CN112884391A - Receiving and dispatching piece planning method and device, electronic equipment and storage medium - Google Patents
Receiving and dispatching piece planning method and device, electronic equipment and storage medium Download PDFInfo
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Abstract
The application discloses a receiving and dispatching planning method and device, electronic equipment and a storage medium. The receiving and dispatching piece planning method comprises the following steps: acquiring user data of a to-be-received dispatch in a preset time period in a target area by a logistics platform; predicting the urging probability of the to-be-received delivery user according to the to-be-received delivery user data; determining the urging type of the to-be-received delivery user according to the urging probability of the to-be-received delivery user; and according to the urging type of the user waiting to receive and dispatch the dispatch, indicating the person receiving and dispatching to receive and dispatch the dispatch. The method and the device can automatically predict the urging probability of the user to receive the delivery; therefore, the urging categories of the to-be-received delivery users can be determined according to the urging probability of the to-be-received delivery users, the urging categories can be classified according to the urging probability to indicate auxiliary delivery members to judge delivery priority, differentiated rescue strategies can be formulated for different probability customer groups, delivery of the waybills with remarkable urging rate can be prioritized, customer complaints are reduced, and customer retention rate is improved.
Description
Technical Field
The present application relates to the field of mobile communications technologies, and in particular, to a receiving and dispatching planning method and apparatus, an electronic device, and a storage medium.
Background
The dispatch appoints customers to give priority to the packages of the logistics enterprise for some reasons. The method mainly comprises the steps of making a call for complaints, WeChat complaints, contacting with a receiver and a dispatcher, or automatically going to a logistics network and automatically taking a part in a transfer station. Therefore, the urge action occupies a great deal of time and energy of customers, websites, customer service and consignors.
The station is in the angle of logistics enterprises, when a package arrives at a network site or is handed over to a dispatching member, how to judge the priority of dispatching, the traditional method is that the dispatching member manually considers the dispatching member from the time effect, the geographic position and the dispatching difficulty, and the dispatching urging behavior is mainly from the perspective of a client, besides the reason of time effect sensitivity, the traditional dispatching scheme can not be accurately early warned in advance, the perception of the client is quantized, the service experience of the client can be influenced by the dispatching urging of the client, the complaint probability is increased, and the retention rate of the user on a logistics platform is influenced.
Disclosure of Invention
The embodiment of the application provides a receiving and dispatching planning method and device, electronic equipment and a storage medium, and the prompting category classification is carried out according to the prompting probability, so that an auxiliary dispatching person can be instructed to judge the dispatching priority, differentiated rescue strategies are formulated for different probability customer groups, the dispatching can be preferentially carried out on the waybill with the remarkable prompting rate, the customer complaint is reduced, and the customer retention rate is improved.
In one aspect, the present application provides a receiving and dispatching planning method, including:
acquiring user data of a to-be-received dispatch in a preset time period in a target area by a logistics platform;
predicting the urging probability of the to-be-received delivery user according to the to-be-received delivery user data;
determining the urging type of the to-be-received delivery user according to the urging probability of the to-be-received delivery user;
and indicating the person who receives and dispatches to receive and dispatch the piece according to the urging type of the user who receives and dispatches the piece.
In some embodiments of the present application, the predicting a promotion probability of a to-be-received dispatch user according to the to-be-received dispatch user data includes:
and inputting the data of the to-be-received delivery user into a preset urging model so as to output the urging probability of the to-be-received delivery user.
In some embodiments of the present application, the incentive model comprises a first incentive model and a second incentive model, the first and second incentive models being different, the incentive probability comprising a first incentive probability and a second incentive probability;
the step of inputting the data of the to-be-received delivery user into a preset urging model so as to output the urging probability of the to-be-received delivery user comprises the following steps:
inputting the data of the to-be-received dispatch user into a preset first urging model so as to output a first urging probability of the to-be-received dispatch user;
and inputting the data of the to-be-received dispatch user into a preset second urging model so as to output a second urging probability of the to-be-received dispatch user.
In some embodiments of the present application, the first motivational model is a LOGISTIC model and the second motivational model is a CNN model.
In some embodiments of the present application, the determining, according to the urging probability of the to-be-received user, an urging category of the to-be-received user includes:
if the first urging probability of the to-be-received delivery user is greater than the first preset probability and the second urging probability is greater than the second preset probability, determining that the to-be-received delivery user is a user with high urging probability;
if the first urging probability of the to-be-received dispatch user is greater than a first preset probability and the second urging probability is less than or equal to a second preset probability, or the first urging probability of the to-be-received dispatch user is less than or equal to the first preset probability and the second urging probability is greater than the second preset probability, determining a user with a medium urging probability in the to-be-received dispatch user;
and if the first urging probability of the to-be-received dispatch user is smaller than the first preset probability and the second urging probability is smaller than the second preset probability, determining that the to-be-received dispatch user is a user with a low urging probability.
In some embodiments of the present application, the instructing a person who receives a dispatch to receive a dispatch according to the urging category of the user who wants to receive a dispatch includes:
if the to-be-received delivery user is a user with high urging probability, determining that the to-be-received delivery user is a first receiving and delivering priority;
if the to-be-received delivery user is a user with medium urging probability, determining that the to-be-received delivery user is a second receiving and delivering priority;
if the to-be-received delivery user is a user with low urging probability, determining that the to-be-received delivery user is a third receiving and delivering priority;
and indicating the person who receives the dispatch to receive the dispatch according to the priority of the dispatch receiving corresponding to the user who receives the dispatch.
In some embodiments of the present application, before the inputting the data of the to-be-received dispatch user into a preset urge model to output an urge probability of the to-be-received dispatch user, the method further includes:
acquiring historical waybill data of the logistics platform in a preset time period;
acquiring a promotion record corresponding to the waybill in the historical waybill data;
generating a training sample set according to each waybill data in the historical waybill data and a promotion record corresponding to each waybill;
and training an initial promotion model by using the training sample set to obtain the promotion model.
In some embodiments of the present application, the user data of the to-be-received dispatch includes at least two of user portrait data, waybill portrait data, received dispatcher portrait data, geographic portrait data, and dynamic routing feature data.
On the other hand, the application also provides a receiving and dispatching planning device, the device includes:
the system comprises an acquisition unit, a receiving unit and a dispatching unit, wherein the acquisition unit is used for acquiring user data of a to-be-received dispatch in a preset time period in a target area of a logistics platform;
the prediction unit is used for predicting the urging probability of the to-be-received delivery user according to the to-be-received delivery user data;
the determining unit is used for determining the urging type of the to-be-received delivery user according to the urging probability of the to-be-received delivery user;
and the indicating unit is used for indicating the person who receives the dispatch to receive the dispatch according to the urging type of the user who receives the dispatch.
In some embodiments of the present application, the prediction unit is specifically configured to:
and inputting the data of the to-be-received delivery user into a preset urging model so as to output the urging probability of the to-be-received delivery user.
In some embodiments of the present application, the incentive model comprises a first incentive model and a second incentive model, the first and second incentive models being different, the incentive probability comprising a first incentive probability and a second incentive probability;
the prediction unit is specifically configured to:
inputting the data of the to-be-received dispatch user into a preset first urging model so as to output a first urging probability of the to-be-received dispatch user;
and inputting the data of the to-be-received dispatch user into a preset second urging model so as to output a second urging probability of the to-be-received dispatch user.
In some embodiments of the present application, the first motivational model is a LOGISTIC model and the second motivational model is a CNN model.
In some embodiments of the present application, the determining unit is specifically configured to:
if the first urging probability of the to-be-received delivery user is greater than the first preset probability and the second urging probability is greater than the second preset probability, determining that the to-be-received delivery user is a user with high urging probability;
if the first urging probability of the to-be-received dispatch user is greater than a first preset probability and the second urging probability is less than or equal to a second preset probability, or the first urging probability of the to-be-received dispatch user is less than or equal to the first preset probability and the second urging probability is greater than the second preset probability, determining a user with a medium urging probability in the to-be-received dispatch user;
and if the first urging probability of the to-be-received dispatch user is smaller than the first preset probability and the second urging probability is smaller than the second preset probability, determining that the to-be-received dispatch user is a user with a low urging probability.
In some embodiments of the present application, the indication unit is specifically configured to:
if the to-be-received delivery user is a user with high urging probability, determining that the to-be-received delivery user is a first receiving and delivering priority;
if the to-be-received delivery user is a user with medium urging probability, determining that the to-be-received delivery user is a second receiving and delivering priority;
if the to-be-received delivery user is a user with low urging probability, determining that the to-be-received delivery user is a third receiving and delivering priority;
and indicating the person who receives the dispatch to receive the dispatch according to the priority of the dispatch receiving corresponding to the user who receives the dispatch.
In some embodiments of the present application, the apparatus further includes a training unit, and the training unit is specifically configured to:
before the data of the to-be-received dispatch user is input into a preset urging model so as to output the urging probability of the to-be-received dispatch user, acquiring historical waybill data of the logistics platform in a preset time period;
acquiring a promotion record corresponding to the waybill in the historical waybill data;
generating a training sample set according to each waybill data in the historical waybill data and a promotion record corresponding to each waybill;
and training an initial promotion model by using the training sample set to obtain the promotion model.
In some embodiments of the present application, the user data of the to-be-received dispatch includes at least two of user portrait data, waybill portrait data, received dispatcher portrait data, geographic portrait data, and dynamic routing feature data.
In another aspect, the present application further provides an electronic device, including a memory and a processor, where the memory stores a computer program, and the computer program, when executed by the processor, causes the processor to perform the following steps:
acquiring user data of a to-be-received dispatch in a preset time period in a target area by a logistics platform;
predicting the urging probability of the to-be-received delivery user according to the to-be-received delivery user data;
determining the urging type of the to-be-received delivery user according to the urging probability of the to-be-received delivery user;
and indicating the person who receives and dispatches to receive and dispatch the piece according to the urging type of the user who receives and dispatches the piece.
In another aspect, the present application further provides a computer-readable storage medium, on which a computer program is stored, where the computer program is loaded by a processor to execute the steps in the receiving and dispatching planning method.
According to the method and the device, the user data of the to-be-received dispatch in the target area within the preset time period of the logistics platform is acquired; predicting the urging probability of the to-be-received delivery user according to the to-be-received delivery user data; determining the urging type of the to-be-received delivery user according to the urging probability of the to-be-received delivery user; and indicating the person who receives and dispatches to receive and dispatch the piece according to the urging type of the user who receives and dispatches the piece. According to the method and the device, under the condition that the existing receiving and dispatching personnel manually consider dispatching and reduce urging, the urging probability of the dispatching user to be received can be automatically predicted based on the dispatching user data to be received; therefore, the urging categories of the to-be-received delivery users can be determined according to the urging probability of the to-be-received delivery users, the urging categories can be classified according to the urging probability to indicate auxiliary delivery members to judge delivery priority, differentiated rescue strategies can be formulated for different probability customer groups, delivery of the waybills with remarkable urging rate can be prioritized, customer complaints are reduced, and customer retention rate is improved.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
Fig. 1 is a schematic view of a logistics system provided in an embodiment of the invention;
FIG. 2 is a flowchart illustrating an embodiment of a receiving and dispatching planning method provided in the embodiment of the present invention;
FIG. 3 is a flowchart of one embodiment of step 202 provided in embodiments of the present invention;
FIG. 4 is a flowchart of one embodiment of step 203 provided in embodiments of the present invention;
FIG. 5 is a schematic diagram of the predicted probability of an embodiment of the CNN model and the LOGISTIC model of the present invention;
fig. 6 is a schematic structural diagram of an embodiment of a receiving and dispatching planning apparatus provided in the embodiment of the present invention;
fig. 7 is a schematic structural diagram of an embodiment of an electronic device according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
In the description that follows, specific embodiments of the present invention are described with reference to steps and symbols executed by one or more computers, unless otherwise indicated. Accordingly, these steps and operations will be referred to, several times, as being performed by a computer, the computer performing operations involving a processing unit of the computer in electronic signals representing data in a structured form. This operation transforms the data or maintains it at locations in the computer's memory system, which may be reconfigured or otherwise altered in a manner well known to those skilled in the art. The data maintains a data structure that is a physical location of the memory that has particular characteristics defined by the data format. However, while the principles of the invention have been described in language specific to above, it is not intended to be limited to the specific form set forth herein, but on the contrary, it is to be understood that various steps and operations described hereinafter may be implemented in hardware.
The term "module" or "unit" as used herein may be considered a software object executing on the computing system. The various components, modules, engines, and services described herein may be viewed as objects implemented on the computing system. The apparatus and method described herein are preferably implemented in software, but may also be implemented in hardware, and are within the scope of the present invention.
As used herein, the singular forms "a", "an", "the" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms "comprises" and/or "comprising," when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof. It will be understood that when an element is referred to as being "connected" or "coupled" to another element, it can be directly connected or coupled to the other element or intervening elements may also be present. Further, "connected" or "coupled" as used herein may include wirelessly connected or wirelessly coupled. As used herein, the term "and/or" includes all or any element and all combinations of one or more of the associated listed items.
The embodiment of the invention provides a receiving and dispatching planning method and device, electronic equipment and a storage medium.
Referring to fig. 1, fig. 1 is a schematic view of a logistics system according to an embodiment of the present invention, where the logistics system may include an electronic device 100, and a receiving and dispatching planning apparatus is integrated in the electronic device 100. In the embodiment of the invention, the electronic device 100 is mainly used for acquiring user data of a to-be-received dispatch in a preset time period in a target area of a logistics platform; predicting the urging probability of the to-be-received delivery user according to the to-be-received delivery user data; determining the urging type of the to-be-received delivery user according to the urging probability of the to-be-received delivery user; and according to the urging type of the user waiting to receive and dispatch the dispatch, indicating the person receiving and dispatching to receive and dispatch the dispatch.
In this embodiment of the present invention, the electronic device 100 may be an independent server or a terminal, and the server may be a server network or a server cluster composed of servers, for example, the server described in this embodiment of the present invention includes, but is not limited to, a computer, a network host, a single network server, multiple network server sets, or a cloud server composed of multiple servers. Among them, the Cloud server is constituted by a large number of computers or web servers based on Cloud Computing (Cloud Computing). In the embodiment of the present invention, the server and the client may implement communication through any communication manner, including but not limited to mobile communication based on the third Generation Partnership Project (3 GPP), Long Term Evolution (LTE), Worldwide Interoperability for Microwave Access (WiMAX), or computer network communication based on the TCP/IP Protocol Suite (TCP/IP), User Datagram Protocol (UDP) Protocol, and the like.
It is to be understood that when the electronic device is a terminal, the electronic device 100 used in the embodiments of the present invention may include both a device having receiving and transmitting hardware, i.e., a device having receiving and transmitting hardware capable of performing two-way communication over a two-way communication link. Such an electronic device 100 may include: a cellular or other communication device having a single line display or a multi-line display or a cellular or other communication device without a multi-line display. The specific electronic device 100 may specifically be a desktop terminal or a mobile terminal, and in some specific application scenarios, the electronic device 100 may also specifically be one of a mobile phone, a tablet computer, a notebook computer, and the like.
Those skilled in the art will appreciate that the application environment shown in fig. 1 is only one application scenario related to the present invention, and does not constitute a limitation to the application scenario of the present invention, and that other application environments may further include more or less servers than those shown in fig. 1, or a network connection relationship of servers, for example, only 1 server and 1 electronic device are shown in fig. 1, and it is understood that the logistics system may further include one or more other servers, or/and one or more clients connected to a network of servers, and is not limited herein.
In addition, as shown in fig. 1, the logistics system may further include a memory 200 for storing data, such as a logistics database, in which logistics data is stored, and the logistics data may include waybill data, pickup and dispatch user data, and the like.
It should be noted that the scenario diagram of the logistics system shown in fig. 1 is only an example, and the logistics system and the scenario described in the embodiment of the present invention are for more clearly illustrating the technical solution of the embodiment of the present invention, and do not form a limitation on the technical solution provided in the embodiment of the present invention, and as a person skilled in the art knows that along with the evolution of the logistics system and the occurrence of a new service scenario, the technical solution provided in the embodiment of the present invention is also applicable to similar technical problems.
The following is a detailed description of specific embodiments.
In the present embodiment, the description will be made from the perspective of a receiving and dispatching planning apparatus, which may be specifically integrated in the electronic device 100.
The invention provides a receiving and dispatching planning method, which comprises the following steps: acquiring user data of a to-be-received dispatch in a preset time period in a target area by a logistics platform; predicting the urging probability of the to-be-received delivery user according to the to-be-received delivery user data; determining the urging type of the to-be-received delivery user according to the urging probability of the to-be-received delivery user; and according to the urging type of the user waiting to receive and dispatch the dispatch, indicating the person receiving and dispatching to receive and dispatch the dispatch.
Referring to fig. 2, a schematic flow chart of an embodiment of a receiving and dispatching planning method according to an embodiment of the present invention is shown, where the receiving and dispatching planning method includes:
201. and acquiring user data of the to-be-received dispatch in a preset time period in the target area by the logistics platform.
In the embodiment of the present invention, any network platform capable of supporting or performing interaction or exchange of logistics service supply and demand information may be regarded as a logistics platform. For example, a logistics company designs an information exchange system for facilitating the contact between the company and its users, so that the users and the company can be conveniently contacted, and the system has the property of a logistics platform, and a professional logistics information service website can also be considered as a typical logistics platform.
The target area may be an area corresponding to the to-be-received and dispatched user, for example, a service area corresponding to a certain logistics network point, an area corresponding to a certain city, and the like, and the specific details are not limited herein. The preset time period may be a preset time interval, for example, a certain time period on a certain day, a certain number of days (for example, a certain e-commerce activity day is exploded, the dispatch is slow, and data of a certain number of days can be collected for analysis), and the like.
In some embodiments of the present invention, the user data of the express to be received and dispatched may include at least two of user portrait data (user characteristics, consignment, complaint, hastening, urging frequency, public number inquiry operation, etc.), waybill portrait data (consignor type, freight rate, insurance price and claim settlement information, time-based labels, settlement methods, etc.), express recipient portrait data (consignor working intensity, complaint frequency, etc.), geographic portrait data (weather, temperature, etc.), and dynamic routing characteristic data (i.e. data during the transportation of the express, such as detainment type, wrong distribution, connection, etc.). Besides basic waybill data, the data corresponding to other users, such as geographic portrait data, and portrait data of the receiving and dispatching personnel, are added to the data of the to-be-received dispatching personnel, so that the influences of package characteristics, habits of the receiving and dispatching personnel, customer preference and the like are comprehensively considered, and the accuracy of the follow-up prediction of the urging probability of the to-be-received dispatching personnel is improved.
202. And predicting the urging probability of the to-be-received delivery user according to the to-be-received delivery user data.
Specifically, the predicting the urging probability of the to-be-received delivery user according to the to-be-received delivery user data includes: and inputting the data of the to-be-received delivery user into a preset urging model so as to output the urging probability of the to-be-received delivery user.
In the embodiment of the present invention, the motivational model may include only one model or a plurality of models, and when one model is included, the motivational model may be a logic model or a Convolutional Neural Networks (CNN) model.
In one embodiment of the present invention, if the promotion model includes two promotion models, the promotion model may include a first promotion model and a second promotion model, the first promotion model and the second promotion model are different, and the promotion probability includes a first promotion probability and a second promotion probability; at this time, as shown in fig. 3, the inputting the data of the to-be-received dispatch user into a preset urging model to output an urging probability of the to-be-received dispatch user may include:
301. and inputting the data of the to-be-received dispatch user into a preset first urging model so as to output a first urging probability of the to-be-received dispatch user.
302. And inputting the data of the to-be-received dispatch user into a preset second urging model so as to output a second urging probability of the to-be-received dispatch user.
Specifically, in the embodiment of the present invention, the first promotion model is a LOGISTIC model, and the second promotion model is a CNN model.
Among them, Convolutional Neural Networks (CNN) are a kind of feed forward Neural Networks (fed forward Neural Networks) containing convolution calculation and having a deep structure, and are one of the representative algorithms of deep learning (deep learning). Convolutional Neural Networks have a feature learning (rendering) capability, and can perform Shift-Invariant classification (Shift-Invariant classification) on input information according to a hierarchical structure thereof, and are therefore also called "Shift-Invariant Artificial Neural Networks (SIANN)". The logstic model is a generalized regression model that has many similarities to multiple linear regression, and the basic form of the model is the same, although also called regression model, it is noted that the logstic model is more applicable to classification problems, but most commonly classified as binary.
In the embodiment of the present invention, no matter the promotion model includes one model or a plurality of models, the model is obtained after training, that is, specifically, before the data of the user of the to-be-received dispatch item is input to a preset promotion model to output the promotion probability of the user of the to-be-received dispatch item, the method for planning the to-be-received dispatch item may further include a training process of the promotion model, and when the promotion model is one, the following is specifically performed: acquiring historical waybill data of the logistics platform in a preset time period; acquiring a promotion record corresponding to the waybill in the historical waybill data; generating a training sample set according to each waybill data in the historical waybill data and a promotion record corresponding to each waybill; and training an initial promotion model by using the training sample set to obtain the promotion model.
When there are a plurality of promotion models, for example, the promotion model includes a first promotion model and a second promotion model, the first promotion model is a logic model, and the second promotion model is a CNN model. The specific training process is as follows:
(1) sample acquisition:
sample source: the logistics platform has the characteristics of real-time waybill transportation, customer receipt and consignment records in the past 6 months, work order urging records of a customer service system, employee receipt and dispatch records, WeChat public number operation records and dynamic routing characteristics;
positive sample: customer urging; negative sample: the customer is not prompted;
sample composition: mixing the raw materials in a ratio of 1: 9, randomly sampling the positive and negative sample proportion;
constructing a model:
the LOGISTIC model is used for obtaining an optimal IV threshold value, candidate variables, an quality ratio and SMOTE parameters;
CNN model: testing a network layer structure, hiding the layer number, activating a function, gradient descending parameters and optimizing DROPOUT parameters;
the LOGISTIC model after parameter adjustment has higher AUC which reaches 0.87 (higher than 0.79 of CNN), and has better overall performance; the LIFT value of the CNN model after parameter adjustment is higher and reaches 50 (higher than 23 of LOGISTIC), and the accuracy rate is higher in a high promotion rate interval.
The AUC (area Under curve) is defined as the area Under the ROC (receiver Operating characterization) curve, and generally, the AUC value is often used as the evaluation criterion of the model because the ROC curve cannot clearly indicate which classifier has a better effect in many cases, and as a numerical value, the classifier having a larger AUC has a better effect. The KS value is the maximum separation distance between two curves in the KS curve, and since the KS value can find the segment with the largest difference in the model, the KS value can only reflect which segment is the most differentiated, but cannot reflect the effect of all segments as a whole. The larger the KS value is, the better the prediction accuracy of the model is, and the KS is more than 0.2, namely the model has better prediction accuracy. The Lift value is a measure to evaluate whether a prediction model is valid; it measures how well a model (or rule) predicts the "response" in a target over a randomly chosen multiple, bounded by 1, with a Lift greater than 1 indicating that the model or rule captures more "response" than the randomly chosen model, a Lift equal to 1 indicating that the model behaves independently of the randomly chosen model, and a Lift less than 1 indicating that the model or rule captures less "response" than the randomly chosen model.
Droupout is a widely used regularization technique for deep learning. Some neural elements are randomly turned off at each iteration: each iteration trains a different model that uses only a portion of the neurons, and as the iteration progresses, neurons become less sensitive to activation by other specific neurons because other neurons may be turned off at any time. The parameter corresponding to the Droupout processing is the Droupout parameter.
And (3) model evaluation:
the two models respectively select the highest AUC and KS combined results to represent the best prediction capability, the CNN observes the difference degree of the LIFT value, and the promotion of sample upgraduations in a high promotion rate interval can be predicted under the high LIFT value.
LOGISTIC model training:
characteristic processing: variable binning, min _ sample: a variable division minimum sample size threshold value is set to be 0.05; alpha is set to 0.05 if the division standard is carried out;
setting an IV threshold value IV _ thre to be 0.1, and taking a variable higher than IV _ thre as a model variable;
generating a training test set: the test set proportion test _ size is 0.3;
performing SMOTE processing on a training set;
model training: a LOGISTIC model was generated.
The SMOTE processing is implemented by adopting SMOTE algorithm processing, the idea of the SMOTE algorithm is to synthesize new minority class samples, the synthesis strategy is to randomly select a sample b from the nearest neighbor of each minority class sample a, and then randomly select a point on a connecting line between a and b as the newly synthesized minority class sample.
(3) CNN model training
Characteristic processing: discrete variable thermal encoding, feature normalization
Generating a training test set: test set proportion test _ size 0.3
Performing SMOTE processing on a training set;
setting model parameters:
an optimizer: a random Gradient (SGD) decrease, a learning rate lr being 0.01, a learning rate decrease rate decay being 1e-6 for each update, and an update momentum being 0.9;
network layer: 1D convolutional layer filters 64, kernel _ size 3, padding same, one BN layer, all connection layer units 64, activation function activation equal to relu, Droupout layer rate 0.1, all connection layer units 128, activation function activation equal to relu, and L2 regularization, and output layer units 1;
loss function: adopting Mean Square Error (MSE);
model training: and (3) carrying out cyclic test, searching an optimal batch processing parameter batch _ size and an iterative parameter epochs combination (sequentially evaluating AUC, KS and LIFT values, finally determining that the batch has the best effect when the batch is between [500,1000] and the epochs has the best effect when the batch is between [15, 25 ]), and generating the CNN model.
Because the promotion behavior has the problem of serious sample unbalance (the promotion is not much higher than the promotion), the embodiment of the invention firstly adopts the stratified sampling configuration with reasonable positive-negative sample ratio (for example, 1: 9), during the characteristic engineering, the LOGISTIC model carries out the box separation processing on the variables, and the CNN model part carries out the SMOTE resampling processing on the samples, thereby improving the prediction capability of the two models.
203. And determining the promotion category of the to-be-received delivery user according to the promotion probability of the to-be-received delivery user.
Further, as shown in fig. 4, the determining the urging category of the user to receive the dispatch according to the urging probability of the user to receive the dispatch may include:
401. and if the first urging probability of the to-be-received dispatch user is greater than the first preset probability and the second urging probability is greater than the second preset probability, determining that the to-be-received dispatch user is a user with high urging probability.
402. And if the first urging probability of the to-be-received dispatch user is greater than a first preset probability and the second urging probability is less than or equal to a second preset probability, or the first urging probability of the to-be-received dispatch user is less than or equal to the first preset probability and the second urging probability is greater than the second preset probability, determining the users with the medium urging probability in the to-be-received dispatch user.
As shown in fig. 5, for the same piece of user data to be sent, a first urging model and a second urging model are respectively used for predicting, that is, both models can respectively predict urging probabilities of the same user, for example, a first urging probability p1 and a second urging probability p2 can be respectively predicted, the urging probabilities are layered in fig. 5, Y1 and X1 are the probabilities of the first urging model and the second urging model under the positive samples with the test set covering the percentage of Y0 and X0, that is, Y1 and X1 are the thresholds of the high urging probabilities corresponding to the first urging model and the second urging model, respectively, a user with a probability higher than the threshold is a user with a high urging probability predicted by the model, that the user with a high urging probability predicted by the model is a user with a high urging probability p1> Y1 and a user with a medium urging probability X6329 > X1, and in the embodiment of the present invention, a high urging probability (p1> Y638) or a high urging probability p 3 > Y2 and X1 is determined according to the probabilities predicted by the first urging model, users with low urging probability (p1< ═ y1 and p2< ═ x1), specifically, the area a in fig. 5 is the area corresponding to users with high urging probability (the prediction probability of the user logimit model p1> y1 and the prediction probability of the CNN model p2> x1 in this area), namely, the areas p1> y1 and p2> x1 in fig. 5, the area B, C is a medium urging probability user corresponding area, wherein, the B region is a region with the CNN model prediction probability p2> x1 and the LOGISTIC model prediction probability p1< ═ y1, namely, in fig. 5, the region p1< ═ y1 and p2> x1, the region C is the region corresponding to the CNN model prediction probability p2< ═ x1, but the logstic model prediction probability p1> y1, namely, in fig. 5, the region p1> y1 and p2< ═ x1, and the region D is a region corresponding to users with low urging probability (in this region, the prediction probabilities of users in the logic model p1< ═ y1 and the prediction probabilities of users in the CNN model p2< ═ x1), that is, the region p1< ═ y1 and p2< + > x1 in fig. 5. It can be understood that the probability division is only an example, and can be divided into more intervals according to the actual service target probability, which is not limited herein. In addition, the promotion described in the embodiment of the present invention may include a promotion and/or a promotion, that is, a behavior of promoting a delivery and/or a promotion, and is not limited herein.
403. And if the first urging probability of the to-be-received dispatch user is smaller than the first preset probability and the second urging probability is smaller than the second preset probability, determining that the to-be-received dispatch user is a user with a low urging probability.
204. And indicating the person who receives and dispatches to receive and dispatch the piece according to the urging type of the user who receives and dispatches the piece.
In some embodiments of the present invention, the instructing a person who receives the dispatch to receive the dispatch according to the urging category of the user who wants to receive the dispatch includes: if the to-be-received delivery user is a user with high urging probability, determining that the to-be-received delivery user is a first receiving and delivering priority; if the to-be-received delivery user is a user with medium urging probability, determining that the to-be-received delivery user is a second receiving and delivering priority; if the to-be-received delivery user is a user with low urging probability, determining that the to-be-received delivery user is a third receiving and delivering priority; and indicating the person who receives the dispatch to receive the dispatch according to the priority of the dispatch receiving corresponding to the user who receives the dispatch.
The method comprises the steps of acquiring user data of the to-be-received dispatch in a preset time period in a target area through a logistics platform; predicting the urging probability of the to-be-received delivery user according to the to-be-received delivery user data; determining the urging type of the to-be-received delivery user according to the urging probability of the to-be-received delivery user; and indicating the person who receives and dispatches to receive and dispatch the piece according to the urging type of the user who receives and dispatches the piece. According to the embodiment of the invention, under the condition that the existing receiving and dispatching personnel manually consider dispatching and reduce urging, the urging probability of the dispatching user to be received can be automatically predicted based on the dispatching user data to be received; therefore, the urging categories of the to-be-received delivery users can be determined according to the urging probability of the to-be-received delivery users, the urging categories can be classified according to the urging probability to indicate auxiliary delivery members to judge delivery priority, differentiated rescue strategies can be formulated for different probability customer groups, delivery of the waybills with remarkable urging rate can be prioritized, customer complaints are reduced, and customer retention rate is improved.
In order to better implement the receiving and dispatching piece planning method provided by the embodiment of the invention, the embodiment of the invention also provides a device based on the receiving and dispatching piece planning method. The meanings of the nouns are the same as those in the receiving and dispatching planning method, and specific implementation details can refer to the description in the method embodiment.
Referring to fig. 6, fig. 6 is a schematic structural diagram of a receiving and dispatching planning apparatus according to an embodiment of the present invention, wherein the receiving and dispatching planning apparatus 600 may include an obtaining unit 601, a predicting unit 602, a determining unit 603, and an indicating unit 604, wherein:
the acquisition unit 601 is configured to acquire to-be-received dispatch user data of a logistics platform in a target area within a preset time period;
the prediction unit 602 is configured to predict a promotion probability of the to-be-received delivery user according to the to-be-received delivery user data;
the determining unit 603 is configured to determine a promotion category of the to-be-received dispatch user according to the promotion probability of the to-be-received dispatch user;
and the indicating unit 604 is used for indicating the person who receives the dispatch to receive the dispatch according to the urging type of the user who receives the dispatch.
In some embodiments of the present application, the prediction unit 602 is specifically configured to:
and inputting the data of the to-be-received delivery user into a preset urging model so as to output the urging probability of the to-be-received delivery user.
In some embodiments of the present application, the incentive model comprises a first incentive model and a second incentive model, the first and second incentive models being different, the incentive probability comprising a first incentive probability and a second incentive probability;
the prediction unit 602 is specifically configured to:
inputting the data of the to-be-received dispatch user into a preset first urging model so as to output a first urging probability of the to-be-received dispatch user;
and inputting the data of the to-be-received dispatch user into a preset second urging model so as to output a second urging probability of the to-be-received dispatch user.
In some embodiments of the present application, the first motivational model is a LOGISTIC model and the second motivational model is a CNN model.
In some embodiments of the present application, the determining unit 603 is specifically configured to:
if the first urging probability of the to-be-received delivery user is greater than the first preset probability and the second urging probability is greater than the second preset probability, determining that the to-be-received delivery user is a user with high urging probability;
if the first urging probability of the to-be-received dispatch user is greater than a first preset probability and the second urging probability is less than or equal to a second preset probability, or the first urging probability of the to-be-received dispatch user is less than or equal to the first preset probability and the second urging probability is greater than the second preset probability, determining a user with a medium urging probability in the to-be-received dispatch user;
and if the first urging probability of the to-be-received dispatch user is smaller than the first preset probability and the second urging probability is smaller than the second preset probability, determining that the to-be-received dispatch user is a user with a low urging probability.
In some embodiments of the present application, the indicating unit 604 is specifically configured to:
if the to-be-received delivery user is a user with high urging probability, determining that the to-be-received delivery user is a first receiving and delivering priority;
if the to-be-received delivery user is a user with medium urging probability, determining that the to-be-received delivery user is a second receiving and delivering priority;
if the to-be-received delivery user is a user with low urging probability, determining that the to-be-received delivery user is a third receiving and delivering priority;
and indicating the person who receives the dispatch to receive the dispatch according to the priority of the dispatch receiving corresponding to the user who receives the dispatch.
In some embodiments of the present application, the apparatus further includes a training unit, and the training unit is specifically configured to:
before the data of the to-be-received dispatch user is input into a preset urging model so as to output the urging probability of the to-be-received dispatch user, acquiring historical waybill data of the logistics platform in a preset time period;
acquiring a promotion record corresponding to the waybill in the historical waybill data;
generating a training sample set according to each waybill data in the historical waybill data and a promotion record corresponding to each waybill;
and training an initial promotion model by using the training sample set to obtain the promotion model.
In some embodiments of the present application, the user data of the to-be-received dispatch includes at least two of user portrait data, waybill portrait data, received dispatcher portrait data, geographic portrait data, and dynamic routing feature data.
In the embodiment of the application, the acquisition unit 601 is used for acquiring the user data of the to-be-received dispatch in the target area within a preset time period of the logistics platform; the prediction unit 602 predicts the urging probability of the to-be-received delivery user according to the to-be-received delivery user data; the determining unit 603 determines the urging type of the to-be-received dispatch user according to the urging probability of the to-be-received dispatch user; the indicating unit 604 indicates the person who receives the dispatch to receive the dispatch according to the urging type of the user who receives the dispatch. According to the method and the device, under the condition that the existing receiving and dispatching personnel manually consider dispatching and reduce urging, the urging probability of the dispatching user to be received can be automatically predicted based on the dispatching user data to be received; therefore, the urging categories of the to-be-received delivery users can be determined according to the urging probability of the to-be-received delivery users, the urging categories can be classified according to the urging probability to indicate auxiliary delivery members to judge delivery priority, differentiated rescue strategies can be formulated for different probability customer groups, delivery of the waybills with remarkable urging rate can be prioritized, customer complaints are reduced, and customer retention rate is improved.
An embodiment of the present invention further provides an electronic device, as shown in fig. 7, which shows a schematic structural diagram of the electronic device according to the embodiment of the present invention, specifically:
the electronic device may include components such as a processor 701 of one or more processing cores, memory 702 of one or more computer-readable storage media, a power supply 703, and an input unit 704. Those skilled in the art will appreciate that the electronic device configuration shown in fig. 7 does not constitute a limitation of the electronic device and may include more or fewer components than shown, or some components may be combined, or a different arrangement of components. Wherein:
the processor 701 is a control center of the electronic device, connects various parts of the whole electronic device by using various interfaces and lines, and performs various functions of the electronic device and processes data by operating or executing software programs and/or modules stored in the memory 702 and calling data stored in the memory 702, thereby performing overall monitoring of the electronic device. Optionally, processor 701 may include one or more processing cores; preferably, the processor 701 may integrate an application processor and a modem processor, wherein the application processor mainly handles operations of storage media, user interfaces, application programs, and the like, and the modem processor mainly handles wireless communications. It will be appreciated that the modem processor described above may not be integrated into the processor 701.
The memory 702 may be used to store software programs and modules, and the processor 701 executes various functional applications and data processing by operating the software programs and modules stored in the memory 702. The memory 702 may mainly include a storage program area and a storage data area, wherein the storage program area may store an application program (such as a sound playing function, an image playing function, etc.) required for operating a storage medium, at least one function, and the like; the storage data area may store data created according to use of the electronic device, and the like. Further, the memory 702 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 device. Accordingly, the memory 702 may also include a memory controller to provide the processor 701 with access to the memory 702.
The electronic device further includes a power source 703 for supplying power to each component, and preferably, the power source 703 is logically connected to the processor 701 through a power management storage medium, so that functions of managing charging, discharging, power consumption, and the like are realized through the power management storage medium. The power supply 703 may also include any component including one or more of a dc or ac power source, a rechargeable storage medium, a power failure detection circuit, a power converter or inverter, a power status indicator, and the like.
The electronic device may also include an input unit 704, and the input unit 704 may be used to receive input numeric or character information and generate keyboard, mouse, joystick, optical or trackball signal inputs related to user settings and function control.
Although not shown, the electronic device may further include a display unit and the like, which are not described in detail herein. Specifically, in this embodiment, the processor 701 in the electronic device loads the executable file corresponding to the process of one or more application programs into the memory 702 according to the following instructions, and the processor 701 runs the application program stored in the memory 702, so as to implement various functions as follows:
acquiring user data of a to-be-received dispatch in a preset time period in a target area by a logistics platform;
predicting the urging probability of the to-be-received delivery user according to the to-be-received delivery user data;
determining the urging type of the to-be-received delivery user according to the urging probability of the to-be-received delivery user;
and indicating the person who receives and dispatches to receive and dispatch the piece according to the urging type of the user who receives and dispatches the piece.
It will be understood by those skilled in the art that all or part of the steps of the methods of the above embodiments may be performed by instructions or by associated hardware controlled by the instructions, which may be stored in a computer readable storage medium and loaded and executed by a processor.
To this end, the embodiment of the present invention provides a computer-readable storage medium, on which a computer program is stored, where the computer program is loaded by a processor to execute the steps in any one of the receiving and dispatching planning methods provided by the embodiment of the present invention. For example, the computer program may be loaded by a processor to perform the steps of:
acquiring user data of a to-be-received dispatch in a preset time period in a target area by a logistics platform;
predicting the urging probability of the to-be-received delivery user according to the to-be-received delivery user data;
determining the urging type of the to-be-received delivery user according to the urging probability of the to-be-received delivery user;
and indicating the person who receives and dispatches to receive and dispatch the piece according to the urging type of the user who receives and dispatches the piece.
The above operations can be implemented in the foregoing embodiments, and are not described in detail herein.
Wherein the computer-readable storage medium may include: read Only Memory (ROM), Random Access Memory (RAM), magnetic or optical disks, and the like.
Since the computer program stored in the computer-readable storage medium can execute the steps in any piece receiving and dispatching planning method provided by the embodiment of the present invention, the beneficial effects that can be achieved by any piece receiving and dispatching planning method provided by the embodiment of the present invention can be achieved, which are detailed in the foregoing embodiments and will not be described herein again.
The receiving and dispatching planning method, the receiving and dispatching planning device, the electronic device and the storage medium provided by the embodiment of the invention are introduced in detail, a specific embodiment is applied in the text to explain the principle and the implementation of the invention, and the description of the embodiment is only used for helping to understand the method and the core idea of the invention; meanwhile, for those skilled in the art, according to the idea of the present invention, there may be variations in the specific embodiments and the application scope, and in summary, the content of the present specification should not be construed as a limitation to the present invention.
Claims (11)
1. A method for dispatch planning, the method comprising:
acquiring user data of a to-be-received dispatch in a preset time period in a target area by a logistics platform;
predicting the urging probability of the to-be-received delivery user according to the to-be-received delivery user data;
determining the urging type of the to-be-received delivery user according to the urging probability of the to-be-received delivery user;
and indicating the person who receives and dispatches to receive and dispatch the piece according to the urging type of the user who receives and dispatches the piece.
2. The receiving and dispatching planning method according to claim 1, wherein the predicting the urging probability of the user to receive and dispatch according to the user data to receive and dispatch comprises:
and inputting the data of the to-be-received delivery user into a preset urging model so as to output the urging probability of the to-be-received delivery user.
3. A method for dispatch planning as claimed in claim 2, wherein the incentive models include a first incentive model and a second incentive model, the first and second incentive models being different, the incentive probabilities including a first incentive probability and a second incentive probability;
the step of inputting the data of the to-be-received delivery user into a preset urging model so as to output the urging probability of the to-be-received delivery user comprises the following steps:
inputting the data of the to-be-received dispatch user into a preset first urging model so as to output a first urging probability of the to-be-received dispatch user;
and inputting the data of the to-be-received dispatch user into a preset second urging model so as to output a second urging probability of the to-be-received dispatch user.
4. A pick-and-place planning method according to claim 3, wherein the first motivational model is a LOGISTIC model and the second motivational model is a CNN model.
5. The method for planning delivery and receipt of claim 3, wherein the determining the category of the promotion of the user who is to be delivered according to the probability of the promotion of the user who is to be delivered comprises:
if the first urging probability of the to-be-received delivery user is greater than the first preset probability and the second urging probability is greater than the second preset probability, determining that the to-be-received delivery user is a user with high urging probability;
if the first urging probability of the to-be-received dispatch user is greater than a first preset probability and the second urging probability is less than or equal to a second preset probability, or the first urging probability of the to-be-received dispatch user is less than or equal to the first preset probability and the second urging probability is greater than the second preset probability, determining a user with a medium urging probability in the to-be-received dispatch user;
and if the first urging probability of the to-be-received dispatch user is smaller than the first preset probability and the second urging probability is smaller than the second preset probability, determining that the to-be-received dispatch user is a user with a low urging probability.
6. The method for planning delivery and receipt of claim 5, wherein the step of instructing delivery and receipt personnel to deliver the delivery and receipt according to the urging categories of the users of the delivery and receipt comprises the following steps:
if the to-be-received delivery user is a user with high urging probability, determining that the to-be-received delivery user is a first receiving and delivering priority;
if the to-be-received delivery user is a user with medium urging probability, determining that the to-be-received delivery user is a second receiving and delivering priority;
if the to-be-received delivery user is a user with low urging probability, determining that the to-be-received delivery user is a third receiving and delivering priority;
and indicating the person who receives the dispatch to receive the dispatch according to the priority of the dispatch receiving corresponding to the user who receives the dispatch.
7. A pick-and-dispatch planning method as claimed in claim 3, wherein before the inputting the user data of the pick-and-dispatch to a preset urging model to output the urging probability of the user of the pick-and-dispatch, the method further comprises:
acquiring historical waybill data of the logistics platform in a preset time period;
acquiring a promotion record corresponding to the waybill in the historical waybill data;
generating a training sample set according to each waybill data in the historical waybill data and a promotion record corresponding to each waybill;
and training an initial promotion model by using the training sample set to obtain the promotion model.
8. The dispatch receiving planning method of any one of claims 1 to 7, wherein the dispatch user data to be received comprises at least two of user portrait data, waybill portrait data, dispatcher portrait data, geographic portrait data, and dynamic routing feature data.
9. A dispatch planning apparatus, the apparatus comprising:
the system comprises an acquisition unit, a receiving unit and a dispatching unit, wherein the acquisition unit is used for acquiring user data of a to-be-received dispatch in a preset time period in a target area of a logistics platform;
the prediction unit is used for predicting the urging probability of the to-be-received delivery user according to the to-be-received delivery user data;
the determining unit is used for determining the urging type of the to-be-received delivery user according to the urging probability of the to-be-received delivery user;
and the indicating unit is used for indicating the person who receives the dispatch to receive the dispatch according to the urging type of the user who receives the dispatch.
10. An electronic device comprising a memory and a processor, the memory having stored therein a computer program that, when executed by the processor, causes the processor to perform the steps of:
acquiring user data of a to-be-received dispatch in a preset time period in a target area by a logistics platform;
predicting the urging probability of the to-be-received delivery user according to the to-be-received delivery user data;
determining the urging type of the to-be-received delivery user according to the urging probability of the to-be-received delivery user;
and indicating the person who receives and dispatches to receive and dispatch the piece according to the urging type of the user who receives and dispatches the piece.
11. A computer-readable storage medium, having stored thereon a computer program which is loaded by a processor to perform the steps in the dispatch planning method of any one of claims 1 to 8.
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