CN115081538A - Customer relationship identification method, device, equipment and medium based on machine learning - Google Patents
Customer relationship identification method, device, equipment and medium based on machine learning Download PDFInfo
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Abstract
The invention relates to the field of artificial intelligence, and provides a customer relationship recognition method, a device, equipment and a medium based on machine learning, which can screen target data from historical customer data, avoid interference of a large amount of invalid and redundant data on subsequent model training, improve the efficiency of model training, further optimize the data, improve the effect of model training, train a preset classification model by using a training sample and a verification sample to obtain a recognition model, obtain data to be recognized according to a customer relationship recognition instruction, input the data to be recognized into the recognition model, determine the customer relationship between a target customer and a target enterprise according to the output data of the recognition model, assist in recognizing the customer relationship based on the model of machine learning training, and improve the accuracy and recognition efficiency of recognition. In addition, the invention also relates to a block chain technology, and the identification model can be stored in the block chain node.
Description
Technical Field
The invention relates to the technical field of artificial intelligence, in particular to a client relationship identification method, a client relationship identification device, client relationship identification equipment and a client relationship identification medium based on machine learning.
Background
In the field of customer management, how to accurately acquire relevant line organizations and individuals of customers becomes a key for developing performance and making the scale of large customers, and particularly in the aspect of comprehensive finance, the cross sales mode which is finally expanded into 1+ N customers by making one customer needs to identify customer relationships.
Taking a public client as an example, in order to identify the public client relationship, a common processing method in the industry at present is to perform a weighting operation by a data analysis technology, and finally obtain a client relationship score.
The processing method has the problems of single dimension, low data saturation, incapability of continuously learning iteration and the like, so that the customer relationship identification is inaccurate, the customers can not be accurately served, the marketing success rate is low and the like.
Disclosure of Invention
In view of the foregoing, it is necessary to provide a method, an apparatus, a device and a medium for identifying a customer relationship based on machine learning, which are intended to solve the problems of low accuracy and efficiency of identifying a customer relationship.
A customer relation identification method based on machine learning comprises the following steps:
acquiring historical client data of a target enterprise, and screening out target data from the historical client data;
processing the target data to obtain a training sample and a verification sample;
training a preset classification model by using the training sample and the verification sample to obtain an identification model;
responding to a customer relationship identification instruction aiming at a target customer, and acquiring data to be identified according to the customer relationship identification instruction;
inputting the data to be recognized into the recognition model, and determining the customer relationship between the target customer and the target enterprise according to the output data of the recognition model;
and feeding back the customer relationship to a specified terminal device.
According to a preferred embodiment of the present invention, the screening target data from the historical customer data includes:
classifying the historical customer data to obtain each type of data;
determining each type of data as nodes to construct a random forest;
determining node importance of each type of data in the historical customer data at each node of each decision tree in the random forest;
determining the importance of each type of data in each decision tree according to the node importance of each type of data at each node of each decision tree;
determining the importance of each type of data in the random forest according to the importance of each type of data in each decision tree;
determining the importance of each type of data in the random forest as the weight of each type of data;
sequencing each type of data according to the sequence of the weight from high to low to obtain a data sequence;
and acquiring data arranged in the front preset bit from the data sequence as the target data.
According to a preferred embodiment of the present invention, before processing the target data, the method further comprises:
determining a data amount of the target data;
when the data volume is smaller than a preset threshold value, performing feature combination on the target data to obtain combined features, wherein the combined features comprise combinations of different types of data with preset quantity; and/or
Pre-training a gradient lifting decision tree by using the target data to obtain a feature tree, and constructing a new feature according to leaf nodes in the feature tree;
and adding the combined features and the newly added features to the target data.
According to a preferred embodiment of the present invention, the processing the target data to obtain a training sample and a verification sample includes:
identifying missing data from the target data, and filling the missing data to obtain first data; wherein the padding the missing data comprises: when the missing data is continuous data, acquiring front and rear data adjacent to the missing data, calculating an average value of the acquired data, and filling the missing data by using the average value, or when the missing data is discrete data, acquiring a mode of all data in a type corresponding to the missing data, and filling the missing data by using the mode;
identifying abnormal data from the first data, and deleting the abnormal data to obtain second data;
labeling the second data to obtain third data;
and acquiring a preset proportion, and splitting the third data according to the preset proportion to obtain the training sample and the verification sample.
According to a preferred embodiment of the present invention, after obtaining the recognition model, the method further comprises:
detecting whether the identification model reaches an updating condition, including: detecting evaluation data of the recognition model in real time, and determining that the updating condition is reached when the evaluation data shows that the accuracy of the recognition model is to be improved; and/or acquiring newly added customer data, and determining that the updating condition is reached when the newly added customer data reaches the configuration quantity; and/or determining that the update condition is reached when a preset time interval is reached;
and when the recognition model is detected to reach the updating condition, performing incremental training on the recognition model by using the newly added customer data.
According to a preferred embodiment of the present invention, the acquiring data to be identified according to the customer relationship identification instruction includes:
determining the customer information of the target customer according to the customer relationship identification instruction;
connecting to a configuration database, and inquiring in the configuration database according to the customer information;
and determining the inquired data as the data to be identified.
According to the preferred embodiment of the present invention, when the customer relationship is fed back to the specified terminal device, the method further includes:
generating a product recommendation strategy and a customer maintenance strategy for the target customer according to the customer relation;
and synchronously feeding back the product recommendation strategy and the customer maintenance strategy to the specified terminal equipment.
A machine learning based customer relationship identification apparatus, the machine learning based customer relationship identification apparatus comprising:
the screening unit is used for acquiring historical client data of a target enterprise and screening the target data from the historical client data;
the processing unit is used for processing the target data to obtain a training sample and a verification sample;
the training unit is used for training a preset classification model by using the training sample and the verification sample to obtain an identification model;
the system comprises an acquisition unit, a processing unit and a processing unit, wherein the acquisition unit is used for responding to a customer relationship identification instruction aiming at a target customer and acquiring data to be identified according to the customer relationship identification instruction;
the determining unit is used for inputting the data to be recognized into the recognition model and determining the customer relationship between the target customer and the target enterprise according to the output data of the recognition model;
and the feedback unit is used for feeding back the customer relationship to the specified terminal equipment.
A computer device, the computer device comprising:
a memory storing at least one instruction; and
a processor executing instructions stored in the memory to implement the machine learning based customer relationship identification method.
A computer-readable storage medium having stored therein at least one instruction for execution by a processor in a computer device to implement the machine learning-based customer relationship identification method.
According to the technical scheme, the client relationship recognition method and the client relationship recognition system can assist in recognizing the client relationship based on the model of machine learning training, and improve recognition accuracy and recognition efficiency.
Drawings
FIG. 1 is a flow chart of a preferred embodiment of the method for identifying customer relationship based on machine learning according to the present invention.
Fig. 2 is a functional block diagram of a preferred embodiment of the client relationship recognition device based on machine learning according to the present invention.
FIG. 3 is a schematic structural diagram of a computer device according to a preferred embodiment of the present invention for implementing a client relationship identification method based on machine learning.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be described in detail with reference to the accompanying drawings and specific embodiments.
Fig. 1 is a flow chart of a preferred embodiment of the method for identifying client relationship based on machine learning according to the present invention. The order of the steps in the flow chart may be changed and some steps may be omitted according to different needs.
The customer relationship identification method based on machine learning is applied to one or more computer devices, which are devices capable of automatically performing numerical calculation and/or information processing according to preset or stored instructions, and the hardware thereof includes, but is not limited to, a microprocessor, an Application Specific Integrated Circuit (ASIC), a Programmable Gate Array (FPGA), a Digital Signal Processor (DSP), an embedded device, and the like.
The computer device may be any electronic product capable of performing human-computer interaction with a user, for example, a Personal computer, a tablet computer, a smart phone, a Personal Digital Assistant (PDA), a game machine, an interactive web Television (IPTV), an intelligent wearable device, and the like.
The computer device may also include a network device and/or a user device. The network device includes, but is not limited to, a single network server, a server group consisting of a plurality of network servers, or a Cloud Computing (Cloud Computing) based Cloud consisting of a large number of hosts or network servers.
The server may be an independent server, or may be a cloud server that provides basic cloud computing services such as a cloud service, a cloud database, cloud computing, a cloud function, cloud storage, a Network service, cloud communication, a middleware service, a domain name service, a security service, a Content Delivery Network (CDN), a big data and artificial intelligence platform, and the like.
Among them, Artificial Intelligence (AI) is a theory, method, technique and application system that simulates, extends and expands human Intelligence using a digital computer or a machine controlled by a digital computer, senses the environment, acquires knowledge and uses the knowledge to obtain the best result.
The artificial intelligence infrastructure generally includes technologies such as sensors, dedicated artificial intelligence chips, cloud computing, distributed storage, big data processing technologies, operation/interaction systems, mechatronics, and the like. The artificial intelligence software technology mainly comprises a computer vision technology, a robot technology, a biological recognition technology, a voice processing technology, a natural language processing technology, machine learning/deep learning and the like.
The Network where the computer device is located includes, but is not limited to, the internet, a wide area Network, a metropolitan area Network, a local area Network, a Virtual Private Network (VPN), and the like.
And S10, acquiring historical customer data of the target enterprise, and screening the target data from the historical customer data.
The historical client can include a key for the development performance and the large client scale of the client to accurately acquire the relevant line organizations and individuals of the client for the public client and other clients, particularly in the field of business for the public client.
In this embodiment, the historical customer data may include external data and internal data.
Wherein, the external data may include, but is not limited to: external data such as the business register, the share relationship, the external investment, the finance, the public opinion, the financing and the like of the client disclosure.
Wherein the intra-pair data may include, but is not limited to: historical cooperation detail data of the client, visit records, strategic cooperation lists and the like.
It will be appreciated that not all of the historical customer data is relevant to the customer relationship, and therefore, there is a need to screen the historical customer data for target data.
In at least one embodiment of the present invention, the filtering out target data from the historical customer data comprises:
classifying the historical customer data to obtain each type of data;
determining each type of data as nodes to construct a random forest;
determining the node importance of each type of data in the historical customer data at each node of each decision tree in the random forest;
determining the importance of each type of data in each decision tree according to the node importance of each type of data at each node of each decision tree;
determining the importance of each type of data in the random forest according to the importance of each type of data in each decision tree;
determining the importance of each type of data in the random forest as the weight of each type of data;
sequencing each type of data according to the sequence of the weight from high to low to obtain a data sequence;
and acquiring data arranged in the front preset bit from the data sequence as the target data.
The front preset bit can be configured by self-definition, such as the front 10 bits.
Of course, in other embodiments, the target data may be filtered in other manners, for example, the target data may be extracted as effective features according to the historical marketing data of the customers.
By screening target data from historical client data, interference of a large amount of invalid and redundant data on subsequent model training can be avoided, and meanwhile, the efficiency of model training is improved.
And S11, processing the target data to obtain a training sample and a verification sample.
In order to ensure the effect of model training, sufficient data needs to be used for training, and therefore, when the data amount of the target data is insufficient, the target data needs to be expanded.
In at least one embodiment of the invention, before processing the target data, the method further comprises:
determining a data amount of the target data;
when the data volume is smaller than a preset threshold value, performing feature combination on the target data to obtain combination features, wherein the combination features comprise combinations of different types of data with preset quantity; and/or
Pre-training a gradient lifting decision tree by using the target data to obtain a feature tree, and constructing a new feature according to leaf nodes in the feature tree;
and adding the combined features and the newly added features to the target data.
For example: different types of data in the target data can be combined two by two.
By expanding the data, the training effect of the model can be prevented from being influenced due to insufficient data volume.
After the target data is screened out, the target data needs to be optimized in order to further adapt the target data to model training.
In at least one embodiment of the present invention, the processing the target data to obtain a training sample and a verification sample includes:
identifying missing data from the target data, and filling the missing data to obtain first data; wherein the padding the missing data comprises: when the missing data is continuous data, acquiring front and rear data adjacent to the missing data, calculating an average value of the acquired data, and filling the missing data by using the average value, or when the missing data is discrete data, acquiring a mode of all data in a type corresponding to the missing data, and filling the missing data by using the mode;
identifying abnormal data from the first data, and deleting the abnormal data to obtain second data;
labeling the second data to obtain third data;
and acquiring a preset proportion, and splitting the third data according to the preset proportion to obtain the training sample and the verification sample.
Wherein the preset ratio may be configured as 7: 3.
the third data obtained after labeling can reflect the strength of the customer relationship, for example: the data can be labeled as strong, medium and weak types according to the strength of the customer relationship.
When time sequence data exists in the target data, the time sequence data can be extracted according to a time sequence.
In the embodiment, the target data are processed, so that the data can be further optimized, and the effect of model training is improved.
And S12, training a preset classification model by using the training sample and the verification sample to obtain an identification model.
The preset classification model may include any classification model with classification function, such as GBDT (Gradient Boosting Decision Tree).
Specifically, in this embodiment, the type of the label may be used as a training target, the preset classification model is trained by using the training sample, and the accuracy of the trained model is verified by using the verification sample, until the target accuracy is reached, the training is stopped, and the model obtained by the current training is determined as the recognition model.
In order to ensure the usability of the trained recognition model, the recognition model needs to be updated.
In at least one embodiment of the invention, after obtaining the recognition model, the method further comprises:
detecting whether the identification model reaches an updating condition, including: detecting evaluation data of the recognition model in real time, and determining that the updating condition is reached when the evaluation data shows that the accuracy of the recognition model is to be improved; and/or acquiring newly added customer data, and determining that the updating condition is reached when the newly added customer data reaches the configuration quantity; and/or determining that the update condition is reached when a preset time interval is reached;
and when the recognition model is detected to reach the updating condition, performing incremental training on the recognition model by using the newly added customer data.
For example: when the evaluation data are 'too low accuracy', 'inaccurate prediction' and the number of evaluations representing inaccurate prediction reaches a certain value, the recognition model can be indicated to need to be updated. When the number of the newly added customer data reaches a certain number, the identification model also needs to be updated in order to be applicable to the newly added customer data. When the preset time interval is reached, the recognition model is used for a certain time, and the recognition model can be updated periodically in order to make the recognition model more practical.
Through the implementation mode, the recognition model can be continuously learned and iterated so as to ensure the accuracy of model recognition.
And S13, responding to the customer relationship identification instruction aiming at the target customer, and acquiring the data to be identified according to the customer relationship identification instruction.
The target customer may include a public customer or other customers, and the present invention is not limited thereto.
In at least one embodiment of the present invention, the acquiring data to be identified according to the customer relationship identification instruction includes:
determining the customer information of the target customer according to the customer relationship identification instruction;
connecting to a configuration database, and inquiring in the configuration database according to the customer information;
and determining the inquired data as the data to be identified.
Wherein the configuration database is used for storing data of any customer.
The configuration database may be a local database or a cloud database, which is not limited in the present invention.
S14, inputting the data to be recognized into the recognition model, and determining the customer relationship between the target customer and the target enterprise according to the output data of the recognition model.
For example: when the relationship between the target client and the target enterprise is determined to be strong according to the output data, the target client and the target enterprise have a high cooperation prospect, so that key maintenance and product recommendation can be performed on the target client, and better service for the client is realized.
And S15, feeding back the customer relationship to the specified terminal equipment.
The specified terminal equipment can be terminal equipment of marketers and the like.
In at least one embodiment of the present invention, when the customer relationship is fed back to the specified terminal device, the method further includes:
generating a product recommendation strategy and a customer maintenance strategy for the target customer according to the customer relationship;
and synchronously feeding back the product recommendation strategy and the customer maintenance strategy to the specified terminal equipment.
Through the embodiment, the product recommendation strategy and the customer maintenance strategy can be automatically generated to assist marketing personnel to carry out more targeted marketing, and the marketing success rate is improved.
It should be noted that, in order to further improve the security of the data and avoid malicious tampering of the data, the identification model may be stored in the blockchain node.
According to the technical scheme, the method can acquire historical client data of a target enterprise, screen out the target data from the historical client data, avoid interference of a large amount of invalid and redundant data on subsequent model training, improve the efficiency of model training, process the target data to obtain training samples and verification samples, further optimize the data, improve the effect of model training, train a preset classification model by using the training samples and the verification samples to obtain an identification model, respond to a client relationship identification instruction aiming at the target client, acquire data to be identified according to the client relationship identification instruction, input the data to be identified into the identification model, determine the client relationship between the target client and the target enterprise according to the output data of the identification model, and feed back the client relationship to a specified terminal device, the model based on machine learning training assists in identifying the customer relationship, and the identification accuracy and identification efficiency are improved.
Fig. 2 is a functional block diagram of a preferred embodiment of the client relationship identification apparatus based on machine learning according to the present invention. The client relationship recognition device 11 based on machine learning includes a screening unit 110, a processing unit 111, a training unit 112, an obtaining unit 113, a determining unit 114, and a feedback unit 115. A module/unit as referred to herein is a series of computer program segments stored in a memory that can be executed by a processor and that can perform a fixed function. In the present embodiment, the functions of the modules/units will be described in detail in the following embodiments.
The filtering unit 110 obtains historical customer data of the target enterprise and filters the target data from the historical customer data.
The historical client can include a key for the development performance and the large client scale of the client to accurately acquire the relevant line organizations and individuals of the client for the public client and other clients, particularly in the field of business for the public client.
In this embodiment, the historical customer data may include external data and internal data.
Wherein the external data may include, but is not limited to: external data such as the business register, the share relationship, the external investment, the finance, the public opinion, the financing and the like of the client disclosure.
Wherein the intra-pair data may include, but is not limited to: historical cooperation detail data of the client, visit records, strategic cooperation lists and the like.
It will be appreciated that not all of the historical customer data is relevant to the customer relationship, and therefore, there is a need to screen the historical customer data for target data.
In at least one embodiment of the present invention, the filtering unit 110 for filtering target data from the historical customer data includes:
classifying the historical customer data to obtain each type of data;
determining each type of data as nodes to construct a random forest;
determining node importance of each type of data in the historical customer data at each node of each decision tree in the random forest;
determining the importance of each type of data in each decision tree according to the node importance of each type of data at each node of each decision tree;
determining the importance of each type of data in the random forest according to the importance of each type of data in each decision tree;
determining the importance of each type of data in the random forest as the weight of each type of data;
sequencing each type of data according to the sequence of the weight from high to low to obtain a data sequence;
and acquiring data arranged in the front preset bit from the data sequence as the target data.
The front preset bit can be configured by self-definition, such as the front 10 bits.
Of course, in other embodiments, the target data may be filtered in other manners, for example, the target data may be extracted as effective features according to the historical marketing data of the customers.
By screening target data from historical client data, interference of a large amount of invalid and redundant data on subsequent model training can be avoided, and meanwhile, the efficiency of model training is improved.
The processing unit 111 processes the target data to obtain a training sample and a verification sample.
In order to ensure the effect of model training, sufficient data needs to be used for training, and therefore, when the data amount of the target data is insufficient, the target data needs to be expanded.
In at least one embodiment of the present invention, before processing the target data, determining a data volume of the target data;
when the data volume is smaller than a preset threshold value, performing feature combination on the target data to obtain combined features, wherein the combined features comprise combinations of different types of data with preset quantity; and/or
Pre-training a gradient lifting decision tree by using the target data to obtain a feature tree, and constructing a new feature according to leaf nodes in the feature tree;
and adding the combined features and the newly added features to the target data.
For example: different types of data in the target data can be combined two by two.
By expanding the data, the training effect of the model can be prevented from being influenced due to insufficient data volume.
After the target data is screened out, the target data needs to be optimized in order to further adapt the target data to model training.
In at least one embodiment of the present invention, the processing unit 111 processes the target data to obtain a training sample and a verification sample, including:
identifying missing data from the target data, and filling the missing data to obtain first data; wherein the padding the missing data comprises: when the missing data is continuous data, acquiring front and rear data adjacent to the missing data, calculating an average value of the acquired data, and filling the missing data by using the average value, or when the missing data is discrete data, acquiring a mode of all data in a type corresponding to the missing data, and filling the missing data by using the mode;
identifying abnormal data from the first data, and deleting the abnormal data to obtain second data;
labeling the second data to obtain third data;
and acquiring a preset proportion, and splitting the third data according to the preset proportion to obtain the training sample and the verification sample.
Wherein the preset ratio may be configured as 7: 3.
the third data obtained after labeling can reflect the strength of the customer relationship, for example: the data can be labeled as strong, medium and weak types according to the strength of the customer relationship.
When time sequence data exists in the target data, the time sequence data can be extracted according to a time sequence.
In the embodiment, the target data are processed, so that the data can be further optimized, and the effect of model training is improved.
The training unit 112 trains a preset classification model by using the training sample and the verification sample to obtain an identification model.
The preset classification model may include any classification model with classification function, such as GBDT (Gradient Boosting Decision Tree).
Specifically, in this embodiment, the type of the label may be used as a training target, the preset classification model is trained by using the training sample, and the accuracy of the trained model is verified by using the verification sample, until the target accuracy is reached, the training is stopped, and the model obtained by the current training is determined as the recognition model.
In order to ensure the usability of the trained recognition model, the recognition model needs to be updated.
In at least one embodiment of the present invention, after obtaining the recognition model, detecting whether the recognition model reaches an update condition includes: detecting evaluation data of the recognition model in real time, and determining that the updating condition is reached when the evaluation data shows that the accuracy of the recognition model is to be improved; and/or acquiring newly added customer data, and determining that the updating condition is reached when the newly added customer data reaches the configuration quantity; and/or determining that the update condition is reached when a preset time interval is reached;
and when the recognition model is detected to reach the updating condition, performing incremental training on the recognition model by using the newly added customer data.
For example: when the evaluation data are 'too low accuracy', 'inaccurate prediction' and the number of evaluations representing inaccurate prediction reaches a certain value, the recognition model can be indicated to need to be updated. When the number of the newly added customer data reaches a certain number, the identification model also needs to be updated in order to be applicable to the newly added customer data. When the preset time interval is reached, the recognition model is used for a certain time, and the recognition model can be updated periodically in order to make the recognition model more practical.
Through the implementation mode, the recognition model can be continuously learned and iterated, so that the accuracy of model recognition is ensured.
In response to a customer relationship identification instruction for a target customer, the acquisition unit 113 acquires data to be identified according to the customer relationship identification instruction.
The target customer may include a public customer, and may also include other customers, which is not limited in the present invention.
In at least one embodiment of the present invention, the acquiring unit 113 acquiring the data to be identified according to the customer relationship identification instruction includes:
determining the customer information of the target customer according to the customer relationship identification instruction;
connecting to a configuration database, and inquiring in the configuration database according to the customer information;
and determining the inquired data as the data to be identified.
Wherein the configuration database is used for storing data of any customer.
The configuration database may be a local database or a cloud database, which is not limited in the present invention.
The determining unit 114 inputs the data to be recognized into the recognition model, and determines the customer relationship between the target customer and the target enterprise according to the output data of the recognition model.
For example: when the relationship between the target client and the target enterprise is determined to be strong according to the output data, the target client and the target enterprise have a high cooperation prospect, so that key maintenance and product recommendation can be performed on the target client, and better service for the client is realized.
The feedback unit 115 feeds back the customer relationship to the specified terminal device.
The specified terminal equipment can be terminal equipment of marketers and the like.
In at least one embodiment of the invention, when the customer relationship is fed back to the appointed terminal equipment, a product recommendation strategy and a customer maintenance strategy for the target customer are generated according to the customer relationship;
and synchronously feeding back the product recommendation strategy and the customer maintenance strategy to the specified terminal equipment.
Through the embodiment, the product recommendation strategy and the customer maintenance strategy can be automatically generated to assist marketing personnel to carry out more targeted marketing, and the marketing success rate is improved.
It should be noted that, in order to further improve the security of the data and avoid malicious tampering of the data, the identification model may be stored in the blockchain node.
According to the technical scheme, the method can acquire historical client data of a target enterprise, screen out the target data from the historical client data, avoid interference of a large amount of invalid and redundant data on subsequent model training, improve the efficiency of model training, process the target data to obtain training samples and verification samples, further optimize the data, improve the effect of model training, train a preset classification model by using the training samples and the verification samples to obtain an identification model, respond to a client relationship identification instruction aiming at the target client, acquire data to be identified according to the client relationship identification instruction, input the data to be identified into the identification model, determine the client relationship between the target client and the target enterprise according to the output data of the identification model, and feed back the client relationship to a specified terminal device, the model based on machine learning training assists in identifying the customer relationship, and the identification accuracy and identification efficiency are improved.
Fig. 3 is a schematic structural diagram of a computer device according to a preferred embodiment of the present invention for implementing a client relationship identification method based on machine learning.
The computer device 1 may comprise a memory 12, a processor 13 and a bus, and may further comprise a computer program, such as a machine learning based customer relationship identification program, stored in the memory 12 and executable on the processor 13.
It will be understood by those skilled in the art that the schematic diagram is merely an example of the computer device 1, and does not constitute a limitation to the computer device 1, the computer device 1 may have a bus-type structure or a star-shaped structure, the computer device 1 may further include more or less other hardware or software than those shown, or different component arrangements, for example, the computer device 1 may further include an input and output device, a network access device, etc.
It should be noted that the computer device 1 is only an example, and other electronic products that are currently available or may come into existence in the future, such as electronic products that can be adapted to the present invention, should also be included in the scope of the present invention, and are included herein by reference.
The memory 12 includes at least one type of readable storage medium, which includes flash memory, removable hard disks, multimedia cards, card-type memory (e.g., SD or DX memory, etc.), magnetic memory, magnetic disks, optical disks, etc. The memory 12 may in some embodiments be an internal storage unit of the computer device 1, for example a removable hard disk of the computer device 1. The memory 12 may also be an external storage device of the computer device 1 in other embodiments, such as a plug-in removable hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), etc. provided on the computer device 1. Further, the memory 12 may also include both an internal storage unit and an external storage device of the computer device 1. The memory 12 can be used not only for storing application software installed in the computer device 1 and various types of data such as codes of a client relationship recognition program based on machine learning, etc., but also for temporarily storing data that has been output or is to be output.
The processor 13 may be composed of an integrated circuit in some embodiments, for example, a single packaged integrated circuit, or may be composed of a plurality of integrated circuits packaged with the same or different functions, including one or more Central Processing Units (CPUs), microprocessors, digital Processing chips, graphics processors, and combinations of various control chips. The processor 13 is a Control Unit (Control Unit) of the computer device 1, connects various components of the entire computer device 1 by using various interfaces and lines, and executes various functions and processes data of the computer device 1 by running or executing programs or modules stored in the memory 12 (for example, executing a client relationship recognition program based on machine learning, etc.), and calling data stored in the memory 12.
The processor 13 executes the operating system of the computer device 1 and various installed application programs. The processor 13 executes the application program to implement the steps in the various machine learning based customer relationship identification method embodiments described above, such as the steps shown in fig. 1.
Illustratively, the computer program may be divided into one or more modules/units, which are stored in the memory 12 and executed by the processor 13 to accomplish the present invention. The one or more modules/units may be a series of computer readable instruction segments capable of performing certain functions, which are used to describe the execution of the computer program in the computer device 1. For example, the computer program may be divided into a screening unit 110, a processing unit 111, a training unit 112, an acquisition unit 113, a determination unit 114, a feedback unit 115.
The integrated unit implemented in the form of a software functional module may be stored in a computer-readable storage medium. The software functional module is stored in a storage medium and includes several instructions to enable a computer device (which may be a personal computer, a computer device, or a network device) or a processor (processor) to execute parts of the client relationship identification method based on machine learning according to the embodiments of the present invention.
The integrated modules/units of the computer device 1 may be stored in a computer-readable storage medium if they are implemented in the form of software functional units and sold or used as separate products. Based on such understanding, all or part of the flow of the method according to the embodiments of the present invention may be implemented by a computer program, which may be stored in a computer-readable storage medium, and when the computer program is executed by a processor, the steps of the method embodiments may be implemented.
Wherein the computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, U-disk, removable hard disk, magnetic disk, optical disk, computer Memory, Read-Only Memory (ROM), random-access Memory, or the like.
Further, the computer-readable storage medium may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function, and the like; the storage data area may store data created according to the use of the blockchain node, and the like.
The block chain is a novel application mode of computer technologies such as distributed data storage, point-to-point transmission, a consensus mechanism, an encryption algorithm and the like. A block chain (Blockchain), which is essentially a decentralized database, is a series of data blocks associated by using a cryptographic method, and each data block contains information of a batch of network transactions, so as to verify the validity (anti-counterfeiting) of the information and generate a next block. The blockchain may include a blockchain underlying platform, a platform product service layer, an application service layer, and the like.
The bus may be a Peripheral Component Interconnect (PCI) bus, an Extended Industry Standard Architecture (EISA) bus, or the like. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of illustration, only one line is shown in FIG. 3, but this does not mean only one bus or one type of bus. The bus is arranged to enable connection communication between the memory 12 and at least one processor 13 or the like.
Although not shown, the computer device 1 may further include a power supply (such as a battery) for supplying power to each component, and preferably, the power supply may be logically connected to the at least one processor 13 through a power management device, so that functions of charge management, discharge management, power consumption management and the like are realized through the power management device. The power supply may also include any component of one or more dc or ac power sources, recharging devices, power failure detection circuitry, power converters or inverters, power status indicators, and the like. The computer device 1 may further include various sensors, a bluetooth module, a Wi-Fi module, and the like, which are not described herein again.
Further, the computer device 1 may further include a network interface, and optionally, the network interface may include a wired interface and/or a wireless interface (such as a WI-FI interface, a bluetooth interface, etc.), which are generally used for establishing a communication connection between the computer device 1 and other computer devices.
Optionally, the computer device 1 may further comprise a user interface, which may be a Display (Display), an input unit, such as a Keyboard (Keyboard), and optionally a standard wired interface, a wireless interface. Alternatively, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, an OLED (Organic Light-Emitting Diode) touch device, or the like. The display, which may also be referred to as a display screen or display unit, is suitable, among other things, for displaying information processed in the computer device 1 and for displaying a visualized user interface.
It is to be understood that the described embodiments are for purposes of illustration only and that the scope of the appended claims is not limited to such structures.
Fig. 3 shows only the computer device 1 with the components 12-13, and it will be understood by a person skilled in the art that the structure shown in fig. 3 does not constitute a limitation of the computer device 1 and may comprise fewer or more components than shown, or a combination of certain components, or a different arrangement of components.
With reference to fig. 1, the memory 12 of the computer device 1 stores a plurality of instructions to implement a method for customer relationship identification based on machine learning, and the processor 13 can execute the plurality of instructions to implement:
acquiring historical client data of a target enterprise, and screening out target data from the historical client data;
processing the target data to obtain a training sample and a verification sample;
training a preset classification model by using the training sample and the verification sample to obtain an identification model;
responding to a customer relationship identification instruction aiming at a target customer, and acquiring data to be identified according to the customer relationship identification instruction;
inputting the data to be recognized into the recognition model, and determining the customer relationship between the target customer and the target enterprise according to the output data of the recognition model;
and feeding back the customer relation to the appointed terminal equipment.
Specifically, the processor 13 may refer to the description of the relevant steps in the embodiment corresponding to fig. 1 for a specific implementation method of the instruction, which is not described herein again.
It should be noted that all data involved in the present application are legally acquired.
In the embodiments provided in the present invention, it should be understood that the disclosed system, apparatus and method may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the modules is only one logical functional division, and other divisions may be realized in practice.
The invention is operational with numerous general purpose or special purpose computing system environments or configurations. For example: personal computers, server computers, hand-held or portable devices, tablet-type devices, multiprocessor systems, microprocessor-based systems, set top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, distributed computing environments that include any of the above systems or devices, and the like. The invention may be described in the general context of computer-executable instructions, such as program modules, being executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. The invention may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote computer storage media including memory storage devices.
The modules described as separate parts may or may not be physically separate, and parts displayed as modules 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 modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment.
In addition, functional modules in the embodiments of the present invention 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 can be realized in a form of hardware, or in a form of hardware plus a software functional module.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential attributes thereof.
The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference signs in the claims shall not be construed as limiting the claim concerned.
Furthermore, it is obvious that the word "comprising" does not exclude other elements or steps, and the singular does not exclude the plural. A plurality of units or means recited in the present invention may also be implemented by one unit or means through software or hardware. The terms first, second, etc. are used to denote names, but not any particular order.
Finally, it should be noted that the above embodiments are only intended to illustrate the technical solutions of the present invention and not to limit the same, and although the present invention is described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.
Claims (10)
1. A customer relation identification method based on machine learning is characterized by comprising the following steps:
acquiring historical client data of a target enterprise, and screening out target data from the historical client data;
processing the target data to obtain a training sample and a verification sample;
training a preset classification model by using the training sample and the verification sample to obtain an identification model;
responding to a customer relationship identification instruction aiming at a target customer, and acquiring data to be identified according to the customer relationship identification instruction;
inputting the data to be recognized into the recognition model, and determining the customer relationship between the target customer and the target enterprise according to the output data of the recognition model;
and feeding back the customer relationship to a specified terminal device.
2. The machine learning-based customer relationship identification method of claim 1, wherein the screening target data from the historical customer data comprises:
classifying the historical customer data to obtain each type of data;
determining each type of data as nodes to construct a random forest;
determining node importance of each type of data in the historical customer data at each node of each decision tree in the random forest;
determining the importance of each type of data in each decision tree according to the node importance of each type of data at each node of each decision tree;
determining the importance of each type of data in the random forest according to the importance of each type of data in each decision tree;
determining the importance of each type of data in the random forest as the weight of each type of data;
sequencing each type of data according to the sequence of the weight from high to low to obtain a data sequence;
and acquiring data arranged in the front preset bit from the data sequence as the target data.
3. The machine-learning based customer relationship identification method of claim 1, wherein prior to processing the target data, the method further comprises:
determining a data amount of the target data;
when the data volume is smaller than a preset threshold value, performing feature combination on the target data to obtain combined features, wherein the combined features comprise combinations of different types of data with preset quantity; and/or
Pre-training a gradient lifting decision tree by using the target data to obtain a feature tree, and constructing a new feature according to leaf nodes in the feature tree;
and adding the combined features and the newly added features to the target data.
4. The customer relationship identification method based on machine learning according to claim 1, wherein the processing the target data to obtain training samples and verification samples comprises:
identifying missing data from the target data, and filling the missing data to obtain first data; wherein the padding the missing data comprises: when the missing data is continuous data, acquiring front and rear data adjacent to the missing data, calculating an average value of the acquired data, and filling the missing data by using the average value, or when the missing data is discrete data, acquiring a mode of all data in a type corresponding to the missing data, and filling the missing data by using the mode;
identifying abnormal data from the first data, and deleting the abnormal data to obtain second data;
labeling the second data to obtain third data;
and acquiring a preset proportion, and splitting the third data according to the preset proportion to obtain the training sample and the verification sample.
5. The machine learning-based customer relationship identification method of claim 1, wherein after obtaining the identification model, the method further comprises:
detecting whether the identification model reaches an updating condition, including: detecting evaluation data of the recognition model in real time, and determining that the updating condition is reached when the evaluation data shows that the accuracy of the recognition model is to be improved; and/or acquiring newly added customer data, and determining that the updating condition is reached when the newly added customer data reaches the configuration quantity; and/or determining that the update condition is reached when a preset time interval is reached;
and when the recognition model is detected to reach the updating condition, performing incremental training on the recognition model by using the newly added customer data.
6. The customer relationship identification method based on machine learning according to claim 1, wherein the obtaining data to be identified according to the customer relationship identification instruction comprises:
determining the customer information of the target customer according to the customer relation identification instruction;
connecting to a configuration database, and inquiring in the configuration database according to the customer information;
and determining the inquired data as the data to be identified.
7. The machine learning-based customer relationship identification method according to claim 1, wherein in feeding back the customer relationship to a specified terminal device, the method further comprises:
generating a product recommendation strategy and a customer maintenance strategy for the target customer according to the customer relationship;
and synchronously feeding back the product recommendation strategy and the customer maintenance strategy to the specified terminal equipment.
8. A client relationship recognition apparatus based on machine learning, comprising:
the screening unit is used for acquiring historical client data of a target enterprise and screening the target data from the historical client data;
the processing unit is used for processing the target data to obtain a training sample and a verification sample;
the training unit is used for training a preset classification model by using the training sample and the verification sample to obtain an identification model;
the system comprises an acquisition unit, a processing unit and a processing unit, wherein the acquisition unit is used for responding to a customer relationship identification instruction aiming at a target customer and acquiring data to be identified according to the customer relationship identification instruction;
the determining unit is used for inputting the data to be recognized into the recognition model and determining the customer relationship between the target customer and the target enterprise according to the output data of the recognition model;
and the feedback unit is used for feeding back the customer relationship to the specified terminal equipment.
9. A computer device, characterized in that the computer device comprises:
a memory storing at least one instruction; and
a processor executing instructions stored in the memory to implement the machine learning based customer relationship identification method of any one of claims 1 to 7.
10. A computer-readable storage medium characterized by: the computer-readable storage medium has stored therein at least one instruction that is executable by a processor in a computer device to implement the machine learning based customer relationship identification method of any one of claims 1 to 7.
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN115795289A (en) * | 2022-12-01 | 2023-03-14 | 北京淘友天下技术有限公司 | Feature recognition method and device, electronic equipment and storage medium |
| CN116701888A (en) * | 2023-08-09 | 2023-09-05 | 国网浙江省电力有限公司丽水供电公司 | Auxiliary model data processing method and system for clean energy enterprises |
| CN117057756A (en) * | 2023-10-11 | 2023-11-14 | 深圳市加推科技有限公司 | Client relationship management method and device based on RPA technology and related medium |
| CN118277964A (en) * | 2024-05-29 | 2024-07-02 | 广东蕾特恩科技发展有限公司 | Client relationship data recommendation system and method based on AI driving |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN115795289A (en) * | 2022-12-01 | 2023-03-14 | 北京淘友天下技术有限公司 | Feature recognition method and device, electronic equipment and storage medium |
| CN115795289B (en) * | 2022-12-01 | 2024-05-28 | 北京淘友天下技术有限公司 | Feature recognition method, device, electronic equipment and storage medium |
| CN116701888A (en) * | 2023-08-09 | 2023-09-05 | 国网浙江省电力有限公司丽水供电公司 | Auxiliary model data processing method and system for clean energy enterprises |
| CN116701888B (en) * | 2023-08-09 | 2023-10-17 | 国网浙江省电力有限公司丽水供电公司 | Auxiliary model data processing method and system for clean energy enterprises |
| CN117057756A (en) * | 2023-10-11 | 2023-11-14 | 深圳市加推科技有限公司 | Client relationship management method and device based on RPA technology and related medium |
| CN118277964A (en) * | 2024-05-29 | 2024-07-02 | 广东蕾特恩科技发展有限公司 | Client relationship data recommendation system and method based on AI driving |
| CN118277964B (en) * | 2024-05-29 | 2024-08-02 | 广东蕾特恩科技发展有限公司 | Client relationship data recommendation system and method based on AI driving |
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