Intelligent optimization and tracking method, device and equipment for express logistics and storage medium

Abstract

The invention relates to the technical field of information processing, in particular to an express logistics intelligent optimizing and tracking method, device and equipment and a storage medium, parcel image information of a logistics parcel is obtained, the parcel image information is analyzed by using an AI image recognition technology, and express bill information and parcel type information are obtained; based on express waybill information and parcel type information, a big data analysis algorithm and a machine learning algorithm are utilized to plan an optimal delivery path, the intelligent degree of path planning is improved, the path planning is more reasonable, the express delivery efficiency is improved, the actual delivery path of a logistics parcel is tracked in real time by utilizing a GPS technology, and the logistics parcel delivery efficiency is improved. According to the method, the actual delivery path is compared with the optimal delivery path, the delivery path deviation value is obtained, when the delivery path deviation value is larger than the preset deviation threshold value, the delivery path deviation information is generated, the deviation information of the delivery path is visually sent to the client in real time, the interactive experience of users is improved, and the participation degree and satisfaction degree of the users are improved.

Description

Technical Field

[0001] The present invention relates to the field of information processing technology, and in particular to a method, device, equipment and storage medium for intelligent optimization and tracking of express logistics. Background Art

[0002] With the rapid development of e-commerce, the express delivery industry has shown a trend of rapid development. The express logistics industry is facing unprecedented challenges and needs to improve the service timeliness and service quality of the express delivery industry in multiple links, including route planning, efficient delivery, package tracking, cost control, etc. However, the traditional express delivery system currently has problems such as untimely package tracking and low sorting efficiency. In addition, the intelligence level of route planning is low, resulting in unreasonable route planning and reduced express delivery efficiency. When the express delivery deviates from the original route for delivery, the deviation information of the delivery route cannot be visually sent to the client in real time, which affects the user's interactive experience and reduces user participation and satisfaction. Summary of the invention

[0003] In order to overcome the shortcomings of the prior art, the purpose of the present invention is to provide a method, device, equipment and storage medium for intelligent optimization and tracking of express logistics, which improves the intelligence level of route planning, makes route planning more reasonable, improves the efficiency of express delivery, and can send the deviation information of the delivery route to the client in real time and visually, thereby enhancing the user's interactive experience and improving user participation and satisfaction.

[0004] The first aspect of the present invention provides an express logistics intelligent optimization and tracking method, comprising: obtaining package image information of a logistics package, analyzing the package image information using AI image recognition technology, and obtaining express order information and package type information; based on the express order information and the package type information, planning the optimal delivery path using big data analysis algorithms and machine learning algorithms, and tracking the actual delivery path of the logistics package in real time using GPS technology; comparing the actual delivery path with the optimal delivery path to obtain a delivery path deviation value, and when the delivery path deviation value is greater than a preset deviation threshold, generating delivery path deviation information; calling a preset visualization display template, filling the delivery path deviation information into the visualization display template, and obtaining path deviation display information; sending the path deviation display information to a client corresponding to the express order information, and uploading the path deviation display information to a blockchain.

[0005] Optionally, in a first implementation manner of the first aspect of the present invention, the obtaining of package image information of a logistics package, analyzing the package image information using AI image recognition technology, and obtaining express delivery note information and package type information includes: obtaining package image information of a logistics package, denoising the package image information, and obtaining package image denoising information; adjusting the contrast of the package image denoising information to obtain package image adjustment information, converting the package image adjustment information into a preset standard format to obtain package image standard information; recognizing text information, package color, and package outer contour in the package image standard information using AI image recognition technology; performing semantic understanding and structured processing on the text information using natural language processing technology to obtain express delivery note information, wherein the express delivery note information includes sender information, shipping address information, recipient information, and receiving address information; calculating the package volume according to the package outer contour, and determining the package shape according to the package outer contour; predicting the type of item in the logistics package according to the package volume, the package shape, and the package color, and generating package type information according to the item type.

[0006] Optionally, in a second implementation method of the first aspect of the present invention, the optimal delivery path is planned based on the express delivery note information and the package type information using big data analysis algorithms and machine learning algorithms, and the actual delivery path of the logistics package is tracked in real time using GPS technology, including: collecting historical package delivery path sample data, cleaning the historical package delivery path sample data to obtain a historical package delivery path cleaning data set; dividing the historical package delivery path cleaning data set into a training set, a validation set and a test set according to a preset ratio; selecting a decision tree algorithm model, training the decision tree algorithm model with the training set to obtain a delivery path planning model; respectively tuning and evaluating the delivery path planning model with the validation set and the test set; inputting the express delivery note information and the package type information into the delivery path planning model for path planning to obtain the optimal delivery path; and tracking the actual delivery path of the logistics package in real time using GPS technology.

[0007] Optionally, in a third implementation method of the first aspect of the present invention, the actual delivery path is compared with the optimal delivery path to obtain a delivery path deviation value, and when the delivery path deviation value is greater than a preset deviation threshold, delivery path deviation information is generated, including: comparing the actual delivery path with the optimal delivery path to obtain a delivery path deviation value; determining whether the delivery path deviation value is greater than a preset deviation threshold; if so, generating delivery path deviation information.

[0008] Optionally, in a fourth implementation method of the first aspect of the present invention, calling a preset visualization template and filling the delivery path deviation information into the visualization template to obtain the path deviation display information includes: calling a preset visualization template; identifying an information filling position in the visualization template; filling the delivery path deviation information into the information filling position to obtain the path deviation display information.

[0009] Optionally, in a fifth implementation method of the first aspect of the present invention, the sending of the path deviation display information to the client corresponding to the express delivery note information, and uploading the path deviation display information to the blockchain, includes: extracting the sender information and the recipient information in the express delivery note information, and determining the corresponding clients respectively according to the sender information and the recipient information; sending the path deviation display information to the client; obtaining a sending timestamp from a timestamp service platform based on the sending time of the path deviation display information; associating the path deviation display information with the sending timestamp to obtain associated information; encrypting the associated information to obtain encrypted information, and uploading the encrypted information to the blockchain.

[0010] Optionally, in a sixth implementation method of the first aspect of the present invention, after sending the path deviation display information to the client corresponding to the express delivery order information and uploading the path deviation display information to the blockchain, it also includes: obtaining real-time traffic condition information and weather forecast information; merging the real-time traffic condition information, the weather forecast information and the delivery path deviation information to obtain merged information; analyzing the merged information to obtain a delivery delay probability; determining whether the delivery delay probability is greater than a preset delay threshold; if so, generating a timeout warning information and sending the timeout warning information to the client.

[0011] The second aspect of the present invention provides an express logistics intelligent optimization and tracking device, including: an acquisition and analysis module, used to acquire package image information of a logistics package, and analyze the package image information using AI image recognition technology to obtain express order information and package type information; a planning and tracking module, used to plan the optimal delivery path based on the express order information and the package type information using big data analysis algorithms and machine learning algorithms, and use GPS technology to track the actual delivery path of the logistics package in real time; a comparison and generation module, used to compare the actual delivery path with the optimal delivery path to obtain a delivery path deviation value, and when the delivery path deviation value is greater than a preset deviation threshold, generate delivery path deviation information; a calling and filling module, used to call a preset visual display template, fill the delivery path deviation information into the visual display template, and obtain path deviation display information; a sending and uploading module, used to send the path deviation display information to a client corresponding to the express order information, and upload the path deviation display information to a blockchain.

[0012] Optionally, in a first implementation manner of the second aspect of the present invention, the acquisition and analysis module includes: an acquisition denoising unit, used to acquire package image information of a logistics package, denoise the package image information, and obtain package image denoising information; an adjustment and conversion unit, used to adjust the contrast of the package image denoising information, obtain package image adjustment information, and convert the package image adjustment information into a preset standard format to obtain package image standard information; a first recognition unit, used to use AI image recognition technology to recognize text information, package color and package outer contour in the package image standard information; a structuring unit, used to use natural language processing technology to perform semantic understanding and structured processing on the text information to obtain express delivery note information, wherein the express delivery note information includes sender information, shipping address information, recipient information and receiving address information; a calculation and determination unit, used to calculate the package volume according to the package outer contour, and determine the package shape according to the package outer contour; a prediction and generation unit, used to predict the type of item in the logistics package according to the package volume, the package shape and the package color, and generate package type information according to the item type.

[0013] Optionally, in a second implementation of the second aspect of the present invention, the planning and tracking module includes: a collection and cleaning unit, used to collect historical parcel delivery path sample data, clean the historical parcel delivery path sample data, and obtain a historical parcel delivery path cleaned data set; a division unit, used to divide the historical parcel delivery path cleaned data set into a training set, a validation set, and a test set according to a preset ratio; a selection training unit, used to select a decision tree algorithm model, train the decision tree algorithm model through the training set, and obtain a delivery path planning model; a tuning and evaluation unit, used to tune and evaluate the delivery path planning model through the validation set and the test set respectively; a planning unit, used to input the express delivery note information and the package type information into the delivery path planning model for path planning, and obtain the optimal delivery path; a tracking unit, used to use GPS technology to track the actual delivery path of the logistics package in real time.

[0014] Optionally, in a third implementation of the second aspect of the present invention, the comparison generation module includes: a comparison unit, used to compare the actual delivery path with the optimal delivery path to obtain a delivery path deviation value; a judgment unit, used to judge whether the delivery path deviation value is greater than a preset deviation threshold; and a generation unit, used to generate delivery path deviation information if so.

[0015] Optionally, in a fourth implementation method of the second aspect of the present invention, the calling and filling module includes: a calling unit, used to call a preset visual display template; a second identification unit, used to identify an information filling position in the visual display template; and a filling unit, used to fill the delivery path deviation information into the information filling position to obtain path deviation display information.

[0016] Optionally, in a fifth implementation of the second aspect of the present invention, the sending and uploading module includes: an extraction and determination unit, used to extract the sender information and the recipient information in the express delivery note information, and determine the corresponding clients according to the sender information and the recipient information; a sending unit, used to send the path deviation display information to the client; an acquisition unit, used to obtain a sending timestamp from a timestamp service platform based on the sending time of the path deviation display information; an association unit, used to associate the path deviation display information with the sending timestamp to obtain associated information; an encryption upload unit, used to encrypt the associated information to obtain encrypted information, and upload the encrypted information to the blockchain.

[0017] Optionally, in a sixth implementation method of the second aspect of the present invention, it also includes: an acquisition module, used to acquire real-time traffic condition information and weather forecast information; a merging module, used to merge the real-time traffic condition information, the weather forecast information and the delivery path deviation information to obtain merged information; an analysis module, used to analyze the merged information to obtain the delivery delay probability; a judgment module, used to judge whether the delivery delay probability is greater than a preset delay threshold; and a generation and sending module, used to generate timeout warning information if so, and send the timeout warning information to the client.

[0018] The third aspect of the present invention provides an express logistics intelligent optimization and tracking device, which includes: a memory and at least one processor, wherein the memory stores instructions; at least one of the processors calls the instructions in the memory to enable the express logistics intelligent optimization and tracking device to perform each step of the express logistics intelligent optimization and tracking method described in any one of the above items.

[0019] A fourth aspect of the present invention provides a computer-readable storage medium having instructions stored thereon, which, when executed by a processor, implement the various steps of any of the above-mentioned methods for intelligent optimization and tracking of express logistics.

[0020] In the technical solution of the present invention, by acquiring the package image information of the logistics package, the package image information is analyzed by using AI image recognition technology to obtain the express order information and the package type information, and based on the express order information and the package type information, the optimal delivery path is planned by using big data analysis algorithm and machine learning algorithm, so as to improve the intelligence degree of path planning, make path planning more reasonable, improve the efficiency of express delivery, use GPS technology to track the actual delivery path of the logistics package in real time, compare the actual delivery path with the optimal delivery path, and obtain the delivery path deviation value. When the delivery path deviation value is greater than the preset deviation threshold, the delivery path deviation information is generated, the preset visualization display template is called, the delivery path deviation information is filled into the visualization display template, and the path deviation display information is obtained, and the path deviation display information is sent to the client corresponding to the express order information, thereby improving the user's interactive experience and improving user participation and satisfaction. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 A first flow chart of the express logistics intelligent optimization and tracking method provided by an embodiment of the present invention;

[0022] Figure 2 A second flow chart of the express logistics intelligent optimization and tracking method provided by an embodiment of the present invention;

[0023] Figure 3A third flow chart of the express logistics intelligent optimization and tracking method provided by an embodiment of the present invention;

[0024] Figure 4 A fourth flow chart of the express logistics intelligent optimization and tracking method provided by an embodiment of the present invention;

[0025] Figure 5 A schematic diagram of the structure of the express logistics intelligent optimization and tracking device provided by an embodiment of the present invention;

[0026] Figure 6 Another structural schematic diagram of the express logistics intelligent optimization and tracking device provided by an embodiment of the present invention;

[0027] Figure 7 A schematic diagram of the structure of an express logistics intelligent optimization and tracking device provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0028] The present invention provides a method, device, equipment and storage medium for intelligent optimization and tracking of express logistics, which improves the intelligence level of route planning, makes route planning more reasonable, improves express delivery efficiency, and can visually send deviation information of the delivery route to the client in real time, thereby enhancing the user's interactive experience and improving user participation and satisfaction.

[0029] The terms "first", "second", "third", "fourth", etc. (if any) in the specification 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 data used in this way can be interchanged where appropriate, so that the embodiments described herein can be implemented in an order other than that illustrated or described herein. In addition, the terms "including" or "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0030] For ease of understanding, the specific process of the embodiment of the present invention is described below. Figure 1 , an embodiment of the express logistics intelligent optimization and tracking method in the embodiment of the present invention includes:

[0031] 101. Obtain the package image information of the logistics package, analyze the package image information using AI image recognition technology, and obtain the express delivery information and package type information;

[0032] In this embodiment, package image information is obtained from data uploaded by a scanning device, camera, smart phone or other device. Before AI image recognition, image preprocessing is required to improve image quality, reduce noise, and highlight key features. The location of the express delivery note is automatically identified from the package image through object detection, and optical character recognition (OCR) technology is used to identify text information in the package image. In addition to text information, AI image recognition also classifies packages, for example, the type of package is inferred based on the package's appearance features such as volume, shape, and color. After image recognition, the obtained package information needs to be structured.

[0033] 102. Based on the express delivery order information and package type information, use big data analysis algorithms and machine learning algorithms to plan the optimal delivery route, and use GPS technology to track the actual delivery route of logistics packages in real time;

[0034] In this embodiment, based on big data analysis algorithms and machine learning algorithms, the delivery route is optimized according to express delivery order and package type information and traffic data, and the location (latitude and longitude) of the delivery vehicle is obtained in real time through the GPS device installed on the delivery vehicle, and the data is transmitted back to the logistics management system.

[0035] 103. Compare the actual delivery path with the optimal delivery path to obtain a delivery path deviation value. When the delivery path deviation value is greater than a preset deviation threshold, generate delivery path deviation information;

[0036] In this embodiment, the delivery path deviation value refers to the difference between the actual delivery path and the optimal delivery path. When calculating the deviation value, a weighted algorithm can be used to weight the deviations of multiple factors such as path length, time, and cost to ultimately obtain a comprehensive deviation value. The deviation threshold is a standard for determining whether the deviation is significant. Once the delivery path deviation value is calculated and the deviation value exceeds a preset threshold, it is necessary to generate delivery path deviation information.

[0037] 104. Calling a preset visualization display template, filling the delivery path deviation information into the visualization display template, and obtaining the path deviation display information;

[0038] In this embodiment, a preset template is called to dynamically fill the actually calculated delivery path deviation information into the visualization display template. In the map display template, the actual path, the optimal path and the deviation between them are marked. The actual path is marked in red on the map, the optimal path is marked in green, and a yellow dotted line is inserted between the two paths to indicate the deviation. The size of the deviation is indicated by numbers or graphics, such as using a bar graph to show the distance difference between the actual path and the optimal path, or using time difference to show the difference between the actual delivery time and the optimal time. The specific cause of the deviation (such as traffic jam, improper path selection, etc.) is obtained and can be marked on the chart or map in the form of text or symbols. For example, a traffic accident or construction section is indicated on the map with a mark or a small icon. In the case of a long time span, it may be necessary to display the deviation trend through a line graph or the like, such as the gradual increase or decrease of the deviation value within a certain time period. After the delivery path deviation information is filled into the template, the path deviation display information is obtained.

[0039] 105. Send the path deviation display information to the client corresponding to the express order information, and upload the path deviation display information to the blockchain;

[0040] In this embodiment, the path deviation display information is sent to the client corresponding to the express delivery order information by push notification, SMS or email, and a map or chart of the path deviation can be directly displayed on the client interface. A suitable blockchain platform is selected to structure the path deviation display information, and the path deviation display information is uploaded to the blockchain through a smart contract.

[0041] In an embodiment of the present invention, by acquiring package image information of a logistics package, the package image information is analyzed using AI image recognition technology to obtain express delivery order information and package type information, and based on the express delivery order information and package type information, the optimal delivery path is planned using big data analysis algorithms and machine learning algorithms, thereby improving the intelligence level of path planning, making path planning more reasonable, and improving express delivery efficiency. The actual delivery path of the logistics package is tracked in real time using GPS technology, and the actual delivery path is compared with the optimal delivery path to obtain a delivery path deviation value. When the delivery path deviation value is greater than a preset deviation threshold, delivery path deviation information is generated, a preset visualization display template is called, and the delivery path deviation information is filled into the visualization display template to obtain path deviation display information, and the path deviation display information is sent to a client corresponding to the express delivery order information, thereby improving the user's interactive experience and improving user participation and satisfaction.

[0042] See also Figure 2 The second embodiment of the express logistics intelligent optimization and tracking method in the embodiment of the present invention includes:

[0043] 201. Obtain package image information of a logistics package, and perform denoising processing on the package image information to obtain package image denoising information;

[0044] In this embodiment, a camera is used to capture images of the package at various stages of the package (such as warehousing, outbound, in transit, sorting, etc.). Once the package image is captured, the image data will be transmitted and stored in a central database or cloud storage platform, and the package image information will be denoised. After the denoising process, the package image will become clearer, excess noise will be removed, useful information will be retained, and denoised package image information will be obtained.

[0045] 202. Adjust the contrast of the package image denoising information to obtain package image adjustment information, and convert the package image adjustment information into a preset standard format to obtain package image standard information;

[0046] In this embodiment, the contrast is adjusted by linearly transforming the brightness value of each pixel, or by adjusting the grayscale distribution of the image so that the pixel distribution of the image is more uniform, thereby improving the contrast of the image, or by applying different contrast adjustment methods in different areas, which is particularly suitable for uneven lighting or when the package image contains multiple areas. The contrast of the image can be adjusted more accurately to obtain package image adjustment information, and the package image adjustment information is converted into a preset standard format (such as JPEG, PNG, TIFF, etc.) to obtain package image standard information.

[0047] 203. Using AI image recognition technology to identify text information, package color and package outline in the package image standard information;

[0048] In this embodiment, for the text information in the package image, OCR technology is used to extract the text, and the color space conversion (such as conversion from RGB space to HSV space) is used to simplify the color recognition process. The main color of the package is identified by calculating the color histogram in the image. Clustering algorithms such as K-means are used to aggregate similar color areas to identify the overall color of the package. For example, for a red package, the system can identify the main color of the package by detecting the pixel density of the red area, and use edge detection algorithms in image processing, such as Canny edge detection, Sobel operator or Laplacian operator, to identify the outer contour of the package. Morphological operations can be used to further optimize the detection of the outer contour, remove noise and extract the complete package shape. After the edge is detected, the outer contour of the package is extracted by a contour extraction algorithm. The size and shape of the package can be further confirmed by calculating the geometric features of the contour (such as area, perimeter, shape, etc.).

[0049] 204. Using natural language processing technology to perform semantic understanding and structural processing on the text information to obtain express delivery order information, which includes sender information, shipping address information, recipient information and receiving address information;

[0050] In this embodiment, the extracted text is segmented using Chinese word segmentation tools such as jieba, and the parts of speech (such as nouns, verbs, etc.) are marked after segmentation. The named entity recognition (NER) technology is used to identify entity information related to the sender, recipient, address, etc., and then relationship extraction is performed. The purpose of relationship extraction is to identify the relationship between entities, such as the relationship between the sender and the sending address, the relationship between the recipient and the receiving address, etc. After completing entity recognition and relationship extraction, the next step is to perform semantic understanding and structured processing of the text.

[0051] 205. Calculate the package volume according to the package outer contour, and determine the package shape according to the package outer contour;

[0052] In this embodiment, if the extracted contour has four sides and the angle is close to 90 degrees, then the package can be considered to be a rectangle or a cuboid, which can be judged by fitting a straight line or polygonal approximation. If the outer contour is close to a circle, the shape of the package can be identified by fitting a circle, and the Hough Transform can be used to detect the circle in the image. For irregular packages, the shape of the package can be identified by calculating the convex hull of the outer contour. The convex hull is a minimum convex polygon that can completely surround the contour and can describe the outer boundary of the irregular shape. After determining the outer contour of the package and analyzing the shape, the volume of the package can be calculated. For irregular packages, one method is to use volume approximation to divide the irregular shape into multiple small cubes, cuboids, cylinders and other small blocks, and then sum the volumes of these small blocks. Another method is to accurately calculate the volume of irregular objects through point cloud reconstruction (such as 3D scanning).

[0053] 206. Predict the type of items in the logistics package according to the package volume, package shape and package color, and generate package type information according to the item type;

[0054] In this embodiment, the volume, shape and color features of the package can be used to predict the item type through machine learning methods, and based on the predicted item type, the system can generate corresponding package type information.

[0055] 207. Collect sample data of historical parcel delivery paths, clean the sample data of historical parcel delivery paths, and obtain a cleaned data set of historical parcel delivery paths;

[0056] In this embodiment, sample data of historical parcel delivery paths are collected and cleaned to process missing data and remove abnormal data and duplicate data. After data cleaning, a cleaned data set of historical parcel delivery paths is obtained.

[0057] 208. Divide the historical package delivery route cleaning data set into a training set, a validation set, and a test set according to a preset ratio;

[0058] In this embodiment, the historical package delivery path cleaning data set is divided into a training set, a validation set and a test set according to a preset ratio, with the training set accounting for 70%-80%, the validation set accounting for 10%-15%, and the test set accounting for 10%-15%.

[0059] 209. Select a decision tree algorithm model, train the decision tree algorithm model through the training set, and obtain a distribution path planning model;

[0060] In this embodiment, a decision tree algorithm model is selected and trained using a training set to obtain a delivery path planning model. The training set is the main data source for training the model. It contains input features and target labels (such as delivery paths). The model will learn how to make decisions based on the input features to predict the delivery path.

[0061] 210. The delivery path planning model is tuned and evaluated through the validation set and test set respectively;

[0062] In this embodiment, the validation set is used to evaluate the model to check the generalization ability of the model during the training process, and the test set is used to evaluate the actual effect of the model to ensure that the model is not overfitted and can maintain good prediction ability on new data.

[0063] 211. Input the express delivery order information and package type information into the delivery route planning model to perform route planning and obtain the optimal delivery route;

[0064] In this embodiment, after the express delivery order information and the package type information are input into the delivery path planning model, the path planning model will calculate the optimal delivery path based on these inputs.

[0065] 212. Use GPS technology to track the actual delivery route of logistics packages in real time;

[0066] In this embodiment, the GPS device on the delivery vehicle will record the vehicle's geographic location data in real time, and then track the actual delivery path of the logistics package in real time based on the geographic location data uploaded by the GPS device.

[0067] In the embodiment of the present invention, by using intelligent means such as AI image recognition technology, natural language processing technology and decision tree algorithm, the entire process can automatically extract information from the package image and analyze it, reducing human intervention and improving efficiency and accuracy. The system not only recognizes the image information of the package, but also integrates the sender, recipient information, package volume, shape and color and other dimensional data to help fully understand the characteristics of the package and provide more accurate input for path planning and item prediction. Through the training and verification of historical delivery path data, combined with information such as package type and volume, the optimal delivery path can be generated, which greatly improves delivery efficiency and accuracy, reduces delivery time and cost, and the application of GPS technology enables the delivery path to be tracked in real time, and can timely adjust and feedback the actual delivery situation, monitor and optimize the accuracy of path planning in real time, ensure that the package is delivered on time, and achieve end-to-end automation from package image acquisition to final delivery path planning, reducing human intervention and improving work efficiency and the overall operation efficiency of the system.

[0068] See also Figure 3 The third embodiment of the express logistics intelligent optimization and tracking method in the embodiment of the present invention includes:

[0069] 301. Compare the actual delivery path with the optimal delivery path to obtain a delivery path deviation value;

[0070] In this embodiment, the optimal path and the actual path are mapped to the same map coordinate system to find the similarities and differences between the two. The location information recorded by the GPS is matched with the road network on the map through a map matching algorithm (such as the Snap-to-Road technology). In this way, the actual driving trajectory can be aligned with the optimal path. The deviation of the path can be evaluated by comparing the distance difference between the actual path and the optimal path. The difference between the two paths can be calculated by Euclidean distance (straight-line distance) or Manhattan distance (driving distance along the street). The difference between the actual driving distance and the optimal path can also be compared by path length. The efficiency of the actual path is evaluated by calculating the difference between the actual driving time and the time required for the optimal path. The curvature of the actual path is compared with the curvature of the optimal path to evaluate the tortuosity of the path. The more curved or away from the optimal route, the greater the deviation of the path.

[0071] 302. Determine whether the delivery path deviation value is greater than a preset deviation threshold;

[0072] In this embodiment, once the delivery path deviation value is calculated, it is determined whether the delivery path deviation value is greater than a preset deviation threshold.

[0073] 303. If yes, generating delivery path deviation information;

[0074] In this embodiment, if the delivery path deviation value is greater than the preset deviation threshold, delivery path deviation information is generated. The delivery path deviation information includes deviation type, deviation location, cause analysis and recommended measures. The deviation type includes distance deviation, time deviation and path distortion deviation.

[0075] 304. Calling a preset visual display template;

[0076] In this embodiment, the generated delivery path deviation information (including deviation type, deviation value, cause analysis, etc.) is sorted and classified, and the system automatically selects a preset visualization display template based on the characteristics of the data and the needs of display.

[0077] 305. Identify the information filling position in the visual display template;

[0078] In this embodiment, the visualization template is parsed to understand the functions and data requirements of each display area or element, and the information filling position in the visualization template that has a mapping relationship with the delivery path deviation information is identified.

[0079] 306. Fill the delivery path deviation information into the information filling position to obtain the path deviation display information;

[0080] In this embodiment, the path deviation information extracted from the data source is matched with the template elements, the numerical data is filled into the coordinate axes or data points of the bar chart or line chart, the geographic coordinates are filled into the paths or marked points on the map, and the classified data is mapped to various parts of the pie chart or bar chart to obtain the path deviation display information.

[0081] 307. Extract the sender information and the recipient information from the express delivery order information, and determine the corresponding clients according to the sender information and the recipient information;

[0082] In this embodiment, basic information of the sender and recipient is extracted from the express delivery order, and this information is matched with the customer database in the system. After the match is successful, the system determines the clients corresponding to the sender and recipient based on their information.

[0083] 308. Send the path deviation display information to the client;

[0084] In this embodiment, the path deviation display information is sent to the client via SMS, email or application APP.

[0085] 309. Based on the sending time of the path deviation display information, obtain a sending timestamp from the timestamp service platform;

[0086] In this embodiment, once the path deviation display information is sent to the client, an accurate sending timestamp is obtained by interacting with the interface of the timestamp service platform.

[0087] 310. Associating the path deviation display information with the sending timestamp to obtain associated information;

[0088] In this embodiment, the prepared path deviation display information is associated with the sending timestamp to obtain the associated information, thereby ensuring that the path deviation display information can accurately reflect its sending time and provide an accurate time basis for subsequent audits and customer inquiries.

[0089] 311. Encrypt the associated information to obtain encrypted information, and upload the encrypted information to the blockchain;

[0090] In this embodiment, the associated information is encrypted using the public key in the key pair to obtain encrypted information, a blockchain platform is selected, and the encrypted information is received through a smart contract and stored in the blockchain.

[0091] In the embodiment of the present invention, the entire process relies on intelligent judgment and automated operation, from path deviation analysis to display information generation and then to encrypted upload, which reduces manual intervention and improves efficiency and accuracy. By comparing and analyzing the actual delivery path and the optimal delivery path, the path deviation can be monitored in real time, and the deviation information can be generated in time, and the information can be fed back to relevant personnel through the client to ensure the transparency and controllability of the delivery process. By calling the visual display template and filling the deviation information into the display position, the information presentation is ensured to be clear and easy to understand, which helps users to quickly understand the delivery status and problems. By encrypting the path deviation information and uploading it to the blockchain, the security and immutability of the data are ensured, the credibility of the data is increased, and tampering and forgery are prevented. By extracting the sender and recipient information and pushing information according to its corresponding client, each customer can receive personalized and accurate delivery information, which improves the user experience and service quality. Through timestamp association and blockchain upload, complete process monitoring and auditing can be achieved to ensure the compliance and traceability of each link.

[0092] See also Figure 4 The fourth embodiment of the express logistics intelligent optimization and tracking method in the embodiment of the present invention includes:

[0093] 401. Obtain real-time traffic information and weather forecast information;

[0094] In this embodiment, by sending a request to the API of the traffic information provider, information such as the traffic conditions, speed limits, traffic accidents, etc. of the current road section is obtained, and by requesting real-time or next few hours' weather data from the API of the weather forecast data provider, weather forecast information that may affect delivery is obtained.

[0095] 402. Combining the real-time traffic condition information, the weather forecast information, and the delivery path deviation information to obtain combined information;

[0096] In this embodiment, the real-time traffic status information, weather forecast information and delivery path deviation information are weighted and then merged to obtain merged information.

[0097] 403. Analyze the combined information to obtain the probability of delivery delay;

[0098] In this embodiment, a mathematical model is constructed using the merged information to quantify traffic conditions, weather conditions, and path deviations. For example, traffic flow can be converted into a "congestion level" indicator, and weather conditions can be converted into a "weather impact index." Through historical data and experience, the relationship between different factors is analyzed, and a regression model (such as linear regression, logistic regression, etc.) is used to predict delay time. According to historical delay data, a probability distribution model of delay time is established. Based on the established delay probability model, the system can calculate the probability of delay and obtain the delivery delay probability.

[0099] 404. Determine whether the delivery delay probability is greater than a preset delay threshold;

[0100] In this embodiment, a delay threshold is preset according to business requirements and risk tolerance, and it is determined whether the probability of delivery delay is greater than the delay threshold.

[0101] 405. If yes, generate a timeout warning message and send the timeout warning message to the client;

[0102] In this embodiment, if the delivery delay probability is greater than the delay threshold, a timeout warning information is generated. The timeout warning information includes delivery order information, delay probability, estimated timeout time, warning instructions and processing suggestions or measures. The timeout warning information is sent to the client via mobile application, email or SMS notification.

[0103] In the embodiments of the present invention, by combining real-time traffic, weather and delivery path deviation information, a more comprehensive delivery status analysis is provided to ensure more accurate delay predictions. By analyzing the combined information to obtain the delay probability and judging whether to trigger an early warning based on a threshold, potential delivery delay problems can be discovered in a timely manner, and intervention can be made in advance. Timeout early warning information can be automatically generated and sent to help relevant personnel or customers make quick response decisions and avoid unnecessary impacts caused by delays. Through real-time early warnings and information feedback, customers can understand the delivery status in a timely manner, thereby improving customer experience and satisfaction.

[0104] The above describes the express logistics intelligent optimization and tracking method in the embodiment of the present invention. The following describes the express logistics intelligent optimization and tracking device in the embodiment of the present invention. Figure 5 In one embodiment of the present invention, an intelligent optimization and tracking device for express logistics includes:

[0105] The acquisition and analysis module 501 is used to acquire the package image information of the logistics package, analyze the package image information using AI image recognition technology, and obtain the express delivery information and package type information;

[0106] The planning and tracking module 502 is used to plan the optimal delivery route based on the express delivery order information and the package type information using the big data analysis algorithm and the machine learning algorithm, and to track the actual delivery route of the logistics package in real time using the GPS technology;

[0107] A comparison generation module 503 is used to compare the actual delivery path with the optimal delivery path to obtain a delivery path deviation value, and when the delivery path deviation value is greater than a preset deviation threshold, generate delivery path deviation information;

[0108] A calling filling module 504 is used to call a preset visualization display template, fill the delivery path deviation information into the visualization display template, and obtain the path deviation display information;

[0109] The sending and uploading module 505 is used to send the path deviation display information to the client corresponding to the express delivery order information, and upload the path deviation display information to the blockchain.

[0110] In this embodiment, by acquiring the package image information of the logistics package, the package image information is analyzed by using AI image recognition technology to obtain the express delivery order information and the package type information, and based on the express delivery order information and the package type information, the optimal delivery path is planned by using big data analysis algorithm and machine learning algorithm, so as to improve the intelligence level of path planning, make path planning more reasonable, and improve the efficiency of express delivery. The actual delivery path of the logistics package is tracked in real time by using GPS technology, and the actual delivery path is compared with the optimal delivery path to obtain the delivery path deviation value. When the delivery path deviation value is greater than the preset deviation threshold, the delivery path deviation information is generated, the preset visualization display template is called, and the delivery path deviation information is filled into the visualization display template to obtain the path deviation display information, and the path deviation display information is sent to the client corresponding to the express delivery order information, thereby improving the user's interactive experience and improving user participation and satisfaction.

[0111] See also Figure 6 Another embodiment of the express logistics intelligent optimization and tracking device in the embodiment of the present invention includes:

[0112] The acquisition and analysis module 501 is used to acquire the package image information of the logistics package, analyze the package image information using AI image recognition technology, and obtain the express delivery information and package type information;

[0113] The planning and tracking module 502 is used to plan the optimal delivery route based on the express delivery order information and the package type information using the big data analysis algorithm and the machine learning algorithm, and to track the actual delivery route of the logistics package in real time using the GPS technology;

[0114] A comparison generation module 503 is used to compare the actual delivery path with the optimal delivery path to obtain a delivery path deviation value, and when the delivery path deviation value is greater than a preset deviation threshold, generate delivery path deviation information;

[0115] A calling filling module 504 is used to call a preset visualization display template, fill the delivery path deviation information into the visualization display template, and obtain the path deviation display information;

[0116] The sending and uploading module 505 is used to send the path deviation display information to the client corresponding to the express delivery order information, and upload the path deviation display information to the blockchain;

[0117] In this embodiment, the acquisition and analysis module 501 includes: an acquisition and denoising unit 5011, which is used to acquire the package image information of the logistics package, perform denoising processing on the package image information, and obtain the package image denoising information; an adjustment and conversion unit 5012, which is used to adjust the contrast of the package image denoising information, obtain the package image adjustment information, and convert the package image adjustment information into a preset standard format to obtain the package image standard information; a first recognition unit 5013, which is used to use AI image recognition technology to recognize the text information, package color and package outer contour in the package image standard information; a structuring unit 5014, which is used to use natural language processing technology to perform semantic understanding and structuring processing on the text information to obtain the express delivery note information, and the express delivery note information includes the sender information, the mailing address information, the recipient information and the receiving address information; a calculation and determination unit 5015, which is used to calculate the package volume according to the package outer contour, and determine the package shape according to the package outer contour; a prediction and generation unit 5016, which is used to predict the type of items in the logistics package according to the package volume, package shape and package color, and generate package type information according to the item type.

[0118] In this embodiment, the planning and tracking module 502 includes: a collection and cleaning unit 5021, which is used to collect historical parcel delivery path sample data, clean the historical parcel delivery path sample data, and obtain a historical parcel delivery path cleaned data set; a division unit 5022, which is used to divide the historical parcel delivery path cleaned data set into a training set, a validation set, and a test set according to a preset ratio; a selection and training unit 5023, which is used to select a decision tree algorithm model, train the decision tree algorithm model through the training set, and obtain a delivery path planning model; a tuning and evaluation unit 5024, which is used to tune and evaluate the delivery path planning model through the validation set and the test set respectively; a planning unit 5025, which is used to input the express delivery order information and the package type information into the delivery path planning model for path planning, and obtain the optimal delivery path; a tracking unit 5026, which is used to use GPS technology to track the actual delivery path of the logistics package in real time.

[0119] In this embodiment, the comparison generation module 503 includes: a comparison unit 5031, which is used to compare the actual delivery path with the optimal delivery path to obtain a delivery path deviation value; a judgment unit 5032, which is used to judge whether the delivery path deviation value is greater than a preset deviation threshold; and a generation unit 5033, which is used to generate delivery path deviation information if so.

[0120] In this embodiment, the calling and filling module 504 includes: a calling unit 5041, used to call a preset visual display template; a second identification unit 5042, used to identify the information filling position in the visual display template; and a filling unit 5043, used to fill the delivery path deviation information into the information filling position to obtain the path deviation display information.

[0121] In this embodiment, the sending and uploading module 505 includes: an extraction and determination unit 5051, which is used to extract the sender information and the recipient information in the express delivery order information, and determine the corresponding clients according to the sender information and the recipient information; a sending unit 5052, which is used to send the path deviation display information to the client; an acquisition unit 5053, which is used to obtain the sending timestamp from the timestamp service platform based on the sending time of the path deviation display information; an association unit 5054, which is used to associate the path deviation display information with the sending timestamp to obtain associated information; an encryption upload unit 5055, which is used to encrypt the associated information, obtain encrypted information, and upload the encrypted information to the blockchain.

[0122] In this embodiment, it also includes: an acquisition module 506, which is used to obtain real-time traffic status information and weather forecast information; a merging module 507, which is used to merge the real-time traffic status information, weather forecast information and delivery path deviation information to obtain merged information; an analysis module 508, which is used to analyze the merged information to obtain the delivery delay probability; a judgment module 509, which is used to judge whether the delivery delay probability is greater than a preset delay threshold; and a generation and sending module 510, which is used to generate timeout warning information if so, and send the timeout warning information to the client.

[0123] above Figure 5 and Figure 6 The express logistics intelligent optimization and tracking device in the embodiment of the present invention is described in detail from the perspective of modular functional entities. The express logistics intelligent optimization and tracking device in the embodiment of the present invention is described in detail from the perspective of hardware processing.

[0124] Figure 7 6 is a schematic diagram of the structure of an express logistics intelligent optimization and tracking device provided by an embodiment of the present invention. The express logistics intelligent optimization and tracking device 600 may have relatively large differences due to different configurations or performances, and may include one or more processors (central processing units, CPU) 610 (for example, one or more processors) and a memory 620, and one or more storage media 630 (for example, one or more mass storage devices) storing application programs 633 or data 632. Among them, the memory 620 and the storage medium 630 can be short-term storage or permanent storage. The program stored in the storage medium 630 may include one or more modules (not shown in the figure), and each module may include a series of instruction operations in the express logistics intelligent optimization and tracking device 600. Furthermore, the processor 610 can be configured to communicate with the storage medium 630, and execute a series of instruction operations in the storage medium 630 on the express logistics intelligent optimization and tracking device 600 to implement the steps of the express logistics intelligent optimization and tracking method provided by the above-mentioned various method embodiments.

[0125] The express logistics intelligent optimization and tracking device 600 may also include one or more power supplies 640, one or more wired or wireless network interfaces 650, one or more input and output interfaces 660, and / or one or more operating systems 631, such as Windows Serve, Mac OS X, Unix, Linux, FreeBSD, etc. It will be appreciated by those skilled in the art that Figure 7 The structure of the express logistics intelligent optimization and tracking device shown does not constitute a limitation on the express logistics intelligent optimization and tracking device, and may include more or fewer components than shown in the figure, or a combination of certain components, or a different arrangement of components.

[0126] The present invention also provides a computer-readable storage medium, which may be a non-volatile computer-readable storage medium or a volatile computer-readable storage medium. Instructions are stored in the computer-readable storage medium. When the instructions are executed on a computer, the computer executes the steps of the express logistics intelligent optimization and tracking method.

[0127] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the above-described system, device, or unit can refer to the corresponding process in the aforementioned method embodiment and will not be repeated here.

[0128] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium, including a number of instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk and other media that can store program codes.

[0129] Finally, it should be noted that the above description is only a preferred example of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art can still modify the technical solutions described in the aforementioned embodiments or replace some of the technical features therein by equivalents. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for intelligent optimization and tracking of express logistics, characterized in that: include: Obtain package image information of the logistics package, analyze the package image information using AI image recognition technology, and obtain express delivery information and package type information; Based on the express delivery information and the package type information, the optimal delivery route is planned using big data analysis algorithms and machine learning algorithms, and the actual delivery route of the logistics package is tracked in real time using GPS technology; Comparing the actual delivery path with the optimal delivery path to obtain a delivery path deviation value, and generating delivery path deviation information when the delivery path deviation value is greater than a preset deviation threshold; Calling a preset visualization display template, filling the delivery path deviation information into the visualization display template, and obtaining the path deviation display information; The path deviation display information is sent to the client corresponding to the express order information, and the path deviation display information is uploaded to the blockchain.

2. The express logistics intelligent optimization and tracking method according to claim 1 is characterized in that: The obtaining of the package image information of the logistics package, analyzing the package image information by using AI image recognition technology, and obtaining the express delivery note information and package type information include: Acquire package image information of the logistics package, and perform denoising processing on the package image information to obtain package image denoising information; adjusting the contrast of the package image denoising information to obtain package image adjustment information, and converting the package image adjustment information into a preset standard format to obtain package image standard information; Using AI image recognition technology to identify text information, package color and package outer contour in the package image standard information; Using natural language processing technology to perform semantic understanding and structural processing on the text information to obtain express delivery order information, wherein the express delivery order information includes sender information, shipping address information, recipient information and receiving address information; Calculating the package volume according to the package outer contour, and determining the package shape according to the package outer contour; The type of item in the logistics package is predicted according to the package volume, the package shape and the package color, and package type information is generated according to the item type.

3. The express logistics intelligent optimization and tracking method according to claim 1 is characterized in that: The method of planning the optimal delivery route based on the express delivery order information and the package type information using big data analysis algorithms and machine learning algorithms, and tracking the actual delivery route of the logistics package in real time using GPS technology, includes: Collecting historical parcel delivery path sample data, and cleaning the historical parcel delivery path sample data to obtain a historical parcel delivery path cleaned data set; Dividing the historical package delivery route cleaning data set into a training set, a validation set, and a test set according to a preset ratio; Select a decision tree algorithm model, and train the decision tree algorithm model using the training set to obtain a delivery path planning model; The delivery path planning model is tuned and evaluated respectively by using the validation set and the test set; Inputting the express delivery order information and the package type information into the delivery route planning model to perform route planning to obtain an optimal delivery route; GPS technology is used to track the actual delivery route of the logistics package in real time.

4. The express logistics intelligent optimization and tracking method according to claim 1 is characterized in that: The actual delivery path is compared with the optimal delivery path to obtain a delivery path deviation value, and when the delivery path deviation value is greater than a preset deviation threshold, delivery path deviation information is generated, including: Compare the actual delivery path with the optimal delivery path to obtain a delivery path deviation value; Determining whether the delivery path deviation value is greater than a preset deviation threshold; If so, the delivery path deviation information is generated.

5. The express logistics intelligent optimization and tracking method according to claim 1 is characterized in that: The calling of a preset visualization display template, filling the delivery path deviation information into the visualization display template, and obtaining the path deviation display information includes: Call the preset visual display template; Identifying information filling positions in the visual display template; The delivery path deviation information is filled into the information filling position to obtain path deviation display information.

6. The express logistics intelligent optimization and tracking method according to claim 1 is characterized in that: The sending of the path deviation display information to the client corresponding to the express order information, and uploading the path deviation display information to the blockchain, includes: Extracting the sender information and the recipient information from the express delivery order information, and determining the corresponding clients according to the sender information and the recipient information; Sending the path deviation display information to the client; Based on the sending time of the path deviation display information, obtaining a sending timestamp from a timestamp service platform; Associating the path deviation display information with the sending timestamp to obtain associated information; The associated information is encrypted to obtain encrypted information, and the encrypted information is uploaded to the blockchain.

7. The express logistics intelligent optimization and tracking method according to claim 1 is characterized in that: After sending the path deviation display information to the client corresponding to the express order information and uploading the path deviation display information to the blockchain, the method further includes: Get real-time traffic information and weather forecast information; Merging the real-time traffic condition information, the weather forecast information and the delivery path deviation information to obtain merged information; Analyzing the combined information to obtain a delivery delay probability; Determining whether the delivery delay probability is greater than a preset delay threshold; If so, a timeout warning message is generated and sent to the client.

8. An intelligent optimization and tracking device for express logistics, characterized in that: include: An acquisition and analysis module is used to acquire the package image information of the logistics package, analyze the package image information using AI image recognition technology, and obtain the express delivery information and package type information; A planning and tracking module is used to plan the optimal delivery route based on the express order information and the package type information using big data analysis algorithms and machine learning algorithms, and to track the actual delivery route of the logistics package in real time using GPS technology; A comparison generation module, used for comparing the actual delivery path with the optimal delivery path to obtain a delivery path deviation value, and generating delivery path deviation information when the delivery path deviation value is greater than a preset deviation threshold; A calling filling module is used to call a preset visual display template, fill the delivery path deviation information into the visual display template, and obtain the path deviation display information; The sending and uploading module is used to send the path deviation display information to the client corresponding to the express delivery order information, and upload the path deviation display information to the blockchain.

9. An intelligent optimization and tracking device for express logistics, characterized in that: The express logistics intelligent optimization and tracking device comprises: a memory and at least one processor, wherein the memory stores instructions; At least one of the processors calls the instructions in the memory so that the express logistics intelligent optimization and tracking device executes each step of the express logistics intelligent optimization and tracking method as described in any one of claims 1-7.

10. A computer-readable storage medium having instructions stored thereon, characterized in that: When the instructions are executed by the processor, the various steps of the express logistics intelligent optimization and tracking method as described in any one of claims 1-7 are implemented.

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