WO2023029507A1

WO2023029507A1 - Data analysis-based service distribution method and apparatus, device, and storage medium

Info

Publication number: WO2023029507A1
Application number: PCT/CN2022/087811
Authority: WO
Inventors: 刘福婷
Original assignee: 康键信息技术（深圳）有限公司
Priority date: 2021-08-31
Filing date: 2022-04-20
Publication date: 2023-03-09
Also published as: CN113706322A

Abstract

The present application relates to the technical field of artificial intelligence and digital medicine, and discloses a data analysis-based service distribution method, comprising: acquiring insurance policy information of a user, and converting the insurance policy information into an insurance policy text; performing word segmentation on the insurance policy text, and performing feature word extraction on words of the text obtained by the word segmentation to obtain a user feature word; constructing a decision tree model using the user feature word as a decision condition; acquiring a usage permission for a preset service, and screening available service for the user from preset services according to the usage permission by using the decision tree model; generating a service list according to the insurance policy information and the available service, and pushing the service list to the user, the insurance policy information potentially being a medical insurance policy. In addition, the present application also relates to blockchain technology, and insurance policy information can be stored on a node of a blockchain. The present application also provides a data analysis-based service distribution apparatus, an electronic device, and a storage medium. The present application can improve accuracy of medical service recommendation.

Description

Service distribution method, device, equipment and storage medium based on data analysis

This application claims the priority of the Chinese patent application with the application number 202111014113.3 submitted to the China Patent Office on August 31, 2021, and the invention title is "service distribution method, device, equipment and storage medium based on data analysis", the entire content of which Incorporated in this application by reference.

technical field

The present application relates to the technical field of artificial intelligence, and in particular to a data analysis-based service distribution method, device, electronic equipment, and computer-readable storage medium.

Background technique

As people's awareness of health management continues to increase, the demand for medical services continues to increase, and the vision of medical insurance is to provide services to users by providing medical services instead of claims. The inventor realized that due to the wide variety of current medical insurance, each The medical services owned by the insurance are not the same, which leads to the situation that when the service is issued to the user, the user may not have the right to enjoy the corresponding service, or even if the user once had the right to enjoy the service, the service has expired now , resulting in low accuracy when recommending medical services to users based on policy data.

Contents of the invention

A service distribution method based on data analysis provided by this application includes:

Obtain the policy information of the user, perform text conversion on the policy information, and obtain the policy text;

Carry out word segmentation to described policy text, and carry out feature word extraction to the text word segmentation that word segmentation obtains, obtain user feature word;

Using the user characteristic words as a decision-making condition to construct a decision tree model;

Obtaining the use authority of the preset service, and using the decision tree model to filter out the service available to the user from the preset service according to the use authority;

A service list is generated according to the policy information and the available services, and the service list is pushed to the user.

The present application also provides a service distribution device based on data analysis, the device includes:

The text conversion module is used to obtain the policy information of the user, and perform text conversion on the policy information to obtain the policy text;

The characteristic word extraction module is used for carrying out word segmentation to described policy text, and carries out characteristic word extraction to the text word segmentation that word segmentation obtains, obtains user characteristic word;

Decision tree construction module, used for constructing a decision tree model using the user feature words as a decision condition;

A service screening module, configured to obtain the use authority of preset services, and use the decision tree model to screen out the services available to the user from the preset services according to the use authority;

A service push module, configured to generate a service list according to the policy information and the available services, and push the service list to the user.

The present application also provides an electronic device, the electronic device comprising:

at least one processor; and,

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 is executed by the at least one processor to enable the at least one processor to perform data analysis based services as described below Distribution method:

The present application also provides a computer-readable storage medium, at least one computer program is stored in the computer-readable storage medium, and the at least one computer program is executed by a processor in an electronic device to implement data-based analysis as described below The service distribution method for:

Description of drawings

FIG. 1 is a schematic flowchart of a service distribution method based on data analysis provided by an embodiment of the present application;

FIG. 2 is a schematic flow diagram of extracting user feature words provided by an embodiment of the present application;

Fig. 3 is a schematic flow chart of constructing a decision tree model provided by an embodiment of the present application;

FIG. 4 is a functional block diagram of a service distribution device based on data analysis provided by an embodiment of the present application;

FIG. 5 is a schematic structural diagram of an electronic device implementing the data analysis-based service distribution method provided by an embodiment of the present application.

The realization, functional features and advantages of the present application will be further described in conjunction with the embodiments and with reference to the accompanying drawings.

Detailed ways

It should be understood that the specific embodiments described here are only used to explain the present application, and are not intended to limit the present application.

An embodiment of the present application provides a service distribution method based on data analysis. The execution subject 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 end and a terminal that can be configured to execute the method provided by the embodiment of the present application. In other words, the service distribution method based on data analysis can be executed by software or hardware installed on the terminal device or server device, and the software can be a block chain 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 can be an independent server, or it can provide cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, content delivery network (Content Delivery) Network, CDN), and cloud servers for basic cloud computing services such as big data and artificial intelligence platforms.

Referring to FIG. 1 , it is a schematic flowchart of a service distribution method based on data analysis provided by an embodiment of the present application. In this embodiment, the service distribution method based on data analysis includes:

S1. Obtain the policy information of the user, perform text conversion on the policy information, and obtain the policy text.

In this embodiment of the present application, the policy information includes data related to the insurance order purchased by the user, for example, data such as the user's age, gender, policy type, and policy amount.

In detail, computer statements (such as Java statements, python statements, etc.) with data capture functions can be used to capture pre-stored policy information that can be obtained with user authorization from a pre-built storage area. The storage area includes but Not limited to databases, blockchains, web caches.

In one of the actual application scenarios of this application, the obtained policy information may include data in various forms such as text, image, audio, etc. In order to improve the subsequent processing efficiency and accuracy of the policy information, the obtained policy information can be Perform text conversion to obtain policy text.

In the embodiment of the present application, the text conversion of the policy information is performed to obtain the policy text, including:

Extracting the data type field of each data in the policy information;

Divide the data in the policy information according to the data type field to obtain text data, image data and audio data;

performing image recognition on the image data in the policy information to obtain image text corresponding to the image data;

Perform speech recognition on the audio data in the policy information to obtain the audio text corresponding to the audio data;

The text data, the image text and the audio text are assembled into a policy text.

In detail, the data type field is a field used to mark the data type of each data; a pre-built regular expression with a data type field extraction function can be used to extract each A data type field of data, the regular expression can be used to realize the function of extracting data with a fixed format in the data.

In the embodiment of the present application, the data in the policy information is divided according to the data type field to obtain text data, image data and audio data, including:

selecting one of the data as the target data one by one from the policy information;

Perform vector conversion on the data type field of the target data to obtain a data type vector;

Calculating distance values between the data type vector and multiple preset data types respectively, the data types including text type, image type and audio type;

Determining the data type with the smallest 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 can be vector-converted by using an artificial intelligence model with a vector conversion function. Cove model), Bert model, etc.

Specifically, the distance values between the data type vector and a plurality of preset data types can be respectively calculated by an algorithm with a distance value calculation function such as a cosine distance algorithm and a Euclidean distance algorithm, and the data type with the smallest distance value is determined to be The type of the target data.

For example, 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 a graph type.

In the embodiment of the present application, OCR (Optical Character Recognition, optical character recognition) technology can be used to perform image recognition on the image data in the policy information, and the image text corresponding to the image data can be obtained; ASR (Automatic Speech Recognition, automatic speech recognition) technology can be used Carrying out voice recognition on the audio data in the policy information to obtain the audio text corresponding to the audio data, and collecting the text data, the image text and the audio text to obtain the policy text.

S2. Perform word segmentation on the policy text, and extract feature words from the word segmentation obtained from the text to obtain user feature words.

In one of the actual application scenarios of this application, since the policy text contains a large amount of text information, if the policy text is directly analyzed, it will occupy a large amount of computing resources, resulting in low analysis efficiency, and a large amount of data is mixed, which will cause The analysis results are imprecise. Therefore, word segmentation can be performed on the policy text, and feature words can be extracted from the result of the word segmentation, so as to reduce the amount of data in the subsequent analysis of the policy text and improve the efficiency and accuracy of the analysis. Spend.

In the embodiment of the present application, as shown in FIG. 2, the described policy text is segmented, and the text segmentation obtained by word segmentation is subjected to feature word extraction to obtain user feature words, including:

S21. Divide the policy text into multiple text clauses according to preset clause symbols;

S22. Retrieve each of the plurality of text clauses in a preset dictionary according to different data lengths, and use the retrieved words as text word segmentation of the policy text;

S23. Count the frequency of occurrence of each text participle, and the position information of each text participle in the policy text;

S24. Calculate the key degree of each text segment word according to the frequency of occurrence and the position information, and set the text segment vocabulary whose key degree is greater than a preset threshold as user characteristic words.

In detail, since the policy text contains a large amount of text data, in order to improve the efficiency of word segmentation of the policy text, a large amount of text data can be segmented using preset sentence symbols, so as to divide the policy text into multiple A text sentence, thereby reducing the amount of data for each word segmentation and improving word segmentation efficiency. The sentence symbols include but are not limited to ",", ".", "...".

Specifically, the dictionary is a pre-acquired dictionary containing multiple word segmentations, each text clause can be retrieved in the dictionary according to different data lengths, and the retrieved word segmentation can be used as the text segmentation of the policy text .

In the embodiment of the present application, the frequency of occurrence is the number of times each text participle appears in all text participles of the policy text; the position information refers to the sequence of positions of each text participle in the policy text, For example, the text participle in the policy text can be numbered according to the order of their positions in the policy text, and the number corresponding to each text participle is the position information of the text participle.

In the embodiment of the present application, the calculation of the key degree of each of the text word segmentation according to the frequency of occurrence and the position information includes:

The following eigenvalue algorithm is used to calculate the key degree of each of the text word segmentation according to the frequency of occurrence and the position information:

K _i =α*A _i +β*B _i

Wherein, K _i is the key degree of the ith participle in the described text participle, A is the frequency of occurrence of the ith participle in the described text participle, Bi _is the positional information of the ith participle in the described text participle, α and β are preset weight coefficients.

In the embodiment of the present application, the word segmentation of the text whose key degree is greater than the preset threshold may be selected, and the selected text segmentation vocabulary set is the user characteristic word of the user.

S3. Construct a decision tree model by using the user characteristic words as a decision condition.

In one of the actual application scenarios of this application, since there are a large number of services, but the user does not have the right to use each service, the user characteristic words can be used as decision conditions to construct a decision tree for subsequent use The constructed decision tree screens out services that can be obtained by the user from a large number of services.

In the embodiment of the present application, as shown in FIG. 3, the construction of a decision tree model using the user characteristic words as a decision condition includes:

S31. Select one of the characteristic words as the target characteristic word one by one from the user characteristic words;

S32. Using a preset compiler to compile the target feature word into a feature parameter, and use the feature parameter to assign a value to a preset decision function;

S33. Using the assigned decision function as a decision condition to generate a decision tree;

S34. Gather the decision trees corresponding to all the characteristic words in the user characteristic words to obtain a decision tree model.

Exemplarily, the decision function may be:

Wherein, f(x) is an output value of the decision function, x is a parameter of the decision function, and g(y) is an input value of the decision function.

In detail, one of the characteristic words can be selected as the target characteristic word one by one from the user characteristic words, and the target characteristic word is compiled into a characteristic parameter by using a preset compiler, and the decision function of the decision function by the characteristic parameter is used Parameter x is assigned, wherein the compiler includes but not limited to Visual Studio compiler, Dev C++ compiler.

Specifically, the following decision tree can be generated by using the assigned decision function as the decision condition:

When the input value g(y) of the decision tree is the same as the parameter x of the decision tree, the output value f(x)=α of the decision tree;

When the input to g(y) of the decision tree is different from the parameter x of the decision tree, the output value of the decision tree is f(x)=β.

S4. Obtain the usage permission of the preset service, and use the decision tree model to screen out the available services for the user from the preset services according to the usage permission.

In the embodiment of the present application, the usage rights include constraints required by the user when obtaining each preset service, for example, time limit, age limit, and the like.

In detail, the step of obtaining the use authority of the preset service is the same as the step of obtaining the user's policy information in S1, so it will not be repeated here.

In the embodiment of the present application, the decision tree can be used to screen a variety of preset services to determine whether the use authority of each preset service in the multiple preset services conforms to the characteristics of the user, and then screen out The user meets the conditions and can use the service.

In the embodiment of the present application, the use of the decision tree to screen out the user's usable services from the preset services according to the usage authority includes:

selecting one of the usage permissions from the preset service usage permissions one by one as an input value;

Select one of the decision trees as the target decision tree one by one from the decision tree model, input the input value into the target decision tree, and obtain the output result of the target decision tree, wherein the output result includes the The input value is the same as the parameter of the target decision tree, or the input value is different from the parameter of the target decision tree;

The output result of collecting all the decision trees is that the preset service corresponding to the usage authority whose input value is the same as the parameter of the target decision tree is an available service.

For example, the decision tree model includes decision tree a ₁ and decision tree a ₂ , the multiple preset services include service A and service B, select decision tree a ₁ as the target decision tree, and select the use authority of service A As an input value; input the input value into the decision tree a ₁ to obtain the output result that the input value output by the decision tree a ₁ is the same as the parameter of the decision tree a ₁ ; input the input value into the Decision tree a ₂ , obtain the output result that the input value output by the decision tree a ₂ is the same as the parameter of the decision tree a ₂ ; then select the practical authority of service B as the input value, and input the input value into the Decision tree b ₁ , obtaining the output result that the input value output by the decision tree b ₁ is different from the parameters of the decision tree b ₁ ; inputting the input value into the decision tree b ₂ , obtaining the decision tree The input value output by _b2 is the same as the output result of the parameter of the decision tree _b2 , then only the input results of service A in all decision trees are the same as the input value and the parameter of the target decision tree If the result is output, it is confirmed that service A is an available service for the user.

S5. Generate a service list according to the policy information and the available services, and push the service list to the user.

In this embodiment of the application, since there may be multiple services available to the user, in order for the user to know more clearly which services they can use, a service list can be generated based on the policy information and the available services, and then The service list is recommended to the user.

For example, the policy information and the available services are filled into a pre-generated blank data table to generate the service list, and the service list is displayed to the user.

The embodiment of the present application can analyze the user's policy information to extract keywords contained in the user's policy information, and then construct a decision tree model through the extracted keywords, and use the constructed decision tree model to perform multiple preset services Accurate condition screening, and then push the selected services to users, realize the precise screening of services, and improve the accuracy of service delivery to users. Therefore, the service distribution method based on data analysis proposed in this application can solve the problem of low accuracy in product recommendation.

As shown in FIG. 4 , it is a functional block diagram of a service distribution device based on data analysis provided by an embodiment of the present application.

The data analysis-based service distribution apparatus 100 described in this application can be installed in an electronic device. According to the realized functions, the data analysis-based service distribution device 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 push module 105 . The module described in this application can also be called a unit, which refers to a series of computer program segments that can be executed by the processor of the electronic device and can complete fixed functions, and are stored in the memory of the electronic device.

In this embodiment, the functions of each module/unit are as follows:

The text conversion module 101 is used to obtain the policy information of the user, and perform text conversion on the policy information to obtain the policy text;

The feature word extraction module 102 is configured to perform word segmentation on the policy text, and perform feature word extraction on the text word segmentation obtained by word segmentation to obtain user feature words;

The decision tree construction module 103 is used to construct a decision tree model using the user characteristic words as a decision condition;

The service screening module 104 is configured to obtain the use authority of preset services, and use the decision tree model to screen out the services available to the user from the preset services according to the use authority;

The service pushing module 105 is configured to generate a service list according to the policy information and the available services, and push the service list to the user.

In detail, each module described in the data analysis-based service distribution device 100 in the embodiment of the present application uses the same technical means as the data analysis-based service distribution method described in Figures 1 to 3 above. , and can produce the same technical effect, which will not be repeated here.

As shown in FIG. 5 , it is a schematic structural diagram of an electronic device implementing a service distribution method based on data analysis provided by an embodiment of the present application.

The electronic device 1 may include a processor 10, a memory 11, a communication bus 12, and a communication interface 13, and may also include a computer program stored in the memory 11 and operable on the processor 10, such as based on data analysis service dispatcher.

Wherein, the processor 10 may be composed of integrated circuits in some embodiments, for example, may be composed of a single packaged integrated circuit, or may be composed of multiple integrated circuits with the same function or different functions packaged, including one or A combination of multiple central processing units (Central Processing unit, CPU), microprocessors, digital processing chips, graphics processors and various control chips, etc. The processor 10 is the control core (Control Unit) of the electronic device, and uses various interfaces and lines to connect the various components of the entire electronic device, and runs or executes programs or modules stored in the memory 11 (such as executing service distribution program based on data analysis, etc.), and call the data stored in the memory 11 to execute various functions of the electronic device and process data.

The memory 11 includes at least one type of readable storage medium, and the readable storage medium includes flash memory, mobile hard disk, multimedia card, card type memory (for example: SD or DX memory, etc.), magnetic memory, magnetic disk, optical disk, etc. , the computer-readable storage medium may be non-volatile or volatile. The storage 11 may be an internal storage unit of the electronic device in some embodiments, such as a mobile hard disk of the electronic device. The memory 11 can also be an external storage device of an electronic device in other embodiments, such as a plug-in mobile hard disk equipped on an electronic device, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD ) card, flash card (Flash Card), etc. Further, the storage 11 may also include both an internal storage unit of the electronic device and an external storage device. The memory 11 can not only be used to store application software and various data installed in electronic equipment, such as codes of service distribution programs based on data analysis, but also can be used to temporarily store data that has been output or will be output.

The communication bus 12 may be a peripheral component interconnect (PCI for short) bus or an extended industry standard architecture (EISA for short) bus or the like. The bus can be divided into address bus, data bus, control bus and so on. The bus is configured to realize connection and communication between the memory 11 and at least one processor 10 and the like.

The communication interface 13 is used for communication between the electronic device and other devices, including a network interface and a user interface. 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 to establish a communication connection between the electronic device and other electronic devices. The user interface may be a display (Display) or an input unit (such as a keyboard (Keyboard)). Optionally, the user interface may also be a standard wired interface or a wireless interface. Optionally, 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, organic light-emitting diode) touch device, and the like. Wherein, the display may also be properly referred to as a display screen or a display unit, and is used for displaying information processed in the electronic device and for displaying a visualized user interface.

FIG. 5 only shows an electronic device with components. Those skilled in the art can understand that the structure shown in FIG. 5 does not constitute a limitation to the electronic device 1, and may include fewer or more components, or combinations of certain components, or different arrangements of components.

For example, although not shown, the electronic device may also include a power supply (such as a battery) for supplying power to various components. Preferably, the power supply may be logically connected to the at least one processor 10 through a power management device, so that Realize functions such as charge management, discharge management, and power consumption management. The power supply may also include one or more DC or AC power supplies, recharging devices, power failure detection circuits, power converters or inverters, power status indicators and other arbitrary components. The electronic device may also include various sensors, a Bluetooth module, a Wi-Fi module, etc., which will not be repeated here.

It should be understood that the embodiments are only for illustration, and are not limited by the structure in terms of the scope of the patent application.

The service distribution program based on data analysis stored in the memory 11 in the electronic device 1 is a combination of multiple instructions, and when running in the processor 10, it can realize:

Specifically, for the specific implementation method of the above instructions by the processor 10, reference may be made to the description of relevant steps in the corresponding embodiments in the drawings, and details are not repeated here.

Further, if the integrated modules/units of the electronic device 1 are realized in the form of software function units and sold or used as independent products, they can 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 the computer program code, a recording medium, a U disk, a removable hard disk, a magnetic disk, an optical disk, a computer memory, a read-only memory (ROM, Read-Only Memory).

The present application also provides a computer-readable storage medium, the readable storage medium stores a computer program, and when the computer program is executed by a processor of an electronic device, it can realize:

In the several embodiments provided in this application, it should be understood that the disclosed devices, devices and methods can be implemented in other ways. For example, the device embodiments described above are only illustrative. For example, the division of the modules is only a logical function division, and there may be other division methods in actual implementation.

The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical units, that is, they may be located in one place, or may be distributed to multiple network units. Part or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

In addition, each functional module in each embodiment of the present application may be integrated into one processing unit, each unit may exist separately physically, or two or more units may be integrated into one unit. The above-mentioned integrated units can be implemented in the form of hardware, or in the form of hardware plus software function modules.

It will be apparent to those skilled in the art that the present application is not limited to the details of the exemplary embodiments described above, but that the present application can be implemented in other specific forms without departing from the spirit or essential characteristics of the present application.

Therefore, the embodiments should be regarded as exemplary and not restrictive in all points of view, and the scope of the application is defined by the appended claims rather than the foregoing description, and it is intended that the scope of the present application be defined by the appended claims rather than by the foregoing description. All changes within the meaning and range of equivalents of the elements are embraced in this application. Any reference sign in a claim should not be construed as limiting the claim concerned.

The blockchain referred to in this application is a new application mode of computer technologies such as distributed data storage, point-to-point transmission, consensus mechanism, and encryption algorithm. Blockchain (Blockchain), essentially a decentralized database, is a series of data blocks associated with each other using cryptographic methods. Each data block contains a batch of network transaction information, which is used to verify its Validity of information (anti-counterfeiting) and generation of the next block. The blockchain can include the underlying platform of the blockchain, the platform product service layer, and the application service layer.

The embodiments of the present application may acquire and process relevant data based on artificial intelligence technology. Among them, artificial intelligence (AI) is a theory, method, technology and application system that uses digital computers or machines controlled by digital computers to simulate, extend and expand human intelligence, perceive the environment, acquire knowledge and use knowledge to obtain the best results. .

In addition, 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 devices stated in the system claims may also be realized by one unit or device through software or hardware. The terms first, second, etc. are used to denote names and do not imply any particular order.

Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application without limitation. Although the present application has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present application can be Make modifications or equivalent replacements without departing from the spirit and scope of the technical solutions of the present application.

Claims

A service distribution method based on data analysis, wherein the method includes:

Obtain the policy information of the user, perform text conversion on the policy information, and obtain the policy text;

Carry out word segmentation to described policy text, and carry out feature word extraction to the text word segmentation that word segmentation obtains, obtain user feature word;

Using the user characteristic words as a decision-making condition to construct a decision tree model;

Obtaining the use authority of the preset service, and using the decision tree model to filter out the service available to the user from the preset service according to the use authority;

A service list is generated according to the policy information and the available services, and the service list is pushed to the user.
The service distribution method based on data analysis according to claim 1, wherein said performing text conversion on said policy information to obtain policy text comprises:

Extracting the data type field of each data in the policy information;

Divide the data in the policy information according to the data type field to obtain text data, image data and audio data;

performing image recognition on the image data in the policy information to obtain image text corresponding to the image data;

Perform speech recognition on the audio data in the policy information to obtain the audio text corresponding to the audio data;

The text data, the image text and the audio text are assembled into a policy text.
The service distribution method based on data analysis according to claim 2, wherein the data in the policy information is divided according to the data type field to obtain text data, image data and audio data, including:

selecting one of the data as the target data one by one from the policy information;

Perform vector conversion on the data type field of the target data to obtain a data type vector;

Calculating distance values between the data type vector and multiple preset data types respectively, the data types including text type, image type and audio type;

Determining the data type with the smallest 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.
The service distribution method based on data analysis according to claim 1, wherein said performing word segmentation on said policy text, and extracting feature words from the text word segmentation obtained by word segmentation, to obtain user feature words, comprising:

dividing the policy text into multiple text clauses according to preset clause symbols;

Retrieving each of the plurality of text clauses in a preset dictionary according to different data lengths, and using the retrieved words as text segmentation of the policy text;

Count the occurrence frequency of each of the text participle, and the position information of each of the text participle in the policy text;

The key degree of each text segment word is calculated according to the occurrence frequency and the position information, and the text segment vocabulary whose key degree is greater than a preset threshold is set as a user feature word.
The service distribution method based on data analysis according to claim 4, wherein said calculating the key degree of each of said text word segmentation according to said occurrence frequency and said location information comprises:

The following eigenvalue algorithm is used to calculate the key degree of each of the text word segmentation according to the frequency of occurrence and the position information:

K i =α*A i +β*B i

Wherein, K i is the key degree of the ith participle in the described text participle, A is the frequency of occurrence of the ith participle in the described text participle, Bi is the positional information of the ith participle in the described text participle, α and β are preset weight coefficients.
The service distribution method based on data analysis according to claim 1, wherein said constructing a decision tree model using said user characteristic words as a decision condition includes:

Select one of the characteristic words as the target characteristic word one by one from the user characteristic words;

Using a preset compiler to compile the target feature word into a feature parameter, and using the feature parameter to assign a preset decision function;

Use the assigned decision function as the decision condition to generate a decision tree;

Collect the decision trees corresponding to all the characteristic words in the user characteristic words to obtain a decision tree model.
The service distribution method based on data analysis according to any one of claims 1 to 6, wherein, using the decision tree to screen out the user's usable services, including:

selecting one of the usage permissions from the preset service usage permissions one by one as an input value;

Select one of the decision trees as the target decision tree one by one from the decision tree model, input the input value into the target decision tree, and obtain the output result of the target decision tree, wherein the output result includes the The input value is the same as the parameter of the target decision tree, or the input value is different from the parameter of the target decision tree;

The output result of collecting all the decision trees is that the preset service corresponding to the usage authority whose input value is the same as the parameter of the target decision tree is an available service.
A device for distributing services based on data analysis, wherein the device includes:

The text conversion module is used to obtain the policy information of the user, and perform text conversion on the policy information to obtain the policy text;

The characteristic word extraction module is used for carrying out word segmentation to described policy text, and carries out characteristic word extraction to the text word segmentation that word segmentation obtains, obtains user characteristic word;

Decision tree construction module, used for constructing a decision tree model using the user feature words as a decision condition;

A service screening module, configured to obtain the use authority of preset services, and use the decision tree model to screen out the services available to the user from the preset services according to the use authority;

A service push module, configured to generate a service list according to the policy information and the available services, and push the service list to the user.
An electronic device, wherein the electronic device includes:

at least one processor; and,

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 is executed by the at least one processor to enable the at least one processor to perform data analysis based services as described below Distribution method:

Obtain the policy information of the user, perform text conversion on the policy information, and obtain the policy text;

Carry out word segmentation to described policy text, and carry out feature word extraction to the text word segmentation that word segmentation obtains, obtain user feature word;

Using the user characteristic words as a decision-making condition to construct a decision tree model;

Obtaining the use authority of the preset service, and using the decision tree model to filter out the service available to the user from the preset service according to the use authority;

A service list is generated according to the policy information and the available services, and the service list is pushed to the user.
The electronic device according to claim 9, wherein the text conversion of the policy information to obtain the policy text includes:

Extracting the data type field of each data in the policy information;

Divide the data in the policy information according to the data type field to obtain text data, image data and audio data;

performing image recognition on the image data in the policy information to obtain image text corresponding to the image data;

Perform speech recognition on the audio data in the policy information to obtain the audio text corresponding to the audio data;

The text data, the image text and the audio text are assembled into a policy text.
The electronic device according to claim 10, wherein the data in the policy information is divided according to the data type field to obtain text data, image data and audio data, including:

selecting one of the data as the target data one by one from the policy information;

Perform vector conversion on the data type field of the target data to obtain a data type vector;

Calculating distance values between the data type vector and multiple preset data types respectively, the data types including text type, image type and audio type;

Determining the data type with the smallest 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.
The electronic device according to claim 9, wherein said word segmentation is performed on the policy text, and feature word extraction is performed on the text word segmentation obtained by word segmentation to obtain user feature words, including:

dividing the policy text into multiple text clauses according to preset clause symbols;

Retrieving each of the plurality of text clauses in a preset dictionary according to different data lengths, and using the retrieved words as text segmentation of the policy text;

Count the occurrence frequency of each of the text participle, and the position information of each of the text participle in the policy text;

The key degree of each text segment word is calculated according to the occurrence frequency and the position information, and the text segment vocabulary whose key degree is greater than a preset threshold is set as a user feature word.
The electronic device according to claim 12, wherein said calculating the key degree of each said text word segmentation according to said occurrence frequency and said location information comprises:

The following eigenvalue algorithm is used to calculate the key degree of each of the text word segmentation according to the frequency of occurrence and the position information:

K i =α*A i +β*B i

Wherein, K i is the key degree of the ith participle in the described text participle, A is the frequency of occurrence of the ith participle in the described text participle, Bi is the positional information of the ith participle in the described text participle, α and β are preset weight coefficients.
The electronic device according to claim 9, wherein said constructing a decision tree model using said user characteristic words as a decision condition comprises:

Select one of the characteristic words as the target characteristic word one by one from the user characteristic words;

Using a preset compiler to compile the target feature word into a feature parameter, and using the feature parameter to assign a preset decision function;

Use the assigned decision function as the decision condition to generate a decision tree;

Collect the decision trees corresponding to all the characteristic words in the user characteristic words to obtain a decision tree model.
A computer-readable storage medium, storing a computer program, wherein, when the computer program is executed by a processor, the following service distribution method based on data analysis is implemented:

Obtain the policy information of the user, perform text conversion on the policy information, and obtain the policy text;

Carry out word segmentation to described policy text, and carry out feature word extraction to the text word segmentation that word segmentation obtains, obtain user feature word;

Using the user characteristic words as a decision-making condition to construct a decision tree model;

Obtaining the use authority of the preset service, and using the decision tree model to filter out the service available to the user from the preset service according to the use authority;

A service list is generated according to the policy information and the available services, and the service list is pushed to the user.
The computer-readable storage medium according to claim 15, wherein the text conversion of the policy information to obtain the policy text includes:

Extracting the data type field of each data in the policy information;

According to the data type field, the data in the policy information is divided to obtain text data, image data and audio data;

performing image recognition on the image data in the policy information to obtain image text corresponding to the image data;

Perform speech recognition on the audio data in the policy information to obtain the audio text corresponding to the audio data;

The text data, the image text and the audio text are assembled into a policy text.
The computer-readable storage medium according to claim 16, wherein the data in the policy information is divided according to the data type field to obtain text data, image data and audio data, comprising:

selecting one of the data as the target data one by one from the policy information;

Perform vector conversion on the data type field of the target data to obtain a data type vector;

Calculating distance values between the data type vector and multiple preset data types respectively, the data types including text type, image type and audio type;

Determining the data type with the smallest 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.
The computer-readable storage medium according to claim 15, wherein said performing word segmentation on said policy text, and extracting feature words from the text word segmentation obtained by word segmentation, to obtain user feature words, comprising:

dividing the policy text into multiple text clauses according to preset clause symbols;

Retrieving each of the plurality of text clauses in a preset dictionary according to different data lengths, and using the retrieved words as text segmentation of the policy text;

Count the occurrence frequency of each of the text participle, and the position information of each of the text participle in the policy text;

The key degree of each text segment word is calculated according to the occurrence frequency and the position information, and the text segment vocabulary whose key degree is greater than a preset threshold is set as a user feature word.
The computer-readable storage medium according to claim 18, wherein said calculating the key degree of each said text word segmentation according to said occurrence frequency and said location information comprises:

The following eigenvalue algorithm is used to calculate the key degree of each of the text word segmentation according to the frequency of occurrence and the position information:

K i =α*A i +β*B i

Wherein, K i is the key degree of the ith participle in the described text participle, A is the frequency of occurrence of the ith participle in the described text participle, Bi is the positional information of the ith participle in the described text participle, α and β are preset weight coefficients.
The computer-readable storage medium according to claim 15, wherein said constructing a decision tree model using said user characteristic words as a decision condition comprises:

Select one of the characteristic words as the target characteristic word one by one from the user characteristic words;

Using a preset compiler to compile the target feature word into a feature parameter, and using the feature parameter to assign a preset decision function;

Use the assigned decision function as the decision condition to generate a decision tree;

Collect the decision trees corresponding to all the characteristic words in the user characteristic words to obtain a decision tree model.