CN115934675A - Dynamic label output method and device, storage medium and electronic equipment - Google Patents
Dynamic label output method and device, storage medium and electronic equipment Download PDFInfo
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Abstract
The invention discloses an output method and device of a dynamic label, a storage medium and electronic equipment, wherein the method comprises the following steps: reading original sample data from a Customer Relationship Management (CRM) system; performing data cleaning and feature extraction on the original sample data, and outputting intermediate sample data meeting preset requirements; inputting the intermediate sample data into a pre-trained label prediction model, and outputting target label data on the CRM system; and responding to a label calling request of a service end, and returning the target label data to the service end. The invention solves the technical problem that the related technology can not generate the dynamic label in real time, and meets the real-time requirement of the service end on the label data.
Description
Technical Field
The invention relates to the technical field of computers, in particular to an output method and device of a dynamic label, a storage medium and electronic equipment.
Background
In the related technology, the digital marketing of the merchant needs to perform data modeling on historical purchase data of a consumer on an e-commerce platform, the main stream e-commerce platform has many limitations on the output of sensitive data of the consumer due to the protection of user privacy, and the merchant cannot derive transaction data from a Customer Relationship Management (CRM) system of the e-commerce platform in real time to perform data modeling.
In the related art, in the prior art, a data domain pre-application needs to be provided for an e-commerce platform, and after the e-commerce platform passes the examination of a plurality of working days, sensitive data required by modeling can be acquired and then the modeling prediction is carried out locally. The existing e-commerce platform customer relation management system does not provide or only provides basic label capability and cannot provide dynamic labels generated through modeling prediction.
In view of the above problems in the related art, no effective solution has been found at present.
Disclosure of Invention
The embodiment of the invention provides a method and a device for outputting a dynamic label, a storage medium and electronic equipment.
According to an aspect of an embodiment of the present application, there is provided an output method of a dynamic tag, including: reading original sample data from a Customer Relationship Management (CRM) system; performing data cleaning and feature extraction on the original sample data, and outputting intermediate sample data meeting preset requirements; inputting the intermediate sample data into a pre-trained label prediction model, and outputting target label data on the CRM system; and responding to a label calling request of a service end, and returning the target label data to the service end.
Further, performing data cleaning and feature extraction on the original sample data, and outputting intermediate sample data meeting preset requirements comprises: performing duplicate removal cleaning and format content cleaning on the original sample data to obtain first sample data; selecting and extracting a sample attribution identification and a sample characteristic from the first sample data; and constructing a feature table by taking the sample attribution identification as a key and the corresponding sample feature as a value, and determining the feature table as intermediate sample data.
Further, inputting the intermediate sample data into a pre-trained label prediction model, and outputting target label data on the CRM system comprises: determining the type of a label to be output; if the label type is a probability label, selecting a probability prediction model from a model library, taking the intermediate sample data as input data of the probability prediction model, and locally outputting first target label data from the probability prediction model in the CRM system; and if the label type is a preference label, selecting a commodity recommendation model from a model library, taking the intermediate sample data as input data of the commodity recommendation model, and locally outputting second target label data from the commodity recommendation model in the CRM system, wherein the model library comprises the probability prediction model and the commodity recommendation model.
Further, responding to a tag call request of a service end, and returning the target tag data to the service end includes one of the following: responding to a first label calling request of a service end, performing variable-length character coding on the target label data to generate json data, and returning the json data to the service end by adopting an Application Programming Interface (API); and responding to a second label calling request of a service end, performing variable-length character coding on the target label data to generate file data, and returning the file data to the service end by adopting a file calling interface.
Further, before reading original sample data from a customer relationship management, CRM, system, the method further comprises: initializing service processor classes of a plurality of scheduling containers, wherein each scheduling container comprises a plurality of container spaces, each container space corresponds to one service processor class, each service processor class corresponds to one label type, and each service processor class comprises a plurality of code data connected by a responsibility chain; after initialization is completed, acquiring service requirement information, and analyzing a processor list and code environment parameters of the scheduling container according to the service requirement information; if the processor list and the code environment parameters are successfully analyzed, analyzing the class parameters of the processor list; and starting the scheduling container after the class parameter analysis is successful.
Further, initializing the service processor class of the scheduling container includes: comparing the minimized scheduling actions, and extracting an abstract template of the service processor according to a comparison result to obtain a virtual class of the service processor; generating a service processor class by adopting the service processor virtual class; and calling a main program of the scheduling container to initialize the service processor classes of all the service processors.
Further, before initializing the service processor classes of the scheduling containers, the method further includes: determining a currently executed target scheduling container; analyzing the container token of the target scheduling container; and positioning the calling time sequence of the target scheduling container in a first-in first-out (FIFO) pipeline by adopting the container token.
According to another aspect of the embodiments of the present application, there is also provided an output device for a dynamic tag, including: the acquisition module is used for reading original sample data from the Customer Relationship Management (CRM) system; the processing module is used for carrying out data cleaning and feature extraction on the original sample data and outputting intermediate sample data meeting preset requirements; the prediction module is used for inputting the intermediate sample data into a pre-trained label prediction model and outputting target label data on the CRM system; and the return module is used for responding to the label calling request of the service end and returning the target label data to the service end.
Further, the processing module includes: the processing unit is used for carrying out duplicate removal cleaning and format content cleaning on the original sample data to obtain first sample data; an extraction unit, configured to select and extract a sample attribution identifier and a sample feature from the first sample data; and the storage unit is used for constructing a feature table by taking the sample attribution identification as key and the corresponding sample feature as value, and determining the feature table as intermediate sample data.
Further, the prediction module comprises: a determination unit for determining a type of a tag to be output; the prediction unit is used for selecting a probability prediction model from a model library if the type of the label is a probability label, taking the intermediate sample data as input data of the probability prediction model, and locally outputting first target label data from the probability prediction model in the CRM system; and if the type of the label is a preference label, selecting a commodity recommendation model from a model library, taking the intermediate sample data as input data of the commodity recommendation model, and locally outputting second target label data from the commodity recommendation model in the CRM system, wherein the model library comprises the probability prediction model and the commodity recommendation model.
Further, the return module includes one of: the first returning unit is used for responding to a first label calling request of a service end, performing variable-length character coding on the target label data to generate json data, and returning the json data to the service end by adopting an Application Programming Interface (API) interface; and the second returning unit is used for responding to a second label calling request of the service end, performing variable-length character coding on the target label data, generating file data, and returning the file data to the service end by adopting a file calling interface.
Further, the apparatus further comprises: the system comprises an initialization module, a Client Relationship Management (CRM) system and a plurality of scheduling containers, wherein the initialization module is used for initializing service processor classes of the scheduling containers before the acquisition module reads original sample data from the CRM system, the scheduling containers comprise a plurality of container spaces, each container space corresponds to one service processor class, each service processor class corresponds to one label type, and each service processor class comprises a plurality of code data connected by a responsibility chain; the first analysis module is used for acquiring service requirement information after initialization is completed, and analyzing a processor list and code environment parameters of the scheduling container according to the service requirement information; the second analysis module is used for analyzing the class parameters of the processor list if the processor list and the code environment parameters are successfully analyzed; and the starting module is used for starting the scheduling container after the class parameter analysis is successful.
Further, the initialization module includes: the extraction unit is used for comparing the minimum scheduling actions and extracting an abstract template of the service processor according to a comparison result to obtain a virtual class of the service processor; a generating unit, configured to generate a service processor class by using the service processor virtual class; and the initialization unit is used for calling the main program of the scheduling container to initialize the service processor classes of all the service processors.
Further, the apparatus further comprises: a determining module, configured to determine a currently executed target scheduling container before the initializing module initializes the service processor classes of the multiple scheduling containers; the third analysis module is used for analyzing the container token of the target scheduling container; and the positioning module is used for positioning the calling time sequence of the target scheduling container in a first-in first-out (FIFO) pipeline by adopting the container token.
According to another aspect of the embodiments of the present application, there is also provided a storage medium including a stored program that executes the above steps when the program is executed.
According to another aspect of the embodiments of the present application, there is also provided an electronic device, including a processor, a communication interface, a memory, and a communication bus, where the processor, the communication interface, and the memory complete communication with each other through the communication bus; wherein: a memory for storing a computer program; a processor for executing the program stored in the memory to execute the steps of the method.
Embodiments of the present application also provide a computer program product containing instructions, which when run on a computer, cause the computer to perform the steps of the above method.
According to the invention, original sample data is read from a Customer Relationship Management (CRM) system, data cleaning and feature extraction are carried out on the original sample data, intermediate sample data meeting preset requirements are output, the intermediate sample data is input into a pre-trained label prediction model, target label data is output on the CRM system, a label calling request of a service end is responded, the target label data is returned to the service end, and a dynamic label required by the service end is output under the condition that sensitive sample data such as transaction data and the like do not exist, so that the technical problem that the dynamic label cannot be generated in real time in the related technology is solved, and the real-time requirement of the service end on the label data is met.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the invention and together with the description serve to explain the invention without limiting the invention. In the drawings:
fig. 1 is a block diagram of a hardware configuration of a server according to an embodiment of the present invention;
FIG. 2 is a flow chart of a method for outputting a dynamic tag according to an embodiment of the present invention;
FIG. 3 is a block flow diagram of an embodiment of the present invention;
FIG. 4 is a block diagram of an output device of a dynamic tag according to an embodiment of the present invention;
fig. 5 is a block diagram of an electronic device implementing an embodiment of the invention.
Detailed Description
In order to make the technical solutions better understood by those skilled in the art, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only partial embodiments of the present application, but not all 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 application. It should be noted that, in the present application, the embodiments and features of the embodiments may be combined with each other without conflict.
It should be noted that the terms "first," "second," and the like in the description and claims of this application and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It should be understood that the data so used may be interchanged under appropriate circumstances such that embodiments of the application described herein may be implemented in sequences other than those illustrated or described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
Example 1
The method provided in the first embodiment of the present application may be executed in a server, a computer, a mobile phone, or a similar computing device. Taking an example of the server running on the server, fig. 1 is a hardware structure block diagram of a server according to an embodiment of the present invention. As shown in fig. 1, the server may include one or more (only one shown in fig. 1) processors 102 (the processors 102 may include, but are not limited to, a processing device such as a microprocessor MCU or a programmable logic device FPGA) and a memory 104 for storing data, and optionally may also include a transmission device 106 for communication functions and an input-output device 108. It will be understood by those skilled in the art that the structure shown in fig. 1 is merely illustrative and is not intended to limit the structure of the server. For example, the server may also include more or fewer components than shown in FIG. 1, or have a different configuration than shown in FIG. 1.
The memory 104 may be used to store a server program, for example, a software program and a module of application software, such as a server program corresponding to an output method of a dynamic tag in an embodiment of the present invention, and the processor 102 executes various functional applications and data processing by running the server program stored in the memory 104, so as to implement the method described above. The memory 104 may include high speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory 104 may further include memory located remotely from the processor 102, which may be connected to a server over a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The transmission device 106 is used to receive or transmit data via a network. Specific examples of the network described above may include a wireless network provided by a communication provider of the server. In one example, the transmission device 106 includes a Network adapter (NIC) that can be connected to other Network devices via a base station to communicate with the internet. In one example, the transmission device 106 may be a Radio Frequency (RF) module, which is used to communicate with the internet in a wireless manner.
In this embodiment, a method for outputting a dynamic tag is provided, and fig. 2 is a flowchart of a method for outputting a dynamic tag according to an embodiment of the present invention, as shown in fig. 2, the flowchart includes the following steps:
step S202, reading original sample data from a Customer Relationship Management (CRM) system;
the CRM system of this embodiment operates on an e-commerce platform (e.g., ari, kyoto, etc.), the original sample data may be transaction data, sales data, order data, member data, commodity data, etc. of a user consumed by a target merchant on the e-commerce platform, and the CRM system may be an independent CRM server or may be integrated on a central server of the e-commerce platform.
Step S204, performing data cleaning and feature extraction on the original sample data, and outputting intermediate sample data meeting preset requirements;
the intermediate sample data of the embodiment refers to sample data meeting the requirements of the label prediction model, and the preset requirements include format requirements, data volume requirements, data quality requirements and the like.
Step S206, inputting the intermediate sample data into a pre-trained label prediction model, and outputting target label data on a CRM system;
the label prediction model of the embodiment is configured on a server where the CRM system is located.
And step S208, responding to the label calling request of the service end, and returning target label data to the service end.
The service end of the embodiment may be a merchant end that needs to use the target tag data, or may be another client end that needs to call the tag data, and since the target tag data does not relate to the privacy information of the user, the target tag data can be quickly and dynamically derived, and tag data synchronization is achieved.
Through the steps, original sample data are read from a Customer Relationship Management (CRM) system, data cleaning and feature extraction are carried out on the original sample data, intermediate sample data meeting preset requirements are output, the intermediate sample data are input into a pre-trained label prediction model, target label data are output on the CRM system, a label calling request of a service end is responded, the target label data are returned to the service end, and dynamic labels required by the service end are output under the condition that sensitive sample data such as transaction data do not exist, so that the technical problem that the dynamic labels cannot be generated in real time in the related technology is solved, and the real-time requirement of the service end on the label data is met.
In an embodiment of this embodiment, performing data cleaning and feature extraction on original sample data, and outputting intermediate sample data that meets preset requirements includes: carrying out duplicate removal cleaning and format content cleaning on original sample data to obtain first sample data; selecting and extracting a sample attribution identification and a sample characteristic from the first sample data; and constructing a feature table by taking the sample attribution identification as a key and the corresponding sample feature as a value, and determining the feature table as intermediate sample data.
Optionally, the sample attribution identifier may be a user identifier, a product identifier, an item identifier, etc., such as user _ id, item _ id, etc., and in one example, the format of the feature table is { user _ id/item _ id, feature _1, feature_2, \8230 }, feature _1, feature_2, \8230, which is a sequence of sample features.
In an example of this embodiment, inputting the intermediate sample data into a pre-trained label prediction model, and outputting the target label data on the CRM system includes: determining the type of a label to be output; if the label type is a probability label, selecting a probability prediction model from a model library, taking intermediate sample data as input data of the probability prediction model, and locally outputting first target label data from the probability prediction model in the CRM system; and if the type of the label is a preference label, selecting a commodity recommendation model from the model library, taking the intermediate sample data as input data of the commodity recommendation model, and outputting second target label data from the commodity recommendation model locally in the CRM system, wherein the model library comprises a probability prediction model and a commodity recommendation model.
According to business requirements, in a data modeling module, a corresponding model is selected for modeling according to the type of the required label, for example, selecting a 'purchasing probability label' calls a purchasing probability prediction model, or selecting a 'commodity preference label' calls a commodity recommendation model and the like.
In some examples, after the target tag data is output on the CRM system, the target tag data may be further post-processed, and a continuous value obtained by tag prediction is discretized, for example, a purchase probability (a value between 0 and 1) is discretized into three (high, medium, and low) purchase probabilities; and storing the label result in a database in a form of { user _ id: label value } for subsequent calling by a label output module.
In the implementation scenario of this embodiment, in response to the tag call request of the service end, the target tag data returned to the service end may be, but is not limited to:
the first method is as follows: responding to a first label calling request of a service end, carrying out variable-length character coding on target label data to generate json data, and returning the json data to the service end by adopting an Application Programming Interface (API);
the second method comprises the following steps: and responding to a second label calling request of the service end, performing variable-length character coding on target label data, generating file data, and returning the file data to the service end by adopting a file calling interface.
The scheme of the embodiment supports two label output modes: the method (1) is used for a label library of a merchant self-system to consume in the form of an API interface, the encoding mode adopts utf-8 encoding, and the data format is json; and the mode (2) is applied by a merchant user in a csv file downloading mode, the encoding mode adopts utf-8 encoding, and the data format is a file.
In an implementation manner of this embodiment, before reading original sample data from the customer relationship management CRM system, the method further includes:
s11, initializing service processor classes of a plurality of scheduling containers, wherein each scheduling container comprises a plurality of container spaces, each container space corresponds to one service processor class, each service processor class corresponds to one label type, and each service processor class comprises a plurality of code data connected by a responsibility chain;
in some examples, initializing the traffic processor class of the scheduling container includes: comparing the minimized scheduling actions, and extracting an abstract template of the service processor according to a comparison result to obtain a virtual class of the service processor; generating a service processor class by adopting the service processor virtual class; calling the main program of the scheduling container initializes the service processor classes of all the service processors.
Optionally, in this embodiment, a FIFO pipe is used to manage the call timing sequence of multiple scheduling containers, so as to ensure the ordered call of hardware resources and prevent hardware overhead from overflowing. Before initializing the service processor classes of a plurality of scheduling containers, the method further comprises the following steps: determining a target scheduling container of current execution; analyzing a container token of the target scheduling container; and positioning the calling time sequence of the target scheduling container by adopting the container token in a first-in first-out FIFO pipeline.
When configuring an execution queue based on a FIFO pipe, the present embodiment includes the following steps:
step 1, configuring FIFO (First Input First Output, first in First out) queue enqueue;
step 2, configuring basic attributes (including but not limited to storage format, encryption mode, etc.) of tokens, wherein each token is used for identifying each object in the FIFO pipeline, namely a scheduling container;
step 3, configuring a queue execution environment (including but not limited to pipeline files, logs, token paths, timeout thresholds, software and hardware versions and the like);
step 4, setting queue initialization parameters (including without limitation occupying hardware resources (cpu, memory), operating system version, queue initialization token number, token encryption mode and the like);
step 5, setting a queue service command list (text file), and initializing a queue (operating system process);
step 6, starting a queue (a command line background running mode);
and 7, re-executing the steps 4-6 after the service command list is modified according to the requirement.
S12, after the initialization is completed, acquiring service requirement information, and analyzing a processor list and code environment parameters of the scheduling container according to the service requirement information;
s13, if the processor list and the code environment parameters are successfully analyzed, analyzing the class parameters of the processor list;
and S14, starting the scheduling container after the class parameters are successfully analyzed.
In some scenarios of this embodiment, a scheduling container (scheduling framework) is used to control an output flow of a dynamic tag, where the scheduling container includes multiple service processor classes, and each service processor class corresponds to one tag type.
In one example, the configuration flow of the scheduling container includes:
step 1, comparing the scheduling minimization actions, and extracting an abstract template of a processor to form a virtual class (such as a base class in a Python code);
step 2, realizing a processor class (abstract template subclass) corresponding to a plurality of services;
step 3, initializing all service processor classes by the main program;
step 4, the main program begins to analyze command line parameters, and the first stage analyzes common parameters such as the required service processor list and the environment; the second stage analyzes the service processor type parameters; loading a parameter table;
step 5, processing errors of the first stage, and if the errors occur, not entering the second stage; after entering the second stage, the processor core scheduling method (serial execution and concurrent execution of the dependent execution queue) is started to be executed;
and 6, adding and deleting the service processor classes and then re-executing the steps 1 to 5.
The invention provides a label prediction method and a label prediction system in a domain where consumer sensitive data cannot be output from an e-commerce platform, wherein data modeling is performed on a CRM (customer relationship management) system of the e-commerce platform, and dynamic labels required by digital marketing of merchants are output, so that the requirement of service instantaneity is met.
Fig. 3 is a schematic diagram of a framework flow according to an embodiment of the present invention, which includes a data loading module, a data modeling module, a tag output module, and a master scheduling module, and the following describes functions of the modules:
and the data loading module loads original data required by modeling from a Customer Relationship Management (CRM) system deployed in the platform domain, and performs data cleaning and characteristic engineering according to modeling requirements so as to output characteristic data meeting model requirements.
And the data modeling module selects a proper model according to the label requirement of the merchant to carry out model training and model prediction, and outputs the label result to the label output module.
And the label output module provides two modes for label output, wherein the mode (1) is used for the consumption of a label library of a merchant self-system in the form of an API (application programming interface) interface, and the mode (2) is used for the application of a merchant user in the form of csv file downloading.
And the master scheduling module provides overall flow scheduling and performs flow management in a timing mode and a random mode. The module consists of two parts: (1) a high-performance and extensible scheduling framework based on a responsibility chain mode supports diversity modeling (including feature engineering). (2) FIFOs pipeline communication based controlled (limited token, sequential) multithreaded, highly concurrent execution queue.
When the data loading module executes data loading, the method comprises the following steps:
the method comprises the following steps: the data loading module loads original data required by modeling from a Customer Relationship Management (CRM) system deployed in a platform domain, wherein the original data mainly comprises order data, member data and commodity data;
step two: the data loading module is used for cleaning the original data, performing feature engineering according to the requirements of a model algorithm in 3.2.2, and storing the features after feature processing in a database in a feature table form for model consumption, wherein the feature table format is { user _ id/item _ id, feature _1, feature_2, \8230 }.
When the data modeling module executes data modeling, the data modeling module comprises the following steps:
the method comprises the following steps: according to business requirements, a merchant user selects a corresponding model for modeling according to a required label type in a data modeling module, for example, selecting a purchase probability label calls a purchase probability prediction model, or selecting a commodity preference label calls a commodity recommendation model;
step two: training a model;
step three: predicting a label;
step four: discretizing the continuous numerical value obtained by label prediction, for example, discretizing the purchase probability (numerical value between 0 and 1) into three purchase probabilities (high, medium and low);
step five: storing the label result in a database in a form of { user _ id: label value } for being called by a 3.2.3 label output module;
when the label output module outputs the label, the label output module comprises the following steps:
the method comprises the following steps: setting calculation of the label and scheduling of an output task through a main scheduling module;
step two: tag output is performed in two ways: the method (1) is used for a label library of a merchant self-system to consume in the form of an API interface, the encoding mode adopts utf-8 encoding, and the data format is json; and the mode (2) is applied by a merchant user in a csv file downloading mode, the encoding mode adopts utf-8 encoding, and the data format is a file.
According to the scheme of the embodiment, the difficulty that sensitive data required by modeling can be acquired to be locally subjected to modeling prediction after the sensitive data is examined and approved for a plurality of working days after the data is required to be filed and pre-applied to the e-commerce platform is overcome; dynamic labels required by marketing of merchants can be output under the condition that sensitive data such as transaction data cannot be output, and the requirement of service instantaneity is met. The dynamic label can be called by an intelligent operation tool and can also be independently output to service personnel for use; the output can be calculated at regular time through the main scheduling module, and can also be calculated according to the requirement. Data modeling can be carried out under the condition that the transaction data does not go out of the e-commerce domain; the real-time business requirement of the merchant can be met.
Through the above description of the embodiments, those skilled in the art can clearly understand that the method according to the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but the former is a better implementation mode in many cases. Based on such understanding, the technical solutions of the present invention may be embodied in the form of a software product, which is stored in a storage medium (e.g., ROM/RAM, magnetic disk, optical disk) and includes instructions for enabling a terminal device (e.g., a mobile phone, a computer, a server, or a network device) to execute the method according to the embodiments of the present invention.
Example 2
In this embodiment, an output device of a dynamic tag is further provided, which is used to implement the foregoing embodiments and preferred embodiments, and the description that has been already made is omitted. As used below, the term "module" may be a combination of software and/or hardware that implements a predetermined function. Although the means described in the embodiments below are preferably implemented in software, an implementation in hardware, or a combination of software and hardware is also possible and contemplated.
Fig. 4 is a block diagram of an output device of a dynamic tag according to an embodiment of the present invention, and as shown in fig. 4, the output device includes: an acquisition module 40, a processing module 42, a prediction module 44, a return module 46, wherein,
the acquisition module 40 is used for reading original sample data from the customer relationship management CRM system;
the processing module 42 is configured to perform data cleaning and feature extraction on the original sample data, and output intermediate sample data meeting preset requirements;
a prediction module 44, configured to input the intermediate sample data into a pre-trained label prediction model, and output target label data on the CRM system;
and a returning module 46, configured to respond to the tag call request of the service end, and return the target tag data to the service end.
Optionally, the processing module includes: the processing unit is used for carrying out duplicate removal cleaning and format content cleaning on the original sample data to obtain first sample data; the extraction unit is used for selecting and extracting a sample attribution identification and a sample characteristic from the first sample data; and the storage unit is used for constructing a feature table by taking the sample attribution identification as a key and the corresponding sample feature as a value, and determining the feature table as intermediate sample data.
Optionally, the prediction module includes: a determination unit for determining a type of a tag to be output; the prediction unit is used for selecting a probability prediction model from a model library if the type of the label is a probability label, taking the intermediate sample data as input data of the probability prediction model, and locally outputting first target label data from the probability prediction model in the CRM system; and if the label type is a preference label, selecting a commodity recommendation model from a model library, taking the intermediate sample data as input data of the commodity recommendation model, and locally outputting second target label data from the commodity recommendation model in the CRM system, wherein the model library comprises the probability prediction model and the commodity recommendation model.
Optionally, the return module includes one of: the first return unit is used for responding to a first label calling request of a service end, carrying out variable-length character coding on the target label data to generate json data, and returning the json data to the service end by adopting an Application Programming Interface (API); and the second returning unit is used for responding to a second label calling request of the service end, performing variable-length character coding on the target label data, generating file data, and returning the file data to the service end by adopting a file calling interface.
Optionally, the apparatus further comprises: the system comprises an initialization module, a Client Relationship Management (CRM) system and a plurality of scheduling containers, wherein the initialization module is used for initializing service processor classes of the scheduling containers before the acquisition module reads original sample data from the CRM system, the scheduling containers comprise a plurality of container spaces, each container space corresponds to one service processor class, each service processor class corresponds to one label type, and each service processor class comprises a plurality of code data connected by a responsibility chain; the first analysis module is used for acquiring service requirement information after initialization is completed, and analyzing a processor list and code environment parameters of the scheduling container according to the service requirement information; the second analysis module is used for analyzing the class parameters of the processor list if the processor list and the code environment parameters are analyzed successfully; and the starting module is used for starting the scheduling container after the class parameter analysis is successful.
Optionally, the initialization module includes: the extraction unit is used for comparing the minimum scheduling actions and extracting an abstract template of the service processor according to a comparison result to obtain a virtual class of the service processor; a generating unit, configured to generate a service processor class by using the service processor virtual class; and the initialization unit is used for calling the main program of the scheduling container to initialize the service processor classes of all the service processors.
Optionally, the apparatus further comprises: a determining module, configured to determine a currently executed target scheduling container before the initializing module initializes the service processor classes of the multiple scheduling containers; the third analysis module is used for analyzing the container token of the target scheduling container; and the positioning module is used for positioning the calling time sequence of the target scheduling container in a first-in first-out (FIFO) pipeline by adopting the container token.
It should be noted that the above modules may be implemented by software or hardware, and for the latter, the following may be implemented, but not limited to: the modules are all positioned in the same processor; alternatively, the modules are respectively located in different processors in any combination.
Example 3
An embodiment of the present invention further provides a storage medium having a computer program stored therein, wherein the computer program is configured to perform the steps in any of the method embodiments described above when executed.
Alternatively, in the present embodiment, the storage medium may be configured to store a computer program for executing the steps of:
s1, reading original sample data from a Customer Relationship Management (CRM) system;
s2, performing data cleaning and feature extraction on the original sample data, and outputting intermediate sample data meeting preset requirements;
s3, inputting the intermediate sample data into a pre-trained label prediction model, and outputting target label data on the CRM system;
and S4, responding to a label calling request of a service end, and returning the target label data to the service end.
Optionally, in this embodiment, the storage medium may include, but is not limited to: various media capable of storing computer programs, such as a usb disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a removable hard disk, a magnetic disk, or an optical disk.
Embodiments of the present invention further provide an electronic device, comprising a memory in which a computer program is stored and a processor configured to execute the computer program to perform the steps in any of the above method embodiments.
Optionally, the electronic device may further include a transmission device and an input/output device, wherein the transmission device is connected to the processor, and the input/output device is connected to the processor.
Optionally, in this embodiment, the processor may be configured to execute the following steps by a computer program:
s1, reading original sample data from a Customer Relationship Management (CRM) system;
s2, performing data cleaning and feature extraction on the original sample data, and outputting intermediate sample data meeting preset requirements;
s3, inputting the intermediate sample data into a pre-trained label prediction model, and outputting target label data on the CRM system;
and S4, responding to the label calling request of the service end, and returning the target label data to the service end.
Optionally, for a specific example in this embodiment, reference may be made to the examples described in the above embodiment and optional implementation, and this embodiment is not described herein again.
Fig. 5 is a block diagram of an electronic device according to an embodiment of the present invention, as shown in fig. 5, including a processor 501, a communication interface 502, a memory 503 and a communication bus 504, where the processor 501, the communication interface 502, and the memory 503 complete communication with each other through the communication bus 504, and the memory 503 is used for storing a computer program; the processor 501 is configured to execute the program stored in the memory 503.
The above-mentioned serial numbers of the embodiments of the present application are merely for description, and do not represent the advantages and disadvantages of the embodiments.
In the above embodiments of the present application, the descriptions of the respective embodiments have respective emphasis, and for parts that are not described in detail in a certain embodiment, reference may be made to related descriptions of other embodiments.
In the embodiments provided in the present application, it should be understood that the disclosed technology can be implemented in other ways. The above-described embodiments of the apparatus are merely illustrative, and for example, the division of the units is only one type of division of logical functions, and there may be other divisions when actually implemented, for example, a plurality of units or components may be combined or may be integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, units or modules, and may be in an electrical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit may be implemented in the form of hardware, or may also be implemented in the form of a software functional unit.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present application may be substantially implemented or contributed to by the prior art, or all or part of the technical solution may be embodied in a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a removable hard disk, a magnetic disk, or an optical disk, and various media capable of storing program codes.
The foregoing is only a preferred embodiment of the present application and it should be noted that, as will be apparent to those skilled in the art, numerous modifications and adaptations can be made without departing from the principles of the present application and such modifications and adaptations are intended to be considered within the scope of the present application.
Claims (10)
1. An output method of a dynamic tag, comprising:
reading original sample data from a Customer Relationship Management (CRM) system;
performing data cleaning and feature extraction on the original sample data, and outputting intermediate sample data meeting preset requirements;
inputting the intermediate sample data into a pre-trained label prediction model, and outputting target label data on the CRM system;
and responding to a label calling request of a service end, and returning the target label data to the service end.
2. The method of claim 1, wherein performing data cleaning and feature extraction on the original sample data, and outputting intermediate sample data meeting preset requirements comprises:
performing duplicate removal cleaning and format content cleaning on the original sample data to obtain first sample data;
selecting and extracting a sample attribution identification and a sample characteristic from the first sample data;
and constructing a feature table by taking the sample attribution identification as a key and the corresponding sample feature as a value, and determining the feature table as intermediate sample data.
3. The method of claim 1, wherein inputting the intermediate sample data into a pre-trained label prediction model, outputting target label data on the CRM system comprises:
determining the type of a label to be output;
if the label type is a probability label, selecting a probability prediction model from a model library, taking the intermediate sample data as input data of the probability prediction model, and locally outputting first target label data from the probability prediction model in the CRM system; and if the label type is a preference label, selecting a commodity recommendation model from a model library, taking the intermediate sample data as input data of the commodity recommendation model, and locally outputting second target label data from the commodity recommendation model in the CRM system, wherein the model library comprises the probability prediction model and the commodity recommendation model.
4. The method of claim 1, wherein the step of returning the target tag data to the service end in response to a tag call request from the service end comprises one of:
responding to a first label calling request of a service end, carrying out variable-length character coding on the target label data to generate json data, and returning the json data to the service end by adopting an Application Programming Interface (API);
and responding to a second label calling request of a service end, performing variable-length character coding on the target label data to generate file data, and returning the file data to the service end by adopting a file calling interface.
5. The method of claim 1, wherein prior to reading original sample data from a customer relationship management, CRM, system, the method further comprises:
initializing service processor classes of a plurality of scheduling containers, wherein each scheduling container comprises a plurality of container spaces, each container space corresponds to one service processor class, each service processor class corresponds to one label type, and each service processor class comprises a plurality of code data connected by a responsibility chain;
after initialization is completed, acquiring service requirement information, and analyzing a processor list and code environment parameters of the scheduling container according to the service requirement information;
if the processor list and the code environment parameters are successfully analyzed, analyzing the class parameters of the processor list;
and starting the scheduling container after the class parameter analysis is successful.
6. The method of claim 5, wherein initializing a traffic processor class for the scheduling container comprises:
comparing the minimized scheduling actions, and extracting an abstract template of the service processor according to a comparison result to obtain a virtual class of the service processor;
generating a service processor class by adopting the service processor virtual class;
and calling the main program of the scheduling container to initialize the service processor classes of all the service processors.
7. The method of claim 5, wherein prior to initializing a traffic processor class for a number of scheduling containers, the method further comprises:
determining a currently executed target scheduling container;
analyzing the container token of the target scheduling container;
and positioning the calling time sequence of the target scheduling container in a first-in first-out (FIFO) pipeline by adopting the container token.
8. An output device for a dynamic tag, comprising:
the acquisition module is used for reading original sample data from the Customer Relationship Management (CRM) system;
the processing module is used for carrying out data cleaning and feature extraction on the original sample data and outputting intermediate sample data meeting preset requirements;
the prediction module is used for inputting the intermediate sample data into a pre-trained label prediction model and outputting target label data on the CRM system;
and the return module is used for responding to the label calling request of the service end and returning the target label data to the service end.
9. A storage medium, characterized in that the storage medium comprises a stored program, wherein the program is operative to perform the steps of the method of any of the preceding claims 1 to 7.
10. An electronic device comprises a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory are communicated with each other through the communication bus; wherein:
a memory for storing a computer program;
a processor for performing the steps of the method of any one of claims 1 to 7 by executing a program stored on a memory.
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| CN108764663A (en) * | 2018-05-15 | 2018-11-06 | 广东电网有限责任公司信息中心 | A kind of power customer portrait generates the method and system of management |
| US20210406220A1 (en) * | 2021-03-25 | 2021-12-30 | Benijing Baidu Netcom Science and Technology Co., Ltd. | Method, apparatus, device, storage medium and computer program product for labeling data |
| CN115344757A (en) * | 2022-02-07 | 2022-11-15 | 花瓣云科技有限公司 | Label prediction method, electronic equipment and storage medium |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN108764663A (en) * | 2018-05-15 | 2018-11-06 | 广东电网有限责任公司信息中心 | A kind of power customer portrait generates the method and system of management |
| US20210406220A1 (en) * | 2021-03-25 | 2021-12-30 | Benijing Baidu Netcom Science and Technology Co., Ltd. | Method, apparatus, device, storage medium and computer program product for labeling data |
| CN115344757A (en) * | 2022-02-07 | 2022-11-15 | 花瓣云科技有限公司 | Label prediction method, electronic equipment and storage medium |
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