CN113657507A - Decision tree-based product distribution method, device, equipment and storage medium - Google Patents
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Abstract
The invention relates to the field of data processing, and discloses a product distribution method based on a decision tree, which comprises the following steps: receiving a distribution instruction of a preset product, acquiring contract content corresponding to the product, extracting contract terms from the contract content, and performing data structuring processing on the contract terms of the product; performing decision tree transformation on the terms after the structured processing, and judging whether the distribution instruction can meet the decision tree; when the distribution instruction can be met, carrying out quantity verification on the product account corresponding to the distribution instruction so as to determine a distribution limit; and measuring the dispensable quantity of the product, changing the distribution instruction according to the dispensable quantity, and distributing the product to a preset user according to the changed distribution instruction. The invention also provides a product distribution device, an electronic device and a storage medium. The invention can improve the efficiency and accuracy of product distribution.
Description
Technical Field
The present invention relates to the field of data processing, and in particular, to a method and an apparatus for allocating products based on a decision tree, an electronic device, and a readable storage medium.
Background
Product distribution is a method of distributing products to be distributed according to a certain rule, such as a trust authority entrusted by an individual or a family, a trust interest distributed instead, and the like. However, the current product allocation only supports manual operation, the workload is large, a uniform allocation management means is not provided, data loss or errors are easily caused, and the product allocation efficiency is low. In addition, no method for dealing with the supply and demand shortage of the product exists in the product distribution process, so that the product distribution error is easily caused, the product distribution accuracy is low, and the unnecessary loss is generated by customers.
Disclosure of Invention
The invention provides a method and a device for distributing products based on a decision tree, electronic equipment and a computer readable storage medium, aiming at improving the efficiency and the accuracy of product distribution.
In order to achieve the above object, the present invention provides a method for allocating products based on a decision tree, comprising:
when an allocation instruction of a preset product is received, acquiring contract content corresponding to the product, extracting contract clauses from the contract content, and performing data structuring processing on the contract clauses to obtain structural combination clauses;
performing decision tree transformation on the structured contract clauses to obtain a contract clause decision tree, and judging whether the distribution instruction can be met according to the contract clause decision tree;
when the distribution instruction can be met, carrying out quantity verification on the product account corresponding to the distribution instruction so as to determine a distribution limit;
estimating the distributable quantity of the product by using a pre-trained quantity estimation model, and judging whether the distributable quantity meets the distribution limit;
when the allocable quantity meets the allocation limit, allocating the product to a preset user according to the allocation instruction;
and when the distributable quantity does not meet the distribution limit, the distribution instruction is changed according to the distributable quantity, and the product is distributed to a preset user according to the changed distribution instruction.
Optionally, the checking the quantity of the product account corresponding to the allocation instruction to determine the allocation limit includes:
carrying out quantity flow measurement and calculation on the product account by using a pre-trained liquidity measurement and calculation model to obtain the account product quantity;
comparing the total quantity in the distribution instruction with the account product quantity line, and judging whether the account product quantity meets the total quantity in the distribution instruction;
if the account product quantity meets the total quantity, judging that the total quantity in the distribution instruction is a distribution limit;
and if the account product quantity does not meet the total quantity, judging the account product quantity to be a distribution limit.
Optionally, the performing decision tree transformation on the structured contract terms to obtain a contract term decision tree includes:
acquiring the clause content of each contract clause of each structure combination identical clause;
and performing recursive calculation on the clause content of each contract clause by using a decision tree algorithm to generate a contract clause decision tree.
Optionally, the recursively calculating the clause content of each contract clause by using a decision tree algorithm to generate a contract clause decision tree, including:
calculating a damping coefficient of the contract content by using a preset first formula;
when the coefficient of the degree of the contract content is larger than or equal to the preset threshold value, calculating the probability that the distribution instruction meets the content of each contract clause by using a preset probability calculation model, and calculating the coefficient of the degree of the content of each contract clause extracted from the contract content according to the probability;
and sequencing the keny coefficients of the clause contents of the contract clauses from small to large, and performing tree division on the contract clauses according to the sequencing to obtain a contract clause decision tree.
Optionally, the data structuring processing is performed on the contract clause of the product to obtain a structured contract clause, including:
acquiring the data type of the clause content of each contract clause, and identifying the hierarchical structure corresponding to each contract clause according to the data type;
according to the hierarchical structure, unstructured data are screened out from the clause content of the contract clauses, the unstructured data are inserted into a pre-constructed table, the classes of the unstructured data inserted into the table are defined, and structural combination clauses are obtained.
Optionally, the identifying, according to the data type, a hierarchy corresponding to each contract term includes:
inquiring a data field of the clause content of the contract clause according to the data type, and identifying a position index of the data field in the contract clause;
and determining the hierarchical structure of the clause content in the contract clauses according to the position index.
Optionally, the changing the allocation instruction according to the allocable number includes:
and selecting users with priority levels larger than a preset threshold, and changing the total number in the distribution instruction according to the distributable number corresponding to the selected users.
In order to solve the above problems, the present invention also provides a product dispensing device comprising:
the clause structuring module is used for acquiring contract contents corresponding to a preset product when receiving a distribution instruction of the product, extracting contract clauses from the contract contents, and performing data structuring processing on the contract clauses of the product to obtain structural combination clauses;
the instruction checking module is used for performing decision tree transformation on the structured contract clauses to obtain a contract clause decision tree and judging whether the distribution instruction can be met according to the contract clause decision tree;
the quantity distribution module is used for carrying out quantity verification on the product account corresponding to the distribution instruction when the distribution instruction can be met so as to determine a distribution limit; estimating the distributable quantity of the product by using a pre-trained quantity estimation model, and judging whether the distributable quantity meets the distribution limit; when the distributable quantity meets the distribution amount, distributing the product to a preset user according to the distribution instruction, or when the distributable quantity does not meet the distribution amount, changing the distribution instruction according to the distributable quantity, and distributing the product to the preset user according to the changed distribution instruction.
In order to solve the above problem, the present invention also provides an electronic device, including:
a memory storing at least one computer program; and
a processor executing a computer program stored in the memory to implement the decision tree based product allocation method described above.
In order to solve the above problem, the present invention further provides a computer-readable storage medium, in which at least one computer program is stored, the at least one computer program being executed by a processor in an electronic device to implement the decision tree-based product allocation method described above.
The embodiment of the invention carries out structural processing on contract contents corresponding to products, carries out decision tree transformation on structural contract clauses, judges whether the distribution instruction meets the contract clause decision tree or not so as to ensure that the distribution instruction can be executed, and improves the efficiency of product distribution. Therefore, the product distribution method based on the decision tree provided by the embodiment of the invention improves the efficiency and accuracy of product distribution.
Drawings
FIG. 1 is a flow chart of a method for allocating products based on a decision tree according to an embodiment of the present invention;
FIG. 2 is a block diagram of a product dispensing device according to one embodiment of the present invention;
fig. 3 is a schematic internal structural diagram of an electronic device implementing a decision tree-based product allocation method according to an embodiment of the present invention;
the implementation, functional features and advantages of the objects of the present invention will be further explained with reference to the accompanying drawings.
Detailed Description
It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
The embodiment of the invention provides a product distribution method based on a decision tree. The execution subject of the decision tree-based product allocation method includes, but is not limited to, at least one of the electronic devices of a server, a terminal, and the like, which can be configured to execute the method provided by the embodiments of the present application. In other words, the decision tree based product allocation method may be performed by software or hardware installed in a terminal device or a server device, and the software may be a blockchain platform. The server includes but is not limited to: a single server, a server cluster, a cloud server or a cloud server cluster, and the like.
Referring to fig. 1, which is a schematic flow chart of a decision tree-based product allocation method according to an embodiment of the present invention, in an embodiment of the present invention, the decision tree-based product allocation method includes:
s1, when a preset product distribution instruction is received, acquiring contract content corresponding to the product, extracting contract terms from the contract content, and performing data structuring processing on the contract terms of the product to obtain structured combination terms.
In embodiments of the invention, the predetermined product may be a product that requires dispensing, such as a family trusted benefit. The dispensing instruction may be a command to rationalise dispensing of the preset product according to the user's wishes.
Further, the contract terms may refer to the basic conditions that the product is allocated. Taking benefit distribution of a trusted product as an example, the contract terms generally include applicant authority, beneficiary qualification verification, protector distribution limit rule, distribution sequence rule, distribution limit verification, whether a principal has a special event or not, beneficiary distribution category rule, repeated initiation of distribution instruction reminding, cumulative application frequency control, medical fund application amount lower limit control, reserve fund cumulative application amount upper limit control, premium agent payment time limit control, product suspension distribution control and the like.
Further, the embodiment of the invention performs data structuring processing on the contract clauses to convert unstructured data in the contract clauses into structured data, so as to facilitate calculation processing of subsequent data. The unstructured data refers to data which are irregular or incomplete in data structure, have no predefined data model and are inconvenient to express by using a database two-dimensional logic table, and comprise office documents, texts, pictures, XML, HTML, various reports, images, audio/video information and the like in all formats, and the structured data, namely row data, are stored in the database and can be logically expressed by using the two-dimensional table structure.
As an embodiment of the present invention, the data structuring processing on the contract clause to obtain a structured contract clause includes: acquiring the data type of the clause content of each contract clause, and identifying the hierarchical structure corresponding to each contract clause according to the data type; according to the hierarchical structure, unstructured data are screened out from the clause content of the contract clauses, the unstructured data are inserted into a pre-constructed table, the classes of the unstructured data inserted into the table are defined, and structural combination clauses are obtained. The data type is used for characterizing the characteristics of the data in the contract clause, for example, the data type of the guarantee measure may be a guarantee type, and the data structure refers to a set of data elements having one or more specific relationships with each other.
In an optional embodiment, the identifying, according to the data type, a hierarchy corresponding to each contract term includes: inquiring a data field of the clause content of the contract clause according to the data type, and identifying a position index of the data field in the contract clause; and determining the hierarchical structure of the clause content in the contract clauses according to the position index.
S2, performing decision tree transformation on the structured contract clauses to obtain a contract clause decision tree, and judging whether the distribution instruction can be met according to the contract clause decision tree.
In the embodiment of the invention, the contract term decision tree can be a decision analysis method for judging the feasibility of the evaluation and distribution instruction risk according to the contract terms, and is a graphical method for intuitively applying probability analysis.
In the embodiment of the invention, the decision tree transformation is carried out on the structured contract, so that whether the distribution instruction meets contract terms or not can be conveniently calculated subsequently, and the performability of the distribution instruction is ensured.
In the embodiment of the present invention, the performing decision tree transformation on the structured contract terms to obtain a contract term decision tree includes: acquiring the clause content of each contract clause of the structural combination clause; and performing recursive calculation on the clause content of each contract clause by using a decision tree algorithm to generate a contract clause decision tree.
Specifically, the term content may be content of contract terms generated according to corresponding products, such as in benefit distribution of trusted products, and the term content may be applicant rights, beneficiary qualification checks, protector distribution quota rules, and the like. The decision tree algorithm may use a common published CART algorithm or the like.
Further, the recursively calculating the clause content of each contract clause by using a decision tree algorithm to generate a contract clause decision tree, including:
calculating a damping coefficient of the contract content by using a preset first formula;
when the coefficient of the degree of the contract content is larger than or equal to the preset threshold value, calculating the probability that the distribution instruction meets the content of each contract clause by using a preset probability calculation model, and calculating the coefficient of the degree of each contract clause extracted from the contract content by using a preset second formula;
and sequencing the keny coefficients of the clause contents of the contract clauses from small to large, and performing tree division on the contract clauses according to the sequencing to obtain a contract clause decision tree.
In the embodiment of the present invention, the preset first formula is as follows:
wherein Gini (D) is the Kernel coefficient of the contract content, D is the contract content, K is the total number of terms of the contract terms, and K is the kth term in the contract termsContract clauses, CkThe clause content of the kth contract clause;
in the embodiment of the present invention, the preset second formula is as follows:
wherein gini (k) is a kini coefficient of the clause content of the kth contract clause in the contract content, and pk is a probability that the distribution instruction satisfies the clause content of the kth contract clause;
in the embodiment of the present invention, when the kini coefficient of the contract content is smaller than the preset threshold, the contract content may be considered to be unsuitable for product distribution, the process may be directly ended, the user is prompted that the distribution instruction is not executable, and an email is generated according to the unexecutable reason and sent to the user.
And S3, when the distribution instruction can not meet the contract clause decision tree, prompting the user that the distribution instruction can not be executed, and generating a mail according to the unexecutable reason and sending the mail to the user.
In this embodiment of the present invention, the non-executable reason may be that one or more contents of the distribution instruction do not satisfy the contract term decision tree.
S4, when the distribution instruction can meet the contract clause decision tree, checking the quantity of the product account corresponding to the distribution instruction to determine the distribution amount.
In this embodiment of the present invention, the product account may be a certain account specified in the allocation instruction. The allocation amount can be the product amount allocated according to the total amount and the account product amount in the allocation instruction and the specific situation of the product account.
In the embodiment of the present invention, the checking the quantity of the product account corresponding to the allocation instruction to determine the allocation amount includes: carrying out quantity flow measurement and calculation on the product account by using a pre-trained liquidity measurement and calculation model to obtain the account product quantity; comparing the total amount in the distribution instruction with the account product amount line, and judging whether the account product amount meets the total amount; if the account product quantity meets the total quantity, judging that the total quantity is a distribution limit; and if the account product quantity does not meet the total quantity, judging the account product quantity to be a distribution limit. The pre-trained mobility measurement model can be obtained by constructing a large amount of data, calculating by using a Bayesian formula, and continuously optimizing the model.
Specifically, the fluidity measurement model comprises a net quantity management model, a fluidity transfer quantification model, a future yield flow prediction model and the like. The total number may be a sum of the numbers to be allocated in the allocation instruction.
S5, estimating the distributable quantity of the product by using a pre-trained quantity estimation model, and judging whether the distributable quantity meets the distribution limit.
In the embodiment of the present invention, the pre-trained quantitative evaluation model may be obtained by constructing a large amount of data and calculating the data by using an EDA algorithm, and continuously optimizing the model. The quantity estimation model may be a model that calculates the quantity of the product using an estimation method.
In the embodiment of the invention, the distribution amount is confirmed again by measuring the distributable amount of the product.
Specifically, S6, when the product profit meets the allocation limit, allocating the product to a preset user according to the allocation instruction.
And S7, when the profit of the product does not meet the allocation limit, changing the allocation instruction according to the allocable quantity, and allocating the product to a preset user according to the changed allocation instruction.
In this embodiment of the present invention, the changing the allocation instruction according to the allocable number includes: and selecting users with priority levels larger than a preset threshold, and changing the total number in the distribution instruction according to the distributable number corresponding to the selected users.
In detail, the adjustment of the total amount in the distribution instruction is realized by preferentially distributing the products to the customers with high importance degree, and the preferential distribution rule can be understood as that the users grade the users in the contract according to the importance degree and filter the users with low grade. If the users are divided into three levels, namely, a first level user, a second level user and a third level user, the importance degrees are respectively that the first level user is greater than the second level user and greater than the third level user, wherein the third level user is a user with too low level. The proportion weight can be the ratio of the distribution amount corresponding to the product to the distribution amount, wherein the distribution amount is the distribution amount after the users with too low grade are filtered. If the allocated amount is 100 ten thousand after the third-level users are filtered, wherein the allocated amount of the first-level users is 30 ten thousand, and the allocated amount of the second-level users is 70 ten thousand, the proportion weight of the first-level users is 30%, the proportion weight of the second-level users is 70%, and the proportion weight of the third-level users is 0%.
The embodiment of the invention carries out structural processing on contract contents corresponding to products, carries out decision tree transformation on structural contract clauses, judges whether the distribution instruction meets the contract clause decision tree or not so as to ensure that the distribution instruction can be executed, and improves the efficiency of product distribution. Therefore, the product distribution method based on the decision tree provided by the embodiment of the invention improves the efficiency and accuracy of product distribution.
Fig. 2 is a functional block diagram of the product dispensing device of the present invention.
The product dispensing device 100 of the present invention may be installed in an electronic device. According to the implemented functions, the product distribution device may include a clause structuring module 101, an instruction checking module 102, and a quantity distribution module 103, which may also be referred to as a unit, and refer to a series of computer program segments that can be executed by a processor of an electronic device and can perform fixed functions, and are stored in a memory of the electronic device.
In the present embodiment, the functions regarding the respective modules/units are as follows:
the clause structuring module 101 is configured to, when receiving an allocation instruction for a preset product, obtain contract content corresponding to the product, extract contract clauses from the contract content, and perform data structuring processing on the contract clauses of the product to obtain structured combination clauses.
In embodiments of the invention, the predetermined product may be a product that requires dispensing, such as a family trusted benefit. The dispensing instruction may be a command to rationalise dispensing of the preset product according to the user's wishes.
Further, the contract terms may refer to the basic conditions that the product is allocated. Taking benefit distribution of a trusted product as an example, the contract terms generally include applicant authority, beneficiary qualification verification, protector distribution limit rule, distribution sequence rule, distribution limit verification, whether a principal has a special event or not, beneficiary distribution category rule, repeated initiation of distribution instruction reminding, cumulative application frequency control, medical fund application amount lower limit control, reserve fund cumulative application amount upper limit control, premium agent payment time limit control, product suspension distribution control and the like.
Further, the embodiment of the invention performs data structuring processing on the contract clauses to convert unstructured data in the contract clauses into structured data, so as to facilitate calculation processing of subsequent data. The unstructured data refers to data which are irregular or incomplete in data structure, have no predefined data model and are inconvenient to express by using a database two-dimensional logic table, and comprise office documents, texts, pictures, XML, HTML, various reports, images, audio/video information and the like in all formats, and the structured data, namely row data, are stored in the database and can be logically expressed by using the two-dimensional table structure.
As an embodiment of the present invention, the data structuring processing on the contract clause to obtain a structured contract clause includes: acquiring the data type of the clause content of each contract clause, and identifying the hierarchical structure corresponding to each contract clause according to the data type; according to the hierarchical structure, unstructured data are screened out from the clause content of the contract clauses, the unstructured data are inserted into a pre-constructed table, the classes of the unstructured data inserted into the table are defined, and structural combination clauses are obtained. The data type is used for characterizing the characteristics of the data in the contract clause, for example, the data type of the guarantee measure may be a guarantee type, and the data structure refers to a set of data elements having one or more specific relationships with each other.
In an optional embodiment, the identifying, according to the data type, a hierarchy corresponding to each contract term includes: inquiring a data field of the clause content of the contract clause according to the data type, and identifying a position index of the data field in the contract clause; and determining the hierarchical structure of the clause content in the contract clauses according to the position index.
The instruction checking module 102 is configured to perform decision tree transformation on the structured contract terms to obtain a contract term decision tree, and determine whether the distribution instruction can be satisfied according to the contract term decision tree.
In the embodiment of the invention, the contract term decision tree can be a decision analysis method for judging the feasibility of the evaluation and distribution instruction risk according to the contract terms, and is a graphical method for intuitively applying probability analysis.
In the embodiment of the invention, the decision tree transformation is carried out on the structured contract, so that whether the distribution instruction meets contract terms or not can be conveniently calculated subsequently, and the performability of the distribution instruction is ensured.
In the embodiment of the present invention, the performing decision tree transformation on the structured contract terms to obtain a contract term decision tree includes: acquiring the clause content of each contract clause of the structural combination clause; and performing recursive calculation on the clause content of each contract clause by using a decision tree algorithm to generate a contract clause decision tree.
Specifically, the term content may be content of contract terms generated according to corresponding products, such as in benefit distribution of trusted products, and the term content may be applicant rights, beneficiary qualification checks, protector distribution quota rules, and the like. The decision tree algorithm may use a common published CART algorithm or the like.
Further, the recursively calculating the clause content of each contract clause by using a decision tree algorithm to generate a contract clause decision tree, including:
calculating a damping coefficient of the contract content by using a preset first formula;
when the coefficient of the degree of the contract content is larger than or equal to the preset threshold value, calculating the probability that the distribution instruction meets the content of each contract clause by using a preset probability calculation model, and calculating the coefficient of the degree of each contract clause extracted from the contract content by using a preset second formula;
and sequencing the keny coefficients of the clause contents of the contract clauses from small to large, and performing tree division on the contract clauses according to the sequencing to obtain a contract clause decision tree.
In the embodiment of the present invention, the preset first formula is as follows:
wherein Gini (D) is the Kernel coefficient of the contract content, D is the contract content, and K is the total number of terms of the contract termsAmount, k is the kth contract term of the contract terms, CkThe clause content of the kth contract clause;
in the embodiment of the present invention, the preset second formula is as follows:
wherein gini (k) is a kini coefficient of the clause content of the kth contract clause in the contract content, and pk is a probability that the distribution instruction satisfies the clause content of the kth contract clause;
in the embodiment of the present invention, when the kini coefficient of the contract content is smaller than the preset threshold, the contract content may be considered to be unsuitable for product distribution, the process may be directly ended, the user is prompted that the distribution instruction is not executable, and an email is generated according to the unexecutable reason and sent to the user.
The quantity distribution module 103 is configured to, when the distribution instruction can be satisfied, perform quantity verification on a product account corresponding to the distribution instruction, determine a distribution amount, estimate the distributable quantity of the product by using a pre-trained quantity estimation model, determine whether the distributable quantity satisfies the distribution amount, distribute the product to a preset user according to the distribution instruction when the distributable quantity satisfies the distribution amount, or modify the distribution instruction according to the distributable quantity when the distributable quantity does not satisfy the distribution amount, and distribute the product to the preset user according to the modified distribution instruction.
In this embodiment of the present invention, the non-executable reason may be that one or more contents of the distribution instruction do not satisfy the contract term decision tree. In this embodiment of the present invention, the product account may be a certain account specified in the allocation instruction. The allocation amount can be the product amount allocated according to the total amount and the account product amount in the allocation instruction and the specific situation of the product account.
In the embodiment of the present invention, the checking the quantity of the product account corresponding to the allocation instruction to determine the allocation amount includes: carrying out quantity flow measurement and calculation on the product account by using a pre-trained liquidity measurement and calculation model to obtain the account product quantity; comparing the total amount in the distribution instruction with the account product amount line, and judging whether the account product amount meets the total amount; if the account product quantity meets the total quantity, judging that the total quantity is a distribution limit; and if the account product quantity does not meet the total quantity, judging the account product quantity to be a distribution limit. The pre-trained mobility measurement model can be obtained by constructing a large amount of data, calculating by using a Bayesian formula, and continuously optimizing the model.
Specifically, the fluidity measurement model comprises a net quantity management model, a fluidity transfer quantification model, a future yield flow prediction model and the like. The total number may be a sum of the numbers to be allocated in the allocation instruction.
In the embodiment of the present invention, the pre-trained quantitative evaluation model may be obtained by constructing a large amount of data and calculating the data by using an EDA algorithm, and continuously optimizing the model. The quantity estimation model may be a model that calculates the quantity of the product using an estimation method.
In the embodiment of the invention, the distribution amount is confirmed again by measuring the distributable amount of the product.
Specifically, when the profit of the product meets the allocation limit, the product is allocated to a preset user according to the allocation instruction.
And when the product profit does not meet the allocation limit, the allocation instruction is changed according to the allocable quantity, and the product is allocated to a preset user according to the changed allocation instruction.
In this embodiment of the present invention, the changing the allocation instruction according to the allocable number includes: and selecting users with priority levels larger than a preset threshold, and changing the total number in the distribution instruction according to the distributable number corresponding to the selected users.
In detail, the adjustment of the total amount in the distribution instruction is realized by preferentially distributing the products to the customers with high importance degree, and the preferential distribution rule can be understood as that the users grade the users in the contract according to the importance degree and filter the users with low grade. If the users are divided into three levels, namely, a first level user, a second level user and a third level user, the importance degrees are respectively that the first level user is greater than the second level user and greater than the third level user, wherein the third level user is a user with too low level. The proportion weight can be the ratio of the distribution amount corresponding to the product to the distribution amount, wherein the distribution amount is the distribution amount after the users with too low grade are filtered. If the allocated amount is 100 ten thousand after the third-level users are filtered, wherein the allocated amount of the first-level users is 30 ten thousand, and the allocated amount of the second-level users is 70 ten thousand, the proportion weight of the first-level users is 30%, the proportion weight of the second-level users is 70%, and the proportion weight of the third-level users is 0%.
Fig. 3 is a schematic structural diagram of an electronic device for implementing a decision tree-based product allocation method according to the present invention.
The electronic device may comprise a processor 10, a memory 11, a communication bus 12 and a communication interface 13, and may further comprise a computer program, such as a product distribution program, stored in the memory 11 and executable on the processor 10.
The memory 11 includes at least one type of readable storage medium, which includes flash memory, removable hard disk, multimedia card, card-type memory (e.g., SD or DX memory, etc.), magnetic memory, magnetic disk, optical disk, etc. The memory 11 may in some embodiments be an internal storage unit of the electronic device, for example a removable hard disk of the electronic device. The memory 11 may also be an external storage device of the electronic device in other embodiments, such as a plug-in mobile hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like, which are provided on the electronic device. Further, the memory 11 may also include both an internal storage unit and an external storage device of the electronic device. The memory 11 may be used not only to store application software installed in the electronic device and various types of data, such as codes of product allocation programs, etc., but also to temporarily store data that has been output or will be output.
The processor 10 may be composed of an integrated circuit in some embodiments, for example, a single packaged integrated circuit, or may be composed of a plurality of integrated circuits packaged with the same or different functions, including one or more Central Processing Units (CPUs), microprocessors, digital Processing chips, graphics processors, and combinations of various control chips. The processor 10 is a Control Unit (Control Unit) of the electronic device, connects various components of the whole electronic device by using various interfaces and lines, and executes various functions and processes data of the electronic device by running or executing programs or modules (e.g., product allocation programs, etc.) stored in the memory 11 and calling data stored in the memory 11.
The communication bus 12 may be a PerIPheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus. The bus may be divided into an address bus, a data bus, a control bus, etc. The communication bus 12 is arranged to enable connection communication between the memory 11 and at least one processor 10 or the like. For ease of illustration, only one thick line is shown, but this does not mean that there is only one bus or one type of bus.
Fig. 3 shows only an electronic device having components, and those skilled in the art will appreciate that the structure shown in fig. 3 does not constitute a limitation of the electronic device, and may include fewer or more components than those shown, or some components may be combined, or a different arrangement of components.
For example, although not shown, the electronic device may further include a power supply (such as a battery) for supplying power to each component, and preferably, the power supply may be logically connected to the at least one processor 10 through a power management device, so that functions of charge management, discharge management, power consumption management and the like are realized through the power management device. The power supply may also include any component of one or more dc or ac power sources, recharging devices, power failure detection circuitry, power converters or inverters, power status indicators, and the like. The electronic device may further include various sensors, a bluetooth module, a Wi-Fi module, and the like, which are not described herein again.
Optionally, the communication interface 13 may include a wired interface and/or a wireless interface (e.g., WI-FI interface, bluetooth interface, etc.), which is generally used to establish a communication connection between the electronic device and other electronic devices.
Optionally, the communication interface 13 may further include a user interface, which may be a Display (Display), an input unit (such as a Keyboard (Keyboard)), and optionally, a standard wired interface, or a wireless interface. Alternatively, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, an OLED (Organic Light-Emitting Diode) touch device, or the like. The display, which may also be referred to as a display screen or display unit, is suitable, among other things, for displaying information processed in the electronic device and for displaying a visualized user interface.
It is to be understood that the described embodiments are for purposes of illustration only and that the scope of the appended claims is not limited to such structures.
The product allocation program stored in the memory 11 of the electronic device is a combination of computer programs that, when executed in the processor 10, enable:
when an allocation instruction of a preset product is received, acquiring contract content corresponding to the product, extracting contract terms from the contract content, and performing data structuring processing on the contract terms of the product to obtain structural combination terms;
performing decision tree transformation on the structured contract clauses to obtain a contract clause decision tree, and judging whether the distribution instruction can be met according to the contract clause decision tree;
when the distribution instruction can be met, carrying out quantity verification on the product account corresponding to the distribution instruction so as to determine a distribution limit;
estimating the distributable quantity of the product by using a pre-trained quantity estimation model, and judging whether the distributable quantity meets the distribution limit;
when the allocable quantity meets the allocation limit, allocating the product to a preset user according to the allocation instruction;
and when the distributable quantity does not meet the distribution limit, the distribution instruction is changed according to the distributable quantity, and the product is distributed to a preset user according to the changed distribution instruction.
Specifically, the processor 10 may refer to the description of the relevant steps in the embodiment corresponding to fig. 1 for a specific implementation method of the computer program, which is not described herein again.
Further, the electronic device integrated module/unit, if implemented in the form of a software functional unit and sold or used as a separate product, may be stored in a computer readable storage medium. The computer readable medium may be non-volatile or volatile. The computer-readable medium may include: any entity or device capable of carrying said computer program code, recording medium, U-disk, removable hard disk, magnetic disk, optical disk, computer Memory, Read-Only Memory (ROM).
Embodiments of the present invention may also provide a computer-readable storage medium, where the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor of an electronic device, the computer program may implement:
when an allocation instruction of a preset product is received, acquiring contract content corresponding to the product, extracting contract terms from the contract content, and performing data structuring processing on the contract terms of the product to obtain structural combination terms;
performing decision tree transformation on the structured contract clauses to obtain a contract clause decision tree, and judging whether the distribution instruction can be met according to the contract clause decision tree;
when the distribution instruction can be met, carrying out quantity verification on the product account corresponding to the distribution instruction so as to determine a distribution limit;
estimating the distributable quantity of the product by using a pre-trained quantity estimation model, and judging whether the distributable quantity meets the distribution limit;
when the allocable quantity meets the allocation limit, allocating the product to a preset user according to the allocation instruction;
and when the distributable quantity does not meet the distribution limit, the distribution instruction is changed according to the distributable quantity, and the product is distributed to a preset user according to the changed distribution instruction.
Further, the computer usable storage medium may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function, and the like; the storage data area may store data created according to the use of the blockchain node, and the like.
In the embodiments provided in the present invention, it should be understood that the disclosed apparatus, device and method can be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the modules is only one logical functional division, and other divisions may be realized in practice.
The modules described as separate parts may or may not be physically separate, and parts displayed as modules may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment.
In addition, functional modules in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, or in a form of hardware plus a software functional module.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential attributes thereof.
The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference signs in the claims shall not be construed as limiting the claim concerned.
The block chain is a novel application mode of computer technologies such as distributed data storage, point-to-point transmission, a consensus mechanism, an encryption algorithm and the like. A block chain (Blockchain), which is essentially a decentralized database, is a series of data blocks associated by using a cryptographic method, and each data block contains information of a batch of network transactions, so as to verify the validity (anti-counterfeiting) of the information and generate a next block. The blockchain may include a blockchain underlying platform, a platform product service layer, an application service layer, and the like.
Furthermore, it is obvious that the word "comprising" does not exclude other elements or steps, and the singular does not exclude the plural. A plurality of units or means recited in the system claims may also be implemented by one unit or means in software or hardware. The terms second, etc. are used to denote names, but not any particular order.
Finally, it should be noted that the above embodiments are only for illustrating the technical solutions of the present invention and not for limiting, and although the present invention is described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications or equivalent substitutions may be made on the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.
Claims (10)
1. A method for decision tree based product distribution, the method comprising:
when an allocation instruction of a preset product is received, acquiring contract content corresponding to the product, extracting contract clauses from the contract content, and performing data structuring processing on the contract clauses to obtain structural combination clauses;
performing decision tree transformation on the structured contract clauses to obtain a contract clause decision tree, and judging whether the distribution instruction can be met according to the contract clause decision tree;
when the distribution instruction can be met, carrying out quantity verification on the product account corresponding to the distribution instruction so as to determine a distribution limit;
estimating the distributable quantity of the product by using a pre-trained quantity estimation model, and judging whether the distributable quantity meets the distribution limit;
when the allocable quantity meets the allocation limit, allocating the product to a preset user according to the allocation instruction;
and when the distributable quantity does not meet the distribution limit, the distribution instruction is changed according to the distributable quantity, and the product is distributed to a preset user according to the changed distribution instruction.
2. The decision tree-based product allocation method of claim 1, wherein the checking the quantity of the product accounts corresponding to the allocation instructions to determine allocation credit comprises:
carrying out quantity flow measurement and calculation on the product account by using a pre-trained liquidity measurement and calculation model to obtain the account product quantity;
comparing the total quantity in the distribution instruction with the account product quantity line, and judging whether the account product quantity meets the total quantity in the distribution instruction;
if the account product quantity meets the total quantity, judging that the total quantity in the distribution instruction is a distribution limit;
and if the account product quantity does not meet the total quantity, judging the account product quantity to be a distribution limit.
3. The decision tree-based product distribution method of claim 1, wherein the decision tree transformation of the structured contract terms to obtain a contract term decision tree comprises:
acquiring the clause content of each contract clause of each structure combination identical clause;
and performing recursive calculation on the clause content of each contract clause by using a decision tree algorithm to generate a contract clause decision tree.
4. The decision tree based product distribution method of claim 3, wherein said recursively computing the clause contents of each of said contract terms using a decision tree algorithm to generate a contract term decision tree, comprises:
calculating a kini coefficient of the contract content;
when the coefficient of the degree of the contract content is larger than or equal to the preset threshold value, calculating the probability that the distribution instruction meets the content of each contract clause by using a preset probability calculation model, and calculating the coefficient of the degree of the content of each contract clause extracted from the contract content according to the probability;
and sequencing the keny coefficients of the clause contents of the contract clauses from small to large, and performing tree division on the contract clauses according to the sequencing to obtain a contract clause decision tree.
5. The decision tree based product distribution method of claim 1, wherein the data structuring of the contract terms to obtain structured contract terms comprises:
acquiring the data type of the clause content of each contract clause, and identifying the hierarchical structure corresponding to each contract clause according to the data type;
according to the hierarchical structure, unstructured data are screened out from the clause content of the contract clauses, the unstructured data are inserted into a pre-constructed table, the classes of the unstructured data inserted into the table are defined, and structural combination clauses are obtained.
6. The decision tree based product distribution method of claim 5, wherein said identifying a hierarchy corresponding to each of said contract terms based on said data type comprises:
inquiring a data field of the clause content of the contract clause according to the data type, and identifying a position index of the data field in the contract clause;
and determining the hierarchical structure of the clause content in the contract clauses according to the position index.
7. The decision tree based product allocation method of claim 1, wherein said modifying said allocation instructions based on said allocable quantities comprises:
acquiring the priority level of each user in the preset users;
and selecting users with priority levels larger than a preset threshold, and changing the total number in the distribution instruction according to the distributable number corresponding to the selected users.
8. A decision tree based product distribution apparatus, comprising:
the clause structuring module is used for acquiring contract contents corresponding to a preset product when receiving a distribution instruction of the product, extracting contract clauses from the contract contents, and performing data structuring processing on the contract clauses of the product to obtain structural combination clauses;
the instruction checking module is used for performing decision tree transformation on the structured contract clauses to obtain a contract clause decision tree and judging whether the distribution instruction can be met according to the contract clause decision tree;
the quantity distribution module is used for carrying out quantity verification on the product account corresponding to the distribution instruction when the distribution instruction can be met so as to determine a distribution limit; estimating the distributable quantity of the product by using a pre-trained quantity estimation model, and judging whether the distributable quantity meets the distribution limit; when the distributable quantity meets the distribution amount, distributing the product to a preset user according to the distribution instruction, or when the distributable quantity does not meet the distribution amount, changing the distribution instruction according to the distributable quantity, and distributing the product to the preset user according to the changed distribution instruction.
9. An electronic device, characterized in that the electronic device comprises:
at least one processor; and the number of the first and second groups,
a memory communicatively coupled to the at least one processor; wherein,
the memory stores computer program instructions executable by the at least one processor to enable the at least one processor to perform the decision tree based product allocation method of any one of claims 1 to 7.
10. A computer-readable storage medium, storing a computer program, wherein the computer program, when executed by a processor, implements the decision tree based product distribution method according to any of claims 1 to 7.
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Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPH11353378A (en) * | 1998-06-09 | 1999-12-24 | Hitachi Ltd | Contractor segmentation method |
| CN102013075A (en) * | 2009-09-07 | 2011-04-13 | 中国工商银行股份有限公司 | Financial product data distributing server and distributing method |
| CN109636612A (en) * | 2018-10-19 | 2019-04-16 | 深圳平安财富宝投资咨询有限公司 | Financing resource allocation methods, device, equipment and medium based on data analysis |
| CN110287665A (en) * | 2019-05-08 | 2019-09-27 | 口碑(上海)信息技术有限公司 | Equipment uses distribution method, device and storage medium, the electronic device of equity |
| CN110796399A (en) * | 2020-01-03 | 2020-02-14 | 支付宝(杭州)信息技术有限公司 | Resource allocation method and device based on block chain |
| CN112581153A (en) * | 2019-09-29 | 2021-03-30 | 拉扎斯网络科技（上海）有限公司 | Resource allocation method, resource allocation device, storage medium, and electronic apparatus |
| US20210097449A1 (en) * | 2020-12-11 | 2021-04-01 | Intel Corporation | Memory-efficient system for decision tree machine learning |
| CN112650763A (en) * | 2020-12-17 | 2021-04-13 | 平安消费金融有限公司 | Configuration method of product quota, related equipment and storage medium |
| CN113191546A (en) * | 2021-04-29 | 2021-07-30 | 远光软件股份有限公司 | Resource allocation prediction method, resource management device and storage medium |
-
2021
- 2021-08-18 CN CN202110950137.3A patent/CN113657507A/en active Pending
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPH11353378A (en) * | 1998-06-09 | 1999-12-24 | Hitachi Ltd | Contractor segmentation method |
| CN102013075A (en) * | 2009-09-07 | 2011-04-13 | 中国工商银行股份有限公司 | Financial product data distributing server and distributing method |
| CN109636612A (en) * | 2018-10-19 | 2019-04-16 | 深圳平安财富宝投资咨询有限公司 | Financing resource allocation methods, device, equipment and medium based on data analysis |
| CN110287665A (en) * | 2019-05-08 | 2019-09-27 | 口碑(上海)信息技术有限公司 | Equipment uses distribution method, device and storage medium, the electronic device of equity |
| CN112581153A (en) * | 2019-09-29 | 2021-03-30 | 拉扎斯网络科技（上海）有限公司 | Resource allocation method, resource allocation device, storage medium, and electronic apparatus |
| CN110796399A (en) * | 2020-01-03 | 2020-02-14 | 支付宝(杭州)信息技术有限公司 | Resource allocation method and device based on block chain |
| US20210097449A1 (en) * | 2020-12-11 | 2021-04-01 | Intel Corporation | Memory-efficient system for decision tree machine learning |
| CN112650763A (en) * | 2020-12-17 | 2021-04-13 | 平安消费金融有限公司 | Configuration method of product quota, related equipment and storage medium |
| CN113191546A (en) * | 2021-04-29 | 2021-07-30 | 远光软件股份有限公司 | Resource allocation prediction method, resource management device and storage medium |
Non-Patent Citations (2)
| Title |
|---|
| SURYAKANTHI TANGIRALA: "Evaluating the Impact of GINI Index and Information Gain on Classification using Decision Tree Classifier Algorithm", 《INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS》, vol. 11, no. 2, pages 612 - 619 * |
| 杨晓梅等: "基尼指数遗传算法求解具有共性的调度问题", 《系统工程学报》, vol. 24, no. 03, pages 322 - 328 * |
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