CN118246584A - Express whole-course aging prediction method, device, equipment and storage medium - Google Patents

Abstract

The invention relates to the technical field of information processing, in particular to a method, a device, equipment and a storage medium for predicting the whole-course aging of express, which are characterized in that by receiving express order information and inputting the express order information into an aging prediction model, the predicted delivery time length is obtained, the whole-course aging prediction approaches to the actual aging, the prediction accuracy is improved, a customer can know the predicted delivery time length accurately in the whole course in advance, the service level is improved, the real-time state change information of the express order information is monitored, the predicted delivery time length is adjusted based on the real-time state change information, the updated delivery time length is obtained, the whole-course predicted delivery time length can be changed along with the actual change, the flexibility is high, the prediction accuracy is further improved, the updated delivery time length and the predicted delivery time length are compared, a difference value is obtained, and early warning information is generated according to the difference value, so that a customer can conveniently take corresponding measures according to the change of the delivery time length, and the customer satisfaction is improved.

Description

Express whole-course aging prediction method, device, equipment and storage medium

Technical Field

The invention relates to the technical field of information processing, in particular to a method, a device, equipment and a storage medium for predicting the whole time of express delivery.

Background

In the express industry, timeliness refers to a time interval from sending out express to receiving a consignee, also called as "speed" or "delivery deadline", timeliness is usually calculated in hours, days or working days, when consumers need to select express companies, they usually consider a plurality of factors such as price, timeliness and quality of service, if an enterprise has excellent timeliness, it can attract more clients through the time, and win a larger market share from competitors, so that the competitiveness is improved in enterprise competition, however, in order to make the bottom of a pocket, the current full-range timeliness prediction is slower than the actual time, deviates from reality, the full-range timeliness prediction of express is inaccurate, clients cannot know accurate estimated delivery to timeliness in advance, service quality and client satisfaction are reduced, the current full-range timeliness prediction is artificially generated, and is not changed along with the actual change, and the accuracy is low.

Disclosure of Invention

In order to overcome the defects of the prior art, the invention aims to provide the express full-range aging prediction method, the device, the equipment and the storage medium, which are used for predicting the full-range aging close to the actual aging, are close to reality, improve the prediction accuracy, enable customers to know the full-range accurate predicted delivery aging in advance, improve the service level, enable the full-range predicted delivery aging to be changed along with the actual aging, have high flexibility, further improve the prediction accuracy and improve the customer satisfaction.

The invention provides a method for predicting the full-course aging of express, which comprises the following steps: acquiring historical waybill data, and identifying influence events, actual transportation routes and actual distribution time lengths respectively corresponding to the waybills in the historical waybill data; constructing an aging prediction model according to the influence event, the actual transportation route and the actual distribution time length, and deploying the aging prediction model; receiving express order information, and inputting the express order information into the aging prediction model to obtain estimated delivery duration; monitoring real-time state change information of the express order information, and adjusting the estimated delivery duration based on the real-time state change information to obtain updated delivery duration; comparing the updated delivery time length with the estimated delivery time length to obtain a difference value, and generating early warning information according to the difference value.

Optionally, in a first implementation manner of the first aspect of the present invention, the acquiring historical waybill data and identifying an impact event, an actual transportation route and an actual delivery duration corresponding to each waybill in the historical waybill data respectively includes: acquiring historical waybill data, and formatting the historical waybill data to obtain standard format waybill data; preprocessing the standard format waybill data to obtain preprocessed data; extracting each waybill data corresponding to each waybill in the preprocessing data; and identifying influence events, actual transportation routes and actual distribution time lengths in each piece of waybill data, wherein the influence events comprise traffic volume exceeding events, weather abnormal events, holiday events and traffic jam events.

Optionally, in a second implementation manner of the first aspect of the present invention, the constructing an aging prediction model according to the impact event, the actual transportation route, and the actual delivery duration, and deploying the aging prediction model includes: analyzing the influence events to obtain influence factor analysis results, and generating event weight configuration information according to the influence factor analysis results; correlating the event weight configuration information, the actual transportation route and the actual distribution duration to obtain a correlation result, and constructing a sample set according to the correlation result; and training by using the sample set to obtain an aging prediction model, and deploying the aging prediction model.

Optionally, in a third implementation manner of the first aspect of the present invention, the receiving the express order information and inputting the express order information into the aging prediction model to obtain the estimated delivery duration includes: receiving express order information, and carrying out data structure analysis on the express order information to obtain order analysis information; cleaning the order analysis information to obtain order cleaning information, and identifying keywords in the order cleaning information; extracting a mail address field and a receipt address field according to the keywords, and respectively carrying out format verification on the mail address field and the receipt address field by adopting a preset address verification rule; when the verification is passed, generating a predicted transportation route according to the mail address field and the receipt address field, and inputting the predicted transportation route into the aging prediction model to obtain a predicted delivery duration.

Optionally, in a fourth implementation manner of the first aspect of the present invention, the monitoring real-time status change information of the express order information, and adjusting the estimated delivery duration based on the real-time status change information, to obtain an updated delivery duration, includes: creating a state change monitor, and monitoring the state change of the express order information by using the state change monitor; when monitoring the real-time state change information of the express order information, acquiring the current real-time state change information; and adjusting the estimated delivery time based on the real-time state change information to obtain updated delivery time.

Optionally, in a fifth implementation manner of the first aspect of the present invention, the comparing the updated delivery duration with the estimated delivery duration to obtain a difference value, and generating early warning information according to the difference value includes: comparing the updated delivery time length with the estimated delivery time length to obtain a difference value; judging whether the difference value is larger than a preset alarm threshold value or not; if yes, generating early warning information according to the difference value, and sending the early warning information to a client corresponding to the express order information.

Optionally, in a sixth implementation manner of the first aspect of the present invention, after comparing the updated delivery duration with the estimated delivery duration to obtain a difference value, and generating early warning information according to the difference value, the method further includes: recording the early warning information to obtain recording information; uploading the recorded information to a blockchain, and calculating state change probability according to the recorded information; and backing up the aging prediction model, and carrying out iterative updating on the aging prediction model according to the state change probability.

The second aspect of the present invention provides an express full-course aging prediction apparatus, including: the system comprises an acquisition and identification module, a storage module and a distribution module, wherein the acquisition and identification module is used for acquiring historical waybill data and identifying influence events, actual transportation routes and actual distribution time lengths respectively corresponding to all waybills in the historical waybill data; the deployment module is used for constructing an aging prediction model according to the influence event, the actual transportation route and the actual distribution time length and deploying the aging prediction model; the receiving and inputting module is used for receiving express order information and inputting the express order information into the aging prediction model to obtain estimated delivery duration; the monitoring and adjusting module is used for monitoring real-time state change information of the express order information, and adjusting the estimated delivery duration based on the real-time state change information to obtain updated delivery duration; the comparison generation module is used for comparing the updated delivery time length with the estimated delivery time length to obtain a difference value and generating early warning information according to the difference value.

Optionally, in a first implementation manner of the second aspect of the present invention, the acquiring identification module includes: the system comprises an acquisition formatting unit, a storage unit and a storage unit, wherein the acquisition formatting unit is used for acquiring historical waybill data and formatting the historical waybill data to obtain standard format waybill data; the preprocessing unit is used for preprocessing the standard format waybill data to obtain preprocessed data; the extraction unit is used for extracting each waybill data corresponding to each waybill in the preprocessing data; the identifying unit is used for identifying the influencing event, the actual transportation route and the actual distribution duration in each piece of waybill data, wherein the influencing event comprises a traffic volume exceeding event, a weather abnormality event, a holiday event and a traffic jam event.

Optionally, in a second implementation manner of the second aspect of the present invention, the constructing and deploying module includes: the analysis generating unit is used for analyzing the influence events to obtain influence factor analysis results and generating event weight configuration information according to the influence factor analysis results; the association construction unit is used for associating the event weight configuration information, the actual transportation route and the actual delivery duration to obtain an association result, and constructing a sample set according to the association result; the training deployment unit is used for training by using the sample set to obtain an aging prediction model and deploying the aging prediction model.

Optionally, in a third implementation manner of the second aspect of the present invention, the receiving input module includes: the receiving and analyzing unit is used for receiving the express order information, and carrying out data structure analysis on the express order information to obtain order analysis information; the cleaning and identifying unit is used for cleaning the order analysis information to obtain order cleaning information and identifying keywords in the order cleaning information; the extraction and verification unit is used for extracting a mail address field and a receipt address field according to the keywords, and respectively carrying out format verification on the mail address field and the receipt address field by adopting a preset address verification rule; and the generation input unit is used for generating a predicted transportation route according to the mail address field and the receipt address field when the verification is passed, and inputting the predicted transportation route into the aging prediction model to obtain the predicted delivery duration.

Optionally, in a fourth implementation manner of the second aspect of the present invention, the monitoring adjustment module includes: the creation monitoring unit is used for creating a state change monitor, and monitoring the state change of the express order information by using the state change monitor; the acquisition unit is used for acquiring the current real-time state change information when monitoring the real-time state change information of the express order information; and the adjusting unit is used for adjusting the estimated delivery time based on the real-time state change information to obtain updated delivery time.

Optionally, in a fifth implementation manner of the second aspect of the present invention, the alignment generating module includes: the comparison unit is used for comparing the updated delivery time length with the estimated delivery time length to obtain a difference value; the judging unit is used for judging whether the difference value is larger than a preset alarm threshold value or not; and the generation and transmission unit is used for generating early warning information according to the difference value if so, and transmitting the early warning information to the client corresponding to the express order information.

Optionally, in a sixth implementation manner of the second aspect of the present invention, the method further includes: the recording module is used for recording the early warning information to obtain recording information; the uploading calculation module is used for uploading the recorded information into a block chain and calculating the state change probability according to the recorded information; and the backup updating module is used for backing up the aging prediction model and iteratively updating the aging prediction model according to the state change probability.

The third aspect of the present invention provides an express full-course aging prediction apparatus, including: a memory and at least one processor, the memory having instructions stored therein; at least one processor invokes the instructions in the memory to cause the express full-time age prediction device to perform the steps of the express full-time age prediction method of any one of the above.

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 steps of the express full-course aging prediction method of any one of the above.

According to the technical scheme, the estimated delivery time length is obtained by receiving the express order information and inputting the express order information into the time efficiency prediction model, the whole-course time efficiency prediction is close to actual time efficiency, the prediction accuracy is improved, a customer can know the whole-course accurate estimated delivery time efficiency in advance, the service level is improved, the real-time state change information of the express order information is monitored, the estimated delivery time length is adjusted based on the real-time state change information, the updated delivery time length is obtained, the whole-course estimated delivery time efficiency can be changed along with the actual time, the flexibility is high, the prediction accuracy is further improved, the updated delivery time length and the estimated delivery time length are compared, a difference value is obtained, the early warning information is generated according to the difference value, the customer can conveniently make corresponding measures according to the change of the delivery time length, and the customer satisfaction is improved.

Drawings

Fig. 1 is a first flowchart of an express full-course aging prediction method provided by an embodiment of the present invention;

Fig. 2 is a second flowchart of an express full-course aging prediction method provided by an embodiment of the present invention;

Fig. 3 is a third flowchart of an express full-course aging prediction method provided by an embodiment of the present invention;

fig. 4 is a fourth flowchart of an express full-course aging prediction method provided by an embodiment of the present invention;

fig. 5 is a schematic structural diagram of an express whole-course aging prediction device provided by an embodiment of the present invention;

Fig. 6 is another schematic structural diagram of an express whole-course aging prediction device provided by an embodiment of the present invention;

Fig. 7 is a schematic structural diagram of an express full-course aging prediction device provided by an embodiment of the present invention.

Detailed Description

The invention provides a method, a device, equipment and a storage medium for predicting the full-course aging of an express, wherein the full-course aging prediction is close to the actual aging and is close to reality, so that the prediction accuracy is improved, a customer can know the full-course accurate predicted delivery aging in advance, the service level is improved, the full-course predicted delivery aging can be changed along with the actual aging, the flexibility is high, the prediction accuracy is further improved, and the customer satisfaction is improved.

The terms "first," "second," "third," "fourth" and the like in the description and in the claims and in the above drawings, if any, are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments described herein may be implemented in other sequences than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed or inherent to such process, method, article, or apparatus.

For easy understanding, a specific flow of an embodiment of the present invention is described below, referring to fig. 1, and an embodiment of a method for predicting express global time efficiency in an embodiment of the present invention includes:

101. Acquiring historical waybill data, and identifying influence events, actual transportation routes and actual delivery time lengths respectively corresponding to the waybills in the historical waybill data;

In this embodiment, historical waybill data is obtained, the historical waybill data is subjected to data cleaning, invalid data and data with abnormal values are removed, then data preprocessing including operations of filling the missing values, adjusting time formats and the like is needed to ensure the integrity and consistency of the data, influencing events in the historical waybill data are identified by establishing various models, for example, a machine learning algorithm is used for detecting out-of-standard traffic events, weather abnormal events, holiday events, traffic jam events and the like, and then the actual transportation route and the actual delivery duration of each waybill data are extracted.

102. Constructing an aging prediction model according to the influence event, the actual transportation route and the actual distribution time length, and deploying the aging prediction model;

in this embodiment, before the aging prediction model is built, feature engineering processing needs to be performed on the influence event, the actual transportation route and the actual distribution duration, the feature engineering processing includes operations such as feature selection, feature conversion, feature combination and the like, so as to extract useful feature information for model training, after model training and evaluation are completed, the aging prediction model is deployed into an actual production environment, a model monitoring system needs to be built after deployment, performance of the model is monitored in real time, and the model is optimized and adjusted according to a monitoring result, so that the model is ensured to continuously and effectively predict distribution aging.

103. Receiving express order information, and inputting the express order information into an aging prediction model to obtain estimated delivery duration;

In this embodiment, an interface or a system for receiving order information is designed, where the order information includes shipper information, receiver information, express mail weight, sending address, delivery time requirement, etc., after receiving the order information, the system needs to preprocess the order information to ensure consistency and integrity of data format, extracts features from the received order information, such as shipping address, receiving address, express mail weight, delivery time requirement, etc., as input features of an aging prediction model, inputs the extracted features into a previously constructed aging prediction model, calls the model to predict, and according to the result output by the model, the system can obtain the estimated delivery time of the order.

104. Monitoring real-time state change information of express order information, and adjusting estimated delivery duration based on the real-time state change information to obtain updated delivery duration;

in this embodiment, a real-time status monitoring system is established, and is configured to monitor a real-time status change of the express order information, and when a change in the status of the express order is detected, trigger an operation of updating the delivery duration, and according to the real-time status change information, the system re-evaluates the current delivery duration and adjusts the estimated delivery duration, for example, if the express encounters traffic jam or weather influence during transportation, the system correspondingly prolongs the delivery duration, and after adjustment, the system obtains the updated delivery duration, where the real-time status change information is considered, so that the delivery duration is closer to an actual delivery condition.

105. Comparing the updated delivery time length with the estimated delivery time length to obtain a difference value, and generating early warning information according to the difference value;

In this embodiment, the updated delivery duration and the estimated delivery duration obtained in the previous step are obtained, the difference value between the two is calculated, the difference value is obtained by subtracting the estimated delivery duration, a threshold value of the difference value is preset and used for judging whether the difference reaches the early warning condition, the calculated difference value is compared with the preset threshold value, if the difference value exceeds the threshold value, it is indicated that the actual delivery duration and the estimated delivery duration have significant differences, when the difference value exceeds the threshold value, the system generates corresponding early warning information, the early warning information can include express delay reminding, delivery scheduling adjustment suggestion and the like, the generated early warning information is notified to related personnel, such as clients, operators, distributors and the like, and the notification mode can be realized through channels of system interface display, short message notification, mail notification and the like.

In the embodiment of the invention, the estimated delivery time length is obtained by receiving the express order information and inputting the express order information into the time efficiency prediction model, the whole-course time efficiency prediction approaches to actual time efficiency, the prediction accuracy is improved, a customer can know the whole-course accurate estimated delivery time efficiency in advance, the service level is improved, the real-time state change information of the express order information is monitored, the estimated delivery time length is adjusted based on the real-time state change information, the updated delivery time length is obtained, the whole-course estimated delivery time efficiency can be changed along with the actual time, the flexibility is high, the prediction accuracy is further improved, the updated delivery time length and the estimated delivery time length are compared, the difference value is obtained, the early warning information is generated according to the difference value, the customer can conveniently make corresponding measures according to the change of the delivery time length, and the customer satisfaction is improved.

Referring to fig. 2, a second embodiment of the express full-time aging prediction method in the embodiment of the present invention includes:

201. acquiring historical waybill data, and formatting the historical waybill data to obtain standard format waybill data;

In this embodiment, a data source of the historical waybill data is obtained, the data source may be a database, a log file, an API interface or other related systems of a carrier, the historical waybill data is extracted from the data source, the historical waybill data is converted to conform to a standard format, if the historical waybill data is from multiple sources or multiple data tables, the system needs to integrate them to obtain a complete data set, the converted and integrated data is verified, the accuracy and integrity of the data are ensured, and the formatted standard format waybill data is stored in a database, a data warehouse or other storage medium of the system for subsequent use and analysis.

202. Preprocessing the standard format waybill data to obtain preprocessed data;

In this embodiment, the extracted historical waybill data is cleaned, repeated, invalid or erroneous data is removed, the standard format waybill data is checked, the missing data is processed, abnormal values or outliers are identified and processed, the data is subjected to dimension reduction processing, so that the complexity and redundancy of a data set are reduced, the data is subjected to smoothing processing, noise and fluctuation in the data are reduced, a more stable data sequence is obtained, and finally the preprocessed data is obtained.

203. Extracting each waybill data corresponding to each waybill in the pretreatment data;

in this embodiment, all the data records included in each waybill are identified according to the waybill number field in the pre-processing data, the waybill numbers are used as keywords, all the data records in the pre-processing data are classified, the data records under the same waybill number are grouped, the data records under the same waybill number are sorted according to time sequence, duplicate records are removed, the data records under each waybill number are integrated into one complete piece of waybill data, the complete piece of waybill data comprises information such as an initial place, a destination place, goods information, a transportation mode and the like, and necessary data formatting processing such as text cleaning, date and time format conversion and the like is performed on each piece of integrated waybill data, so that subsequent data analysis and application are facilitated.

204. Identifying an influence event, an actual transportation route and an actual distribution duration in each piece of waybill data, wherein the influence event comprises a traffic volume exceeding event, a weather abnormality event, a holiday event and a traffic jam event;

In this embodiment, by analyzing information such as the number of cargoes and the destination in the waybill data, identifying whether there is a condition that the traffic exceeds the standard, analyzing the traffic situation at that time, identifying whether there is a traffic jam event such as road congestion, accident and the like, combining date, time and place information in the waybill data, acquiring corresponding weather data, identifying whether there is a weather abnormality event such as influence on transportation caused by severe weather (heavy rain, snowstorm and the like), matching date information in the waybill data by using calendar information or specific holiday data, identifying whether there is a holiday event, possibly affecting a transportation route and a delivery time during the holiday, combining start place and destination place information in the waybill data, analyzing the traffic situation at that time, identifying whether there is a traffic jam event such as road congestion, accident and the like, combining actual transportation situation and road condition information, identifying and recording the actual transportation route including transit station, route selection and the like, calculating the time information in the waybill data, calculating the delivery time to calculate the actual delivery time according to the time information in the holiday data, namely, analyzing the actual delivery time to the actual delivery time, and the actual delivery time is more accurately calculated to be more effective in consideration of the actual delivery time, and the actual delivery time is more effective to the real time and the actual delivery time is more ensured.

205. Analyzing the influence events to obtain influence factor analysis results, and generating event weight configuration information according to the influence factor analysis results;

In this embodiment, for the influence events in each waybill data, including the traffic volume exceeding event, the weather abnormal event, the holiday event and the traffic jam event, identify and classify the influence events, analyze the influence degree and the influence range of each influence event on the waybill under different conditions, determine the influence degree of each influence factor on the waybill, based on historical data or expertise, carry out deep analysis on each influence factor, including the aspects of the occurrence frequency, the duration, the influence range and the like, utilize a statistical analysis method or a machine learning model to model and analyze the association relation between each influence factor and the actual distribution duration, find the contribution degree of each influence factor on the distribution duration, assign corresponding weights to each influence event according to the analysis result of the influence factors, reflect the influence degree of the influence factors on the actual distribution duration, configure the weights based on the methods such as experience, the analysis result of data or subjective evaluation, ensure the objectivity and accuracy of the weights, represent the event in a numerical manner, such as a percentage form or a numerical form after normalization, integrate the configuration information of each influence weight into the configuration event and form, and generate the subsequent configuration information of the weight configuration table for the decision table.

206. Correlating event weight configuration information, an actual transportation route and an actual distribution time length to obtain a correlation result, and constructing a sample set according to the correlation result;

In this embodiment, the previously generated event weight configuration information is associated with an actual transportation route in each waybill data, the concrete performance of each influencing factor in the transportation process is determined according to the information such as a transit site and a path selection passed by the actual route, whether each transit site or path segment is influenced by a certain influencing event is analyzed, the comprehensive influence degree of each transit site or path segment is calculated by combining the event weight configuration information, the event weight configuration information is associated with the actual delivery time length in each waybill data, the total influence of each influencing event in each waybill on the delivery time length is calculated according to the corresponding relation between the event weight configuration information and the actual delivery time length, the contribution degree of different influencing factors on the delivery time length is analyzed, the weight ratio of each influencing factor in the delivery time length is determined, each waybill data is taken as a sample according to the association result, and each sample contains the relevant information such as the event weight configuration information, the actual delivery route and the actual delivery time length, and the whole execution condition of one waybill is reflected, and the whole execution condition of the received influencing event, the actual delivery route and the final delivery time length are included.

207. Training by using a sample set to obtain an aging prediction model, and deploying the aging prediction model;

In this embodiment, data processing and feature engineering are performed on the constructed sample set, including data cleaning, missing value filling, feature selection and the like, the relationship between each feature and the actual delivery duration is further explored through methods such as data visualization, statistical analysis and the like, key factors influencing the delivery duration are searched, a machine learning algorithm suitable for a business scene, such as linear regression, a decision tree, a random forest and the like, model training is performed by using the sample set, performance and generalization capability of the model are evaluated through methods such as cross verification, an optimal model is selected and parameter adjustment is performed, a trained aging prediction model is deployed in a production environment, and prediction service is provided for the actual business.

In the embodiment of the invention, through analyzing the historical waybill data, the factors which possibly influence the transportation timeliness can be identified in advance, the useful information is found and extracted, the model and the algorithm are further established, the more accurate timeliness prediction is realized, and the automatic operation management can be realized by deploying the model and the algorithm into the system, so that the efficiency and the accuracy are improved.

Referring to fig. 3, a third embodiment of the express full-time aging prediction method in the embodiment of the present invention includes:

301. Receiving express order information, and carrying out data structure analysis on the express order information to obtain order analysis information;

In this embodiment, an interface or service is provided to receive the order information of the express mail, which may be through an API interface, a message queue, a database, etc., according to the data structure of the order information of the express mail, a corresponding analysis algorithm or program is designed to analyze the order information, and a programming language or tool, such as Python, java, XML analysis library, etc., is used to analyze the order information, so as to convert the order information into an operable data structure.

302. Cleaning the order analysis information to obtain order cleaning information, and identifying keywords in the order cleaning information;

In this embodiment, data cleaning is performed on the order analysis information, including removing duplicate data, processing missing values, correcting error data, and the like, according to service requirements, operations such as format conversion, unified data unit, standard data naming, and the like are required, then text processing is performed, such as removing special symbols, disabling words, and the like, so as to facilitate subsequent keyword recognition, further processing is performed on the cleaned order information, such as word segmentation, part-of-speech tagging, entity recognition, and the like, the order information is processed by using a natural language processing tool or library, such as NLTK, space, and the like, key information in the order information is extracted, keywords in the order cleaning information are identified by using text mining techniques, such as TF-IDF, word frequency statistics, and the like, keywords are extracted from the order information by means of constructing a word bag model, performing topic modeling, and the like, a weight threshold of the keywords is set, and keywords with the most representativeness and importance are screened.

303. Extracting a mail address field and a receipt address field according to the keywords, and respectively carrying out format verification on the mail address field and the receipt address field by adopting a preset address verification rule;

In this embodiment, by using the aforementioned keyword recognition method, the sender address field and the receiver address field are extracted from the order cleaning information, the address field can be located by recognizing the information such as the place name and the street name in the keyword, the format check rules of the sender address field and the receiver address field are preset, including but not limited to the integrity of the province area, the correctness of the postal code, and the like, the information which each field should contain, such as the information such as the province area, the city, the county, the street, the house number, and the like, are determined, the preset address check rules are applied to the extracted sender address field, the integrity and the accuracy of each part are verified one by one, whether the name of the province area is correct, whether the postal code accords with the specification, the street number, and the like is complete, and similarly, the extracted receiver address field is also subjected to format check according to the preset rules, so as to ensure the accuracy and the integrity of the address information.

304. When the verification passes, generating a predicted transportation route according to the mail address field and the receipt address field, and inputting the predicted transportation route into an aging prediction model to obtain a predicted delivery time;

In this embodiment, when verification passes, based on the sending address field and the receiving address field that pass the verification, the map API or the logistics route planning tool is used to generate an estimated transportation route, where the route planning generally includes information of a start point, a route point (such as a logistics transfer station), an end point, and the like, so as to ensure that the goods can be quickly delivered to a destination according to an optimal route, the generated estimated transportation route is input into an aging prediction model, where the model can predict a delivery duration of the goods based on various aspects such as historical data, traffic conditions, seasonal factors, and the like, and the aging prediction model outputs an estimated delivery duration, that is, a time required for the goods to reach the receiving address from the sending address according to the input estimated transportation route and other relevant factors.

305. Creating a state change monitor, and monitoring the state change of the express order information by using the state change monitor;

In this embodiment, a state change monitor is created, and a corresponding monitoring method is written in the state change monitor, so as to capture the change of the state of the order information of the express mail, where the monitoring method can process the change of the state of the order, such as updating the state of the order, generating a log record, triggering a notification, and when the state of the order information of the express mail changes, the system triggers the corresponding state change event.

306. When monitoring real-time state change information of express order information, acquiring current real-time state change information;

In this embodiment, when the system monitors that the state of the order information of the express mail changes, corresponding processing logic is written in the monitor, the current real-time state information is obtained and stored in the system or other processing is performed, and the latest state information of the current order is obtained by calling an order information interface, querying a database and the like.

307. Adjusting the estimated delivery time based on the real-time state change information to obtain updated delivery time;

In this embodiment, the obtained real-time state change information is analyzed, the state change related to the delivery duration is identified, whether the original estimated delivery duration needs to be adjusted is determined according to different state changes, if the real-time state change information indicates that the delivery progress of the order is faster or slower, the estimated delivery duration needs to be adjusted accordingly, the adjustment of the delivery duration can be performed by comparing with historical data, calculating with an algorithm or a rule-based method, and the like, and after the new delivery duration is determined, the updated delivery duration is stored in the system.

In the embodiment of the invention, the accuracy of the order information can be ensured by analyzing and cleaning the data structure of the order information of the express mail, the whole process adopts automatic processing, the adjustment from the analysis of the order information to the estimated delivery time is automatically carried out by a program, the efficiency is improved, the possibility of manual errors is reduced, the state change monitor is established and the state change information of the order information of the express mail is acquired in real time, the state of the order can be known in time, the estimated delivery time is adjusted, the real-time property and accuracy of delivery are improved, the estimated delivery time is generated according to the mail address field and the receipt address field, the estimated delivery time is adjusted by combining the real-time state change information, the optimization of the delivery route and the possible delay condition of advance response are facilitated, and the timeliness and reliability of delivery can be improved by timely adjusting the delivery time length, so that the satisfaction degree and the loyalty degree of customers are improved.

Referring to fig. 4, a fourth embodiment of the express full-time aging prediction method in the embodiment of the present invention includes:

401. comparing the updated delivery time length with the estimated delivery time length to obtain a difference value;

In this embodiment, the values of the latest updated delivery duration and the estimated delivery duration are obtained, the updated delivery duration is the latest delivery duration adjusted according to the real-time state change information, the estimated delivery duration is the initial value set previously or the value adjusted last time, the updated delivery duration and the estimated delivery duration are compared, the difference value between them is calculated, the positive value indicates that the actual delivery duration is longer than the estimated delivery duration, and the negative value indicates that the actual delivery duration is shorter than the estimated delivery duration.

402. Judging whether the difference value is larger than a preset alarm threshold value or not;

In this embodiment, an alarm threshold is set for the difference value, so as to trigger an alarm when the difference value exceeds the threshold, the setting of the alarm threshold should be adjusted according to the actual situation, and in consideration of factors such as a delivery strategy, a customer requirement, etc., after the difference value of the updated delivery duration and the estimated delivery duration is obtained, the difference value needs to be compared with the alarm threshold, and if the difference value is greater than the threshold, an abnormal situation is considered to occur, and an alarm mechanism needs to be triggered, so that measures are taken in time to handle the abnormal situation.

403. If yes, generating early warning information according to the difference value, and sending the early warning information to a client corresponding to the express order information;

in this embodiment, if the difference value is greater than the preset alarm threshold, corresponding early warning information is generated according to the difference value, where the early warning information may include content such as a delivery delay condition, a possible problem, a suggested processing measure, etc., after the early warning information is generated, client information corresponding to the express order information needs to be acquired so as to send the early warning information to related clients, the client information may be acquired through an order system or a client information database, the generated early warning information is sent to a client corresponding to the express order information, and the early warning information may be sent to the client through a mail, a short message, an App push, etc., so as to remind the clients of the plan that the delivery condition is concerned and may be affected.

404. Recording the early warning information to obtain recording information;

in this embodiment, a format of recording early warning information is defined so as to record the early warning information according to a certain structure, where the recording format includes fields of early warning time, order number, client information, difference value, early warning content, etc., after the early warning information is generated and sent to the client, the early warning information needs to be recorded, the generated early warning information is stored according to the recording format, and can be recorded by selecting a database, a log file or other suitable modes, when the recording information needs to be acquired, the storage position of the early warning information record can be queried, the corresponding recording information can be acquired, and the early warning recording information of a specific time period or a specific order can be acquired by querying according to key information such as the early warning time, the order number, etc.

405. Uploading the recorded information to a blockchain, and calculating the state change probability according to the recorded information;

in this embodiment, a suitable blockchain platform, such as an ethernet, super ledger, etc., is selected, and before record information is ready to be uploaded to the blockchain, a blockchain data structure is designed, including the information content stored in each block, and the association between the blocks, the record information is written to the blockchain using an interface or tool provided by the blockchain platform, and the state change probability is calculated from the record information.

406. Backing up the time-effect prediction model, and carrying out iterative updating on the time-effect prediction model according to the state change probability;

In this embodiment, the current aging prediction model is backed up, so that a reliable original version is ensured to be available for rollback and comparison when iterative updating is performed, the performance and accuracy of the current aging prediction model can be evaluated according to the record information uploaded into the blockchain before and the state change probability obtained by calculation of the intelligent contract, how to iteratively update the aging prediction model can be determined based on the state change probability and the model evaluation result, and the model is retrained by using the historical data by using machine learning or deep learning technology so as to adapt to the latest state change and trend.

In the embodiment of the invention, the real-time monitoring and early warning of the delivery timeliness can be realized by comparing the updated delivery time length with the estimated delivery time length and judging whether the difference value is larger than the preset alarm threshold value, the timely discovery capability of potential problems is improved, when the difference value is larger than the preset alarm threshold value, early warning information is timely generated and sent to the client, the client is helped to know the condition of delivery delay in the first time, and is ready for coping, the transparency and the guarantee of the client service are improved, the early warning information is recorded and uploaded to a block chain, the authenticity and the non-tamper modification of the early warning information can be guaranteed, the state change probability is calculated according to the recorded information, the iterative update is carried out on the time-efficiency prediction model, the accuracy and the applicability of the time-efficiency prediction model are improved continuously, the prediction capability of the delivery timeliness is improved, the efficiency is improved through automatic early warning, the recording and the model updating flow, and the manual intervention and the processing time are reduced, and the efficiency and the quality of the whole operation are improved.

The method for predicting the full-time aging of the express in the embodiment of the present invention is described above, and the device for predicting the full-time aging of the express in the embodiment of the present invention is described below, referring to fig. 5, one embodiment of the device for predicting the full-time aging of the express in the embodiment of the present invention includes:

The acquiring and identifying module 501 is configured to acquire historical waybill data, and identify an impact event, an actual transportation route and an actual delivery duration corresponding to each waybill in the historical waybill data;

The deployment module 502 is configured to construct an aging prediction model according to the influence event, the actual transportation route and the actual delivery duration, and deploy the aging prediction model;

The receiving and inputting module 503 is configured to receive the order information of the express mail, and input the order information of the express mail into the aging prediction model, so as to obtain an estimated delivery duration;

the monitoring adjustment module 504 is configured to monitor real-time status change information of the express order information, and adjust the estimated delivery duration based on the real-time status change information to obtain updated delivery duration;

the comparison generating module 505 is configured to compare the updated delivery duration with the estimated delivery duration, obtain a difference value, and generate early warning information according to the difference value.

In the embodiment, the predicted delivery time is obtained by receiving the express order information and inputting the express order information into the time-effect prediction model, the whole-course time-effect prediction approaches actual time-effect, the prediction accuracy is improved, a customer can know the predicted delivery time-effect accurately in the whole course in advance, the service level is improved, the real-time state change information of the express order information is monitored, the predicted delivery time-effect is adjusted based on the real-time state change information, the updated delivery time-effect is obtained, the whole-course predicted delivery time-effect can be changed along with the actual time-effect, the flexibility is high, the prediction accuracy is further improved, the updated delivery time-effect and the predicted delivery time-effect are compared, the difference value is obtained, the early warning information is generated according to the difference value, the customer can conveniently make corresponding measures according to the change of the delivery time-effect, and the customer satisfaction is improved.

Referring to fig. 6, another embodiment of an express full-course aging prediction apparatus in an embodiment of the present invention includes:

The acquiring and identifying module 501 is configured to acquire historical waybill data, and identify an impact event, an actual transportation route and an actual delivery duration corresponding to each waybill in the historical waybill data;

The deployment module 502 is configured to construct an aging prediction model according to the influence event, the actual transportation route and the actual delivery duration, and deploy the aging prediction model;

The receiving and inputting module 503 is configured to receive the order information of the express mail, and input the order information of the express mail into the aging prediction model, so as to obtain an estimated delivery duration;

the monitoring adjustment module 504 is configured to monitor real-time status change information of the express order information, and adjust the estimated delivery duration based on the real-time status change information to obtain updated delivery duration;

The comparison generating module 505 is configured to compare the updated delivery duration with the estimated delivery duration to obtain a difference value, and generate early warning information according to the difference value;

In this embodiment, the acquisition identification module 501 includes: the obtaining formatting unit 5011 is configured to obtain historical waybill data, and format the historical waybill data to obtain standard format waybill data; a preprocessing unit 5012, configured to preprocess standard format waybill data to obtain preprocessed data; an extracting unit 5013, configured to extract each piece of waybill data corresponding to each waybill in the preprocessed data; the identifying unit 5014 is configured to identify an influencing event, an actual transportation route, and an actual delivery duration in each waybill data, where the influencing event includes a traffic volume exceeding event, a weather abnormality event, a holiday event, and a traffic jam event.

In this embodiment, building the deployment module 502 includes: the analysis generating unit 5021 is used for analyzing the influence events to obtain influence factor analysis results and generating event weight configuration information according to the influence factor analysis results; the association construction unit 5022 is used for associating the event weight configuration information, the actual transportation route and the actual delivery duration to obtain an association result, and constructing a sample set according to the association result; the training deployment unit 5023 is configured to train by using the sample set to obtain an aging prediction model, and deploy the aging prediction model.

In the present embodiment, the receiving input module 503 includes: the receiving and analyzing unit 5031 is configured to receive order information of a express mail, and perform data structure analysis on the order information of the express mail to obtain order analysis information; the cleaning identification unit 5032 is configured to clean the order analysis information to obtain order cleaning information, and identify keywords in the order cleaning information; the extraction verification unit 5033 is configured to extract a sender address field and a receiver address field according to the keyword, and perform format verification on the sender address field and the receiver address field respectively by adopting a preset address verification rule; and the generation input unit 5034 is configured to generate an estimated transportation route according to the consignment address field and the consignment address field when the verification passes, and input the estimated transportation route into the aging prediction model to obtain an estimated delivery duration.

In this embodiment, the snoop adjustment module 504 includes: a creation monitoring unit 5041, configured to create a state change monitor, and monitor, with the state change monitor, a state change of the express order information; an obtaining unit 5042, configured to obtain current real-time status change information when monitoring real-time status change information of the express order information; the adjusting unit 5043 is configured to adjust the estimated delivery duration based on the real-time status change information, so as to obtain an updated delivery duration.

In this embodiment, the alignment generation module 505 includes: the comparison unit 5051 is configured to compare the updated delivery duration with the estimated delivery duration to obtain a difference value; a judging unit 5052, configured to judge whether the difference value is greater than a preset alarm threshold; and the generation and transmission unit 5053 is configured to generate early warning information according to the difference value if yes, and transmit the early warning information to the client corresponding to the express order information.

In this embodiment, further comprising: the recording module 506 is configured to record the early warning information to obtain recording information; the uploading calculation module 507 is configured to upload the record information into the blockchain, and calculate a state change probability according to the record information; and the backup updating module 508 is used for backing up the time-lapse prediction model and iteratively updating the time-lapse prediction model according to the state change probability.

The express full-range aging prediction device in the embodiment of the present invention is described in detail from the perspective of the modularized functional entity in fig. 5 and fig. 6, and the express full-range aging prediction device in the embodiment of the present invention is described in detail from the perspective of hardware processing.

Fig. 7 is a schematic structural diagram of an express full-time-efficiency prediction device according to an embodiment of the present invention, where the express full-time-efficiency prediction device 600 may have a relatively large difference due to different configurations or performances, and may include one or more processors (central processing units, CPU) 610 (e.g., one or more processors) and a memory 620, and one or more storage mediums 630 (e.g., one or more mass storage devices) storing applications 633 or data 632. Wherein the memory 620 and the storage medium 630 may be transitory or persistent storage. The program stored on the storage medium 630 may include one or more modules (not shown), each of which may include a series of instruction operations in the express full-time aging prediction apparatus 600. Still further, the processor 610 may be configured to communicate with the storage medium 630, and execute a series of instruction operations in the storage medium 630 on the express full-range aging prediction device 600, so as to implement the steps of the express full-range aging prediction method provided in the above method embodiments.

The express full time aging prediction device 600 may also include one or more power sources 640, one or more wired or wireless network interfaces 650, one or more input/output interfaces 660, and/or one or more operating systems 631, such as Windows Serve, mac OS X, unix, linux, freeBSD, and the like. It will be appreciated by those skilled in the art that the express full time period prediction apparatus structure shown in fig. 7 is not limiting and may include more or fewer components than shown, or may be combined with certain components or a different arrangement of components.

The invention also provides a computer readable storage medium, which can be a nonvolatile computer readable storage medium, and can also be a volatile computer readable storage medium, wherein instructions are stored in the computer readable storage medium, and when the instructions run on a computer, the instructions cause the computer to execute the steps of the express full-course aging prediction method.

It will be clearly understood by those skilled in the art that, for convenience and brevity of description, the specific working process of the system or apparatus and unit described above may refer to the corresponding process in the foregoing method embodiment, which is not repeated herein.

The integrated units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied essentially or in part or all of the technical solution or in part in the form of a software product stored in a storage medium, including instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a read-only memory (ROM), a random access memory (random access memory, RAM), a magnetic disk, or an optical disk, or other various media capable of storing program codes.

Finally, it should be noted that: the foregoing is merely a preferred example of the present invention, and the present invention is not limited thereto, but it is to be understood that modifications and equivalents of some of the technical features described in the foregoing embodiments may be made by those skilled in the art, although the present invention has been described in detail with reference to the foregoing embodiments. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims (10)

1. The express full-course aging prediction method is characterized by comprising the following steps of:

Acquiring historical waybill data, and identifying influence events, actual transportation routes and actual distribution time lengths respectively corresponding to the waybills in the historical waybill data;

constructing an aging prediction model according to the influence event, the actual transportation route and the actual distribution time length, and deploying the aging prediction model;

receiving express order information, and inputting the express order information into the aging prediction model to obtain estimated delivery duration;

Monitoring real-time state change information of the express order information, and adjusting the estimated delivery duration based on the real-time state change information to obtain updated delivery duration;

Comparing the updated delivery time length with the estimated delivery time length to obtain a difference value, and generating early warning information according to the difference value.

2. The express delivery whole-course aging prediction method according to claim 1, wherein the obtaining historical waybill data and identifying the influence event, the actual transportation route and the actual delivery duration corresponding to each waybill in the historical waybill data respectively comprise:

Acquiring historical waybill data, and formatting the historical waybill data to obtain standard format waybill data;

Preprocessing the standard format waybill data to obtain preprocessed data;

extracting each waybill data corresponding to each waybill in the preprocessing data;

And identifying influence events, actual transportation routes and actual distribution time lengths in each piece of waybill data, wherein the influence events comprise traffic volume exceeding events, weather abnormal events, holiday events and traffic jam events.

3. The express delivery whole-course aging prediction method according to claim 1, wherein the constructing an aging prediction model according to the influence event, the actual transportation route and the actual delivery duration, and deploying the aging prediction model comprises:

Analyzing the influence events to obtain influence factor analysis results, and generating event weight configuration information according to the influence factor analysis results;

Correlating the event weight configuration information, the actual transportation route and the actual distribution duration to obtain a correlation result, and constructing a sample set according to the correlation result;

And training by using the sample set to obtain an aging prediction model, and deploying the aging prediction model.

4. The express delivery whole-course aging prediction method according to claim 1, wherein the receiving express order information and inputting the express order information into the aging prediction model to obtain the estimated delivery duration comprises:

Receiving express order information, and carrying out data structure analysis on the express order information to obtain order analysis information;

Cleaning the order analysis information to obtain order cleaning information, and identifying keywords in the order cleaning information;

extracting a mail address field and a receipt address field according to the keywords, and respectively carrying out format verification on the mail address field and the receipt address field by adopting a preset address verification rule;

when the verification is passed, generating a predicted transportation route according to the mail address field and the receipt address field, and inputting the predicted transportation route into the aging prediction model to obtain a predicted delivery duration.

5. The express delivery whole-course aging prediction method according to claim 1, wherein the monitoring the real-time state change information of the express order information, adjusting the estimated delivery duration based on the real-time state change information, and obtaining an updated delivery duration comprises:

Creating a state change monitor, and monitoring the state change of the express order information by using the state change monitor;

When monitoring the real-time state change information of the express order information, acquiring the current real-time state change information;

and adjusting the estimated delivery time based on the real-time state change information to obtain updated delivery time.

6. The express delivery whole-course aging prediction method according to claim 1, wherein the comparing the updated delivery duration with the estimated delivery duration to obtain a difference value, and generating early warning information according to the difference value comprises:

Comparing the updated delivery time length with the estimated delivery time length to obtain a difference value;

Judging whether the difference value is larger than a preset alarm threshold value or not;

If yes, generating early warning information according to the difference value, and sending the early warning information to a client corresponding to the express order information.

7. The method for predicting the full-course aging of an express delivery according to claim 1, wherein the comparing the updated delivery duration with the estimated delivery duration to obtain a difference value, and generating early warning information according to the difference value, further comprises:

Recording the early warning information to obtain recording information;

uploading the recorded information to a blockchain, and calculating state change probability according to the recorded information;

And backing up the aging prediction model, and carrying out iterative updating on the aging prediction model according to the state change probability.

8. Whole ageing prediction unit of express delivery, its characterized in that includes:

the system comprises an acquisition and identification module, a storage module and a distribution module, wherein the acquisition and identification module is used for acquiring historical waybill data and identifying influence events, actual transportation routes and actual distribution time lengths respectively corresponding to all waybills in the historical waybill data;

the deployment module is used for constructing an aging prediction model according to the influence event, the actual transportation route and the actual distribution time length and deploying the aging prediction model;

The receiving and inputting module is used for receiving express order information and inputting the express order information into the aging prediction model to obtain estimated delivery duration;

The monitoring and adjusting module is used for monitoring real-time state change information of the express order information, and adjusting the estimated delivery duration based on the real-time state change information to obtain updated delivery duration;

the comparison generation module is used for comparing the updated delivery time length with the estimated delivery time length to obtain a difference value and generating early warning information according to the difference value.

9. Express delivery whole-course aging prediction equipment, a serial communication port, express delivery whole-course aging prediction equipment includes: a memory and at least one processor, the memory having instructions stored therein;

At least one of the processors invokes the instructions in the memory to cause the express full time effect prediction device to perform the steps of the express full time effect prediction method of any one of claims 1-7.

10. A computer readable storage medium having instructions stored thereon, which when executed by a processor, implement the steps of the express full range aging prediction method of any one of claims 1-7.