CN109359966B - Method and device for detecting abnormal charging of logistics packages - Google Patents
Method and device for detecting abnormal charging of logistics packages Download PDFInfo
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Abstract
The invention is suitable for the technical field of logistics management, and provides a method for detecting abnormal charging of logistics packages, which comprises the following steps: selecting standard characteristics according to the related characteristic data of the corresponding relation table of the expense characteristics and the related characteristics; establishing a characteristic statistical model of historical logistics data according to the standard characteristics and data in a corresponding relation table of the cost characteristics and the related characteristics, wherein the characteristic statistical model is used for representing the association of the standard characteristics, the cost characteristic categories and threshold values corresponding to the cost characteristic categories; acquiring standard characteristics and cost characteristics of the current package, and acquiring a threshold value corresponding to the cost characteristic category of the current package according to the standard characteristics and the characteristic statistical model of the current package; and determining whether the cost characteristic of the current parcel is abnormal or not according to the cost characteristic of the current parcel and a threshold value corresponding to the cost characteristic category to which the current parcel belongs.
Description
Technical Field
The invention belongs to the technical field of logistics management, and particularly relates to a method and equipment for detecting abnormal charging of logistics packages.
Background
In the existing logistics management equipment, the logistics cost of each parcel is calculated according to data information such as a departure place, a destination, the size of the parcel, the weight of the parcel, a logistics mode, time and the like, but the existing logistics management equipment cannot detect whether the calculated logistics cost is reasonable and accurate or not, and whether the logistics cost is abnormal or not.
Disclosure of Invention
The embodiment of the invention provides a method and equipment for detecting abnormal charging of logistics packages, and aims to solve the problem that the conventional logistics management equipment cannot detect whether the calculated logistics cost is reasonable and accurate.
The embodiment of the invention is realized in such a way that a method for detecting abnormal charging of logistics packages comprises the following steps:
obtaining historical logistics data of logistics equipment, and obtaining a corresponding relation table of cost characteristics and related characteristics according to the historical logistics data;
selecting standard characteristics according to the related characteristic data of the corresponding relation table of the expense characteristics and the related characteristics;
establishing a characteristic statistical model of historical logistics data according to the standard characteristics and data in a corresponding relation table of the cost characteristics and the related characteristics, wherein the characteristic statistical model is used for representing the association of the standard characteristics, the cost characteristic categories and threshold values corresponding to the cost characteristic categories;
acquiring standard characteristics and cost characteristics of the current package, and acquiring a threshold value corresponding to the cost characteristic category of the current package according to the standard characteristics and the characteristic statistical model of the current package;
and determining whether the cost characteristic of the current parcel is abnormal or not according to the cost characteristic of the current parcel and a threshold value corresponding to the cost characteristic category to which the current parcel belongs.
The method for detecting the abnormal charging of the logistics packages obtains a threshold value corresponding to the cost characteristic category of the current packages according to the standard characteristics and the cost characteristics of the current packages, the standard characteristics of the current packages and the characteristic statistical model by establishing a characteristic statistical model of historical logistics data; according to the cost characteristics of the current parcel and the threshold value corresponding to the cost characteristic category to which the current parcel belongs, whether the cost characteristics of the current parcel are abnormal or not can be determined, and the function of detecting whether the logistics cost is reasonable or not is achieved.
The embodiment of the invention also provides equipment for detecting abnormal charging of the logistics packages, which comprises a memory, a processor and a computer program which is stored on the memory and can run on the processor, wherein the processor executes the program to realize the method.
Drawings
Fig. 1 is a flowchart of an embodiment of a method for detecting abnormal billing of a logistics package according to the invention.
Figure 2 is a box diagram of one embodiment of the cost feature of the present invention.
FIG. 3 is a box diagram of one embodiment of the normal cost feature of the present invention.
Fig. 4 is a flowchart of another embodiment of the method for detecting abnormal billing of a logistics package of the present invention.
Figure 5 is a schematic diagram of one embodiment of the expense feature of the present invention.
Fig. 6 is a schematic structural diagram of an embodiment of the device for detecting abnormal billing of the logistics package according to the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
The invention provides a method for detecting abnormal charging of a logistics package, as shown in fig. 1, the method comprises the following steps:
102, selecting standard characteristics from related characteristic data of a corresponding relation table of the expense characteristics and the related characteristics;
103, establishing a feature statistical model of historical logistics data according to the standard features and data in the corresponding relation table of the cost features and the related features, wherein the feature statistical model is used for expressing the association of the standard features, the cost feature categories and threshold values corresponding to each cost feature category;
104, acquiring the standard characteristic and the cost characteristic of the current parcel, and acquiring a threshold value corresponding to the cost characteristic category of the current parcel according to the standard characteristic and the characteristic statistical model of the current parcel;
and 105, determining whether the cost characteristic of the current parcel is abnormal or not according to the cost characteristic of the current parcel and a threshold value corresponding to the cost characteristic category to which the current parcel belongs.
In step 101, historical logistics data of the logistics device is collected, wherein the historical logistics data of the logistics device comprises a departure place, a destination, a package size, a package weight, a logistics mode, a duration and the like. And obtaining a corresponding relation table of the expense characteristics and the related characteristics according to the historical logistics data, wherein the related characteristics are other logistics characteristics except the expense characteristics, such as distance, departure place, destination, parcel size, parcel weight and the like, and the corresponding relation table of the expense characteristics and the related characteristics is shown in the table 1.
TABLE 1
| Cost characteristics | Distance between two adjacent plates | Departure place | Destination | Size of package | Weight of package | 。。。 |
| 5 Yuan | 100 km | Xi ' an | Weinan (a Chinese character of 'wei' an) | 10*10 | 1.5 kg | 。。。 |
| 3 |
20 km | Xi ' an | Xi ' an | 5*5 | 1 kg of | 。。。 |
| 。。。 |
In a specific implementation, after step 101, the method further comprises the steps of:
and step 1011, cleaning the data in the corresponding relation table of the expense characteristics and the related characteristics to obtain a correct corresponding relation table of the expense characteristics and the related characteristics.
Specifically, the data in the corresponding relation table of the cost characteristics and the related characteristics is cleaned column by column, and the data outside the cost mean value ± N × variance can be cleaned as outliers by using a ROI or box diagram mode during cleaning. More specifically, data cleaning is carried out on all data column by column, the value range of normal data can be obtained through a box diagram during cleaning, and points outside the range are removed. Taking the price as an example, a box diagram of the cost characteristics is drawn, as shown in fig. 2, if a point with a value of 500 is taken as an abnormal point, the abnormal point needs to be removed, and after the abnormal point is removed, a box diagram of a normal cost characteristic value range can be obtained, as shown in fig. 3.
In a specific implementation, as shown in fig. 4, the step 102 specifically includes:
the step 103 specifically includes:
and step 1031, establishing a feature statistical model of historical logistics data according to the standard features and the data in the correct corresponding relation table of the cost features and the related features.
In step 1031, the feature statistical model is used to represent the association of the standard features, the fee feature categories, and the threshold values corresponding to each fee feature category. That is to say, the standard feature of the package is obtained, the cost feature category of the package can be obtained according to the standard feature of the package and the feature statistical model, then the threshold value corresponding to the cost feature category of the package can be obtained according to the cost feature category of the package and the feature statistical model, and the cost of the package can be judged to be abnormal when the cost value of the package is not within the threshold value corresponding to the cost feature category of the package by comparing the cost feature of the package and the threshold value corresponding to the cost feature category of the package.
In step 1021, the cost features and the related features are selected, and the last M% of the features in the feature sorting are removed to obtain the final cost feature. Specifically, the correlation coefficients of the cost characteristics and different correlation characteristics are calculated through the Relieff algorithm of the R language, and the specific relationship is shown in Table 2. For example, the last 2 ranked features are removed, i.e. the distance and the parcel size can be selected from the related features as the standard features according to the correlation coefficient.
TABLE 2
| Name of related feature | Correlation |
| Distance between two adjacent plates | 0.7 |
| Size of package | 0.65 |
| Destination | 0.4 |
| |
0 |
In a specific implementation, the step 1031 further includes the following steps:
clustering data in the correct corresponding relation table of the standard features and the cost features and the related features according to the standard features to obtain a corresponding relation table of the standard features and the cost feature categories, and counting the cost mean value and the cost variance under each cost feature category;
obtaining a threshold value corresponding to each expense characteristic category according to the expense mean value and the expense variance under each expense characteristic category;
and establishing a characteristic statistical model of historical logistics data according to the corresponding relation table of the standard characteristics and the expense characteristic categories and the threshold value corresponding to each expense characteristic category.
Specifically, the clustering algorithm specifically comprises: PAM and K-Means.
That is, clustering algorithms such as PAM or K-Means are used to cluster the related features, and the cost mean and the cost variance under different categories are counted. For example, distance and parcel size, the two standard features are clustered, where the number of clusters is assumed to be 4, and each cost feature class is class 3, for example, the cost mean and the cost variance of class 3 are respectively: the cost mean is 10.16 and the cost variance is 7.
In a specific implementation, the threshold value corresponding to each cost feature category is obtained according to the cost mean and the cost variance under each cost feature category, and specifically is as follows: and taking the cost mean value +/-N cost variance of each cost characteristic category as a corresponding threshold value of each cost characteristic category. Assuming that the cost features are normal values within the cost mean ± 0.75 × variance, there is an anomaly in the point cost within the red circle in fig. 5, i.e., the cost is normal values within the interval (10.16-0.75 × 7, 10.16+0.75 × 7).
In a specific implementation, in step 104 and step 105, for the current package, a standard feature and a cost feature of the current package are obtained, a cost feature category to which the current package belongs is obtained according to the standard feature of the current package and the feature statistical model, that is, the cost feature category to which the current package belongs is calculated according to KNN, then a threshold value corresponding to the cost feature category to which the current package belongs can be obtained according to the feature statistical model and the current package, and whether the cost feature of the current package is abnormal or not is determined according to the cost feature of the current package and the threshold value corresponding to the cost feature category to which the current package belongs, that is, whether the cost is reasonable or not is determined. Say a sample point is 150 km away and 10 x 10, it should belong to category 3, and the cost would normally be in the range of 10.16-0.75 x 7, 10.16+0.75 x 7.
In a specific implementation, the present invention further provides an apparatus for detecting abnormal billing of a logistics package according to an embodiment, as shown in fig. 6, the apparatus includes a memory 601, a processor 602, and a computer program stored in the memory and executable on the processor, and the processor executes the program to implement the method as described above.
In a specific implementation, the invention also provides an embodiment non-transitory computer-readable storage medium, on which a computer program is stored, the program being executed by a processor for implementing the method as described above.
Any process or method descriptions in flow charts or otherwise described herein may be understood as representing modules, segments, or portions of code which include one or more executable instructions for implementing steps of a custom logic function or process, and alternate implementations are included within the scope of the preferred embodiment of the present invention in which functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order, depending on the functionality involved, as would be understood by those reasonably skilled in the art of the present invention.
The logic and/or steps represented in the flowcharts or otherwise described herein, e.g., an ordered listing of executable instructions that can be considered to implement logical functions, can be embodied in any computer-readable medium for use by or in connection with an instruction execution apparatus, device, or apparatus, such as a computer-based apparatus, processor-containing apparatus, or other device that can fetch the instructions from the instruction execution apparatus, device, or apparatus and execute the instructions. For the purposes of this description, a "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution apparatus, device, or apparatus. More specific examples (a non-exhaustive list) of the computer-readable medium would include the following: an electrical connection (electronic device) having one or more wires, a portable computer diskette (magnetic device), a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable medium could even be paper or another suitable medium upon which the program is printed, as the program can be electronically captured, via for instance optical scanning of the paper or other medium, then compiled, interpreted or otherwise processed in a suitable manner if necessary, and then stored in a computer memory.
It should be understood that portions of the present invention may be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, the various steps or methods may be implemented in software or firmware stored in memory and executed by a suitable instruction execution device. If implemented in hardware, as in another embodiment, any one or combination of the following techniques, which are known in the art, may be used: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application specific integrated circuit having an appropriate combinational logic gate circuit, a Programmable Gate Array (PGA), a Field Programmable Gate Array (FPGA), or the like.
It will be understood by those skilled in the art that all or part of the steps carried by the method for implementing the above embodiments may be implemented by hardware related to instructions of a program, which may be stored in a computer readable storage medium, and when the program is executed, the program includes one or a combination of the steps of the method embodiments.
In addition, functional units in the embodiments of the present invention may be integrated into one processing module, or each unit may exist alone physically, or two or more units are integrated into one module. The integrated module can be realized in a hardware mode, and can also be realized in a software functional module mode. The integrated module, if implemented in the form of a software functional module and sold or used as a stand-alone product, may also be stored in a computer readable storage medium.
The storage medium mentioned above may be a read-only memory, a magnetic or optical disk, etc. Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention, and that variations, modifications, substitutions and alterations can be made to the above embodiments by those of ordinary skill in the art within the scope of the present invention.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents and improvements made within the spirit and principle of the present invention are intended to be included within the scope of the present invention.
Claims (7)
1. A method for detecting abnormal charging of logistics packages is characterized in that: the method comprises the following steps:
obtaining historical logistics data of logistics equipment, and obtaining a corresponding relation table of cost characteristics and related characteristics according to the historical logistics data; wherein, the related characteristics are other logistics characteristics except the cost characteristics in the historical logistics data;
selecting standard characteristics according to the related characteristic data of the corresponding relation table of the expense characteristics and the related characteristics; wherein the standard feature is selected from the related features according to the correlation between the related features and the expense features;
establishing a characteristic statistical model of historical logistics data according to the standard characteristics and data in a corresponding relation table of the cost characteristics and the related characteristics, wherein the characteristic statistical model is used for representing the association of the standard characteristics, the cost characteristic categories and threshold values corresponding to the cost characteristic categories;
acquiring standard characteristics and cost characteristics of the current package, and acquiring a threshold value corresponding to the cost characteristic category of the current package according to the standard characteristics and the characteristic statistical model of the current package;
and determining whether the cost characteristic of the current parcel is abnormal or not according to the cost characteristic of the current parcel and a threshold value corresponding to the cost characteristic category to which the current parcel belongs.
2. The method of claim 1, further comprising the steps of: cleaning data in the corresponding relation table of the expense characteristics and the related characteristics to obtain a correct corresponding relation table of the expense characteristics and the related characteristics;
the step of selecting the standard feature from the relevant feature data of the corresponding relationship table of the fee feature and the relevant feature specifically includes:
selecting standard features from the relevant feature data of the correct corresponding relation table of the expense features and the relevant features;
the step of establishing a characteristic statistical model of historical logistics data according to the standard characteristics and the data in the corresponding relation table of the cost characteristics and the related characteristics comprises the following specific steps:
and establishing a characteristic statistical model of historical logistics data according to the standard characteristics and the data in the correct corresponding relation table of the cost characteristics and the related characteristics.
3. The method of claim 2, wherein: the step of selecting the standard feature according to the relevant feature data of the correct corresponding relation table of the expense feature and the relevant feature comprises the following steps:
performing correlation calculation on the related feature data in the correct corresponding relation table of the cost features and the related features to obtain a correlation coefficient of each related feature;
and selecting standard features from the relevant features according to the correlation coefficient of each relevant feature.
4. The method of claim 2, wherein: the characteristic statistical model of the historical logistics data is established according to the standard characteristics and the data in the correct corresponding relation table of the cost characteristics and the related characteristics, and the method comprises the following steps:
clustering data in the correct corresponding relation table of the standard features and the cost features and the related features according to the standard features to obtain a corresponding relation table of the standard features and the cost feature categories, and counting the cost mean value and the cost variance under each cost feature category;
obtaining a threshold value corresponding to each expense characteristic category according to the expense mean value and the expense variance under each expense characteristic category;
and establishing a characteristic statistical model of historical logistics data according to the corresponding relation table of the standard characteristics and the expense characteristic categories and the threshold value corresponding to each expense characteristic category.
5. The method of claim 4, wherein: the clustering algorithm specifically comprises the following steps: PAM and K-Means.
6. The method according to claim 4, wherein the threshold value corresponding to each cost feature class is obtained according to the cost mean and the cost variance under each cost feature class, and specifically comprises:
and taking the cost mean value plus or minus N-cost variance of each cost characteristic category as a corresponding threshold value of each cost characteristic category, wherein N is 0.75.
7. The utility model provides a detect unusual equipment of commodity circulation parcel charging which characterized in that: the apparatus comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor executes the program for implementing the method according to claims 1-6.
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| CN111160635A (en) * | 2019-12-19 | 2020-05-15 | 金陵科技学院 | Regional logistics demand influence factor prediction method based on Relieff algorithm |
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