information update lags behind the expected time window, an early warning signal is immediately triggered. The triggering conditions for the early warning signal can be flexibly configured based on actual circumstances, such as when the duration of an outage exceeds a certain threshold or when multiple status updates are delayed. The early warning signal is promptly notified to logistics service providers, management personnel, and consumers via various channels (such as SMS, email, and app push notifications), ensuring rapid information delivery.

[0138] In the embodiments of this specification, Figure 5 As shown, Figure 5 The figure is a flowchart of an early warning method, including:

[0139] S501: Constructing an early warning processing strategy, wherein the early warning processing strategy includes early warning strategies corresponding to multiple early warning levels;

[0140] S503: Determine a target warning strategy corresponding to the current warning level according to the warning processing strategy;

[0141] S505: If the current warning level is a high warning level, execute a warning strategy of urgently dispatching a spare vehicle or contacting a manual customer for intervention.

[0142] In the embodiments of this specification, the warning level will be dynamically adjusted based on the duration of the outage to reflect the urgency of the issue. For severe outages, the system will automatically initiate contingency plans, such as dispatching backup vehicles and contacting customer service for intervention, to minimize logistics delays and consumer dissatisfaction. Multiple warning levels can be pre-set, and corresponding warning strategies can be generated for each level. There can be a one-to-one relationship between warning levels and warning strategies.

[0143] In the embodiments of this specification, a feedback mechanism for updating logistics information can be established to collect multi-source data such as consumer feedback and logistics execution results, regularly evaluate the accuracy and response efficiency of the early warning system, and continuously iterate and optimize the algorithm model and early warning strategy.

[0144] To ensure the accuracy and responsiveness of the early warning system, a comprehensive logistics information update and feedback mechanism will be established. This mechanism will collect feedback data from consumers, logistics service providers, managers, and other sources, including consumer satisfaction ratings and logistics execution performance statistics. Regular evaluation and analysis of this feedback data will identify issues and deficiencies in the early warning system, enabling iterative optimization of the algorithm model and early warning strategy. For example, the triggering conditions and notification methods of early warning signals can be adjusted based on consumer feedback; and delivery routes and transportation resource allocation strategies can be optimized based on logistics execution performance data.

[0145] Illustratively, this embodiment provides a logistics data early warning and optimization method, including:

[0146] Step 1: Build a logistics information monitoring platform that integrates big data processing and machine learning algorithms. This platform collects and integrates data from multiple logistics channels in real time, including but not limited to express delivery numbers, location information, status update times, etc., and uses algorithmic models to predict the normal frequency and expected time window of logistics status updates;

[0147] To build an efficient and accurate logistics information monitoring platform, we first integrated advanced big data processing technologies and machine learning algorithms. The core of this platform lies in the real-time collection and integration of massive amounts of data from multiple logistics channels, including but not limited to express delivery tracking numbers, location information (such as GPS coordinates), status update times, transit node dwell times, weather conditions, and traffic congestion indices. This data is stored in a distributed database to ensure high concurrent processing capabilities and data security. At the data processing level, stream processing technologies (such as Apache Kafka and Spark Streaming) are used to preprocess and cleanse the real-time data streams, removing noise and outliers and improving data quality. Subsequently, machine learning algorithms (such as time series analysis, LSTM neural networks, and random forests) are used to deeply mine this data to predict the normal frequency and expected time window of logistics status updates. These predictive models are trained based on historical data and dynamically adjusted and optimized as new data is added to improve prediction accuracy.

[0148] Step 2: Based on the intelligent monitoring system, set personalized warning rules. When the system detects that the update of a certain logistics information lags behind the expected time window, it immediately triggers a warning signal and notifies the logistics service provider, management personnel, and consumers via SMS, email, or app push. The warning level is dynamically adjusted according to the duration of the outage. In serious cases, emergency plans such as emergency dispatch or manual intervention are activated.

[0149] In intelligent monitoring systems, setting early warning rules is key to ensuring a timely response to logistics outages. Based on the prediction results in step one, a series of personalized early warning rules can be established, taking into account factors such as different logistics channels, transportation methods, seasonal variations, and holidays. When the system detects that a particular shipment's logistics information update lags behind the expected time window, an early warning signal is immediately triggered. The triggering conditions for the early warning signal can be flexibly configured based on actual circumstances, such as when the outage duration exceeds a certain threshold or when multiple consecutive status updates are delayed. Early warning signals are promptly notified to logistics service providers, management personnel, and consumers via multiple channels (such as SMS, email, and app push notifications) to ensure rapid information dissemination. The alert level is dynamically adjusted based on the duration of the outage to reflect the urgency of the issue. For severe outages, the system automatically activates contingency plans, such as dispatching backup vehicles and contacting customer service for intervention, to minimize logistics delays and consumer dissatisfaction.

[0150] Step 3: Combine early warning signals with historical data analysis to identify bottleneck areas and high-risk periods in the logistics network. Use algorithms to optimize delivery routes and adjust transportation resource allocation, such as increasing transportation frequency in hotspots and deploying more temporary sorting points, to fundamentally reduce the probability of logistics disruptions.

[0151] After receiving early warning signals, historical data analysis is combined to identify bottleneck areas and high-risk periods in the logistics network. This involves in-depth mining and correlation analysis of large amounts of logistics data to identify potential transportation bottlenecks and risk factors. Algorithmic optimization of delivery routes can reduce unnecessary detours and waiting time, improving transportation efficiency. Simultaneously, based on historical data and real-time traffic conditions, transportation resource allocation strategies can be adjusted, such as increasing transportation frequency in hot spots and deploying more temporary sorting points, to cope with sudden demand and reduce the probability of logistics interruptions. Furthermore, machine learning algorithms can be used to continuously optimize the logistics network, improving its flexibility and responsiveness through continuous learning and adjustment of algorithm parameters.

[0152] Step 4. Establish a feedback mechanism for logistics information updates, collect multi-source data such as consumer feedback and logistics execution results, regularly evaluate the accuracy and response efficiency of the early warning system, and continuously iterate and optimize the algorithm model and early warning strategy.

[0153] To ensure the accuracy and responsiveness of the early warning system, a comprehensive logistics information update and feedback mechanism will be established. This mechanism will collect feedback data from consumers, logistics service providers, managers, and other sources, including consumer satisfaction ratings and logistics execution performance statistics. Regular evaluation and analysis of this feedback data will identify issues and deficiencies in the early warning system, enabling iterative optimization of the algorithm model and early warning strategy. For example, the triggering conditions and notification methods of early warning signals can be adjusted based on consumer feedback; and delivery routes and transportation resource allocation strategies can be optimized based on logistics execution performance data.

[0154] It can be seen from the technical solutions provided by the above embodiments of this specification that the embodiments of this specification obtain target logistics data of target logistics orders in target logistics channels; input the target logistics data into the logistics information prediction model to perform logistics status update frequency prediction processing and expected time window prediction processing to obtain the target logistics status update frequency and target expected time window; based on the target logistics status update frequency, detect the real-time update information of the target logistics order; if the update time corresponding to the real-time update information lags behind the target expected time window, trigger an early warning signal; based on the early warning signal and the historical logistics data corresponding to the target logistics order, perform data analysis to identify bottleneck areas and high-risk periods in the logistics network; based on the bottleneck areas and high-risk periods, optimize the logistics path corresponding to the target logistics order. The present invention optimizes the resource allocation strategy, effectively alleviates the pressure during logistics peak periods, and reduces customer complaints caused by logistics interruptions.

[0155] The embodiment of this specification also provides a logistics data early warning and optimization device, such as Figure 6 As shown, the device includes:

[0156] The target logistics data acquisition module 610 is used to acquire the target logistics data of the target logistics order in the target logistics channel;

[0157] A target time update module 620 is configured to input the target logistics data into a logistics information prediction model to perform logistics status update frequency prediction processing and expected time window prediction processing to obtain a target logistics status update frequency and a target expected time window;

[0158] An information detection module 630 is configured to detect real-time update information of the target logistics order based on the target logistics status update frequency;

[0159] An early warning signal triggering module 640 is configured to trigger an early warning signal if the update time corresponding to the real-time update information lags behind the target expected time window;

[0160] A data analysis module 650 is configured to perform data analysis based on the warning signal and the historical logistics data corresponding to the target logistics order to identify bottleneck areas and high-risk periods in the logistics network;

[0161] The path optimization module 660 is used to optimize the logistics path corresponding to the target logistics order based on the bottleneck area and the high-risk period.

[0162] Optionally, the device further includes:

[0163] a time difference determination module, configured to determine a time difference between the update time and the target expected time window;

[0164] an early warning information generating module, configured to determine a current early warning level according to the time difference, and generate early warning information according to the early warning signal and the current early warning level;

[0165] The warning information sending module is used to push the warning information to the terminal corresponding to the target logistics order.

[0166] Optionally, the device further includes:

[0167] A strategy building module is used to build an early warning processing strategy, wherein the early warning processing strategy includes early warning strategies corresponding to multiple early warning levels;

[0168] An early warning strategy determination module is used to determine a target early warning strategy corresponding to the current early warning level according to the early warning processing strategy;

[0169] The strategy execution module is used to execute the warning strategy of emergency dispatching spare vehicles or contacting manual customer intervention if the current warning level is a high warning level.

[0170] Optionally, the path optimization module includes:

[0171] a target path screening unit, configured to screen out target paths corresponding to the bottleneck area and the high-risk period from the logistics paths corresponding to the target logistics orders;

[0172] a path updating unit, configured to delete the target path from the logistics path and update the logistics path of the target logistics order;

[0173] A strategy adjustment unit is used to adjust the allocation strategy of transportation resources based on the historical logistics data corresponding to the target logistics order and the current real-time traffic conditions; the allocation strategy includes increasing the transportation frequency and the number of temporary sorting points for the bottleneck area.

[0174] Optionally, the device further includes:

[0175] A sample data acquisition module is used to obtain a sample logistics data set corresponding to multiple sample logistics channels; each sample logistics data in the sample logistics data set is marked with a sample logistics status update frequency label and a sample expected time window label;

[0176] A result determination module is used to input the sample logistics data set into a preset machine learning model to perform logistics status update frequency prediction processing and expected time window prediction processing to obtain the sample logistics status update frequency result and the sample expected time window result;

[0177] a first loss determination module, configured to determine first loss data according to a difference between the sample logistics state update frequency result and the sample logistics state update frequency label;

[0178] a second loss determination module, configured to determine second loss data based on a difference between the sample expected time window result and the sample expected time window label;

[0179] a target loss determination module, configured to determine target loss data based on the first loss data and the second loss data;

[0180] A model training module is used to adjust the parameters of the preset machine learning model according to the target loss data until the training end conditions are met to obtain the logistics information prediction model.

[0181] Optionally, the device further includes:

[0182] A data update module is used to obtain sample updated logistics data sets corresponding to multiple sample logistics channels at every preset time interval;

[0183] A model updating module, configured to update and train the logistics information prediction model based on the sample updated logistics data set to obtain an updated logistics information prediction model;

[0184] The target time update module is also used to input the target logistics data into the updated logistics information prediction model to perform logistics status update frequency prediction processing and expected time window prediction processing to obtain the target logistics status update frequency and the target expected time window.

[0185] Optionally, the device further includes:

[0186] A sample data acquisition module is used to obtain sample warning signals and sample historical logistics data of sample logistics orders; the sample logistics orders are marked with sample bottleneck area labels and sample high-risk time period labels;

[0187] A data input module is used to input the sample warning signal and sample historical logistics data into a preset model for prediction processing to obtain sample bottleneck area results and sample high-risk period results;

[0188] An early warning model training module is used to train the preset model based on the difference between the sample bottleneck area result and the sample bottleneck area label, and the difference between the sample high-risk period result and the sample high-risk period label to obtain an early warning model;

[0189] The data analysis module is further used to input the early warning signal and the historical logistics data corresponding to the target logistics order into the early warning model for data analysis to identify bottleneck areas and high-risk periods in the logistics network.

[0190] The device and method embodiments in the device embodiments are based on the same inventive concept.

[0191] An embodiment of this specification provides an electronic device, which includes a processor and a memory, wherein the memory stores at least one instruction or at least one program, and the at least one instruction or at least one program is loaded and executed by the processor to implement the logistics data warning and optimization method provided in the above method embodiment.

[0192] An embodiment of the present invention also provides a computer storage medium, which can be set in a terminal to store at least one instruction or at least one program related to a logistics data warning and optimization method in a method embodiment. The at least one instruction or at least one program is loaded and executed by the processor to implement the logistics data warning and optimization method provided by the above method embodiment.

[0193] Embodiments of the present invention further provide a computer program product or computer program, which includes computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to implement the logistics data early warning and optimization method provided in the above-mentioned method embodiment.

[0194] Optionally, in the embodiments of this specification, the storage medium may be located in at least one of the multiple network servers of the computer network. Optionally, in this embodiment, the storage medium may include, but is not limited to, various media that can store program code, such as a USB flash drive, a read-only memory (ROM), a random access memory (RAM), a mobile hard disk, a magnetic disk, or an optical disk.

[0195] The memory described in the embodiments of this specification can be used to store software programs and modules, and the processor executes various functional applications and data processing by running the software programs and modules stored in the memory. The memory may mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, application programs required for functions, etc.; the data storage area can store data created according to the use of the device, etc. In addition, the memory may include a high-speed random access memory and may also include a non-volatile memory, such as at least one disk storage device, a flash memory device, or other volatile solid-state storage device. Accordingly, the memory may also include a memory controller to provide the processor with access to the memory.

[0196] The logistics data early warning and optimization method embodiments provided in the embodiments of this specification can be executed in a mobile terminal, a computer terminal, a server or a similar computing device. Taking running on a server as an example, Figure 7 This is a hardware structure diagram of a server for a logistics data early warning and optimization method provided in the embodiment of this specification. Figure 7As shown, the server 700 may have relatively large differences due to different configurations or performances, and may include one or more central processing units (CPUs) 710 (the central processing unit 710 may include but is not limited to a processing device such as a microprocessor MCU or a programmable logic device FPGA), a memory 730 for storing data, and one or more storage media 720 (such as one or more mass storage devices) for storing application programs 723 or data 722. Among them, the memory 730 and the storage medium 720 can be temporary storage or permanent storage. The program stored in the storage medium 720 may include one or more modules, each module may include a series of instruction operations on the server. Furthermore, the central processing unit 710 can be configured to communicate with the storage medium 720 to execute a series of instruction operations in the storage medium 720 on the server 700. The server 700 may also include one or more power supplies 760, one or more wired or wireless network interfaces 750, one or more input and output interfaces 740, and / or one or more operating systems 721, such as Windows Server™, Mac OS X™, Unix™, Linux™, FreeBSD™, etc.

[0197] The input / output interface 740 can be used to receive or send data via a network. Specific examples of the aforementioned network may include a wireless network provided by the communication provider of the server 700. In one embodiment, the input / output interface 740 includes a network interface controller (NIC), which can be connected to other network devices via a base station to communicate with the Internet. In one embodiment, the input / output interface 740 can be a radio frequency (RF) module for wirelessly communicating with the Internet.

[0198] It can be understood by those skilled in the art that Figure 7 The structure shown is only for illustration and does not limit the structure of the above electronic device. Figure 7 More or fewer components than shown, or with Figure 7 Different configurations shown.

[0199] It can be seen from the embodiments of the logistics data warning and optimization method, device, electronic device or storage medium provided by the present invention that the present invention obtains the target logistics data of the target logistics order in the target logistics channel; inputs the target logistics data into the logistics information prediction model to perform logistics status update frequency prediction processing and expected time window prediction processing to obtain the target logistics status update frequency and target expected time window; based on the target logistics status update frequency, detects the real-time update information of the target logistics order; if the update time corresponding to the real-time update information lags behind the target expected time window, triggers a warning signal; based on the warning signal and the historical logistics data corresponding to the target logistics order, performs data analysis to identify bottleneck areas and high-risk periods in the logistics network; based on the bottleneck areas and high-risk periods, optimizes the logistics path corresponding to the target logistics order. The present invention optimizes the resource allocation strategy, effectively alleviates the pressure during the logistics peak period, and reduces customer complaints caused by logistics interruptions.

[0200] It should be noted that the order in which the embodiments of this specification are presented is for illustrative purposes only and does not represent the superiority or inferiority of the embodiments. Furthermore, the foregoing descriptions are of specific embodiments of this specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims may be performed in an order different from that in the embodiments and still achieve the desired results. Furthermore, the processes depicted in the accompanying drawings do not necessarily require the specific order or sequential order shown to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0201] The various embodiments in this specification are described in a progressive manner. Similar portions between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from the other embodiments. In particular, the device, equipment, and storage medium embodiments are generally similar to the method embodiments, so their descriptions are relatively simplified. For relevant portions, refer to the descriptions of the method embodiments.

[0202] Those skilled in the art will understand that all or part of the steps to implement the above embodiments may be accomplished by hardware, or by a program to instruct the relevant hardware. The program may be stored in a computer storage medium, and the above-mentioned storage medium may be a read-only memory, a disk, or an optical disk, etc.

[0203] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A logistics data early warning and optimization method, characterized in that: The method comprises: Obtain target logistics data of target logistics orders in target logistics channels; Input the target logistics data into the logistics information prediction model to perform logistics status update frequency prediction processing and expected time window prediction processing to obtain the target logistics status update frequency and the target expected time window; Based on the target logistics status update frequency, detecting the real-time update information of the target logistics order; If the update time corresponding to the real-time update information lags behind the target expected time window, triggering an early warning signal; Based on the early warning signal and the historical logistics data corresponding to the target logistics order, data analysis is performed to identify bottleneck areas and high-risk periods in the logistics network; Based on the bottleneck area and high-risk time period, the logistics path corresponding to the target logistics order is optimized.

2. The method according to claim 1, characterized in that: The method further comprises: determining a time difference between the update time and the target expected time window; Determining a current warning level according to the time difference, and generating warning information according to the warning signal and the current warning level; The warning information is pushed to the terminal corresponding to the target logistics order.

3. The method according to claim 1, characterized in that The method further comprises: Constructing an early warning processing strategy, wherein the early warning processing strategy includes early warning strategies corresponding to each of a plurality of early warning levels; According to the warning processing strategy, determining the target warning strategy corresponding to the current warning level; If the current warning level is a high warning level, a warning strategy of emergency dispatch of a spare vehicle or contacting a manual customer for intervention is executed.

4. The method according to any one of claims 1 to 3, characterized in that: The optimizing process of the logistics path corresponding to the target logistics order based on the bottleneck area and the high-risk period includes: Filtering out target paths corresponding to the bottleneck area and high-risk period from the logistics paths corresponding to the target logistics order; Deleting the target path from the logistics path, and updating the logistics path of the target logistics order; The allocation strategy of transportation resources is adjusted according to the historical logistics data corresponding to the target logistics order and the current real-time traffic conditions; the allocation strategy includes increasing the transportation frequency and the number of temporary sorting points for the bottleneck area.

5. The method according to any one of claims 1 to 3, characterized in that: The training method of the logistics information prediction model includes: Obtain a sample logistics data set corresponding to a plurality of sample logistics channels; each sample logistics data in the sample logistics data set is annotated with a sample logistics status update frequency label and a sample expected time window label; Input the sample logistics data set into a preset machine learning model to perform logistics status update frequency prediction processing and expected time window prediction processing to obtain sample logistics status update frequency results and sample expected time window results; Determining first loss data according to a difference between the sample logistics status update frequency result and the sample logistics status update frequency label; Determining second loss data according to a difference between the sample expected time window result and the sample expected time window label; determining target loss data according to the first loss data and the second loss data; The parameters of the preset machine learning model are adjusted according to the target loss data until the training end conditions are met to obtain the logistics information prediction model.

6. The method according to claim 5, characterized in that The method further comprises: At each preset time interval, a sample updated logistics data set corresponding to a plurality of sample logistics channels is obtained; Based on the sample updated logistics data set, the logistics information prediction model is updated and trained to obtain an updated logistics information prediction model; The step of inputting the target logistics data into a logistics information prediction model to perform logistics status update frequency prediction processing and expected time window prediction processing to obtain a target logistics status update frequency and a target expected time window includes: The target logistics data is input into the updated logistics information prediction model to perform logistics status update frequency prediction processing and expected time window prediction processing to obtain the target logistics status update frequency and the target expected time window.

7. The method according to any one of claims 1 to 3, characterized in that: The method further comprises: Obtaining sample early warning signals and sample historical logistics data of sample logistics orders; the sample logistics orders are marked with sample bottleneck area labels and sample high-risk time period labels; Input the sample warning signal and sample historical logistics data into a preset model for prediction processing to obtain sample bottleneck area results and sample high-risk period results; According to the difference between the sample bottleneck area result and the sample bottleneck area label, and the difference between the sample high-risk period result and the sample high-risk period label, the preset model is trained to obtain an early warning model; The performing of data analysis based on the early warning signal and the historical logistics data corresponding to the target logistics order to identify bottleneck areas and high-risk periods in the logistics network includes: The early warning signal and the historical logistics data corresponding to the target logistics order are input into the early warning model for data analysis to identify bottleneck areas and high-risk periods in the logistics network.

8. A logistics data early warning and optimization device, characterized in that: The device comprises: A target logistics data acquisition module is used to acquire target logistics data of target logistics orders in target logistics channels; A target time update module is used to input the target logistics data into a logistics information prediction model to perform logistics status update frequency prediction processing and expected time window prediction processing to obtain a target logistics status update frequency and a target expected time window; An information detection module, used to detect the real-time update information of the target logistics order based on the update frequency of the target logistics status; A warning signal triggering module, configured to trigger a warning signal if the update time corresponding to the real-time update information lags behind the target expected time window; A data analysis module, used to perform data analysis based on the warning signal and the historical logistics data corresponding to the target logistics order, and identify bottleneck areas and high-risk periods in the logistics network; The path optimization module is used to optimize the logistics path corresponding to the target logistics order based on the bottleneck area and the high-risk period.

9. An electronic device, characterized in that: The device includes: a processor and a memory, wherein the memory stores at least one instruction or at least one program, and the at least one instruction or at least one program is loaded and executed by the processor to implement the logistics data warning and optimization method as described in any one of claims 1-7.

10. A computer storage medium, characterized in that: The computer storage medium stores at least one instruction or at least one program, and the at least one instruction or at least one program is loaded and executed by the processor to implement the logistics data early warning and optimization method as described in any one of claims 1-7.

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