CN113706322A - Service distribution method, device, equipment and storage medium based on data analysis - Google Patents
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Abstract
The invention relates to the technical field of artificial intelligence and digital medical treatment, and discloses a service distribution method based on data analysis, which comprises the following steps: acquiring policy information of a user, and converting the policy information into a policy text; performing word segmentation on the policy text, and performing feature word extraction on text word segmentation obtained by word segmentation to obtain user feature words; constructing a decision tree model by taking the user characteristic words as decision conditions; acquiring the use permission of the preset service, and screening the usable service of the user from the preset service by using a decision tree model according to the use permission; and generating a service list according to the policy information and the available service, and pushing the service list to the user, wherein the policy information can be a medical policy. In addition, the invention also relates to a block chain technology, and the policy information can be stored in the nodes of the block chain. The invention also provides a service distribution device based on data analysis, an electronic device and a storage medium. The invention can improve the accuracy of medical service recommendation.
Description
Technical Field
The present invention relates to the field of artificial intelligence technologies, and in particular, to a service distribution method and apparatus based on data analysis, an electronic device, and a computer-readable storage medium.
Background
With the increasing health management awareness of people, the demand for medical services is increasing, and in addition, the vision of medical insurance is to provide medical services to users instead of claims, but due to the wide variety of medical insurance, the medical services owned by each insurance are different, so that when the services are issued to the users, the users may not be authorized to enjoy the corresponding services, or even if the users have been authorized to enjoy the services, the services are due, and the accuracy of recommending the medical services to the users according to policy data is not high.
Disclosure of Invention
The invention provides a service distribution method and device based on data analysis and a computer readable storage medium, and mainly aims to solve the problem of low accuracy in medical service recommendation.
In order to achieve the above object, the present invention provides a service distribution method based on data analysis, including:
acquiring policy information of a user, and performing text conversion on the policy information to obtain a policy text;
performing word segmentation on the policy text, and performing feature word extraction on text word segmentation obtained by word segmentation to obtain user feature words;
constructing a decision tree model by taking the user feature words as decision conditions;
acquiring the use permission of preset services, and screening the usable services of the user from the preset services by using the decision tree model according to the use permission;
and generating a service list according to the policy information and the usable service, and pushing the service list to the user.
Optionally, the text converting the policy information to obtain a policy text includes:
extracting a data type field of each datum in the policy information;
dividing data in the policy information according to the data type field to obtain text data, image data and audio data;
carrying out image recognition on the image data in the policy information to obtain an image text corresponding to the image data;
performing voice recognition on audio data in the policy information to obtain an audio text corresponding to the audio data;
and aggregating the text data, the image text and the audio text into a policy text.
Optionally, the dividing the data in the policy information according to the data type field to obtain text data, image data, and audio data includes:
selecting one data from the policy information one by one as target data;
carrying out vector conversion on the data type field of the target data to obtain a data type vector;
respectively calculating distance values between the data type vector and a plurality of preset data types, wherein the data types comprise a text type, an image type and an audio type;
and determining the data type with the minimum distance value as the type of the target data, and dividing the data in the policy information according to the type to obtain text data, image data and audio data.
Optionally, the segmenting the policy text and extracting the feature words of the segmented text to obtain the user feature words includes:
dividing the policy text into a plurality of text clauses according to preset clause symbols;
searching each text clause in the plurality of text clauses in a preset dictionary according to different data lengths, and taking the searched words as text clauses of the policy text;
counting the occurrence frequency of each text participle and the position information of each text participle in the policy text;
and calculating the criticality of each text word according to the occurrence frequency and the position information, and collecting the text words with the criticality larger than a preset threshold value as user characteristic words.
Optionally, the calculating the criticality of each text segment according to the occurrence frequency and the location information includes:
calculating the criticality of each text word segmentation according to the occurrence frequency and the position information by using a characteristic value algorithm as follows:
Ki＝α*Ai+β*βi
wherein, KiThe key degree of the ith word in the text word segmentation, A is the frequency of occurrence of the ith word in the text word segmentation, BiAnd alpha and beta are preset weight coefficients for the position information of the ith word in the text word.
Optionally, the constructing a decision tree model by using the user feature word as a decision condition includes:
one feature word is selected from the user feature words one by one to serve as a target feature word;
compiling the target characteristic words into characteristic parameters by using a preset compiler, and assigning values to a preset decision function by using the characteristic parameters;
generating a decision tree by using the assigned decision function as a decision condition;
and collecting the decision trees corresponding to all the characteristic words in the user characteristic words to obtain a decision tree model.
Optionally, the screening out available services of the user from the preset services according to the usage right by using the decision tree includes:
selecting one of the use authorities of the preset service one by one as an input value;
selecting one decision tree from the decision tree models one by one as a target decision tree, and inputting the input value into the target decision tree to obtain an output result of the target decision tree, wherein the output result comprises that the input value is the same as the parameters of the target decision tree or the input value is different from the parameters of the target decision tree;
and collecting the preset service corresponding to the use authority with the input value being the same as the parameter of the target decision tree as the usable service according to the output results of all the decision trees.
In order to solve the above problem, the present invention further provides a service distribution apparatus based on data analysis, the apparatus including:
the text conversion module is used for acquiring policy information of a user and performing text conversion on the policy information to obtain a policy text;
the characteristic word extraction module is used for segmenting words of the policy text and extracting the characteristic words of the segmented text to obtain user characteristic words;
the decision tree construction module is used for constructing a decision tree model by taking the user characteristic words as decision conditions;
the service screening module is used for acquiring the use permission of the preset service and screening the usable service of the user from the preset service by utilizing the decision tree model according to the use permission;
and the service pushing module is used for generating a service list according to the policy information and the usable service and pushing the service list to the user.
In order to solve the above problem, the present invention also provides an electronic device, including:
at least one processor; and the number of the first and second groups,
a memory communicatively coupled to the at least one processor; wherein,
the memory stores a computer program executable by the at least one processor, the computer program being executable by the at least one processor to enable the at least one processor to perform the data analysis-based service distribution method described above.
In order to solve the above problem, the present invention also provides a computer-readable storage medium, in which at least one computer program is stored, the at least one computer program being executed by a processor in an electronic device to implement the data analysis-based service distribution method described above.
The policy information of the user can be analyzed to extract the keywords contained in the policy information of the user, the decision tree model is constructed through the extracted keywords, the constructed decision tree model is used for carrying out accurate conditional screening on a plurality of preset services, and the screened services are pushed to the user, so that the accurate screening of the services is realized, and the accuracy of service distribution of the user is improved. Therefore, the service distribution method, the service distribution device, the electronic equipment and the computer-readable storage medium based on data analysis provided by the invention can solve the problem of low precision in product recommendation.
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Fig. 1 is a schematic flow chart of a service distribution method based on data analysis according to an embodiment of the present invention;
fig. 2 is a schematic flow chart of extracting a user feature word according to an embodiment of the present invention;
FIG. 3 is a schematic flow chart of a decision tree model according to an embodiment of the present invention;
FIG. 4 is a functional block diagram of a service distribution apparatus based on data analysis according to an embodiment of the present invention;
fig. 5 is a schematic structural diagram of an electronic device implementing the service distribution method based on data analysis according to an embodiment of the present invention.
The implementation, functional features and advantages of the objects of the present invention will be further explained with reference to the accompanying drawings.
Detailed Description
It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
The embodiment of the application provides a service distribution method based on data analysis. The executing body of the service distribution method based on data analysis includes, but is not limited to, at least one of electronic devices such as a server and a terminal, which can be configured to execute the method provided by the embodiments of the present application. In other words, the data analysis-based service distribution method may be performed by software or hardware installed in a terminal device or a server device, and the software may be a blockchain platform. The server includes but is not limited to: a single server, a server cluster, a cloud server or a cloud server cluster, and the like. 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.
Fig. 1 is a schematic flow chart of a service distribution method based on data analysis according to an embodiment of the present invention. In this embodiment, the service distribution method based on data analysis includes:
and S1, acquiring policy information of the user, and performing text conversion on the policy information to obtain a policy text.
In the embodiment of the present invention, the policy information includes data related to the insurance order purchased by the user, for example, data such as the age, sex, policy type, and policy amount of the user.
In detail, the policy information that is pre-stored and that the user authorization can be obtained can be fetched from pre-constructed storage areas, including but not limited to databases, block chains, network caches, using computer sentences with data fetching functionality (e.g., Java sentences, python sentences, etc.).
In one practical application scenario of the invention, the acquired policy information may include data in various forms such as text, image, audio and the like, and in order to improve the subsequent processing efficiency and accuracy of the policy information, the acquired policy information may be subjected to text conversion to obtain a policy text.
In the embodiment of the present invention, the performing text conversion on the policy information to obtain a policy text includes:
extracting a data type field of each datum in the policy information;
dividing data in the policy information according to the data type field to obtain text data, image data and audio data;
carrying out image recognition on the image data in the policy information to obtain an image text corresponding to the image data;
performing voice recognition on audio data in the policy information to obtain an audio text corresponding to the audio data;
and aggregating the text data, the image text and the audio text into a policy text.
In detail, the data type field is a field used for marking the data type of each piece of data; and extracting the data type field of each piece of data from the policy information by utilizing a pre-constructed regular expression with a data type field extraction function, wherein the regular expression can be used for realizing the function of extracting the data with a fixed format in the data.
In this embodiment of the present invention, the dividing the data in the policy information according to the data type field to obtain text data, image data, and audio data includes:
selecting one data from the policy information one by one as target data;
carrying out vector conversion on the data type field of the target data to obtain a data type vector;
respectively calculating distance values between the data type vector and a plurality of preset data types, wherein the data types comprise a text type, an image type and an audio type;
and determining the data type with the minimum distance value as the type of the target data, and dividing the data in the policy information according to the type to obtain text data, image data and audio data.
In detail, the data type field of the target data may be vector-converted using an artificial intelligence Model having a vector conversion function, including an NLP (Natural Language Processing) Model, an HMM (Hidden Markov Model), a bert Model, and the like.
Specifically, the distance values between the data type vector and a plurality of preset data types can be respectively calculated through algorithms with distance value calculation functions, such as a cosine distance algorithm, a euclidean distance algorithm, and the like, and the data type with the minimum distance value is determined as the type of the target data.
For example, if the distance value between the data type vector of the target data and the text type is 20, the distance value between the data type vector of the target data and the image type is 90, and the distance value between the data type vector of the target data and the audio type is 25, it can be confirmed that the data type of the target data is the graphic type.
In the embodiment of the invention, image Recognition can be carried out on the image data in the policy information by using an OCR (Optical Character Recognition) technology to obtain the image text corresponding to the image data; and performing voice Recognition on the audio data in the policy information by using an ASR (Automatic Speech Recognition) technology to obtain an audio text corresponding to the audio data, and collecting the text data, the image text and the audio text to obtain a policy text.
And S2, performing word segmentation on the policy text, and performing feature word extraction on the text word segmentation obtained by word segmentation to obtain a user feature word.
In one practical application scenario of the present invention, since the policy document contains a large amount of text information, if the policy document is directly analyzed, a large amount of computing resources are occupied, which results in low analysis efficiency, and a large amount of data is mixed, which results in inaccurate analysis results, so that word segmentation processing can be performed on the policy document, and feature words can be extracted from the word segmentation results, so as to reduce the data amount when the policy document is subsequently analyzed, and improve the analysis efficiency and accuracy.
In the embodiment of the present invention, referring to fig. 2, the performing word segmentation on the policy document and performing feature word extraction on the text word segmentation obtained by word segmentation to obtain a user feature word includes:
s21, dividing the policy document into a plurality of text clauses according to preset clause symbols;
s22, retrieving each text clause in the plurality of text clauses in a preset dictionary according to different data lengths, and taking the retrieved words as the text clauses of the policy text;
s23, counting the occurrence frequency of each text participle and the position information of each text participle in the policy text;
s24, calculating the criticality of each text participle according to the occurrence frequency and the position information, and collecting the text participles with the criticality larger than a preset threshold value as user characteristic words.
In detail, since the policy text contains a large amount of text data, in order to improve the efficiency of segmenting words of the policy text, a large amount of text data with preset segmentation symbols can be utilized for segmentation, so that the policy text is divided into a plurality of text segments, the data amount during word segmentation each time is further reduced, and the word segmentation efficiency is improved. "," … … ".
Specifically, the dictionary is a dictionary which is obtained in advance and contains a plurality of participles, each text clause can be searched in the dictionary according to different data lengths, and the searched participles are used as text participles of the policy text.
In the embodiment of the present invention, the occurrence frequency is the number of times that each text participle appears in all text participles of the policy text; the position information refers to a sequence of positions of each text participle in the policy text, for example, the text participles in the policy text may be numbered according to the sequence of positions in the policy text, and a number corresponding to each text participle is the position information of the text participle.
In this embodiment of the present invention, the calculating the criticality of each text word according to the occurrence frequency and the location information includes:
calculating the criticality of each text word segmentation according to the occurrence frequency and the position information by using a characteristic value algorithm as follows:
Ki＝α*Ai+β*βi
wherein, KiThe key degree of the ith word in the text word segmentation, A is the frequency of occurrence of the ith word in the text word segmentation, BiAnd alpha and beta are preset weight coefficients for the position information of the ith word in the text word.
In the embodiment of the invention, the text participles with the criticality larger than the preset threshold value can be selected, and the selected text participles are collected as the user characteristic words of the user.
And S3, constructing a decision tree model by taking the user characteristic words as decision conditions.
In one practical application scenario of the present invention, since there are a large number of services, but the user does not have a right of use for each service, the user feature word can be used as a decision condition to construct a decision tree, so that the constructed decision tree can be subsequently utilized to screen out services that can be acquired by the user from the large number of services.
In the embodiment of the present invention, as shown in fig. 3, the constructing a decision tree model by using the user feature word as a decision condition includes:
s31, selecting one feature word from the user feature words one by one as a target feature word;
s32, compiling the target feature words into feature parameters by using a preset compiler, and assigning values to a preset decision function by using the feature parameters;
s33, generating a decision tree by using the assigned decision function as a decision condition;
and S34, collecting the decision trees corresponding to all the characteristic words in the user characteristic words to obtain a decision tree model.
Illustratively, the decision function may be:
wherein f (x) is the output value of the decision function, x is the parameter of the decision function, and g (y) is the input value of the decision function.
In detail, one of the feature words may be selected from the user feature words one by one as a target feature word, the target feature word is compiled into a feature parameter by using a preset compiler, and a parameter x of the decision function is assigned by using the feature parameter, where the compiler includes, but is not limited to, a Visual Studio compiler and a Dev C + + compiler.
Specifically, the following decision tree can be generated by using the assigned decision function as a decision condition:
when the input value g (y) of the decision tree is the same as the parameter x of the decision tree, the decision tree output value f (x) α;
when the input to g (y) of the decision tree is not the same as the parameter x of the decision tree, the decision tree outputs a value f (x) β.
S4, obtaining the use authority of the preset service, and screening the usable service of the user from the preset service according to the use authority by using the decision tree model.
In the embodiment of the present invention, the usage right includes a constraint condition, such as a time limit, an age limit, and the like, required by the user when obtaining each preset service.
In detail, the step of obtaining the usage right of the preset service is the same as the step of obtaining the policy information of the user in S1, and is not described herein again.
In the embodiment of the invention, the decision tree can be used for screening various preset services to judge whether the use authority of each preset service in the various preset services conforms to the characteristics of the user, and further, the service which can be used and meets the conditions is screened out.
In this embodiment of the present invention, the screening out the service available to the user from the preset services according to the usage right by using the decision tree includes:
selecting one of the use authorities of the preset service one by one as an input value;
selecting one decision tree from the decision tree models one by one as a target decision tree, and inputting the input value into the target decision tree to obtain an output result of the target decision tree, wherein the output result comprises that the input value is the same as the parameters of the target decision tree or the input value is different from the parameters of the target decision tree;
and collecting the preset service corresponding to the use authority with the input value being the same as the parameter of the target decision tree as the usable service according to the output results of all the decision trees.
For example, the decision tree model includes a decision tree a1And decision tree a2The plurality of preset services comprise a service A and a service B, and a decision tree a is selected1Selecting the use authority of the service A as an input value for a target decision tree; inputting an input value to the decision tree a1Obtaining the decision tree a1The output input value and the decision tree a1The output results with the same parameters; inputting an input value to the decision tree a2Obtaining the decision tree a2The output input value and the decision tree a2The output results with the same parameters; then selecting the practical authority of the service B as an input value, and inputting the input value into the decision tree B1Obtaining the decision tree b1The output input value and the decision tree b1Output results with different parameters; inputting input values into the decision tree b2Obtaining the decision tree b2The output input value and the decision tree b2If the parameters of the service A are the same, the input results of the service A in all the decision trees are the input values and the targetsAnd marking the output result with the same parameters of the decision tree, and then confirming that the service A is the usable service of the user.
S5, generating a service list according to the policy information and the usable service, and pushing the service list to the user.
In the embodiment of the invention, as the number of the available services of the user can be multiple, in order to enable the user to know which services can be used by the user more clearly, the service list can be generated according to the policy information and the available services, and the user is further expected to recommend the service list.
For example, the policy information and the available services are filled in a pre-generated blank data table to generate the service list, and the service list is displayed to the user.
The policy information of the user can be analyzed to extract the keywords contained in the policy information of the user, the decision tree model is constructed through the extracted keywords, the constructed decision tree model is used for carrying out accurate conditional screening on a plurality of preset services, and the screened services are pushed to the user, so that the accurate screening of the services is realized, and the accuracy of service distribution of the user is improved. Therefore, the service distribution method based on data analysis provided by the invention can solve the problem of low precision in product recommendation.
Fig. 4 is a functional block diagram of a service distribution apparatus based on data analysis according to an embodiment of the present invention.
The service distribution apparatus 100 based on data analysis according to the present invention may be installed in an electronic device. According to the implemented functions, the data analysis-based service distribution apparatus 100 may include a text conversion module 101, a feature word extraction module 102, a decision tree construction module 103, a service screening module 104, and a service pushing module 105. The module of the present invention, which may also be referred to as a unit, refers to a series of computer program segments that can be executed by a processor of an electronic device and that can perform a fixed function, and that are stored in a memory of the electronic device.
In the present embodiment, the functions regarding the respective modules/units are as follows:
the text conversion module 101 is configured to obtain policy information of a user, perform text conversion on the policy information, and obtain a policy text;
the feature word extraction module 102 is configured to perform word segmentation on the policy document, and perform feature word extraction on text word segmentation obtained by word segmentation to obtain a user feature word;
the decision tree construction module 103 is configured to construct a decision tree model by using the user feature word as a decision condition;
the service screening module 104 is configured to obtain a usage right of a preset service, and screen a service available to the user from the preset service according to the usage right by using the decision tree model;
the service pushing module 105 is configured to generate a service list according to the policy information and the available service, and push the service list to the user.
In detail, when the modules in the service distribution apparatus 100 based on data analysis according to the embodiment of the present invention are used, the same technical means as the service distribution method based on data analysis described in fig. 1 to 3 are adopted, and the same technical effects can be produced, which is not described herein again.
Fig. 5 is a schematic structural diagram of an electronic device implementing a service distribution method based on data analysis according to an embodiment of the present invention.
The electronic device 1 may comprise a processor 10, a memory 11, a communication bus 12 and a communication interface 13, and may further comprise a computer program, such as a service distribution program based on data analysis, stored in the memory 11 and executable on the processor 10.
In some embodiments, the processor 10 may be composed of an integrated circuit, for example, a single packaged integrated circuit, or may be composed of a plurality of integrated circuits packaged with the same function or different functions, and includes one or more Central Processing Units (CPUs), a microprocessor, a digital Processing chip, a graphics processor, a combination of various control chips, and the like. The processor 10 is a Control Unit (Control Unit) of the electronic device, connects various components of the electronic device by using various interfaces and lines, and executes various functions and processes data of the electronic device by running or executing programs or modules (for example, executing a service distribution program based on data analysis, and the like) stored in the memory 11 and calling data stored in the memory 11.
The memory 11 includes at least one type of readable storage medium including 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 11 may in some embodiments be an internal storage unit of the electronic device, for example a removable hard disk of the electronic device. The memory 11 may also be an external storage device of the electronic device in other embodiments, such as a plug-in mobile hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like, which are provided on the electronic device. Further, the memory 11 may also include both an internal storage unit and an external storage device of the electronic device. The memory 11 may be used not only to store application software installed in the electronic device and various types of data, such as codes of a service distribution program based on data analysis, etc., but also to temporarily store data that has been output or is to be output.
The communication bus 12 may be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus. The bus may be divided into an address bus, a data bus, a control bus, etc. The bus is arranged to enable connection communication between the memory 11 and at least one processor 10 or the like.
The communication interface 13 is used for communication between the electronic device and other devices, and includes a network interface and a user interface. Optionally, the network interface may include a wired interface and/or a wireless interface (e.g., WI-FI interface, bluetooth interface, etc.), which are typically used to establish a communication connection between the electronic device and other electronic devices. The user interface 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 electronic device and for displaying a visualized user interface.
Fig. 5 only shows an electronic device with components, and it will be understood by a person skilled in the art that the structure shown in fig. 5 does not constitute a limitation of the electronic device 1, and may comprise fewer or more components than shown, or a combination of certain components, or a different arrangement of components.
For example, although not shown, the electronic device 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 10 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 electronic device may further include various sensors, a bluetooth module, a Wi-Fi module, and the like, which are not described herein again.
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.
The data analysis based service distribution program stored in the memory 11 of the electronic device 1 is a combination of instructions, which when executed in the processor 10, can implement:
acquiring policy information of a user, and performing text conversion on the policy information to obtain a policy text;
performing word segmentation on the policy text, and performing feature word extraction on text word segmentation obtained by word segmentation to obtain user feature words;
constructing a decision tree model by taking the user feature words as decision conditions;
acquiring the use permission of preset services, and screening the usable services of the user from the preset services by using the decision tree model according to the use permission;
and generating a service list according to the policy information and the usable service, and pushing the service list to the user.
Specifically, the specific implementation method of the instruction by the processor 10 may refer to the description of the relevant steps in the embodiment corresponding to the drawings, which is not described herein again.
Further, the integrated modules/units of the electronic device 1, if implemented in the form of software functional units and sold or used as separate products, may be stored in a computer readable storage medium. The computer readable storage medium may be volatile or non-volatile. For example, the computer-readable medium may include: any entity or device capable of carrying said computer program code, recording medium, U-disk, removable hard disk, magnetic disk, optical disk, computer Memory, Read-Only Memory (ROM).
The present invention also provides a computer-readable storage medium, storing a computer program which, when executed by a processor of an electronic device, may implement:
acquiring policy information of a user, and performing text conversion on the policy information to obtain a policy text;
performing word segmentation on the policy text, and performing feature word extraction on text word segmentation obtained by word segmentation to obtain user feature words;
constructing a decision tree model by taking the user feature words as decision conditions;
acquiring the use permission of preset services, and screening the usable services of the user from the preset services by using the decision tree model according to the use permission;
and generating a service list according to the policy information and the usable service, and pushing the service list to the user.
In the embodiments provided in the present invention, it should be understood that the disclosed apparatus, device and method can 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 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.
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 embodiment of the application can acquire and process related data based on an artificial intelligence technology. 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.
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 system claims may also be implemented by one unit or means in 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 for illustrating the technical solutions of the present invention and not for limiting, 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 may be made on 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 method for service distribution based on data analysis, the method comprising:
acquiring policy information of a user, and performing text conversion on the policy information to obtain a policy text;
performing word segmentation on the policy text, and performing feature word extraction on text word segmentation obtained by word segmentation to obtain user feature words;
constructing a decision tree model by taking the user feature words as decision conditions;
acquiring the use permission of preset services, and screening the usable services of the user from the preset services by using the decision tree model according to the use permission;
and generating a service list according to the policy information and the usable service, and pushing the service list to the user.
2. The data analysis-based service distribution method of claim 1, wherein the text-converting the policy information to obtain a policy text comprises:
extracting a data type field of each datum in the policy information;
dividing data in the policy information according to the data type field to obtain text data, image data and audio data;
carrying out image recognition on the image data in the policy information to obtain an image text corresponding to the image data;
performing voice recognition on audio data in the policy information to obtain an audio text corresponding to the audio data;
and aggregating the text data, the image text and the audio text into a policy text.
3. The data analysis-based service distribution method according to claim 2, wherein the dividing data in the policy information according to the data type field to obtain text data, image data and audio data comprises:
selecting one data from the policy information one by one as target data;
carrying out vector conversion on the data type field of the target data to obtain a data type vector;
respectively calculating distance values between the data type vector and a plurality of preset data types, wherein the data types comprise a text type, an image type and an audio type;
and determining the data type with the minimum distance value as the type of the target data, and dividing the data in the policy information according to the type to obtain text data, image data and audio data.
4. The service delivery method based on data analysis according to claim 1, wherein the segmenting the policy text and extracting the feature words of the segmented text to obtain the user feature words comprises:
dividing the policy text into a plurality of text clauses according to preset clause symbols;
searching each text clause in the plurality of text clauses in a preset dictionary according to different data lengths, and taking the searched words as text clauses of the policy text;
counting the occurrence frequency of each text participle and the position information of each text participle in the policy text;
and calculating the criticality of each text word according to the occurrence frequency and the position information, and collecting the text words with the criticality larger than a preset threshold value as user characteristic words.
5. The data analysis-based service distribution method of claim 4, wherein the calculating the criticality of each of the text participles according to the frequency of occurrence and the location information comprises:
calculating the criticality of each text word segmentation according to the occurrence frequency and the position information by using a characteristic value algorithm as follows:
Ki＝α*Ai+β*Bi
wherein, KiThe key degree of the ith word in the text word segmentation, A is the frequency of occurrence of the ith word in the text word segmentation, BiAnd alpha and beta are preset weight coefficients for the position information of the ith word in the text word.
6. The service distribution method based on data analysis according to claim 1, wherein the building of the decision tree model using the user feature words as decision conditions comprises:
one feature word is selected from the user feature words one by one to serve as a target feature word;
compiling the target characteristic words into characteristic parameters by using a preset compiler, and assigning values to a preset decision function by using the characteristic parameters;
generating a decision tree by using the assigned decision function as a decision condition;
and collecting the decision trees corresponding to all the characteristic words in the user characteristic words to obtain a decision tree model.
7. The service distribution method based on data analysis as claimed in any one of claims 1 to 6, wherein the using the decision tree to screen out the service available to the user from the preset services according to the usage right comprises:
selecting one of the use authorities of the preset service one by one as an input value;
selecting one decision tree from the decision tree models one by one as a target decision tree, and inputting the input value into the target decision tree to obtain an output result of the target decision tree, wherein the output result comprises that the input value is the same as the parameters of the target decision tree or the input value is different from the parameters of the target decision tree;
and collecting the preset service corresponding to the use authority with the input value being the same as the parameter of the target decision tree as the usable service according to the output results of all the decision trees.
8. A service distribution apparatus based on data analysis, the apparatus comprising:
the text conversion module is used for acquiring policy information of a user and performing text conversion on the policy information to obtain a policy text;
the characteristic word extraction module is used for segmenting words of the policy text and extracting the characteristic words of the segmented text to obtain user characteristic words;
the decision tree construction module is used for constructing a decision tree model by taking the user characteristic words as decision conditions;
the service screening module is used for acquiring the use permission of the preset service and screening the usable service of the user from the preset service by utilizing the decision tree model according to the use permission;
and the service pushing module is used for generating a service list according to the policy information and the usable service and pushing the service list to the user.
9. An electronic device, characterized in that the electronic device comprises:
at least one processor; and the number of the first and second groups,
a memory communicatively coupled to the at least one processor; wherein,
the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the data analysis based service distribution method of any one of claims 1 to 7.
10. A computer-readable storage medium storing a computer program, wherein the computer program, when executed by a processor, implements the data analysis-based service distribution method according to any one of claims 1 to 7.
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| CN202111014113.3A CN113706322B (en) | 2021-08-31 | Service distribution method, device, equipment and storage medium based on data analysis | |
| PCT/CN2022/087811 WO2023029507A1 (en) | 2021-08-31 | 2022-04-20 | Data analysis-based service distribution method and apparatus, device, and storage medium |
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| CN202111014113.3A CN113706322B (en) | 2021-08-31 | Service distribution method, device, equipment and storage medium based on data analysis |
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114492663A (en) * | 2022-02-15 | 2022-05-13 | 平安国际智慧城市科技股份有限公司 | Intelligent event distribution method, device, equipment and storage medium |
| CN115100670A (en) * | 2022-07-21 | 2022-09-23 | 中国平安人寿保险股份有限公司 | Policy information processing method and system, electronic device, and storage medium |
| CN115168848A (en) * | 2022-09-08 | 2022-10-11 | 南京鼎山信息科技有限公司 | Interception feedback processing method based on big data analysis interception |
| WO2023029507A1 (en) * | 2021-08-31 | 2023-03-09 | 康键信息技术（深圳）有限公司 | Data analysis-based service distribution method and apparatus, device, and storage medium |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111275524A (en) * | 2020-01-19 | 2020-06-12 | 北京众信易保科技有限公司 | Insurance product recommendation method and system |
| CN111444944A (en) * | 2020-03-16 | 2020-07-24 | 中国平安人寿保险股份有限公司 | Information screening method, device, equipment and storage medium based on decision tree |
| CN113268615A (en) * | 2021-05-25 | 2021-08-17 | 平安银行股份有限公司 | Resource label generation method and device, electronic equipment and storage medium |
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111275524A (en) * | 2020-01-19 | 2020-06-12 | 北京众信易保科技有限公司 | Insurance product recommendation method and system |
| CN111444944A (en) * | 2020-03-16 | 2020-07-24 | 中国平安人寿保险股份有限公司 | Information screening method, device, equipment and storage medium based on decision tree |
| CN113268615A (en) * | 2021-05-25 | 2021-08-17 | 平安银行股份有限公司 | Resource label generation method and device, electronic equipment and storage medium |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2023029507A1 (en) * | 2021-08-31 | 2023-03-09 | 康键信息技术（深圳）有限公司 | Data analysis-based service distribution method and apparatus, device, and storage medium |
| CN114492663A (en) * | 2022-02-15 | 2022-05-13 | 平安国际智慧城市科技股份有限公司 | Intelligent event distribution method, device, equipment and storage medium |
| CN114492663B (en) * | 2022-02-15 | 2024-06-07 | 平安国际智慧城市科技股份有限公司 | Event intelligent allocation method, device, equipment and storage medium |
| CN115100670A (en) * | 2022-07-21 | 2022-09-23 | 中国平安人寿保险股份有限公司 | Policy information processing method and system, electronic device, and storage medium |
| CN115168848A (en) * | 2022-09-08 | 2022-10-11 | 南京鼎山信息科技有限公司 | Interception feedback processing method based on big data analysis interception |
| CN115168848B (en) * | 2022-09-08 | 2022-12-16 | 南京鼎山信息科技有限公司 | Interception feedback processing method based on big data analysis interception |
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|---|---|
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