CN116245112A - Logistics information identification method and device and computer equipment - Google Patents
Logistics information identification method and device and computer equipment Download PDFInfo
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Abstract
The invention relates to the field of artificial intelligence, and discloses a method, a device and computer equipment for identifying logistics information, which are used for improving the accuracy of identifying the logistics information. The method comprises the following steps: performing feature mapping matching on the logistics feature integrated library to obtain at least one second stream information and at least one corresponding second stream track; inputting user decision information into a preset decision information analysis model to analyze the user decision information, so as to obtain a decision information analysis result; according to the analysis result of the decision information, decision feedback is carried out on at least one second stream information, and at least one target stream track corresponding to the at least one second stream track is constructed; and carrying out logistic anomaly detection on at least one second stream information according to at least one target logistic track to obtain an anomaly detection result, and generating a corresponding anomaly logistic processing strategy according to the anomaly detection result.
Description
Technical Field
The present invention relates to the field of artificial intelligence, and in particular, to a method, an apparatus, and a computer device for identifying logistics information.
Background
Currently, many e-commerce platforms and express companies provide order tracking functions, but these functions generally only provide simple sign-up status and logistics track information, and cannot update logistics positions or remind of abnormal situations in real time. In the prior art, users need to manually input a waybill number to track the logistics information, and codes used by different logistics companies are different, so that the way is easy to make mistakes.
Disclosure of Invention
The invention provides a method, a device and computer equipment for identifying logistics information, which are used for improving the accuracy of identifying the logistics information.
The first aspect of the present invention provides a method for identifying logistics information, where the method for identifying logistics information includes:
acquiring a plurality of first logistics information based on a plurality of preset logistics data sources, and respectively carrying out dynamic track analysis on the plurality of first logistics information to obtain a first logistics track corresponding to each first logistics information;
extracting features of the plurality of first logistics information to obtain at least one logistics feature corresponding to each first logistics information, and integrating the features of the at least one logistics feature corresponding to each first logistics information to generate a logistics feature integrated library;
Receiving a logistics operation request input by a user terminal, acquiring user decision information, and performing feature mapping matching on the logistics feature integrated library according to the user decision information to obtain at least one second stream information and at least one corresponding second stream track;
inputting the user decision information into a preset decision information analysis model for user decision information analysis to obtain a corresponding decision information analysis result, wherein the decision information analysis result comprises the following steps: user decision type and user decision factor;
according to the user decision type and the user decision factor, performing decision feedback on the at least one second stream information, and constructing at least one target stream track corresponding to the at least one second stream track;
and carrying out logistic anomaly detection on the at least one second stream information according to the at least one target logistic track to obtain an anomaly detection result, and generating a corresponding anomaly logistic processing strategy according to the anomaly detection result.
With reference to the first aspect, in a first implementation manner of the first aspect of the present invention, the obtaining a plurality of first logistics information based on a plurality of preset logistics data sources, and performing dynamic track analysis on the plurality of first logistics information to obtain a first logistics track corresponding to each first logistics information respectively, includes:
Setting a data format and a transmission protocol of each logistics data source based on a plurality of preset logistics data sources, and calling a target API interface according to the data format and the transmission protocol to acquire a plurality of first logistics information;
scanning the plurality of first logistics information through a preset keyword recognition model to obtain at least one keyword corresponding to each first logistics information;
according to the at least one keyword, carrying out semantic matching in a preset track database to obtain corresponding at least one keyword semantic information;
and inquiring the first logistics track corresponding to each piece of first logistics information according to the at least one piece of keyword semantic information.
With reference to the first aspect, in a second implementation manner of the first aspect of the present invention, the feature extracting the plurality of first logistics information to obtain at least one logistics feature corresponding to each first logistics information, and feature information integrating the at least one logistics feature corresponding to each first logistics information to generate a logistics feature integrated library, where the feature integrating library includes:
extracting features of the plurality of first logistics information to obtain at least one logistics feature corresponding to each first logistics information;
Performing feature traversal on at least one logistics feature corresponding to each piece of first logistics information to obtain a feature traversal result;
based on the feature traversing result, carrying out feature identification construction on the at least one logistics feature to obtain at least one feature identification;
and integrating the characteristic information of the at least one logistics characteristic according to the at least one characteristic identifier to generate a logistics characteristic integrated library.
With reference to the first aspect, in a third implementation manner of the first aspect of the present invention, the receiving a logistic operation request input by a user terminal and obtaining user decision information, performing feature mapping matching on the logistic feature integrated library according to the user decision information, to obtain at least one second stream information and at least one corresponding second stream track, where the step includes;
when detecting that a user enters an interactive page of a user terminal, acquiring a logistics operation request, and analyzing the logistics operation request to obtain corresponding user decision information;
judging whether the user decision information meets a preset logistics order verification condition or not;
if yes, feature mapping matching is carried out between the user decision information and the logistics feature integrated library, and at least one second stream information is obtained;
And determining at least one corresponding second stream track according to the at least one second stream information.
With reference to the first aspect, in a fourth implementation manner of the first aspect of the present invention, the inputting the user decision information into a preset decision information analysis model performs user decision information analysis to obtain a corresponding decision information analysis result, where the decision information analysis result includes: user decision type and user decision factor, including:
inputting the user decision information into a preset decision information analysis model, wherein the decision information analysis model comprises: a two-way long short-time memory layer, a three-layer convolution layer and a prediction layer;
extracting features of the user decision information through the decision information analysis model to obtain a plurality of decision features;
classifying the plurality of decision features to obtain decision classification standards;
calculating decision results of the plurality of decision features according to the decision classification standard to generate corresponding decision information analysis results, wherein the decision information analysis results comprise: user decision type and user decision factor.
With reference to the first aspect, in a fifth implementation manner of the first aspect of the present invention, the performing decision feedback on the at least one second stream information according to the user decision type and the user decision factor, and constructing at least one target stream track corresponding to the at least one second stream track includes:
Constructing a corresponding decision rule according to the user decision type and the user decision factor;
according to the decision rule, performing decision feedback on the at least one second stream information to obtain decision feedback information;
and constructing at least one target logistics track corresponding to the at least one second logistics track according to the decision feedback information.
With reference to the first aspect, in a sixth implementation manner of the first aspect of the present invention, the performing, according to the at least one target logistics track, a logistics anomaly detection on the at least one second stream information to obtain an anomaly detection result, and generating a corresponding anomaly logistics processing policy according to the anomaly detection result, includes:
carrying out logistic anomaly detection on the at least one second stream information according to the at least one target logistic track to obtain an anomaly detection result;
and matching the corresponding abnormal logistics processing strategy from a plurality of preset candidate abnormal processing strategies according to the abnormal detection result.
The second aspect of the present invention provides a device for identifying logistics information, the device for identifying logistics information comprising:
the acquisition module is used for acquiring a plurality of first logistics information based on a plurality of preset logistics data sources, and respectively carrying out dynamic track analysis on the plurality of first logistics information to obtain a first logistics track corresponding to each first logistics information;
The extraction module is used for carrying out feature extraction on the plurality of first logistics information to obtain at least one logistics feature corresponding to each piece of first logistics information, and carrying out feature information integration on at least one logistics feature corresponding to each piece of first logistics information to generate a logistics feature integrated library;
the matching module is used for receiving a logistics operation request input by a user terminal, acquiring user decision information, and carrying out feature mapping matching on the logistics feature integrated library according to the user decision information to obtain at least one second stream information and at least one corresponding second stream track;
the analysis module is used for inputting the user decision information into a preset decision information analysis model to perform user decision information analysis to obtain a corresponding decision information analysis result, wherein the decision information analysis result comprises the following components: user decision type and user decision factor;
the feedback module is used for carrying out decision feedback on the at least one second stream information according to the user decision type and the user decision factor, and constructing at least one target stream track corresponding to the at least one second stream track;
the detection module is used for carrying out logistic anomaly detection on the at least one second stream information according to the at least one target logistic track to obtain an anomaly detection result, and generating a corresponding anomaly logistic processing strategy according to the anomaly detection result.
A third aspect of the present invention provides a computer apparatus comprising: a memory and at least one processor, the memory having instructions stored therein; the at least one processor invokes the instructions in the memory to cause the computer device to perform the method of identifying logistic information as described above.
A fourth aspect of the present invention provides a computer readable storage medium having instructions stored therein which, when run on a computer, cause the computer to perform the above-described method of identifying logistics information.
In the technical scheme provided by the invention, feature mapping matching is carried out on a logistics feature integrated library to obtain at least one second stream information and at least one corresponding second stream track; inputting user decision information into a preset decision information analysis model to analyze the user decision information, so as to obtain a decision information analysis result; according to the analysis result of the decision information, decision feedback is carried out on at least one second stream information, and at least one target stream track corresponding to the at least one second stream track is constructed; according to the logistics information processing method, the logistics information processing system and the logistics information processing system, logistics abnormality detection is carried out on at least one second physical stream information according to at least one target logistics track to obtain an abnormality detection result, and a corresponding abnormality logistics processing strategy is generated according to the abnormality detection result. The method comprises the steps of API interface integration, automatic identification coding, visual display, abnormal reminding and the like, and through integrating information of a plurality of logistics data sources and providing comprehensive tracking service, a user can more conveniently track logistics information, and user experience and use satisfaction are improved.
Drawings
FIG. 1 is a schematic diagram of an embodiment of a method for identifying logistic information according to an embodiment of the present invention;
FIG. 2 is a flow chart of dynamic trajectory analysis for a plurality of first logistics information respectively in an embodiment of the present invention;
FIG. 3 is a flow chart of feature map matching of a logistic feature integrated library according to user decision information in an embodiment of the present invention;
FIG. 4 is a flowchart of user decision information analysis performed by inputting user decision information into a preset decision information analysis model in an embodiment of the present invention;
FIG. 5 is a schematic diagram of an embodiment of a device for identifying logistic information according to an embodiment of the present invention;
FIG. 6 is a schematic diagram of one embodiment of a computer device in an embodiment of the invention.
Detailed Description
The embodiment of the invention provides a method, a device and computer equipment for identifying logistics information, which are used for improving the accuracy of identifying the logistics information. 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 ease of understanding, a specific flow of an embodiment of the present invention is described below, referring to fig. 1, and one embodiment of a method for identifying logistic information in an embodiment of the present invention includes:
s101, acquiring a plurality of first logistics information based on a plurality of preset logistics data sources, and respectively carrying out dynamic track analysis on the plurality of first logistics information to obtain a first logistics track corresponding to each first logistics information;
it is to be understood that the execution body of the present invention may be a physical distribution information identification device, and may also be a terminal or a server, which is not limited herein. The embodiment of the invention is described by taking a server as an execution main body as an example.
Specifically, the server acquires a plurality of first logistics information through a plurality of preset logistics data sources, and the process needs to acquire the first logistics information from different data sources, such as transportation records of logistics companies, warehouse entry and warehouse exit records and the like, so as to facilitate subsequent dynamic track analysis. Then, the server analyzes the dynamic track of each piece of first logistics information to obtain a first logistics track corresponding to each piece of first logistics information. The process needs to analyze the track of each piece of logistics information, including information such as logistics nodes, transportation modes, transportation time, transportation routes and the like, and finally obtains a first logistics track corresponding to each piece of first logistics information.
S102, extracting features of a plurality of first logistics information to obtain at least one logistics feature corresponding to each first logistics information, and integrating the features of at least one logistics feature corresponding to each first logistics information to generate a logistics feature integrated library;
specifically, the server performs feature extraction on a plurality of first logistics information to obtain at least one logistics feature corresponding to each first logistics information, wherein the server extracts meaningful features such as logistics time, transportation mode, transportation distance and the like from each logistics information, and further, the server performs feature information integration on at least one logistics feature corresponding to each first logistics information to generate a logistics feature integrated library, wherein the server combines and integrates each logistics feature to generate the logistics feature integrated library, and in the process of generating the logistics feature integrated library, the relationship between different features such as the relationship between logistics time and transportation distance is according to.
S103, receiving a logistics operation request input by a user terminal, acquiring user decision information, and performing feature mapping matching on a logistics feature integrated library according to the user decision information to acquire at least one second stream information and at least one corresponding second stream track;
The server receives the HTTP request from the user terminal through the interface, obtains the logistic operation request and the decision information provided by the user by using the corresponding API, encapsulates the logistic operation request and the decision information into JSON format data, and sends the JSON format data to the back end. And the back end uses a machine learning model to perform feature mapping matching on the features provided by the user, and uses an algorithm to find out second stream information meeting the conditions from the stream feature integrated library. After finding out the second stream information meeting the conditions, the back end uses a machine learning algorithm to analyze the dynamic track of the second stream information and obtain a corresponding second stream track. And finally, the back end packages the second stream information and the corresponding second stream track into data in a JSON format and sends the data to the front end for a user to check.
S104, inputting user decision information into a preset decision information analysis model to analyze the user decision information, and obtaining a corresponding decision information analysis result, wherein the decision information analysis result comprises the following steps: user decision type and user decision factor;
specifically, the server inputs the user decision information into a preset decision information analysis model for analysis, and a corresponding decision information analysis result is obtained. The server analyzes the decision information provided by the user through a machine learning technology, and extracts the user decision type and decision factors from the decision information by using an algorithm. The decision type refers to a decision type made by a user in a logistics operation, such as inquiring a logistics state, modifying a logistics plan, and the like. The decision factor refers to factors considered by the user when making a decision, such as logistics time, transportation mode, and the like.
S105, performing decision feedback on at least one second stream information according to the user decision type and the user decision factor, and constructing at least one target stream track corresponding to the at least one second stream track;
specifically, the server performs decision feedback on at least one second stream information according to the user decision type and decision factor obtained before, and constructs a target stream track corresponding to the at least one second stream track. The method comprises the steps that a server machine learning algorithm finds second object flow information which accords with a user decision type and a decision factor from an object flow feature integrated library, a dynamic track analysis algorithm is used for constructing a corresponding object flow track, in the feedback process, the second object flow information is determined according to actual demand data of a user and logistics capacity evaluation data of an enterprise, and the server dynamically plans according to time, distance, cost and the like of different logistics links when constructing the object flow track, and finally at least one object flow track corresponding to at least one second object flow track is obtained.
106. And carrying out logistic anomaly detection on at least one second stream information according to at least one target logistic track to obtain an anomaly detection result, and generating a corresponding anomaly logistic processing strategy according to the anomaly detection result.
Specifically, the server performs logistic anomaly detection on at least one second stream information according to at least one target logistic track to obtain an anomaly detection result, and generates a corresponding anomaly logistic processing strategy according to the anomaly detection result. Specifically, first, it is necessary to acquire the related data of the target stream trajectory and the related data of the at least one second stream information. The related data can comprise data information such as a logistics path, a time stamp, logistics nodes, a transport means and the like, further, the server analyzes the logistics data through a machine learning algorithm, compares differences between a target logistics track and a second logistics track, and performs abnormality detection according to factors such as time, distance and cost of different logistics links in the detection process. Further, it should be noted that, the analysis result may include information such as an anomaly type, an anomaly degree, an anomaly reason, etc., and in the embodiment of the present invention, a corresponding anomaly logistics processing policy may be formulated according to different anomaly conditions. For example, if there is a logistic delay, measures such as accelerating the logistic progress or replacing the logistic channels can be considered; if there is a physical distribution damage, repackaging or physical distribution replacement can be considered. In the process of formulating the strategy, an optimal treatment scheme is obtained according to the logistics capacity and the cost of the enterprise.
In the embodiment of the invention, feature mapping matching is carried out on a logistics feature integrated library to obtain at least one second stream information and at least one corresponding second stream track; inputting user decision information into a preset decision information analysis model to analyze the user decision information, so as to obtain a decision information analysis result; according to the analysis result of the decision information, decision feedback is carried out on at least one second stream information, and at least one target stream track corresponding to the at least one second stream track is constructed; according to the logistics information processing method, the logistics information processing system and the logistics information processing system, logistics abnormality detection is carried out on at least one second physical stream information according to at least one target logistics track to obtain an abnormality detection result, and a corresponding abnormality logistics processing strategy is generated according to the abnormality detection result. The method comprises the steps of API interface integration, automatic identification coding, visual display, abnormal reminding and the like, and through integrating information of a plurality of logistics data sources and providing comprehensive tracking service, a user can more conveniently track logistics information, and user experience and use satisfaction are improved.
In a specific embodiment, the process of executing step S101 may specifically include the following steps:
(1) Setting a data format and a transmission protocol of each logistics data source based on a plurality of preset logistics data sources, and calling a target API interface according to the data format and the transmission protocol to acquire a plurality of first logistics information;
(2) Scanning a plurality of first logistics information through a preset keyword recognition model to obtain at least one keyword corresponding to each first logistics information;
(3) According to at least one keyword, carrying out semantic matching in a preset track database to obtain corresponding at least one keyword semantic information;
(4) And inquiring the first logistics track corresponding to each piece of first logistics information according to the semantic information of at least one keyword.
Specifically, for each logistics data source, the server needs to set a data format and a transmission protocol thereof so as to call a target API interface to acquire first logistics information, for example, the server can use JSON format and HTTP protocol to perform data transmission, and use a preset keyword recognition model to scan the first logistics information so as to recognize keywords therein. The keywords can be specific information such as logistics nodes, time stamps, transportation means and the like, and the identified keywords are subjected to semantic matching with a preset track database so as to find logistics track information related to the keywords. The semantic matching can use natural language processing technology, the context information of the keywords is utilized for matching, and the corresponding first logistics track is inquired according to logistics track information obtained by the semantic matching. The first logistics track may include logistics path, time stamp, logistics node, transportation means, etc. information. Through the steps, the first logistics information can be effectively obtained from a plurality of logistics data sources, and track inquiry is carried out according to the keywords and the semantic information. The method is beneficial to enterprises to obtain logistics information in time and improves logistics service quality and operation efficiency.
In a specific embodiment, as shown in fig. 2, the process of executing step S102 may specifically include the following steps:
s201, extracting features of a plurality of first logistics information to obtain at least one logistics feature corresponding to each first logistics information;
s202, performing feature traversal on at least one logistics feature corresponding to each piece of first logistics information to obtain a feature traversal result;
s203, constructing a feature identifier for at least one logistics feature based on a feature traversal result to obtain at least one feature identifier;
s204, integrating the characteristic information of at least one logistics characteristic according to at least one characteristic identifier to generate a logistics characteristic integrated library.
Specifically, the server performs feature extraction on the plurality of first logistics information to obtain at least one logistics feature corresponding to each piece of first logistics information. The logistic characteristics can be information such as weight, quantity, volume, transport means, logistic nodes, time stamps and the like, and characteristic traversal is carried out on at least one logistic characteristic corresponding to each piece of first logistic information, so that characteristic traversal results are obtained. The feature traversal is to make statistics such as maximum, minimum, average and the like on numerical type features, count and duty ratio and the like on fractional type features, make statistics such as time difference and time span and the like on time stamps, and make feature identification construction on at least one logistics feature based on a feature traversal result to obtain at least one feature identification. The feature identifier is used to classify the physical distribution feature, for example, weight is classified into a light weight class and a heavy weight class, and time stamp is classified into a working day class and a non-working day class. And integrating the characteristic information of at least one logistics characteristic according to the at least one characteristic identifier to generate a logistics characteristic integrated library. The feature integrated library can be classified according to the feature identifiers, for example, weights are classified into a light type and a heavy type, and statistical information of the light type and the heavy type of weights is stored in the feature integrated library respectively. Through the steps, the logistics features in the plurality of first logistics information can be extracted, the feature traversal and the identification construction are carried out, and finally the logistics feature integrated library is generated.
In a specific embodiment, as shown in fig. 3, the process of executing step S103 may specifically include the following steps:
s301, when detecting that a user enters an interactive page of a user terminal, acquiring a logistics operation request, and analyzing the logistics operation request to obtain corresponding user decision information;
s302, judging whether user decision information meets preset logistics order verification conditions or not;
s303, if yes, performing feature mapping matching between user decision information and a logistics feature integrated library to obtain at least one second stream information;
s304, determining at least one corresponding second stream track according to the at least one second stream information.
Specifically, when detecting that a user enters an interactive page of a user terminal, the server acquires a logistics operation request and analyzes the logistics operation request to obtain corresponding user decision information: the method comprises the steps of monitoring an interactive page of a user terminal, acquiring a logistics operation request submitted by a user, analyzing the request, extracting key information including information such as a logistics order number, a delivery address, a receiving address, article types, article quantity and the like, and combining the information to obtain corresponding user decision information. Judging whether the user decision information meets the preset logistics order verification condition or not: the step is to verify the user decision information and judge whether the user decision information meets the preset logistics order verification condition. This process takes into account factors such as the validity, legitimacy, integrity, etc. of the logistics order to ensure the correctness and reliability of the logistics order. If yes, performing feature mapping matching between the user decision information and the logistics feature integrated library to obtain at least one second stream information, wherein the step is to perform feature mapping matching on the user decision information and the logistics feature integrated library to obtain the second stream information meeting the requirements. The process needs to consider factors such as feature vectors, similarity measurement, matching algorithm and the like of user decision information and the logistic feature integrated library so as to ensure the accuracy and reliability of matching. Finally, the server determines at least one corresponding second stream trajectory according to the at least one second stream information.
In a specific embodiment, as shown in fig. 4, the process of executing step S104 may specifically include the following steps:
s401, inputting user decision information into a preset decision information analysis model, wherein the decision information analysis model comprises: a two-way long short-time memory layer, a three-layer convolution layer and a prediction layer;
s402, extracting features of user decision information through a decision information analysis model to obtain a plurality of decision features;
s403, classifying the plurality of decision features to obtain decision classification standards;
s404, calculating decision results of the plurality of decision features according to the decision classification standard, and generating corresponding decision information analysis results, wherein the decision information analysis results comprise: user decision type and user decision factor.
Specifically, the server inputs user decision information into a preset decision information analysis model, wherein the decision information analysis model comprises: a two-way long short-term memory layer, a three-layer convolution layer and a prediction layer: the method comprises the steps of inputting user decision information into a preset decision information analysis model, and carrying out feature extraction and decision calculation on the user decision information by using the model. The decision information analysis model comprises a plurality of layers and parts such as a two-way long and short time memory layer, a three-layer convolution layer, a prediction layer and the like, and corresponding decision characteristics and decision classification standards are obtained through layer-by-layer processing of user decision information. And extracting features of the user decision information through a decision information analysis model to obtain a plurality of decision features. The decision features comprise information such as behavior features, historical decision features, preference features, demand features and the like of the user, and more accurate and reliable decision analysis results are obtained through comprehensive analysis and processing of the features. And classifying the plurality of decision features to obtain decision classification standards. The process needs to consider factors such as decision type, decision factors, decision difficulty and the like of the user so as to ensure the accuracy and reliability of the classification standard. Calculating decision results of the plurality of decision features according to the decision classification standards to generate corresponding decision information analysis results, wherein the decision information analysis results comprise: user decision type and user decision factor: the step is to calculate the decision result of the plurality of decision features according to the decision classification standard, and finally generate the corresponding decision information analysis result. The result comprises information such as user decision types, user decision factors and the like, and more specific and effective decision suggestions and guidance can be provided for the user.
In a specific embodiment, the process of executing step S105 may specifically include the following steps:
(1) Constructing a corresponding decision rule according to the user decision type and the user decision factor;
(2) According to the decision rule, performing decision feedback on at least one second stream information to obtain decision feedback information;
(3) And constructing at least one target logistics track corresponding to the at least one second logistics track according to the decision feedback information.
Specifically, the server can obtain the corresponding decision rule through analysis of the decision type and the decision factor of the user. The decision rules can be formulated by the user or by factors such as historical data and expertise. Decision rules can help users make decisions more quickly and accurately in subsequent decision making processes. And according to the decision rule, decision feedback can be carried out on the second stream information. In the process, the user can analyze and evaluate the second stream information according to the direction of the decision rule, so as to obtain decision feedback information. Such feedback information may include suggestions, ideas, improvements, etc., that may help the user better complete the decision making process. According to the obtained decision feedback information, the second stream track can be further analyzed and processed, so that a corresponding target stream track is obtained. In the process, factors such as time, cost, efficiency and the like of logistics and factors such as actual demands and preferences of users need to be considered, so that the accuracy and reliability of the target logistics track are ensured.
In a specific embodiment, the process of executing step S106 may specifically include the following steps:
(1) Carrying out logistic anomaly detection on at least one second stream information according to at least one target logistic track to obtain an anomaly detection result;
(2) And matching the corresponding abnormal logistics processing strategy from a plurality of preset candidate abnormal processing strategies according to the abnormal detection result.
Specifically, according to the target logistics track, logistics abnormality detection can be performed on the second logistics information. In this process, it is necessary to consider the possible abnormal situations in the logistic process, such as delay, damage, loss, etc., and the degree and influence of the abnormality. Through analysis and evaluation of the abnormal conditions, a corresponding abnormal detection result can be obtained, and according to the obtained abnormal detection result, a corresponding abnormal logistics processing strategy can be matched from a plurality of preset candidate abnormal processing strategies. These exception handling strategies may include countermeasures, such as compensation, retransmission, reimbursement, etc., which need to be selected and matched according to the severity and specifics of the exception condition. By selecting a proper abnormality processing strategy, the problem of abnormal logistics can be effectively solved in time, and the quality and efficiency of logistics are improved.
The method for identifying the logistics information in the embodiment of the present invention is described above, and the device for identifying the logistics information in the embodiment of the present invention is described below, referring to fig. 5, one embodiment of the device for identifying the logistics information in the embodiment of the present invention includes:
the acquiring module 501 is configured to acquire a plurality of first logistics information based on a plurality of preset logistics data sources, and perform dynamic track analysis on the plurality of first logistics information respectively to obtain a first logistics track corresponding to each first logistics information;
the extracting module 502 is configured to perform feature extraction on the plurality of first logistics information to obtain at least one logistics feature corresponding to each piece of first logistics information, and perform feature information integration on at least one logistics feature corresponding to each piece of first logistics information to generate a logistics feature integrated library;
the matching module 503 is configured to receive a logistic operation request input by a user terminal, obtain user decision information, and perform feature mapping matching on the logistic feature integrated library according to the user decision information to obtain at least one second stream information and at least one corresponding second stream track;
the analysis module 504 is configured to input the user decision information into a preset decision information analysis model to perform user decision information analysis, so as to obtain a corresponding decision information analysis result, where the decision information analysis result includes: user decision type and user decision factor;
The feedback module 505 is configured to perform decision feedback on the at least one second stream information according to the user decision type and the user decision factor, and construct at least one target stream track corresponding to the at least one second stream track;
the detection module 506 is configured to perform, according to the at least one target logistics track, a logistics anomaly detection on the at least one second stream information, obtain an anomaly detection result, and generate a corresponding anomaly logistics processing policy according to the anomaly detection result.
Performing feature mapping matching on the logistics feature integrated library through the cooperation of the components to obtain at least one second stream information and at least one corresponding second stream track; inputting user decision information into a preset decision information analysis model to analyze the user decision information, so as to obtain a decision information analysis result; according to the analysis result of the decision information, decision feedback is carried out on at least one second stream information, and at least one target stream track corresponding to the at least one second stream track is constructed; according to the logistics information processing method, the logistics information processing system and the logistics information processing system, logistics abnormality detection is carried out on at least one second physical stream information according to at least one target logistics track to obtain an abnormality detection result, and a corresponding abnormality logistics processing strategy is generated according to the abnormality detection result. The method comprises the steps of API interface integration, automatic identification coding, visual display, abnormal reminding and the like, and through integrating information of a plurality of logistics data sources and providing comprehensive tracking service, a user can more conveniently track logistics information, and user experience and use satisfaction are improved.
The above-mentioned figure 5 describes the identifying device of the logistic information in the embodiment of the present invention in detail from the point of view of the modularized functional entity, and the following describes the computer device in the embodiment of the present invention in detail from the point of view of the hardware processing.
Fig. 6 is a schematic diagram of a computer device according to an embodiment of the present invention, where the computer device 600 may have a relatively large difference due to configuration or performance, 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 media 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 computer device 600. Still further, the processor 610 may be configured to communicate with a storage medium 630 and execute a series of instruction operations in the storage medium 630 on the computer device 600.
The computer device 600 may also include one or more power supplies 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 computer device structure shown in FIG. 6 is not limiting of the computer device and may include more or fewer components than shown, or may combine certain components, or a different arrangement of components.
The present invention also provides a computer device including a memory and a processor, the memory storing computer readable instructions that, when executed by the processor, cause the processor to perform the steps of the method for identifying logistics information in the above embodiments.
The present invention also provides a computer readable storage medium, which may be a non-volatile computer readable storage medium, or may be a volatile computer readable storage medium, in which instructions are stored which, when executed on a computer, cause the computer to perform the steps of the method for identifying logistics information.
It will be clear to those skilled in the art that, for convenience and brevity of description, specific working procedures of the above-described systems, apparatuses and units may refer to corresponding procedures in the foregoing method embodiments, which are 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 acceS memory, RAM), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
The above embodiments are only for illustrating the technical solution of the present invention, and not for limiting the same; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention.
Claims (10)
1. The method for identifying the logistics information is characterized by comprising the following steps of:
acquiring a plurality of first logistics information based on a plurality of preset logistics data sources, and respectively carrying out dynamic track analysis on the plurality of first logistics information to obtain a first logistics track corresponding to each first logistics information;
extracting features of the plurality of first logistics information to obtain at least one logistics feature corresponding to each first logistics information, and integrating the features of the at least one logistics feature corresponding to each first logistics information to generate a logistics feature integrated library;
receiving a logistics operation request input by a user terminal, acquiring user decision information, and performing feature mapping matching on the logistics feature integrated library according to the user decision information to obtain at least one second stream information and at least one corresponding second stream track;
Inputting the user decision information into a preset decision information analysis model for user decision information analysis to obtain a corresponding decision information analysis result, wherein the decision information analysis result comprises the following steps: user decision type and user decision factor;
according to the user decision type and the user decision factor, performing decision feedback on the at least one second stream information, and constructing at least one target stream track corresponding to the at least one second stream track;
and carrying out logistic anomaly detection on the at least one second stream information according to the at least one target logistic track to obtain an anomaly detection result, and generating a corresponding anomaly logistic processing strategy according to the anomaly detection result.
2. The method for identifying logistics information according to claim 1, wherein the obtaining a plurality of first logistics information based on a plurality of preset logistics data sources, and performing dynamic track analysis on the plurality of first logistics information to obtain a first logistics track corresponding to each first logistics information respectively, includes:
setting a data format and a transmission protocol of each logistics data source based on a plurality of preset logistics data sources, and calling a target API interface according to the data format and the transmission protocol to acquire a plurality of first logistics information;
Scanning the plurality of first logistics information through a preset keyword recognition model to obtain at least one keyword corresponding to each first logistics information;
according to the at least one keyword, carrying out semantic matching in a preset track database to obtain corresponding at least one keyword semantic information;
and inquiring the first logistics track corresponding to each piece of first logistics information according to the at least one piece of keyword semantic information.
3. The method for identifying logistic information according to claim 1, wherein the feature extracting the plurality of first logistic information to obtain at least one logistic feature corresponding to each first logistic information, and feature integrating the at least one logistic feature corresponding to each first logistic information to generate a logistic feature integrated library, includes:
extracting features of the plurality of first logistics information to obtain at least one logistics feature corresponding to each first logistics information;
performing feature traversal on at least one logistics feature corresponding to each piece of first logistics information to obtain a feature traversal result;
based on the feature traversing result, carrying out feature identification construction on the at least one logistics feature to obtain at least one feature identification;
And integrating the characteristic information of the at least one logistics characteristic according to the at least one characteristic identifier to generate a logistics characteristic integrated library.
4. The method for identifying logistics information according to claim 1, wherein the steps of receiving a logistics operation request input by a user terminal and obtaining user decision information, performing feature mapping matching on the logistics feature integrated library according to the user decision information to obtain at least one second stream information and at least one corresponding second stream track, and the method comprises the steps of;
when detecting that a user enters an interactive page of a user terminal, acquiring a logistics operation request, and analyzing the logistics operation request to obtain corresponding user decision information;
judging whether the user decision information meets a preset logistics order verification condition or not;
if yes, feature mapping matching is carried out between the user decision information and the logistics feature integrated library, and at least one second stream information is obtained;
and determining at least one corresponding second stream track according to the at least one second stream information.
5. The method for identifying logistics information according to claim 1, wherein the inputting the user decision information into a preset decision information analysis model performs user decision information analysis to obtain a corresponding decision information analysis result, and the decision information analysis result includes: user decision type and user decision factor, including:
Inputting the user decision information into a preset decision information analysis model, wherein the decision information analysis model comprises: a two-way long short-time memory layer, a three-layer convolution layer and a prediction layer;
extracting features of the user decision information through the decision information analysis model to obtain a plurality of decision features;
classifying the plurality of decision features to obtain decision classification standards;
calculating decision results of the plurality of decision features according to the decision classification standard to generate corresponding decision information analysis results, wherein the decision information analysis results comprise: user decision type and user decision factor.
6. The method for identifying logistics information according to claim 1, wherein said performing decision feedback on the at least one second logistics information according to the user decision type and the user decision factor, and constructing at least one target logistics track corresponding to the at least one second logistics track, includes:
constructing a corresponding decision rule according to the user decision type and the user decision factor;
according to the decision rule, performing decision feedback on the at least one second stream information to obtain decision feedback information;
And constructing at least one target logistics track corresponding to the at least one second logistics track according to the decision feedback information.
7. The method for identifying logistics information according to claim 1, wherein the performing, according to the at least one target logistics track, logistics anomaly detection on the at least one second logistics information to obtain an anomaly detection result, and generating a corresponding anomaly logistics processing strategy according to the anomaly detection result, includes:
carrying out logistic anomaly detection on the at least one second stream information according to the at least one target logistic track to obtain an anomaly detection result;
and matching the corresponding abnormal logistics processing strategy from a plurality of preset candidate abnormal processing strategies according to the abnormal detection result.
8. A device for identifying logistic information, wherein the device for identifying logistic information comprises:
the acquisition module is used for acquiring a plurality of first logistics information based on a plurality of preset logistics data sources, and respectively carrying out dynamic track analysis on the plurality of first logistics information to obtain a first logistics track corresponding to each first logistics information;
the extraction module is used for carrying out feature extraction on the plurality of first logistics information to obtain at least one logistics feature corresponding to each piece of first logistics information, and carrying out feature information integration on at least one logistics feature corresponding to each piece of first logistics information to generate a logistics feature integrated library;
The matching module is used for receiving a logistics operation request input by a user terminal, acquiring user decision information, and carrying out feature mapping matching on the logistics feature integrated library according to the user decision information to obtain at least one second stream information and at least one corresponding second stream track;
the analysis module is used for inputting the user decision information into a preset decision information analysis model to perform user decision information analysis to obtain a corresponding decision information analysis result, wherein the decision information analysis result comprises the following components: user decision type and user decision factor;
the feedback module is used for carrying out decision feedback on the at least one second stream information according to the user decision type and the user decision factor, and constructing at least one target stream track corresponding to the at least one second stream track;
the detection module is used for carrying out logistic anomaly detection on the at least one second stream information according to the at least one target logistic track to obtain an anomaly detection result, and generating a corresponding anomaly logistic processing strategy according to the anomaly detection result.
9. A computer device, the computer device comprising: a memory and at least one processor, the memory having instructions stored therein;
The at least one processor invokes the instructions in the memory to cause the computer device to perform the method of identifying logistic information as claimed in any one of claims 1 to 7.
10. A computer readable storage medium having instructions stored thereon, wherein the instructions when executed by a processor implement the method of identifying logistic information as claimed in any one of claims 1 to 7.
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