Abnormal Waybill Identification Method, Device, Electronic Device and Storage Medium

Abstract

The present application provides a method, apparatus, electronic device and storage medium for identifying abnormal waybills. The method for identifying abnormal waybills includes: obtaining historical waybill information and historical user complaint information of a plurality of historical waybills within a preset time period, wherein the historical waybill information includes historical abnormal information of handheld terminal operations; performing correlation analysis on the historical waybill information and historical user complaint information of the plurality of historical waybills to obtain a plurality of correlation analysis results on the correlation between the waybill information and the user complaint information; determining the correlation analysis results that meet the preset abnormal conditions as target correlation analysis results; and performing abnormal identification on the current waybill based on the target correlation analysis results and the current waybill information of the current waybill to obtain an abnormal waybill. When the waybill information of the current waybill is obtained, the present application can predict the corresponding user complaint information according to the target correlation analysis results before the user makes a complaint, and identify the abnormal waybill in real time.

Description

Technical Field

[0001] This application relates to the field of logistics technology, and particularly to a method, device, electronic device and storage medium for identifying abnormal waybills. Background Art

[0002] Operation - type anomalies refer to problems with express deliveries caused by the occurrence of abnormal operations or abnormal numbers of normal operations during the express logistics process, such as express delivery delays, timeliness complaints, and express delivery returns. Usually, after an express delivery problem occurs, it is too late for manual discovery, and problems such as logistics delays and complaints have already occurred.

[0003] That is, the existing methods for identifying abnormal waybills mainly rely on user feedback, which has a certain lag and cannot identify abnormal waybills in real - time when waybill operations are abnormal. Summary of the Invention

[0004] This application aims to provide a method, device, electronic device and storage medium for identifying abnormal waybills, aiming to solve the problem that the existing methods for identifying abnormal waybills cannot identify abnormal waybills in real - time when waybill operations are abnormal.

[0005] On the one hand, this application provides a method for identifying abnormal waybills, and the identification method includes:

[0006] Obtain the historical waybill information and historical user complaint information of multiple historical waybills within a preset time period, where the historical waybill information includes historical abnormal gun - scanning operation information;

[0007] Perform correlation analysis on the historical waybill information and historical user complaint information of the multiple historical waybills to obtain multiple correlation analysis results of the correlation between waybill information and user complaint information;

[0008] Determine the correlation analysis results that meet the preset abnormal conditions as target correlation analysis results;

[0009] Perform abnormal identification on the current waybill based on the target correlation analysis results and the current waybill information of the current waybill to obtain abnormal waybills.

[0010] Wherein, the historical abnormal gun - scanning operation information includes whether each type of gun - scanning operation of the waybill is abnormal;

[0011] The obtaining of the historical waybill information and historical user complaint information of multiple historical waybills within a preset time period includes:

[0012] Obtain the historical gun - scanning operation information of the historical waybills, where the historical gun - scanning operation information includes the operation times of each type of gun - scanning operation for each historical waybill;

[0013] Determine the range of operation abnormal times for each type of PDA operation based on the Gaussian distribution of the historical PDA operation information;

[0014] Determine the historical PDA operation abnormal information of each historical waybill based on the historical PDA operation information of the historical waybill and the range of operation abnormal times.

[0015] Among them, the historical waybill information further includes historical waybill category information; the historical PDA operation information includes the operation times of each type of PDA operation for historical waybills of each waybill category;

[0016] The determining the range of operation abnormal times for each type of PDA operation based on the Gaussian distribution of the historical PDA operation information includes:

[0017] Determine the range of operation abnormal times for each type of PDA operation of each waybill category based on the Gaussian distribution of the historical PDA operation information.

[0018] Among them, the determining the historical PDA operation abnormal information of each historical waybill based on the historical PDA operation information of the historical waybill and the range of operation abnormal times includes:

[0019] Respectively determine each historical waybill as a target historical waybill, and determine each type of PDA operation as a target type of PDA operation;

[0020] Obtain the operation times of the target historical waybill for the target type of PDA operation;

[0021] Judge whether the operation times of the target historical waybill for the target type of PDA operation belong to the corresponding range of operation abnormal times;

[0022] If the operation times of the target historical waybill for the target type of PDA operation belong to the corresponding range of operation abnormal times, determine the historical PDA operation abnormal information of the target historical waybill for the target type of PDA operation as normal; if the operation times of the target historical waybill for the target type of PDA operation do not belong to the corresponding range of operation abnormal times, determine the historical PDA operation abnormal information of the target historical waybill for the target type of PDA operation as abnormal.

[0023] Among them, the historical waybill information includes historical waybill category information and historical PDA operation abnormal information;

[0024] The performing a correlation analysis on the historical waybill information and historical user complaint information of the multiple historical waybills to obtain multiple correlation analysis results of the correlation between the waybill information and the user complaint information includes:

[0025] Determine the historical waybill category information, historical abnormal PDA operation information, and historical user complaint information of each of the historical waybills as an item set, and obtain multiple item sets corresponding to the multiple historical waybills;

[0026] Input the multiple item sets into a preset association analysis model to obtain multiple association analysis results of the correlation between the waybill category information, PDA operation abnormal information, and user complaint information, where the association analysis model includes an Apriori association degree model.

[0027] Among them, the association analysis results include the support, confidence, and lift of each item set, and the historical user complaint information includes the category of user complaints;

[0028] The preset abnormal conditions include: the support is greater than the preset support, and / or the confidence is greater than the preset confidence and / or the lift is greater than the preset lift, and the category of user complaints belongs to the preset category label.

[0029] Among them, the abnormal identification of the current waybill based on the target association analysis result and the current waybill information of the current waybill to obtain an abnormal waybill includes:

[0030] Obtain the current waybill information of the current waybill;

[0031] Based on the current waybill information of the current waybill, determine whether there is corresponding target waybill information in the target association analysis result;

[0032] When there is corresponding target waybill information in the target association analysis result, determine the current waybill as an abnormal waybill.

[0033] On the one hand, the present application provides an abnormal waybill identification device, and the abnormal waybill identification device includes:

[0034] An acquisition unit, configured to acquire the historical waybill information and historical user complaint information of multiple historical waybills within a preset time period, where the historical waybill information includes historical abnormal PDA operation information;

[0035] An association analysis unit, configured to perform an association analysis on the historical waybill information and historical user complaint information of the multiple historical waybills to obtain multiple association analysis results of the correlation between the waybill information and user complaint information;

[0036] A determination unit, configured to determine the association analysis result that meets the preset abnormal conditions as the target association analysis result;

[0037] An abnormal identification unit, configured to perform abnormal identification on the current waybill based on the target association analysis result and the current waybill information of the current waybill to obtain an abnormal waybill.

[0038] Among them, the historical abnormal information of the handheld scanner operation includes whether the handheld scanner operations of various types of waybills are abnormal; the obtaining unit is further configured to: obtain the historical handheld scanner operation information of the historical waybill, where the historical handheld scanner operation information includes the operation times of each historical waybill for each type of handheld scanner operation;

[0039] Determine the range of abnormal operation times for each type of handheld scanner operation based on the Gaussian distribution of the historical handheld scanner operation information;

[0040] Determine the historical abnormal information of the handheld scanner operation of each historical waybill based on the historical handheld scanner operation information of the historical waybill and the range of abnormal operation times.

[0041] Among them, the historical waybill information further includes historical waybill category information; the historical handheld scanner operation information includes the operation times of each historical waybill of each waybill category for each type of handheld scanner operation;

[0042] The obtaining unit is further configured to: determine the range of abnormal operation times for each type of handheld scanner operation of each waybill category based on the Gaussian distribution of the historical handheld scanner operation information.

[0043] Among them, the determining the historical abnormal information of the handheld scanner operation of each historical waybill based on the historical handheld scanner operation information of the historical waybill and the range of abnormal operation times includes:

[0044] Respectively determine each historical waybill as a target historical waybill, and determine each type of handheld scanner operation as a target type of handheld scanner operation;

[0045] Obtain the operation times of the target historical waybill for the target type of handheld scanner operation;

[0046] Judge whether the operation times of the target historical waybill for the target type of handheld scanner operation belong to the corresponding range of abnormal operation times;

[0047] If the operation times of the target historical waybill for the target type of handheld scanner operation belong to the corresponding range of abnormal operation times, determine the historical abnormal information of the handheld scanner operation of the target historical waybill for the target type of handheld scanner operation as normal; if the operation times of the target historical waybill for the target type of handheld scanner operation do not belong to the corresponding range of abnormal operation times, determine the historical abnormal information of the handheld scanner operation of the target historical waybill for the target type of handheld scanner operation as abnormal.

[0048] Among them, the historical waybill information includes historical waybill category information and historical abnormal information of the handheld scanner operation; the obtaining unit is further configured to:

[0049] Determine the historical waybill category information, historical abnormal PDA operation information, and historical user complaint information of each of the historical waybills as an item set, and obtain multiple item sets corresponding to the multiple historical waybills;

[0050] Input the multiple item sets into a preset association analysis model to obtain multiple association analysis results of the relevance among the waybill category information, abnormal PDA operation information, and user complaint information, where the association analysis model includes an Apriori association degree model.

[0051] Among them, the association analysis results include the support degree, confidence degree, and lift degree of each item set, and the historical user complaint information includes the category of user complaints;

[0052] The preset abnormal conditions include: the support degree is greater than a preset support degree, and / or the confidence degree is greater than a preset confidence degree and / or the lift degree is greater than a preset lift degree, and the category of user complaints belongs to a preset category label.

[0053] Among them, the abnormal identification unit is further configured to: obtain the current waybill information of the current waybill;

[0054] Based on the current waybill information of the current waybill, determine whether there is corresponding target waybill information for the target association analysis result;

[0055] When there is corresponding target waybill information for the target association analysis result, determine the current waybill as an abnormal waybill.

[0056] On the one hand, the present application further provides an electronic device, where the electronic device includes:

[0057] One or more processors;

[0058] A memory; and

[0059] One or more applications, where the one or more applications are stored in the memory and are configured to be executed by the processor to implement the abnormal waybill identification method described in any item of the first aspect.

[0060] On the one hand, the present application further provides a computer-readable storage medium, on which a computer program is stored, and the computer program is loaded by a processor to execute the steps in the abnormal waybill identification method described in any item of the first aspect.

[0061] The present application provides a method for identifying abnormal waybills. The method for identifying abnormal waybills first obtains the historical waybill information and historical user complaint information of historical waybills, performs a correlation analysis on the historical waybill information and historical user complaint information, and obtains multiple correlation analysis results of the correlation between the waybill information and the user complaint information. The multiple correlation analysis results can reflect the corresponding relationship between the waybill information and the user complaint information. The target correlation analysis results with abnormal scenarios are screened from the multiple correlation analysis results through preset abnormal conditions, and then the target correlation analysis results are used to identify the abnormality of the current waybill. When the waybill information of the current waybill is obtained, the corresponding user complaint information can be predicted according to the target correlation analysis results before the user makes a complaint, and the abnormal waybill can be identified in real time. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the following drawings are only some embodiments of the present application. For those skilled in the art, without creative efforts, other drawings can be obtained according to these drawings.

[0063] Figure 1 It is a schematic diagram of the scenario of the system for identifying abnormal waybills provided by the embodiment of the present application;

[0064] Figure 2 It is a schematic flowchart of an embodiment of the method for identifying abnormal waybills provided by the embodiment of the present application;

[0065] Figure 3 It is a schematic structural diagram of an embodiment of the device for identifying abnormal waybills provided by the embodiment of the present application;

[0066] Figure 4 It is a schematic structural diagram of an embodiment of the electronic device provided by the embodiment of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0067] The following will clearly and completely describe the technical solutions in the embodiments of the present application with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only some embodiments of the present application, rather than all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative efforts belong to the scope of protection of the present application.

[0068] In the description of the present application, it should be understood that the orientation or positional relationship indicated by terms such as "center", "longitudinal", "transverse", "length", "width", "thickness", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc. is based on the orientation or positional relationship shown in the drawings. It is only for the convenience of describing the present application and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be construed as a limitation to the present application. In addition, the terms "first" and "second" are only used for descriptive purposes and cannot be construed as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include one or more features. In the description of the present application, "a plurality" means two or more, unless otherwise specifically defined.

[0069] In the present application, the term "exemplary" is used to mean "serving as an example, illustration, or explanation". Any embodiment described as "exemplary" in the present application is not necessarily to be construed as more preferred or advantageous than other embodiments. The following description is provided to enable any person skilled in the art to make and use the present application. In the following description, details are set forth for the purpose of explanation. It should be understood that those of ordinary skill in the art can recognize that the present application can be implemented without the use of these specific details. In other instances, well-known structures and processes are not elaborated in detail to avoid obscuring the description of the present application with unnecessary details. Therefore, the present application is not intended to be limited to the embodiments shown, but is to be accorded the widest scope consistent with the principles and features disclosed in the present application.

[0070] It should be noted that since the method of the embodiment of the present application is executed in an electronic device, the processing objects of each electronic device exist in the form of data or information. For example, time, which is actually time information. It can be understood that in subsequent embodiments, if dimensions, quantities, positions, etc. are mentioned, they are all corresponding data existences for the electronic device to process, and specific details are not elaborated here.

[0071] The embodiment of the present application provides a method, device, electronic device, and storage medium for identifying abnormal waybills, which will be described in detail below.

[0072] Please refer to Figure 1 , Figure 1 , which is a schematic diagram of the scenario of the abnormal waybill identification system provided by the embodiment of the present application. The abnormal waybill identification system may include an electronic device 100, and an abnormal waybill identification device is integrated in the electronic device 100, such as Figure 1 the electronic device in

[0073] In the embodiments of the present application, the electronic device 100 may be an independent server or a server network or server cluster composed of servers. For example, the electronic device 100 described in the embodiments of the present application includes, but is not limited to, a computer, a network host, a single network server, a set of multiple network servers, or a cloud server composed of multiple servers. Among them, the cloud server is composed of a large number of computers or network servers based on cloud computing.

[0074] Those skilled in the art can understand that Figure 1 the application environment shown is only an application scenario of the solution of the present application and does not constitute a limitation on the application scenario of the solution of the present application. Other application environments may also include more or fewer electronic devices than Figure 1 shown in, for example Figure 1 only 1 electronic device is shown in. It can be understood that the abnormal waybill recognition system may also include one or more other servers, which are not specifically limited here.

[0075] In addition, as Figure 1 shown, the abnormal waybill recognition system may also include a memory 200 for storing data.

[0076] It should be noted that Figure 1 the scenario schematic diagram of the abnormal waybill recognition system shown is only an example. The abnormal waybill recognition system and scenario described in the embodiments of the present application are for more clearly explaining the technical solution of the embodiments of the present application and do not constitute a limitation on the technical solution provided by the embodiments of the present application. Those skilled in the art know that with the evolution of the abnormal waybill recognition system and the emergence of new business scenarios, the technical solution provided by the embodiments of the present application is also applicable to similar technical problems.

[0077] First, an abnormal waybill recognition method is provided in the embodiments of the present application. The execution subject of the abnormal waybill recognition method is an abnormal waybill recognition device. The abnormal waybill recognition device is applied to an electronic device. The abnormal waybill recognition method includes:

[0078] Obtain the historical waybill information and historical user complaint information of multiple historical waybills within a preset time period, where the historical waybill information includes historical abnormal gun operation information;

[0079] Perform correlation analysis on the historical waybill information and historical user complaint information of multiple historical waybills to obtain multiple correlation analysis results of the correlation between the waybill information and the user complaint information;

[0080] Determine the correlation analysis results that meet the preset abnormal conditions as the target correlation analysis results;

[0081] Based on the target association analysis result and the current waybill information of the current waybill, perform anomaly recognition on the current waybill to obtain an abnormal waybill.

[0082] See Figure 2 , Figure 2 which is a schematic flowchart of an embodiment of the method for identifying an abnormal waybill provided by an embodiment of the present application. As Figure 2 shown, the method for identifying an abnormal waybill includes:

[0083] S201. Obtain the historical waybill information and historical user complaint information of multiple historical waybills within a preset time period, where the historical waybill information includes historical abnormal PDA operation information.

[0084] In an embodiment of the present application, the preset time period can be a historical one-month period, a two-month period, a one-week period, etc., which is set according to specific circumstances.

[0085] In a specific embodiment, the abnormal PDA operation information includes whether each type of PDA operation of the waybill is abnormal. Among them, the types of PDA operations can include: abnormal item handover, small package scanning, unloading operation, etc., which can be set according to specific circumstances. For example, the abnormal PDA operation information of waybill X is: abnormal item handover, normal; small package scanning, abnormal; unloading operation, normal. The historical user complaint information is the information fed back by the user for the historical waybill, and the categories of historical user complaints can include: no complaint C3, complaint with the first complaint reason C0, complaint with the second complaint reason C1. The categories of historical user complaints can be set according to specific circumstances.

[0086] In a specific embodiment, obtaining the historical waybill information and historical user complaint information of multiple historical waybills within a preset time period includes:

[0087] (1) Obtain the historical PDA operation information of the historical waybill, where the historical PDA operation information includes the number of operations of each type of PDA operation for each historical waybill.

[0088] In a specific embodiment, for example, the historical PDA operation information of waybill X is: abnormal item handover, 2 times; small package scanning, 2 times; unloading operation, 2 times. The historical PDA operation information of waybill Y is: abnormal item handover, 1 time; small package scanning, 1 time; unloading operation, 1 time.

[0089] (2) Determine the range of abnormal operation times for each type of PDA operation based on the Gaussian distribution of the number of operations of each type of PDA operation.

[0090] The Gaussian distribution was first obtained by Abraham de Moivre in the asymptotic formula for the binomial distribution. C.F. Gauss derived it from another perspective when studying measurement errors. P.S. Laplace and Gauss studied its properties. It is a very important probability distribution in the fields of mathematics, physics, engineering, etc., and has a significant influence in many aspects of statistics. The normal curve is bell-shaped, low at both ends, high in the middle, and symmetric about the y-axis. Because of its bell shape, it is often called the bell curve. If the random variable X follows a normal distribution with a mathematical expectation of μ and a variance of σ^2, it is denoted as N(μ, σ^2). Its probability density function is that the expected value μ of the normal distribution determines its position, and its standard deviation σ determines the amplitude of the distribution. When μ = 0 and σ = 1, the normal distribution is the standard normal distribution.

[0091] Fitting is to connect a series of points on a plane with a smooth curve. Since there are countless possibilities for this curve, there are various fitting methods. The fitted curve can generally be represented by a function, and there are different fitting names according to the different functions. Common fitting methods include the least squares curve fitting method, etc. In MATLAB, polyfit can also be used to fit polynomials. Fitting, interpolation, and approximation are the three basic tools of numerical analysis. Generally speaking, the differences between them are as follows: fitting is to approach a known point sequence as a whole; interpolation is to know the point sequence and pass through the point sequence completely; approximation is to know a curve or a point sequence, and make the constructed function infinitely close to them through approximation.

[0092] In the embodiments of the present application, the operation times of each historical waybill for the target type of bar code gun operation are counted, and the operation times of each historical waybill for the target type of bar code gun operation are fitted to obtain the Gaussian distribution of the historical bar code gun operation information of the target type of bar code gun operation. For example, the Gaussian model parameters of the Gaussian distribution of the historical bar code gun operation information of the target type of bar code gun operation are (μ - 3σ, μ + 3σ). The Gaussian model parameters of the Gaussian distribution are determined as the operation abnormal times range.

[0093] Each type of bar code gun operation is respectively determined as the target type of bar code gun operation to obtain the operation abnormal times range of each target type of bar code gun operation. A corresponding operation abnormal times range is set for each type of bar code gun operation, which improves the accuracy of the operation abnormal times range and can improve the accuracy of abnormal judgment. For example, three types of bar code gun operations, namely abnormal piece handover, small package barcode scanning, and unloading operation, correspond to three operation abnormal times ranges.

[0094] In another specific embodiment, the historical waybill information further includes historical waybill category information, and the historical waybill category information represents the waybill category of the waybill; the historical PDA operation information includes the number of operations of performing various types of PDA operations on historical waybills of each waybill category. Determining the range of abnormal operation times for each type of PDA operation based on the Gaussian distribution of the historical PDA operation information includes: determining the range of abnormal operation times for each type of PDA operation for each waybill category based on the Gaussian distribution of the historical PDA operation information. Specifically, determining the range of abnormal operation times for each type of PDA operation for each waybill category based on the Gaussian distribution of the historical PDA operation information. For example, the PDA operations include three types of PDA operations: abnormal item handover, small package scanning code, and unloading operation; if there are three categories of waybill categories, there are a total of 9 ranges of abnormal operation times for each type of PDA operation for each waybill category. Further, a corresponding range of abnormal operation times is set for each type of PDA operation for each waybill category, which further improves the accuracy of the range of abnormal operation times and can improve the accuracy of abnormal judgment.

[0095] Specifically, obtaining the historical waybill information and historical user complaint information of multiple historical waybills within a preset time period includes: obtaining the flow direction information, timeliness type, and weight segment of multiple historical waybills. Classifying the multiple historical waybills based on the preset classification rules and the flow direction information, timeliness type, and weight segment of the multiple historical waybills to obtain the historical waybill category information. The flow direction information can be divided into city - rural, rural - city, rural - rural, etc.; the timeliness type can be divided into same - day delivery, next - day delivery, etc.; the weight segment can be divided into overweight, normal weight, etc. according to the weight of the waybill. It can be set according to specific circumstances. For example, classifying multiple waybills according to the preset classification rules, the waybill category of waybill X1 is A1; the waybill category of waybill X2 is A2; the waybill category of waybill X3 is A3.

[0096] (3) Determining the PDA operation abnormal information of each historical waybill based on the historical PDA operation information and the range of abnormal operation times of the historical waybill.

[0097] In the embodiment of the present application, determining the PDA operation abnormal information of each historical waybill based on the historical PDA operation information and the range of abnormal operation times of the historical waybill includes:

[0098] Respectively determine each historical waybill as the target historical waybill, and determine each type of handheld scanner operation as the target type of handheld scanner operation. Obtain the number of operations for the target historical waybill to perform the target type of handheld scanner operation. Determine whether the number of operations for the target historical waybill to perform the target type of handheld scanner operation belongs to the corresponding range of abnormal operation times. If the number of operations for the target historical waybill to perform the target type of handheld scanner operation belongs to the corresponding range of abnormal operation times, determine the abnormal information of the handheld scanner operation for the target historical waybill to perform the target type of handheld scanner operation as normal; if the number of operations for the target historical waybill to perform the target type of handheld scanner operation does not belong to the corresponding range of abnormal operation times, determine the abnormal information of the handheld scanner operation for the target historical waybill to perform the target type of handheld scanner operation as abnormal.

[0099] For example, the historical handheld scanner operation information of waybill X is: abnormal item handover, 2 times; small package barcode scanning, 2 times; unloading operation, 2 times. The historical handheld scanner operation information of waybill Y is: abnormal item handover, 1 time; small package barcode scanning, 1 time; unloading operation, 1 time. Determine waybill X as the target historical waybill, and determine the unloading operation as the target type of handheld scanner operation. The historical handheld scanner operation information of waybill X is: unloading operation, 2 times. Assume that the corresponding range of abnormal operation times for the unloading operation is 1.5 times - 2 times. Then the number of operations for waybill X to perform the unloading operation belongs to the corresponding range of abnormal operation times, and determine the abnormal information of the handheld scanner operation for waybill X to perform the unloading operation as normal.

[0100] Since each historical waybill will be used as the target waybill, the abnormal information of the handheld scanner operation for each historical waybill can be obtained by traversing. For example, the abnormal information of the historical handheld scanner operation of waybill X is: abnormal item handover, normal; small package barcode scanning, abnormal; unloading operation, normal.

[0101] S202. Perform an association analysis on the historical waybill information and historical user complaint information of multiple historical waybills to obtain multiple association analysis results on the correlation between the waybill information and the user complaint information.

[0102] In a specific embodiment, the historical waybill information includes historical waybill category information and abnormal information of historical handheld scanner operations. Perform an association analysis on the historical waybill information and historical user complaint information of each historical waybill to obtain the association analysis results between the waybill information and the user complaint information, including:

[0103] (1) Determine the historical waybill category information, abnormal information of historical handheld scanner operations, and historical user complaint information of each historical waybill as an item set, and obtain multiple item sets corresponding to multiple historical waybills.

[0104] Association analysis is a simple and practical analysis technique that discovers the associations or correlations existing in a large dataset, thereby describing the rules and patterns of the co-occurrence of certain attributes in a thing. Association analysis is to discover interesting associations and relevant connections between item sets from a large amount of data. A typical example of association analysis is basket analysis. This process analyzes customers' purchasing habits by discovering the connections between different items that customers put into their shopping baskets. By understanding which items are frequently purchased by customers simultaneously, this discovery of associations can help retailers formulate marketing strategies. Other applications also include price list design, product promotion, product placement, and customer segmentation based on purchasing patterns.

[0105] Item: Each item in a transaction is called an item, such as Cola, Egg, etc. Specifically, an item can be the waybill category, whether each type of barcode scanner operation is abnormal, and the category of user complaints.

[0106] Item set: A set containing zero or more items is called an item set, such as {Cola, Egg, Ham}. For example, A1, A2, A3 represent the waybill category. B1, B2, B3 represent whether each type of barcode scanner operation is abnormal. B1 represents the handover of abnormal items, abnormal; B1 represents the handover of abnormal items, normal. The categories of historical user complaints can include: no complaint C3, complaint for the first reason C0, complaint for the second reason C1. The item set can be {A1, B2, C0}, indicating that when A1 and B2 appear, there is a very high probability that C0 will appear.

[0107] (2) Input multiple item sets into a preset association analysis model to obtain multiple association analysis results on the associations between waybill category information, barcode scanner operation abnormality information, and user complaint information. Among them, the association analysis model includes the Apriori association degree model.

[0108] Specifically, the association analysis results include the support, confidence, and lift of each item set, and the user complaint information includes the category of user complaints.

[0109] Support: Support indicates the proportion of transactions that contain both A and B among all transactions. If P(A) represents the proportion of A transactions, the formula is expressed as:

[0110] Support(A,B) = P(A∩B)

[0111] Confidence: Confidence (credibility) indicates the proportion of transactions that contain B among transactions that contain A, that is, the proportion of transactions that contain A among transactions that contain both A and B. The formula is expressed as:

[0112] Confidence(A→B) = Support(A,B) / Support(B)

[0113] Lift, Lift represents the ratio of "the proportion of transactions containing B among transactions containing A" to "the proportion of transactions containing B". When the value of Lift is greater than 1, it indicates a positive correlation between transaction A and transaction B, and an increase in transaction A will lead to an increase in transaction B. When Lift is equal to 1, it means there is no relationship between the two. When Lift is less than 1, it means an increase in transaction A will lead to a decrease in transaction B. The formula is expressed as:

[0114] Lift(A→B)=Confidence(A,B) / Support(A)

[0115] For example: For five item sets, operation A appears three times, operation B appears four times, and operations A and B appear together twice. Then Support(A)=3 / 5, Support(B)=4 / 5, Support(A,B)=2 / 5. Continuing to calculate the confidence: Confidence(A->B)=Support(A,B) / Support(B)=1 / 2. It means the probability that operation B appears when operation A appears. Finally, calculate the lift: Lift(A->B)=Confidence(A->B) / Support(A)=5 / 6.

[0116] The Apriori algorithm is a basic algorithm for mining frequent item sets required to generate Boolean association rules and is also one of the most well-known association rule mining algorithms. The Apriori algorithm is named based on the prior knowledge about the characteristics of frequent item sets. It uses an iterative method called level-wise search, where k-item sets are used to explore (k + 1)-item sets. First, find the set of frequent 1-item sets, denoted as L1. L1 is used to find the set of frequent 2-item sets L2, and then L3, and so on, until no frequent k-item sets can be found. Scanning the database once is required to find each Lk.

[0117] Of course, the FP-growth algorithm can also be used. Due to the inherent defects of the Apriori method, even after optimization, its efficiency is still not satisfactory. In 2000, Han Jiawei et al. proposed the FP-growth algorithm for discovering frequent patterns based on the Frequent Pattern Tree (abbreviated as FP-tree). In the FP-growth algorithm, by scanning the transaction database twice, the frequent items contained in each transaction are compressed and stored in the FP-tree in descending order of their support degrees. In the subsequent process of discovering frequent patterns, there is no need to scan the transaction database again, but only to search in the FP-Tree, and the frequent patterns are directly generated by recursively calling the FP-growth method. Therefore, no candidate patterns need to be generated during the entire discovery process. This algorithm overcomes the problems existing in the Apriori algorithm and is also significantly better than the Apriori algorithm in terms of execution efficiency.

[0118] S203. Determine the target association analysis result by identifying the association analysis result that meets the preset abnormal conditions.

[0119] In a specific embodiment, the preset abnormal conditions include: the category of user complaints belongs to the preset category label, and the support degree is greater than the preset support degree, and / or the confidence degree is greater than the preset confidence degree, and / or the lift degree is greater than the preset lift degree. The preset support degree, preset confidence degree, preset lift degree, and preset category label can be set according to specific circumstances. For example, the preset lift degree is 1.

[0120] For example, if {A1, B2, C0} exists in the target association analysis result, {A1, B2, C0} means that when A1 and B2 appear, there is a very high probability that C0 will appear.

[0121] S204. Based on the target association analysis result and the current waybill information of the current waybill, perform abnormal identification on the current waybill to obtain an abnormal waybill.

[0122] (1) Obtain the current waybill information of the current waybill.

[0123] The current waybill information includes current abnormal gun operation information and current waybill category information.

[0124] (2) Based on the current waybill information of the current waybill, determine whether there is corresponding target waybill information in the target association analysis result.

[0125] Specifically, determine whether the abnormal information of the gun scanning operation in each item set in the target correlation analysis result matches the current abnormal information of the gun scanning operation to obtain the first matching result; determine whether the waybill category information in each item set in the target correlation analysis result matches the current waybill category information to obtain the second matching result; when both matching results are a match, determine that there is corresponding target waybill information in the target correlation analysis result. For example, if the current waybill information is {A1, B2} and {A1, B2, C0} exists in the target correlation analysis result, then there is corresponding target waybill information in the target correlation analysis result.

[0126] (3) When there is corresponding target waybill information in the target correlation analysis result, determine the current waybill as an abnormal waybill.

[0127] When there is corresponding target waybill information in the target correlation analysis result, such as {A1, B2, C0} existing. {A1, B2, C0} means that when A1 and B2 appear, there is a very high probability that C0 will appear. Currently, {A1, B2} has occurred, and there is a very high probability that C0 will appear in the current waybill, that is, the current waybill is complained by the user for the first complaint reason. This waybill is an abnormal waybill. When obtaining the waybill information of the current waybill, before the user's feedback, determine the abnormal order in real time for subsequent processing to avoid being complained by the user.

[0128] To better implement the method for identifying abnormal waybills in the embodiments of the present application, based on the method for identifying abnormal waybills, an apparatus for identifying abnormal waybills is further provided in the embodiments of the present application, as Figure 3 shown. Figure 3 FIG. is a schematic structural diagram of an embodiment of an apparatus for identifying abnormal waybills provided in the embodiments of the present application. The apparatus for identifying abnormal waybills includes:

[0129] An obtaining unit 401, configured to obtain historical waybill information and historical user complaint information of a plurality of historical waybills within a preset time period, where the historical waybill information includes historical abnormal information of gun scanning operations;

[0130] A correlation analysis unit 402, configured to perform a correlation analysis on the historical waybill information and historical user complaint information of a plurality of historical waybills to obtain a plurality of correlation analysis results on the correlation between the waybill information and the user complaint information;

[0131] A determination unit 403, configured to determine the correlation analysis result that meets the preset abnormal condition as the target correlation analysis result;

[0132] An abnormal identification unit 404, configured to perform abnormal identification on the current waybill based on the target correlation analysis result and the current waybill information of the current waybill to obtain an abnormal waybill.

[0133] Among them, the historical abnormal information of the PDA operations includes whether the PDA operations of various types of waybills are abnormal; the obtaining unit 401 is further configured to: obtain the historical PDA operation information of historical waybills, where the historical PDA operation information includes the operation times of various types of PDA operations for each historical waybill;

[0134] Determine the range of abnormal operation times of various types of PDA operations based on the Gaussian distribution of the historical PDA operation information;

[0135] Determine the historical abnormal information of the PDA operations of each historical waybill based on the historical PDA operation information of the historical waybill and the range of abnormal operation times.

[0136] Among them, the historical waybill information further includes historical waybill category information; the historical PDA operation information includes the operation times of various types of PDA operations for the historical waybills of each waybill category;

[0137] The obtaining unit 401 is further configured to: determine the range of abnormal operation times of various types of PDA operations for each waybill category based on the Gaussian distribution of the historical PDA operation information.

[0138] Among them, determining the historical abnormal information of the PDA operations of each historical waybill based on the historical PDA operation information of the historical waybill and the range of abnormal operation times includes:

[0139] Respectively determine each historical waybill as a target historical waybill, and determine each type of PDA operation as a target type of PDA operation;

[0140] Obtain the operation times of the target type of PDA operation for the target historical waybill;

[0141] Judge whether the operation times of the target historical waybill for the target type of PDA operation belong to the corresponding range of abnormal operation times;

[0142] If the operation times of the target historical waybill for the target type of PDA operation belong to the corresponding range of abnormal operation times, determine the historical abnormal information of the PDA operation of the target historical waybill for the target type of PDA operation as normal; if the operation times of the target historical waybill for the target type of PDA operation do not belong to the corresponding range of abnormal operation times, determine the historical abnormal information of the PDA operation of the target historical waybill for the target type of PDA operation as abnormal.

[0143] Among them, the historical waybill information includes historical waybill category information and historical abnormal information of PDA operations; the obtaining unit 401 is further configured to:

[0144] Determine the historical waybill category information, the historical abnormal information of PDA operations, and the historical user complaint information of each historical waybill as an item set, and obtain multiple item sets corresponding to multiple historical waybills;

[0145] Input multiple item sets into a preset association analysis model to obtain multiple association analysis results on the correlation between waybill category information, abnormal gun operation information, and user complaint information. Among them, the association analysis model includes an Apriori association degree model.

[0146] Among them, the association analysis results include the support degree, confidence degree, and lift degree of each item set. The historical user complaint information includes the categories of user complaints.

[0147] The preset abnormal conditions include: the support degree is greater than the preset support degree, and / or the confidence degree is greater than the preset confidence degree, and / or the lift degree is greater than the preset lift degree, and the category of the user complaint belongs to the preset category label.

[0148] Among them, the abnormal recognition unit 404 is further configured to: obtain the current waybill information of the current waybill.

[0149] Based on the current waybill information of the current waybill, determine whether there is corresponding target waybill information for the target association analysis result.

[0150] When there is corresponding target waybill information for the target association analysis result, determine the current waybill as an abnormal waybill.

[0151] The embodiment of the present application further provides an electronic device, which integrates any abnormal waybill recognition device provided by the embodiment of the present application. As Figure 4 shown, it shows the structural schematic diagram of the electronic device involved in the embodiment of the present application. Specifically:

[0152] The electronic device may include a processor 401 with one or more processing cores, a memory 402 with one or more computer-readable storage media, a power supply 403, an input unit 404, and other components. Those skilled in the art can understand that the structural diagram of the electronic device shown in the figure does not constitute a limitation on the electronic device, and it may include more or fewer components than shown in the figure, or combine certain components, or have different component arrangements.

[0153] Among them:

[0154] The processor 401 is the control center of the electronic device, connecting various parts of the entire electronic device through various interfaces and circuits. By running or executing software programs and / or modules stored in the memory 402, and by invoking the data stored in the memory 402, it executes various functions of the electronic device and processes data, thereby monitoring the electronic device as a whole. Optionally, the processor 401 may include one or more processing cores; preferably, the processor 401 may integrate an application processor and a modem processor. Among them, the application processor mainly processes the operating system, user interface, application programs, etc., and the modem processor mainly processes wireless communications. It can be understood that the above-mentioned modem processor may not be integrated into the processor 401 either.

[0155] The memory 402 can be used to store software programs and modules. The processor 401 executes various functional applications and data processing by running the software programs and modules stored in the memory 402. The memory 402 may mainly include a program storage area and a data storage area. Among them, the program storage area can store the operating system, application programs required for at least one function (such as the sound playback function, image playback function, etc.); the data storage area can store the data created according to the use of the electronic device. In addition, the memory 402 may include high-speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other volatile solid-state storage devices. Correspondingly, the memory 402 may also include a memory controller to provide the processor 401 with access to the memory 402.

[0156] The electronic device further includes a power supply 403 for supplying power to each component. Preferably, the power supply 403 can be logically connected to the processor 401 through a power management system, so as to implement functions such as management of charging, discharging, and power consumption management through the power management system. The power supply 403 may also include any components such as one or more DC or AC power supplies, a recharge system, a power failure detection circuit, a power converter or inverter, and a power status indicator.

[0157] The electronic device may further include an input unit 404, which can be used to receive input digital or character information, and generate keyboard, mouse, joystick, optical or trackball signal inputs related to user settings and function controls.

[0158] Although not shown, the electronic device may further include a display unit, etc., which will not be elaborated here. Specifically, in this embodiment, the processor 401 in the electronic device will load the executable files corresponding to the processes of one or more application programs into the memory 402 according to the following instructions, and the processor 401 will run the application programs stored in the memory 402 to achieve various functions as follows:

[0159] Obtain the historical waybill information and historical user complaint information of multiple historical waybills within a preset time period, where the historical waybill information includes historical abnormal PDA operation information;

[0160] Conduct correlation analysis on the historical waybill information and historical user complaint information of multiple historical waybills to obtain multiple correlation analysis results on the correlation between waybill information and user complaint information;

[0161] Determine the correlation analysis results that meet the preset abnormal conditions as the target correlation analysis results;

[0162] Based on the target correlation analysis results and the current waybill information of the current waybill, perform abnormal identification on the current waybill to obtain an abnormal waybill.

[0163] Those of ordinary skill in the art can understand that all or part of the steps in the various methods of the above embodiments can be completed by instructions, or by controlling relevant hardware through instructions. These instructions can be stored in a computer-readable storage medium and loaded and executed by a processor.

[0164] For this reason, an embodiment of the present application provides a computer-readable storage medium, which may include: read-only memory (ROM, Read Only Memory), random access memory (RAM, Random Access Memory), a magnetic disk or an optical disc, etc. A computer program is stored thereon, and the computer program is loaded by a processor to execute the steps in any of the abnormal waybill identification methods provided by the embodiments of the present application. For example, when the computer program is loaded by a processor, the following steps can be executed:

[0165] Obtain the historical waybill information and historical user complaint information of multiple historical waybills within a preset time period, where the historical waybill information includes historical abnormal PDA operation information;

[0166] Conduct correlation analysis on the historical waybill information and historical user complaint information of multiple historical waybills to obtain multiple correlation analysis results on the correlation between waybill information and user complaint information;

[0167] Determine the correlation analysis results that meet the preset abnormal conditions as the target correlation analysis results;

[0168] Based on the target correlation analysis results and the current waybill information of the current waybill, perform abnormal identification on the current waybill to obtain an abnormal waybill.

[0169] In the above embodiments, the descriptions of the respective embodiments have their own emphases. For parts not detailed in a certain embodiment, reference can be made to the detailed descriptions of other embodiments above, and details will not be repeated here.

[0170] In specific implementation, each of the above units or structures can be implemented as an independent entity, or can be arbitrarily combined and implemented as the same or several entities. For the specific implementation of each of the above units or structures, reference can be made to the foregoing method embodiments, which will not be elaborated herein.

[0171] For the specific implementation of each of the above operations, reference can be made to the foregoing embodiments, which will not be elaborated herein.

[0172] The above has introduced in detail a method, apparatus, electronic device, and storage medium for identifying an abnormal waybill provided by an embodiment of the present application. Specific examples are used herein to elaborate on the principle and implementation manner of the present application. The description of the above embodiments is only used to help understand the method and its core idea of the present application; at the same time, for those skilled in the art, according to the idea of the present application, there will be changes in the specific implementation manner and application scope. In summary, the content of this specification should not be construed as a limitation to the present application.

Claims

1. A method for identifying abnormal waybills, characterized in that, The identification method includes: Obtaining historical waybill information and historical user complaint information of a plurality of historical waybills within a preset time period, wherein the historical waybill information includes historical abnormal PDA operation information; the historical abnormal PDA operation information includes whether each type of PDA operation of the waybill is abnormal; Performing correlation analysis on the historical waybill information and the historical user complaint information of the plurality of historical waybills to obtain a plurality of correlation analysis results on the correlation between the waybill information and the user complaint information; Determining the correlation analysis results that meet the preset abnormal conditions as target correlation analysis results; Performing abnormal identification on the current waybill based on the target correlation analysis results and the current waybill information of the current waybill to obtain an abnormal waybill.

2. The identification method according to claim 1, characterized in that, The obtaining of the historical waybill information and the historical user complaint information of a plurality of historical waybills within a preset time period includes: Obtaining the historical PDA operation information of the historical waybills, wherein the historical PDA operation information includes the number of operations of each type of PDA operation for each historical waybill; Determining the range of abnormal operation times of each type of PDA operation based on the Gaussian distribution of the historical PDA operation information; Determining the historical abnormal PDA operation information of each historical waybill based on the historical PDA operation information of the historical waybill and the range of abnormal operation times.

3. The identification method according to claim 2, characterized in that, The historical waybill information further includes historical waybill category information; the historical PDA operation information includes the number of operations of each type of PDA operation for the historical waybills of each waybill category; The determining the range of abnormal operation times of each type of PDA operation based on the Gaussian distribution of the historical PDA operation information includes: Determining the range of abnormal operation times of each type of PDA operation for each waybill category based on the Gaussian distribution of the historical PDA operation information.

4. The identification method according to claim 2, characterized in that, The determining the historical abnormal PDA operation information of each historical waybill based on the historical PDA operation information of the historical waybill and the range of abnormal operation times includes: Respectively determining each historical waybill as a target historical waybill and each type of PDA operation as a target type of PDA operation; Obtaining the number of operations of the target historical waybill for the target type of PDA operation; Judging whether the number of operations of the target historical waybill for the target type of PDA operation belongs to the corresponding range of abnormal operation times; If the number of operations of the target historical waybill for the target type of PDA operation belongs to the corresponding range of abnormal operation times, determining the historical abnormal PDA operation information of the target historical waybill for the target type of PDA operation as normal; if the number of operations of the target historical waybill for the target type of PDA operation does not belong to the corresponding range of abnormal operation times, determining the historical abnormal PDA operation information of the target historical waybill for the target type of PDA operation as abnormal.

5. The identification method according to claim 1, characterized in that, The historical waybill information includes historical waybill category information and historical abnormal PDA operation information; The performing correlation analysis on the historical waybill information and the historical user complaint information of the plurality of historical waybills to obtain a plurality of correlation analysis results on the correlation between the waybill information and the user complaint information includes: Determine the historical waybill category information, historical handheld terminal operation exception information, and historical user complaint information of each of the historical waybills as an item set, and obtain multiple item sets corresponding to the multiple historical waybills; Input the multiple item sets into a preset association analysis model to obtain multiple association analysis results on the correlation between the waybill category information, handheld terminal operation exception information, and user complaint information, where the association analysis model includes an Apriori correlation model.

6. The identification method according to claim 5, characterized in that, The association analysis results include the support, confidence, and lift of each item set, and the historical user complaint information includes the category of user complaints; The preset exception conditions include: the support is greater than a preset support, and / or the confidence is greater than a preset confidence and / or the lift is greater than a preset lift, and the category of user complaints belongs to a preset category label.

7. The identification method according to claim 1, characterized in that, The abnormal identification of the current waybill based on the target association analysis result and the current waybill information of the current waybill to obtain an abnormal waybill includes: Obtain the current waybill information of the current waybill; Based on the current waybill information of the current waybill, determine whether there is corresponding target waybill information in the target association analysis result; When there is corresponding target waybill information in the target association analysis result, determine the current waybill as an abnormal waybill.

8. An apparatus for identifying abnormal waybills, characterized in that, The abnormal waybill identification device includes: An acquisition unit, configured to acquire the historical waybill information and historical user complaint information of multiple historical waybills within a preset time period, where the historical waybill information includes historical handheld terminal operation exception information; the historical handheld terminal operation exception information includes whether each type of handheld terminal operation of the waybill is abnormal; An association analysis unit, configured to perform an association analysis on the historical waybill information and historical user complaint information of the multiple historical waybills to obtain multiple association analysis results on the correlation between the waybill information and user complaint information; A determination unit, configured to determine the association analysis result that meets the preset exception conditions as the target association analysis result; An abnormal identification unit, configured to perform an abnormal identification on the current waybill based on the target association analysis result and the current waybill information of the current waybill to obtain an abnormal waybill.

9. An electronic device, characterized in that, The electronic device includes: One or more processors; A memory; and One or more applications, where the one or more applications are stored in the memory and are configured to be executed by the processor to implement the abnormal waybill identification method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, A computer program is stored thereon, and the computer program is loaded by the processor to execute the steps in the abnormal waybill identification method according to any one of claims 1 to 7.

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