CN111429085A - Contract data generation method, device, electronic device and storage medium - Google Patents

Abstract

The present invention provides a method, device, electronic device and storage medium for generating contract data. The method can perform de-abnormal preprocessing on the original contract information set when receiving the original contract information set to obtain a standard contract information set, further construct a data analysis function according to the function mapping relationship, and then analyze the data analysis function according to the data analysis function. The standard contract information set is identified, classified and stored to obtain a corresponding contract table list set, and data warehouse modeling is performed according to the corresponding contract table list set to obtain a contract configuration wide table set, and the contract configuration wide table set is used to generate For contract data, since multiple modeling processes are involved, the accuracy rate of contract data generation is improved, and the integrity of the contract data is further verified to obtain target contract data, which improves the integrity of the contract data.

Description

Contract data generation method, device, electronic device and storage medium

technical field

The present invention relates to the technical field of data processing, and in particular, to a method, device, electronic device and storage medium for generating contract data.

Background technique

The traditional method of generating contract data is usually carried out according to certain rules, combined with manual identification and analysis, which not only takes a long time, but also accumulates a lot of hard codes, and cannot quickly determine the accuracy of the plan. The abnormal link of data reverse rollout not only causes higher repair costs, but also reduces system stability.

SUMMARY OF THE INVENTION

In view of the above content, it is necessary to provide a contract data generation method, device, electronic device and storage medium, which can accurately generate contract data.

A method for generating contract data, the method comprising:

When the original contract information set is received, de-abnormality preprocessing is performed on the original contract information set to obtain a standard contract information set;

Build a data analysis function according to the function mapping relationship;

Perform identification, classification and storage processing on the standard contract information set according to the data analysis function to obtain a corresponding contract table list set;

Perform data warehouse modeling according to the corresponding contract table list set to obtain a contract configuration wide table set;

generating contract data using the contract configuration wide table set;

Perform integrity check on the contract data to obtain target contract data.

According to a preferred embodiment of the present invention, performing anomaly-removing preprocessing on the original contract information set to obtain a standard contract information set includes:

Perform an absolute value operation on the data in the original contract information set to obtain a data set;

Convert the data in the data set into percentages to obtain the standard contract information set.

According to a preferred embodiment of the present invention, the construction of the data analysis function according to the function mapping relationship includes:

The data analysis function is constructed using the following formula:

表示在所述标准合约信息集中含有配置类别属性L_j的所有特征组成的集合，W_T表示所述原始合约信息集的文件名中所包含的特征词的集合，R_T(D)表示应用数据分析方法，R_B(D)表示BOW文件内容表示方法。Among them, R represents the data analysis function, D represents the file content, L represents the category,

Represents the set of all features that contain configuration category attribute L _j in the standard contract information set, W _T represents the set of feature words contained in the file name of the original contract information set, R _T (D) represents application data Analysis method, _RB (D) represents the BOW file content representation method.

According to a preferred embodiment of the present invention, performing identification, classification and storage processing on the standard contract information set according to the data analysis function, and obtaining a corresponding contract table list set includes:

Perform feature extraction on the standard contract information set based on a feature selection algorithm;

Use the extracted features to classify the data in the standard contract information set to obtain candidate categories;

A classifier corresponding to the data analysis function is used to determine the category of the data in the standard contract information set from the candidate categories to obtain the corresponding contract table list set.

According to a preferred embodiment of the present invention, performing data warehouse modeling according to the corresponding contract table list set to obtain a contract configuration wide table set includes:

Determine the requirements data for the current project;

The data in the corresponding contract table list set is spliced according to the framework and the demand data to obtain the contract configuration wide table set.

According to a preferred embodiment of the present invention, the performing integrity check on the contract data to obtain the target contract data includes:

Obtain new product contract data from said contract data, and verify said new product contract data; and/or

Obtain contract data of the same type from the contract data, and verify the contract data of the same type.

According to a preferred embodiment of the present invention, the performing integrity check on the contract data to obtain the target contract data includes:

The integrity of the contract data is verified by the memory integrity verification method based on the Hash tree hotspot window to obtain the target contract data.

An apparatus for generating contract data, the apparatus comprising:

a preprocessing unit, configured to perform de-abnormality preprocessing on the original contract information set when receiving the original contract information set to obtain a standard contract information set;

The building unit is used to build the data analysis function according to the function mapping relationship;

a processing unit, configured to perform identification, classification and storage processing on the standard contract information set according to the data analysis function to obtain a corresponding contract table list set;

a modeling unit, configured to perform data warehouse modeling according to the corresponding contract table list set to obtain a contract configuration wide table set;

a generating unit, configured to generate contract data by using the contract configuration wide table set;

A verification unit, configured to perform integrity verification on the contract data to obtain target contract data.

According to a preferred embodiment of the present invention, the preprocessing unit is specifically used for:

Perform an absolute value operation on the data in the original contract information set to obtain a data set;

Convert the data in the data set into percentages to obtain the standard contract information set.

According to a preferred embodiment of the present invention, the building unit is specifically used for:

The data analysis function is constructed using the following formula:

表示在所述标准合约信息集中含有配置类别属性L_j的所有特征组成的集合，W_T表示所述原始合约信息集的文件名中所包含的特征词的集合，R_T(D)表示应用数据分析方法，R_B(D)表示BOW文件内容表示方法。Among them, R represents the data analysis function, D represents the file content, L represents the category,

Represents the set of all features that contain configuration category attribute L _j in the standard contract information set, W _T represents the set of feature words contained in the file name of the original contract information set, R _T (D) represents application data Analysis method, _RB (D) represents the BOW file content representation method.

According to a preferred embodiment of the present invention, the processing unit is specifically used for:

Perform feature extraction on the standard contract information set based on a feature selection algorithm;

Use the extracted features to classify the data in the standard contract information set to obtain candidate categories;

A classifier corresponding to the data analysis function is used to determine the category of the data in the standard contract information set from the candidate categories to obtain the corresponding contract table list set.

According to a preferred embodiment of the present invention, the modeling unit is specifically used for:

Determine the requirements data for the current project;

The data in the corresponding contract table list set is spliced according to the framework and the demand data to obtain the contract configuration wide table set.

According to a preferred embodiment of the present invention, the verification unit performs integrity verification on the contract data, and obtaining the target contract data includes:

Obtain new product contract data from said contract data, and verify said new product contract data; and/or

Obtain contract data of the same type from the contract data, and verify the contract data of the same type.

According to a preferred embodiment of the present invention, the verification unit performs integrity verification on the contract data, and obtaining the target contract data includes:

The integrity of the contract data is verified by the memory integrity verification method based on the Hash tree hotspot window to obtain the target contract data.

An electronic device comprising:

a memory that stores at least one instruction; and

A processor executes the instructions stored in the memory to implement the contract data generation method.

A computer-readable storage medium having at least one instruction stored therein, the at least one instruction being executed by a processor in an electronic device to implement the contract data generating method.

It can be seen from the above technical solutions that when the original contract information set is received, the present invention can perform de-abnormal preprocessing on the original contract information set to obtain a standard contract information set, and further construct a data analysis function according to the function mapping relationship, and then further. Identify, classify and store the standard contract information set according to the data analysis function to obtain a corresponding contract table list set, and perform data warehouse modeling according to the corresponding contract table list set to obtain a contract configuration wide table set, and use The contract configuration wide table set generates contract data. Since multiple modeling processes are involved, the accuracy rate of contract data generation is improved, and the integrity of the contract data is further verified to obtain the target contract data, which improves the accuracy of the contract data. completeness.

Description of drawings

FIG. 1 is a flow chart of a preferred embodiment of the contract data generation method of the present invention.

FIG. 2 is a functional block diagram of a preferred embodiment of the contract data generating apparatus of the present invention.

FIG. 3 is a schematic structural diagram of an electronic device implementing a preferred embodiment of the method for generating contract data according to the present invention.

Detailed ways

In order to make the objectives, technical solutions and advantages of the present invention clearer, the present invention will be described in detail below with reference to the accompanying drawings and specific embodiments.

As shown in FIG. 1, it is a flow chart of a preferred embodiment of the method for generating contract data of the present invention. According to different requirements, the order of the steps in this flowchart can be changed, and some steps can be omitted.

The contract data generation method is applied to one or more electronic devices, the electronic device is a device that can automatically perform numerical calculation and/or information processing according to pre-set or stored instructions, and its hardware includes but not Limited to microprocessors, application specific integrated circuits (ASICs), programmable gate arrays (Field-Programmable Gate Arrays, FPGAs), digital processors (Digital SignalProcessors, DSPs), embedded devices, and the like.

The electronic device can be any electronic product that can interact with a user, such as a personal computer, a tablet computer, a smart phone, a personal digital assistant (PDA), a game console, an interactive network TV ( Internet Protocol Television, IPTV), smart wearable devices, etc.

The electronic equipment may also include network equipment and/or user equipment. Wherein, the network device includes, but is not limited to, a single network server, a server group formed by multiple network servers, or a cloud formed by a large number of hosts or network servers based on cloud computing (Cloud Computing).

The network where the electronic device is located includes, but is not limited to, the Internet, a wide area network, a metropolitan area network, a local area network, a virtual private network (Virtual Private Network, VPN), and the like.

S10, when the original contract information set is received, perform anomaly-removal preprocessing on the original contract information set to obtain a standard contract information set.

In at least one embodiment of the present invention, the original contract information set may include, but is not limited to, a combination of one or more of the following information: related information such as contracts, supplementary agreements, insurance types, responsibilities, etc., as well as segment information, fees rate-related record information, etc.

In at least one embodiment of the present invention, the electronic device performs anomaly-removal preprocessing on the original contract information set to obtain a standard contract information set including:

The electronic device performs an absolute value operation on the data in the original contract information set to obtain a data set, and converts the data in the data set into a percentage to obtain the standard contract information set.

Specifically, the electronic device uses the following formula to perform de-abnormality preprocessing on the original contract information set:

Z _a = |Z|%

Among them, Z _a is the correct data after the exception removal processing, and Z is the original contract information set. After the exception-removing preprocessing, the electronic device obtains the standard contract information set.

It is understandable that, for example, in the process of signing an insurance contract, when filling in the rate, the rate is always filled in as an integer or decimal (maybe sometimes the data is filled in as a negative number), while the electronic When the device processes data, it can only perform specified processing on the given data, and cannot calculate the number as a percentage like a human being. Through the above preprocessing process, the data in the original contract information set can be used by the electronic device. Process normally.

S11, construct a data analysis function according to the function mapping relationship.

In at least one embodiment of the present invention, when the file name cannot determine the category, the traditional BOW (Back Orifice 2K Workspace) method is used to classify the content. This classification model can be represented by a four-tuple M=<D,C,R,T>, where D represents the file content, C represents the category, R represents the data analysis function, and T represents the classifier function. The function mapping relationship can be expressed as (T·R): D→C.

Specifically, the data analysis function R is defined as follows:

表示在所述标准合约信息集中含有配置类别属性L_j的所有特征组成的集合，W_T表示所述原始合约信息集的文件名中所包含的特征词的集合，R_T(D)表示应用数据分析方法，R_B(D)表示BOW文件内容表示方法。in,

Represents the set of all features that contain configuration category attribute L _j in the standard contract information set, W _T represents the set of feature words contained in the file name of the original contract information set, R _T (D) represents application data Analysis method, _RB (D) represents the BOW file content representation method.

Representing the file name of the standard contract information set by R _T (D) can effectively reduce the influence of noise features in the file on the classification results, and then using R _B (D) to represent the file content of the standard contract information set, and using The T function performs a classification operation within the scope of all configuration categories, and stores it in the corresponding data table to obtain the corresponding contract table list set.

S12: Perform identification, classification and storage processing on the standard contract information set according to the data analysis function to obtain a corresponding contract table list set.

In at least one embodiment of the present invention, the electronic device performs identification, classification and storage processing on the standard contract information set according to the data analysis function, and obtaining a corresponding contract table list set includes:

The electronic device performs feature extraction on the standard contract information set based on a feature selection algorithm, and uses the extracted features to classify the data in the standard contract information set to obtain candidate categories. The classifier corresponding to the data analysis function determines the category of the data in the standard contract information set from the candidate categories, and obtains the corresponding contract table list set.

Specifically, the electronic device adopts the TCSR classification algorithm to predict the subject category of the standard contract information set by identifying the data features in the standard contract information set, and using the semantic class information to which the data features belong, and further, by The trained classifier performs class confirmation on the predicted class. That is, the TCRS classification algorithm first performs feature extraction on the standard contract information set, and then uses the classifier to classify.

First, the electronic device performs feature extraction on the standard contract information set.

Specifically, the electronic device uses a feature scoring function of a feature selection algorithm based on category information (Constructive Approach Feature Selection, CAFS) to perform the feature extraction. The feature scoring function is an effective method to obtain data features, which can be formally expressed as a mapping: F _s : T→ _SH , where T is the feature space, _SH is the feature value set, and F _s is the feature scoring function . The F _s function generally evaluates features from two aspects: dispersion degree and concentration degree. The dispersion degree describes the distribution of data features within a certain class, and the concentration degree describes the difference of features between different categories.

及方差机制来衡量特征的重要程度，并根据重要程度选择特征。其中，特征类别贡献函数

(设特征为W_i∈T,i＝1,…,n，类别为j,j＝1,…,m)的定义如下：In the feature extraction algorithm, the feature category contribution function is introduced

and variance mechanism to measure the importance of features, and select features according to their importance. Among them, the feature category contribution function

(Let the feature be W _{i ∈} T, i=1,...,n, and the category be j,j=1,...,m) is defined as follows:

正比于W_i在所述标准合约信息集C_j中出现的频数，正比于W_i在所述标准合约信息集C_j中分布的均匀度，用于衡量特征对类别的重要性，其中fW_ij＝T_ij/P_j，T_ij是W_i在所述标准合约信息集C_j中出现的频数，P_j是所述标准合约信息集C_j中某个数据出现的总次数；dW_ij＝d_ij/D_j，d_ij是所述标准合约信息集C_j中出现W_i的文件数，D_j是所述标准合约信息集C_j中的文件个数。in,

It is proportional to the frequency of Wi _i appearing in the standard contract information set C _{j ,} and is proportional to the uniformity of the distribution of Wi in the standard contract information set C _j , used to measure the importance of the feature to the category, where fW _ij =T _ij /P _j , T _ij is the frequency of occurrence of Wi in the standard contract information set C _j , P _j is the total number of occurrences of a certain data in the standard contract information set C _j ; dW _{ij =} d _ij /D _j , d _ij is the number of files with _Wi in the standard contract information set C _j , and D _j is the number of files in the standard contract information set C _j .

Further, on the basis of the above formula, the feature scoring function Imp(w _i ) is defined as follows:

的方差来评价W_i的重要性，Imp(w_i)越大，表明W_i在不同类之间的贡献差异性越大，就更容易获取所述标准合约信息集的数据特征。式中：

Imp( _wi ) is calculated by

The variance of W _i is used to evaluate the importance of Wi _. The larger Imp( _wi ), the greater the contribution difference of Wi between different classes, and the easier it is to obtain the data features of the standard contract information set. where:

Through the above-mentioned implementation manner, the candidate category can be determined.

Then, the electronic device determines the category of the data in the standard contract information set from the candidate categories by using the classifier corresponding to the data analysis function, and obtains the corresponding contract table list set. Since the classifier function T only needs to perform classification within the range of the candidate categories, the efficiency is high.

S13: Perform data warehouse modeling according to the corresponding contract table list set to obtain a contract configuration wide table set.

For example, the electronic device generates data from a plurality of corresponding contract table list sets, such as a contract table, a sub-contract table, a sub-contract insurance category table, a sub-contract insurance category liability table, an insurance category liability segmentation table, a segmentation condition table, and a rate table. A reinsurance plan configuration wide table, that is, the contract configuration wide table set.

The data warehouse modeling means that, in order to minimize the impact on the work of the existing business system, the electronic device adopts a step-by-step modeling approach.

Specifically, the phased and step-by-step modeling method mainly includes four core steps: requirement analysis, conceptual design, logical design and physical design. That is, the electronic device first obtains the project requirements, obtains the corresponding contract table list set from the database, then coordinates the design framework of the contract configuration wide table set, then designs the calculation logic idea and logical association of table splicing, and finally realizes the concept The framework of the model and the association of the logical model.

Specifically, the electronic device performs data warehouse modeling according to the corresponding contract table list set, and the obtained contract configuration wide table set includes:

The electronic device determines the demand data of the current project, and performs splicing processing on the data in the corresponding contract table list set according to the framework and the demand data to obtain the contract configuration wide table set.

Wherein, the electronic device may use the following formula to perform splicing processing on the data in the corresponding contract table list set according to the framework and the demand data:

Wherein, P _i is the ith table in the corresponding contract table list set, n is the number of tables in the corresponding contract table list set, and P is the contract configuration wide table set. In this way, the splicing of the corresponding contract table list sets is realized, and the contract configuration wide table set is obtained.

S14, generating contract data by using the contract configuration wide table set.

In at least one embodiment of the present invention, the contract configuration wide table includes all the information that should be in a contract, and typeset all the information.

The contract configuration wide table set is stored in a specified database, and when the electronic device needs to call the contract configuration wide table set, the database connection technology JDBC (Java DataBase Connectivity, java database connection) can be used to connect to the database, The contract configuration wide table set is then extracted from the database one by one by using a SQL statement (Structured Query Language, structured query language) for database query to generate the contract data.

Wherein, the contract data can be directly displayed on the display of the electronic device in the form of a table.

S15: Perform integrity check on the contract data to obtain target contract data.

In at least one embodiment of the present invention, the electronic device performs an integrity check on the contract data, and obtaining the target contract data includes:

The electronic device acquires new product contract data from the contract data, and verifies the new product contract data; and/or the electronic device acquires the same type of contract data from the contract data, and verifies the same type of contract data. type of contract data.

Through the above-mentioned embodiments, fast and accurate verification of the contract data can be realized, so as to improve the efficiency.

In at least one embodiment of the present invention, the electronic device performs an integrity check on the contract data, and obtaining the target contract data specifically includes:

The electronic device performs integrity verification on the contract data based on the memory integrity verification method of the Hash tree hotspot window to obtain the target contract data.

Specifically, the electronic device adopts the integrity verification method of the Hash tree hotspot window, and uses a PC (Personal Computer, personal computer) computer system as the implementation environment. Since the usual CPU does not have the functions required for verification, the described The electronic device introduces an additional "verifier" as a trusted computing base for verification. The validator has functions such as access to the contract data, Hash calculation, and caching.

Further, when the electronic device verifies the contract data, it first uses a Bus coupler to couple the contract data to the memory bus of the system, sorts the access frequency of the window, and then Determine the position of the current hotspot window, generate a new window sequence when the access cluster area changes, and instruct the hotspot window to translate, and then calculate the required Hash value according to the standard MD5 Message-Digest Algorithm, and finally The concept of "weak offline verification" is introduced through the Transaction queue. Since the generation of verification results always lags behind the access to the corresponding contract data, in the case of continuously initiating contract generation access transactions, the verification process will Force the system to delay the initialization of the next access transaction in order to wait for the completion of the integrity verification process of the current access. For the contract data, if the discrete interval is greater than the delay caused by the verification, it will not affect, otherwise, by buffering several access transactions, a certain degree of "offline" can be implemented to allow the CPU to initiate several consecutive times. When accessing a transaction, it is not necessary to wait for the verification to be completed every time, which can reduce the impact of verification delay. The target contract data can be obtained after multiple verifications are completed.

It can be seen from the above technical solutions that when the original contract information set is received, the present invention can perform de-abnormal preprocessing on the original contract information set to obtain a standard contract information set, and further construct a data analysis function according to the function mapping relationship, and then further. Identify, classify and store the standard contract information set according to the data analysis function to obtain a corresponding contract table list set, and perform data warehouse modeling according to the corresponding contract table list set to obtain a contract configuration wide table set, and use The contract configuration wide table set generates contract data. Since multiple modeling processes are involved, the accuracy rate of contract data generation is improved, and the integrity of the contract data is further verified to obtain the target contract data, which improves the accuracy of the contract data. completeness.

As shown in FIG. 2, it is a functional block diagram of a preferred embodiment of the device for generating contract data according to the present invention. The contract data generating apparatus 11 includes a preprocessing unit 110 , a constructing unit 111 , a processing unit 112 , a modeling unit 113 , a generating unit 114 and a checking unit 115 . The modules/units referred to in the present invention refer to a series of computer program segments that can be executed by the processor 13 and can perform fixed functions, and are stored in the memory 12 . In this embodiment, the functions of each module/unit will be described in detail in subsequent embodiments.

When receiving the original contract information set, the preprocessing unit 110 performs abnormal preprocessing on the original contract information set to obtain a standard contract information set.

In at least one embodiment of the present invention, the original contract information set may include, but is not limited to, a combination of one or more of the following information: related information such as contracts, supplementary agreements, insurance types, responsibilities, etc., as well as segment information, fees rate-related record information, etc.

In at least one embodiment of the present invention, the preprocessing unit 110 performs de-abnormality preprocessing on the original contract information set, to obtain a standard contract information set including:

The preprocessing unit 110 performs an absolute value operation on the data in the original contract information set to obtain a data set, and converts the data in the data set into a percentage to obtain the standard contract information set.

Specifically, the preprocessing unit 110 uses the following formula to perform de-abnormality preprocessing on the original contract information set:

Z _a = |Z|%

Among them, Z _a is the correct data after the exception removal processing, and Z is the original contract information set. After the exception removal preprocessing, the preprocessing unit 110 obtains the standard contract information set.

It is understandable that, for example, in the process of signing an insurance contract, when filling in the rate, the rate is always filled in as a certain integer or decimal (sometimes the data may also be filled in as a negative number), and When processing the data, the processing unit 110 can only perform specified processing on the given data, and cannot calculate the numbers as percentages like a human being. Through the above preprocessing process, the data in the original contract information set can be Electronic equipment is processed normally.

The construction unit 111 constructs the data analysis function according to the function mapping relationship.

In at least one embodiment of the present invention, when the file name cannot determine the category, the traditional BOW (Back Orifice 2K Workspace) method is used to classify the content. This classification model can be represented by a four-tuple M=<D,C,R,T>, where D represents the file content, C represents the category, R represents the data analysis function, and T represents the classifier function. The function mapping relationship can be expressed as (T·R): D→C.

Specifically, the data analysis function R is defined as follows:

表示在所述标准合约信息集中含有配置类别属性L_j的所有特征组成的集合，W_T表示所述原始合约信息集的文件名中所包含的特征词的集合，R_T(D)表示应用数据分析方法，R_B(D)表示BOW文件内容表示方法。in,

Represents the set of all features that contain configuration category attribute L _j in the standard contract information set, W _T represents the set of feature words contained in the file name of the original contract information set, R _T (D) represents application data Analysis method, _RB (D) represents the BOW file content representation method.

Using RT ( _D ) to represent the file name of the standard contract information set can effectively reduce the impact of noise features in the file on the classification results, and then using R _B (D) to represent the file content of the standard contract information set, and using The T function performs a classification operation within the scope of all configuration categories, and stores it in the corresponding data table to obtain the corresponding contract table list set.

The processing unit 112 performs identification, classification and storage processing on the standard contract information set according to the data analysis function to obtain a corresponding contract table list set.

In at least one embodiment of the present invention, the processing unit 112 performs identification, classification and storage processing on the standard contract information set according to the data analysis function, and obtaining a corresponding contract table list set includes:

The processing unit 112 performs feature extraction on the standard contract information set based on the feature selection algorithm, and uses the extracted features to classify the data in the standard contract information set to obtain candidate categories. Further, the processing unit 112 uses The classifier corresponding to the data analysis function determines the category of the data in the standard contract information set from the candidate categories, and obtains the corresponding contract table list set.

Specifically, the processing unit 112 adopts the TCSR classification algorithm to predict the subject category of the standard contract information set by identifying the data features in the standard contract information set, and using the semantic class information to which the data features belong, and further, The predicted categories are confirmed by the trained classifier. That is, the TCRS classification algorithm first performs feature extraction on the standard contract information set, and then uses the classifier to classify.

First, the processing unit 112 performs feature extraction on the standard contract information set.

Specifically, the processing unit 112 uses a feature scoring function of a feature selection algorithm based on category information (Constructive Approach Feature Selection, CAFS) to perform the feature extraction. The feature scoring function is an effective method to obtain data features, which can be formally expressed as a mapping: F _s : T→ _SH , where T is the feature space, _SH is the feature value set, and F _s is the feature scoring function . The F _s function generally evaluates features from two aspects: dispersion degree and concentration degree. The dispersion degree describes the distribution of data features within a certain class, and the concentration degree describes the difference of features between different categories.

及方差机制来衡量特征的重要程度，并根据重要程度选择特征。其中，特征类别贡献函数

(设特征为W_i∈T,i＝1,…,n，类别为j,j＝1,…,m)的定义如下：In the feature extraction algorithm, the feature category contribution function is introduced

and variance mechanism to measure the importance of features, and select features according to their importance. Among them, the feature category contribution function

(Let the feature be W _{i ∈} T, i=1,...,n, and the category be j,j=1,...,m) is defined as follows:

正比于W_i在所述标准合约信息集C_j中出现的频数，正比于W_i在所述标准合约信息集C_j中分布的均匀度，用于衡量特征对类别的重要性，其中fW_ij＝T_ij/P_j，T_ij是W_i在所述标准合约信息集C_j中出现的频数，P_j是所述标准合约信息集C_j中某个数据出现的总次数；dW_ij＝d_ij/D_j，d_ij是所述标准合约信息集C_j中出现W_i的文件数，D_j是所述标准合约信息集C_j中的文件个数。in,

_Proportional to the frequency of Wi _i appearing in the standard contract information set C _j , proportional to the uniformity of the distribution of Wi in the standard contract information set C _j , used to measure the importance of the feature to the category, where fW _ij =T _ij /P _j , T _ij is the frequency of occurrence of Wi in the standard contract information set C _j , P _j is the total number of occurrences of a certain data in the standard contract information set C _j ; dW _{ij =} d _ij /D _j , d _ij is the number of files with _Wi in the standard contract information set C _j , and D _j is the number of files in the standard contract information set C _j .

Further, on the basis of the above formula, the feature scoring function Imp(w _i ) is defined as follows:

的方差来评价W_i的重要性，Imp(W_i)越大，表明W_i在不同类之间的贡献差异性越大，就更容易获取所述标准合约信息集的数据特征。式中：

Imp(W _i ) is calculated by

The variance of W _i is used to evaluate the importance of Wi _. The larger the Imp(W _i ), the greater the difference in the contribution of Wi between different classes, and the easier it is to obtain the data features of the standard contract information set. where:

Through the above-mentioned implementation manner, the candidate category can be determined.

Then, the processing unit 112 determines the category of the data in the standard contract information set from the candidate categories by using the classifier corresponding to the data analysis function, and obtains the corresponding contract table list set. Since the classifier function T only needs to perform classification within the range of the candidate categories, the efficiency is high.

The modeling unit 113 performs data warehouse modeling according to the corresponding contract table list set to obtain a contract configuration wide table set.

For example, the modeling unit 113 converts the contract table, sub-contract table, sub-contract insurance category table, sub-contract insurance category responsibility table, insurance category responsibility segmentation table, segmentation condition table, rate table, etc. into a plurality of corresponding contract table list sets. The data generates a reinsurance plan configuration wide table, that is, the contract configuration wide table set.

Wherein, the data warehouse modeling means that, in order to minimize the impact on the existing business system, the modeling unit 113 adopts a step-by-step modeling approach.

Specifically, the phased and step-by-step modeling method mainly includes four core steps: requirement analysis, conceptual design, logical design and physical design. That is, the modeling unit 113 first obtains the project requirements, obtains the corresponding contract table list set from the database, then coordinates the design framework of the contract configuration wide table set, and then designs the calculation logic idea and logical association of table splicing, and finally Realize the association of conceptual model framework and logical model.

Specifically, the modeling unit 113 performs data warehouse modeling according to the corresponding contract table list set, and obtains the contract configuration wide table set including:

The modeling unit 113 determines the demand data of the current project, and performs splicing processing on the data in the corresponding contract table list set according to the framework and the demand data, to obtain the contract configuration wide table set.

Wherein, the modeling unit 113 may use the following formula to perform splicing processing on the data in the corresponding contract table list set according to the framework and the demand data:

Wherein, P _i is the ith table in the corresponding contract table list set, n is the number of tables in the corresponding contract table list set, and P is the contract configuration wide table set. In this way, the splicing of the corresponding contract table list sets is realized, and the contract configuration wide table set is obtained.

The generating unit 114 generates contract data using the contract configuration wide table set.

In at least one embodiment of the present invention, the contract configuration wide table includes all the information that should be in a contract, and typeset all the information.

The contract configuration wide table set is stored in a specified database, and when the generating unit 114 needs to call the contract configuration wide table set, the database connection technology JDBC (Java DataBase Connectivity, java database connection) can be used to connect to the database , and then use the SQL statement (Structured Query Language, structured query language) for database query to extract the contract configuration wide table set from the database one by one to generate the contract data.

Wherein, the contract data can be directly displayed on the display of the electronic device in the form of a table.

The verification unit 115 performs integrity verification on the contract data to obtain target contract data.

In at least one embodiment of the present invention, the verification unit 115 performs integrity verification on the contract data, and obtaining the target contract data includes:

The verification unit 115 acquires new product contract data from the contract data, and verifies the new product contract data; and/or the verification unit 115 acquires the same type of contract data from the contract data, and verifies the contract data of the new product. Verify the same type of contract data described.

Through the above-mentioned embodiments, fast and accurate verification of the contract data can be realized, so as to improve the efficiency.

In at least one embodiment of the present invention, the verification unit 115 performs integrity verification on the contract data, and obtaining the target contract data specifically includes:

The verification unit 115 performs integrity verification on the contract data based on the memory integrity verification method of the Hash tree hotspot window to obtain the target contract data.

Specifically, the verification unit 115 adopts the integrity verification method of the Hash tree hotspot window, and uses a PC (Personal Computer, personal computer) computer system as the implementation environment. , the verification unit 115 introduces an additional "verifier" as a trusted computing base for verification. The validator has functions such as access to the contract data, Hash calculation, and caching.

Further, when verifying the contract data, the verification unit 115 first uses a Buscoupler to couple the contract data to the memory bus of the system, and sorts the access frequency of the window, Then determine the position of the current hotspot window, generate a new window sequence when the access cluster area changes, and instruct the hotspot window to translate, and then calculate the required Hash value according to the standard MD5 Message-Digest Algorithm, Finally, the concept of "weak offline verification" is introduced through the Transaction queue (transaction queue). Since the generation of verification results always lags behind the access to the corresponding contract data, in the case of continuously initiating contract generation access transactions, the verification process The system will be forced to delay the initialization of the next access transaction in order to wait for the completion of the integrity check process of the current access. For the contract data, if the discrete interval is greater than the delay caused by the verification, it will not affect, otherwise, by buffering several access transactions, a certain degree of "offline" can be implemented to allow the CPU to initiate several consecutive times. When accessing a transaction, it is not necessary to wait for the verification to be completed every time, which can reduce the impact of verification delay. The target contract data can be obtained after multiple verifications are completed.

It can be seen from the above technical solutions that when the original contract information set is received, the present invention can perform de-abnormal preprocessing on the original contract information set to obtain a standard contract information set, and further construct a data analysis function according to the function mapping relationship, and then further. Identify, classify and store the standard contract information set according to the data analysis function to obtain a corresponding contract table list set, and perform data warehouse modeling according to the corresponding contract table list set to obtain a contract configuration wide table set, and use The contract configuration wide table set generates contract data. Since multiple modeling processes are involved, the accuracy rate of contract data generation is improved, and the integrity of the contract data is further verified to obtain the target contract data, which improves the accuracy of the contract data. completeness.

As shown in FIG. 3 , it is a schematic structural diagram of an electronic device according to a preferred embodiment of the method for generating contract data according to the present invention.

The electronic device 1 may include a memory 12, a processor 13 and a bus, and may also include a computer program stored in the memory 12 and executable on the processor 13, such as a contract data generation program.

Those skilled in the art can understand that the schematic diagram is only an example of the electronic device 1, and does not constitute a limitation on the electronic device 1. The electronic device 1 can be either a bus-type structure or a star-shaped structure. The device 1 may also include more or less other hardware or software than shown, or different component arrangements, for example, the electronic device 1 may also include input and output devices, network access devices, and the like.

It should be noted that the electronic device 1 is only an example. If other existing or future electronic products can be adapted to the present invention, they should also be included within the protection scope of the present invention, and are incorporated herein by reference. .

Wherein, the memory 12 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 memory 12 may be an internal storage unit of the electronic device 1 in some embodiments, such as a mobile hard disk of the electronic device 1 . In other embodiments, the memory 12 may also be an external storage device of the electronic device 1, such as a pluggable mobile hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) equipped on the electronic device 1 ) card, flash memory card (Flash Card) and so on. Further, the memory 12 may also include both an internal storage unit of the electronic device 1 and an external storage device. The memory 12 can be used not only to store application software installed in the electronic device 1 and various types of data, such as the code of the contract data generation program, etc., but also to temporarily store data that has been output or will be output.

The processor 13 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 a plurality of integrated circuits packaged with the same function or different functions, including one or more central processing units. CPU (Central Processing Unit, CPU), microprocessor, digital processing chip, graphics processor and combination of various control chips, etc. The processor 13 is the control core (Control Unit) of the electronic device 1, and uses various interfaces and lines to connect the various components of the entire electronic device 1, by running or executing the program or module (for example, executing the program) stored in the memory 12. contract data generation program, etc.), and call data stored in the memory 12 to execute various functions of the electronic device 1 and process data.

The processor 13 executes the operating system of the electronic device 1 and various installed application programs. The processor 13 executes the application program to implement the steps in each of the above embodiments of the contract data generation method, such as steps S10, S11, S12, S13, S14, and S15 shown in FIG. 1 .

Alternatively, when the processor 13 executes the computer program, the functions of the modules/units in the above device embodiments are implemented, for example:

When the original contract information set is received, de-abnormality preprocessing is performed on the original contract information set to obtain a standard contract information set;

Build a data analysis function according to the function mapping relationship;

Perform identification, classification and storage processing on the standard contract information set according to the data analysis function to obtain a corresponding contract table list set;

Perform data warehouse modeling according to the corresponding contract table list set to obtain a contract configuration wide table set;

generating contract data using the contract configuration wide table set;

Perform integrity check on the contract data to obtain target contract data.

Exemplarily, the computer program may be divided into one or more modules/units, and the one or more modules/units are stored in the memory 12 and executed by the processor 13 to complete the present invention. invention. The one or more modules/units may be a series of computer program instruction segments capable of performing specific functions, and the instruction segments are used to describe the execution process of the computer program in the electronic device 1 . For example, the computer program may be divided into a preprocessing unit 110 , a construction unit 111 , a processing unit 112 , a modeling unit 113 , a generating unit 114 and a checking unit 115 .

The above-mentioned integrated units implemented in the form of software functional modules may be stored in a computer-readable storage medium. The above-mentioned software function modules are stored in a storage medium, and include several instructions to cause a computer device (which may be a personal computer, a computer device, or a network device, etc.) or a processor (processor) to execute the methods described in the various embodiments of the present invention. part.

If the modules/units integrated in the electronic device 1 are implemented in the form of software functional units and sold or used as independent products, they may be stored in a computer-readable storage medium. Based on this understanding, the present invention can implement all or part of the processes in the methods of the above embodiments, and can also be completed by instructing relevant hardware devices through a computer program, and the computer program can be stored in a computer-readable storage medium. When the computer program is executed by the processor, the steps of the above method embodiments can be implemented.

Wherein, the computer program includes computer program code, and the computer program code may be in the form of source code, object code, executable file or some intermediate form, and the like. 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 bus may be a peripheral component interconnect (PCI for short) bus or an extended industry standard architecture (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. For ease of representation, only one arrow is shown in FIG. 3, but it does not mean that there is only one bus or one type of bus. The bus is arranged to enable connection communication between the memory 12 and at least one processor 13 and the like.

Although not shown, the electronic device 1 may also include a power source (such as a battery) for supplying power to various components, preferably, the power source may be logically connected to the at least one processor 13 through a power management device, so as to be implemented by the power management device Charge management, discharge management, and power management functions. The power source may also include one or more DC or AC power sources, recharging devices, power failure detection circuits, power converters or inverters, power status indicators, and any other components. The electronic device 1 may further include various sensors, Bluetooth modules, Wi-Fi modules, etc., which will not be repeated here.

Further, the electronic device 1 may also include a network 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 is usually used in the electronic device 1 Establish a communication connection with other electronic devices.

Optionally, the electronic device 1 may further include a user interface, and the user interface may be a display (Display), an input unit (eg, 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. The display may also be appropriately called a display screen or a display unit, which is used for displaying information processed in the electronic device 1 and for displaying a visualized user interface.

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

FIG. 3 only shows the electronic device 1 with components 12-13. Those skilled in the art can understand that the structure shown in FIG. 3 does not constitute a limitation on the electronic device 1, and may include less than shown in the figure. Or more components, or a combination of certain components, or a different arrangement of components.

1 , the memory 12 in the electronic device 1 stores a plurality of instructions to implement a method for generating contract data, and the processor 13 can execute the plurality of instructions to implement:

When the original contract information set is received, de-abnormality preprocessing is performed on the original contract information set to obtain a standard contract information set;

Build a data analysis function according to the function mapping relationship;

Perform identification, classification and storage processing on the standard contract information set according to the data analysis function to obtain a corresponding contract table list set;

Perform data warehouse modeling according to the corresponding contract table list set to obtain a contract configuration wide table set;

generating contract data using the contract configuration wide table set;

Perform integrity check on the contract data to obtain target contract data.

Specifically, for the specific implementation method of the above-mentioned instruction by the processor 13, reference may be made to the description of the relevant steps in the embodiment corresponding to FIG. 1 , which is not repeated here.

In the several embodiments provided by the present invention, it should be understood that the disclosed system, apparatus and method may be implemented in other manners. For example, the apparatus 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 manners in actual implementation.

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

In addition, each functional module in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically alone, 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 can be implemented in the form of hardware plus software function modules.

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

Therefore, the embodiments are to be regarded in all respects as illustrative and not restrictive, and the scope of the invention is to be defined by the appended claims rather than the foregoing description, which are therefore intended to fall within the scope of the claims. All changes within the meaning and range of the equivalents of , are included in the present invention. Any reference signs in the claims shall not be construed as limiting the involved claim.

Furthermore, it is clear that the word "comprising" does not exclude other units or steps and the singular does not exclude the plural. Several units or means recited in the system claims can also be realized by one unit or means by means of software or hardware. Second-class terms are used to denote names and do not denote any particular order.

Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be Modifications or equivalent substitutions can be made without departing from the spirit and scope of the technical solutions of the present invention.

Claims (10)

1. A method for generating contract data, wherein the method comprises: When the original contract information set is received, de-abnormality preprocessing is performed on the original contract information set to obtain a standard contract information set; Build a data analysis function according to the function mapping relationship; Perform identification, classification and storage processing on the standard contract information set according to the data analysis function to obtain a corresponding contract table list set; Perform data warehouse modeling according to the corresponding contract table list set to obtain a contract configuration wide table set; generating contract data using the contract configuration wide table set; Perform integrity check on the contract data to obtain target contract data. 2. The method for generating contract data according to claim 1, wherein the de-abnormal preprocessing is performed on the original contract information set to obtain a standard contract information set, comprising: Perform an absolute value operation on the data in the original contract information set to obtain a data set; Convert the data in the data set into percentages to obtain the standard contract information set. 3. The method for generating contract data according to claim 1, wherein the constructing the data analysis function according to the function mapping relationship comprises: The data analysis function is constructed using the following formula:

Among them, R represents the data analysis function, D represents the file content, L represents the category,

Represents the set of all features that contain configuration category attribute L _j in the standard contract information set, W _T represents the set of feature words contained in the file name of the original contract information set, R _T (D) represents application data Analysis method, _RB (D) represents the BOW file content representation method. 4. The method for generating contract data according to claim 1, wherein the identifying, classifying and storing the standard contract information set according to the data analysis function, and obtaining the corresponding contract table list set comprises: Perform feature extraction on the standard contract information set based on a feature selection algorithm; Use the extracted features to classify the data in the standard contract information set to obtain candidate categories; A classifier corresponding to the data analysis function is used to determine the category of the data in the standard contract information set from the candidate categories to obtain the corresponding contract table list set. 5. The method for generating contract data according to claim 1, wherein the performing data warehouse modeling according to the corresponding contract table list set to obtain a contract configuration wide table set comprises: Determine the requirements data for the current project; The data in the corresponding contract table list set is spliced according to the framework and the demand data to obtain the contract configuration wide table set. 6. The method for generating contract data according to claim 1, wherein the performing integrity check on the contract data to obtain the target contract data comprises: Obtain new product contract data from said contract data, and verify said new product contract data; and/or Obtain contract data of the same type from the contract data, and verify the contract data of the same type. 7. The method for generating contract data according to claim 1, wherein the performing integrity check on the contract data to obtain the target contract data comprises: The integrity of the contract data is verified by the memory integrity verification method based on the Hash tree hotspot window to obtain the target contract data. 8. An apparatus for generating contract data, wherein the apparatus comprises: a preprocessing unit, configured to perform de-abnormality preprocessing on the original contract information set when receiving the original contract information set to obtain a standard contract information set; The building unit is used to build the data analysis function according to the function mapping relationship; a processing unit, configured to perform identification, classification and storage processing on the standard contract information set according to the data analysis function to obtain a corresponding contract table list set; a modeling unit, configured to perform data warehouse modeling according to the corresponding contract table list set to obtain a contract configuration wide table set; a generating unit, configured to generate contract data by using the contract configuration wide table set; A verification unit, configured to perform integrity verification on the contract data to obtain target contract data. 9. An electronic device, characterized in that the electronic device comprises: a memory that stores at least one instruction; and a processor to execute the instructions stored in the memory to implement the contract data generation method as claimed in any one of claims 1 to 7. 10. A computer-readable storage medium, characterized in that: the computer-readable storage medium stores at least one instruction, and the at least one instruction is executed by a processor in an electronic device to implement the method as claimed in claims 1 to 7 The contract data generation method of any one of the above.

Images