CN112561461A - Government affair approval method, system, device and storage medium based on machine learning - Google Patents
Government affair approval method, system, device and storage medium based on machine learning Download PDFInfo
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Abstract
According to the government affair approval method and system based on machine learning, the material elements of government affair materials and the logic relation among the material elements are obtained; training the material elements of the government affair materials and the logic relation between the material elements by using a machine learning algorithm to obtain a material element rule model; inputting the government affair materials into the material element rule model, and outputting the logic relation among the material elements of the government affair materials and the probability of the logic relation; when the probability of the logical relation corresponding to the material element is larger than a preset threshold value, the logical relation is stored in a rule base of a government affair approval system, manual examination is carried out on the rule, and the logical rule meeting the requirement is reserved to form the rule base; and uploading government affair application materials, and intelligently examining and approving the material elements of the government affair application materials by using the logical relation stored in the rule base of the government affair examination and approval system. Through the government affair approval rule based on machine learning training, the number of input items of a user is reduced, and the approval speed of electronic government affair application items is increased.
Description
Technical Field
The invention belongs to the field of electronic government affairs, and particularly relates to a government affair approval method, a system, a device and a storage medium based on machine learning.
Background
At present, the national government departments sequentially go on line with respective government affair approval platforms, and individuals and enterprises fill application forms on line, so that users need to fill related information step by step, and the declaration time period is long and the efficiency is low. The electronic government affair management system and method (CN111325517A) in the prior art provide an intelligent and efficient government affair management platform scheme, solve the problem that a large number of forms need to be filled when an application user submits application materials, and are easy to make mistakes depending on content information input by the application user; when the approver approves the application items, a policy basis for checking whether the information submitted by the application user conforms to the government affairs items is needed, and the approver wastes time and labor and is easy to generate errors.
Disclosure of Invention
In view of this, the present disclosure provides a method, a system, an apparatus, and a storage medium for government affair approval based on machine learning, which reduce user input items and improve approval speed of e-government application items based on government affair approval rules trained by machine learning.
According to an aspect of the present disclosure, there is provided a machine learning-based government affair approval method, the method including:
acquiring material elements of government affair materials and logic relations among the material elements; training the material elements of the government affairs materials and the logic relation between the material elements by using a machine learning algorithm to obtain a material element rule model;
inputting the government materials into the material element rule model, and outputting logical relations between the material elements of the government materials and probabilities of the logical relations; when the probability of the logical relation corresponding to the material element is larger than a preset threshold value, storing the logical relation into a rule base of the government affair approval system, manually checking the rule, and reserving the logical rules meeting the requirements to form the rule base;
uploading government affair application materials, and carrying out intelligent examination and approval on material elements of the government affair application materials by utilizing the logic relation stored in the rule base of the government affair examination and approval system.
In one possible implementation, the material elements include a text type element, a number type element, an amount type element, and a date type element.
In one possible implementation, the approval rule logic includes: equal to, not equal to, greater than, less than, greater than or equal to, less than or equal to, including, not including, intersecting.
In one possible implementation, the method further includes:
and after the intelligent examination and approval is passed, sending the order to an examination and approval person for rechecking government affair material elements, storing the application material into a material library after the rechecking is passed, simultaneously or after the rechecking of the application elements, carrying out examination and approval of the manual rules, and after the approval of the manual rules is passed, informing an application user that the item application is accepted, otherwise, returning to the application user.
According to another aspect of the present disclosure, there is provided a machine learning-based government affairs approval system, the system including:
the rule model training module is used for acquiring material elements of government affair materials and logic relations among the material elements; training the material elements of the government affairs materials and the logic relation between the material elements by using a machine learning algorithm to obtain a material element rule model;
a comparison module for inputting the government affairs materials into the material element rule model and outputting the logic relationship between the material elements of the government affairs materials and the probability of the logic relationship; when the probability of the logical relation corresponding to the material element is larger than a preset threshold value, storing the logical relation into a rule base of the government affair approval system, manually checking the rule, and reserving the logical rules meeting the requirements to form the rule base;
and the intelligent approval module is used for uploading government affair application materials and intelligently approving the material elements of the government affair application materials by utilizing the logical relation stored in the rule base of the government affair approval system.
According to another aspect of the present disclosure, there is provided a machine learning-based government affairs approval apparatus, including:
a processor;
a memory for storing processor-executable instructions;
wherein the processor is configured to implement the method described above when executing the instructions.
According to another aspect of the present disclosure, a non-transitory computer-readable storage medium is provided, having stored thereon computer program instructions, which when executed by a processor, perform the method described above.
According to the government affair approval method based on machine learning, the material elements of government affair materials and the logic relation among the material elements are obtained; training the material elements of the government affairs materials and the logic relation between the material elements by using a machine learning algorithm to obtain a material element rule model; inputting the government materials into the material element rule model, and outputting logical relations between the material elements of the government materials and probabilities of the logical relations; when the probability of the logical relation corresponding to the material element is larger than a preset threshold value, storing the logical relation into a rule base of the government affair approval system, manually checking the rule, and reserving the logical rules meeting the requirements to form the rule base; uploading government affair application materials, and carrying out intelligent examination and approval on material elements of the government affair application materials by utilizing the logic relation stored in the rule base of the government affair examination and approval system. Through the government affair approval rule based on machine learning training, the number of input items of a user is reduced, and the approval speed of electronic government affair application items is increased.
Other features and aspects of the present disclosure will become apparent from the following detailed description of exemplary embodiments, which proceeds with reference to the accompanying drawings.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate exemplary embodiments, features, and aspects of the disclosure and, together with the description, serve to explain the principles of the disclosure.
Fig. 1 shows a flowchart of a machine learning-based government approval method according to an embodiment of the present disclosure.
FIG. 2 illustrates a schematic diagram of application element approval rule setting for machine learning-based government approval according to an embodiment of the present disclosure;
FIG. 3 illustrates a schematic diagram of the setup of application material for a machine learning based government approval application in accordance with an embodiment of the present disclosure;
FIG. 4 illustrates a schematic diagram of a setup of application elements for machine learning-based government approval according to an embodiment of the present disclosure; FIG. 5 shows a schematic diagram of an automatic approval of a machine learning based government approval according to an embodiment of the present disclosure;
fig. 6 illustrates a diagram of a machine learning based government approval system according to an embodiment of the present disclosure.
Fig. 7 shows a block diagram of a machine learning-based government approval apparatus according to an embodiment of the present disclosure.
Fig. 8 shows a block diagram of a machine learning-based government approval apparatus according to an embodiment of the present disclosure.
Detailed Description
Various exemplary embodiments, features and aspects of the present disclosure will be described in detail below with reference to the accompanying drawings. In the drawings, like reference numbers can indicate functionally identical or similar elements. While the various aspects of the embodiments are presented in drawings, the drawings are not necessarily drawn to scale unless specifically indicated.
The word "exemplary" is used exclusively herein to mean "serving as an example, embodiment, or illustration. Any embodiment described herein as "exemplary" is not necessarily to be construed as preferred or advantageous over other embodiments.
Furthermore, in the following detailed description, numerous specific details are set forth in order to provide a better understanding of the present disclosure. It will be understood by those skilled in the art that the present disclosure may be practiced without some of these specific details. In some instances, methods, means, elements and circuits that are well known to those skilled in the art have not been described in detail so as not to obscure the present disclosure.
The government affair materials can be various application forms (new house brand application forms and the like), certificates (identity cards, retirement cards, registration engineers and the like), certificates (house (building) property certificates, various qualification certificates and the like), reports and the like which are submitted by the application users for proving the qualification of applying for the government affairs.
The material elements may be fields on the government material such as name on an identification card, identification number, date of birth, age, rights type, etc.
Fig. 1 shows a flowchart of a machine learning-based government approval method according to an embodiment of the present disclosure. The government affair approval method based on machine learning can be applied to electronic government affair approval systems, such as newly developed building (group) title house license transaction systems, building crane filing approval systems, commodity room pre-sale approval systems, third-level qualification approval systems of real estate development enterprises and the like, and the newly developed building (group) title house license transaction systems are taken as an example for explanation.
As shown in fig. 1, step S11: acquiring material elements of government affair materials and logic relations among the material elements, and training the material elements of the government affair materials and the logic relations among the material elements by utilizing a machine learning algorithm to obtain the material element rule model.
In an example, the logic rule may include, among other things: the logic rules of equal to, unequal to, greater than, less than, greater than or equal to, greater than or equal to, less than or equal to, including, not including, intersecting, etc. can be confirmed by automatic check of a computer, otherwise, the rules are confirmed as manual rules.
Acquiring historical government affair data successfully approved by an electronic government affair system, analyzing material elements of historical government affair materials, and using logic relations among the material elements as input data of a material element rule model. For example, the auditor previously uploads the successfully approved documents, i.e., the government affairs materials, analyzes the rules of the government affairs approval, and obtains the material elements of the government affairs materials required by the rules of the approval, the logical relationship among the material elements, and the corresponding logical rules of judgment. If the government affair approval system traverses the uploaded elements of the text types of all the government affair materials, searching the existing logic rules of the material elements in the central rule base of the government affair approval system, and if the logic rules exist and another element of the logic rules is in the uploaded government affair materials or the elements of the central rule base of the government affair approval system, adopting the logic rules; if the other element is not in the uploaded material or is not an element of a central rule base of the government affair approval system, only adopting the logical relationship of the logical rule, and finding the element which is in accordance with the logical relationship in other text type elements uploaded at this time to form a logical rule; if the relation is not found, the relation of 'equal' is used by default, and then elements which accord with the relation of 'equal' are found in other text type elements uploaded at this time to form a logic rule.
If the government affair approval system traverses the elements of the date types of all the uploaded materials, searching the existing logic rules of the material elements in the central rule base, and if the logic rules exist and the other element of the logic rules is also in the uploaded materials at this time or the elements of the central rule base of the government affair approval system, adopting the logic rules; if the other element is not in the uploaded material or is not an element of a central rule base of the government affair approval system, only adopting the logical relationship of the logical rule, and then finding the element which is in accordance with the logical relationship in other date type elements uploaded at this time to form a logical rule; if the logic rule cannot be found, the relation of 'more than or equal to' is used by default, and then elements which accord with the relation of 'more than or equal to' are found in other uploaded material elements to form a logic rule. More government approval materials with historical success are analyzed to obtain more material elements and corresponding relations, and a machine learning algorithm (such as a binary tree, a K-proximity algorithm and the like) is utilized to train the logical relations between the material elements and the material elements of the government materials and the logical rules corresponding to the material elements to obtain a material element rule model.
Step S12: inputting the government affair materials into the material element rule model, and outputting the logic relation corresponding to the material elements of the government affair materials and the probability of the logic relation; and when the probability of the logical relation corresponding to the material element is greater than a preset threshold value, storing the logical relation into a rule base of the government affair approval system, manually checking the rule, and reserving the logical rules meeting the requirements to form the rule base.
The material element rule model obtained in the step S11 can be used to fit and generate most of the automatic confirmation rules of the government material elements for government approval, and the examiners can improve the settings of the remaining automatic rules and manual rules on the basis, so that the workload of the examiners can be greatly reduced, and the speed of the government approval can be increased. The government material elements may include text type elements, number type elements, amount type elements, date type elements, etc., among others. The logical relationship for each material element may include a wide variety of relationships, such as different requirements for time and date, requirements before the date, requirements after the date, etc. in different government approval events. By analyzing a large amount of government affair materials approved by the government affairs, the probability value of the corresponding logic relation of each material element can be analyzed, when the probability value is larger than a preset threshold value, an auditor removes the inconsistent logic rules in a manual rechecking mode, and the logic rules which accord with the application items are stored in a central rule base of the government affair approval system to serve as the automatic approval logic rules of the government affair approval system. The preset threshold may be 0.8 or 0.9, and is set according to different government affair approval tasks, which is not limited herein.
And step S13, uploading government affair application materials, and carrying out intelligent approval on the material elements of the government affair application materials by using the logic relation stored in the rule base of the government affair approval system.
In one example, government affair application materials uploaded by users or enterprises are obtained, whether the application element information determined according to the application materials meets the requirements of the application setting information is automatically judged according to logical relations stored in a rule base of the government affair approval system, and when the requirements of the application setting information are met, the logical rules are automatically confirmed; the automatic validation of the logic rules for the government application materials may further comprise manual rule validation when the government approval requirements are met. The manual rule confirmation can be used for confirming application materials for enterprise application users according to approval standards of the declared government application matters, or confirming materials except for automatic approval of some electronic government systems, such as environment data materials, qualification materials and the like.
The method further includes step S14: and after the intelligent examination and approval is passed, sending the order to an examination and approval person for rechecking government affair material elements, storing the application material into a material library after the rechecking is passed, simultaneously or after the rechecking of the application elements, carrying out examination and approval of the manual rules, and after the approval of the manual rules is passed, informing an application user that the item application is accepted, otherwise, returning to the application user.
Application example
For example, as shown in fig. 2, the stakeholders who do not exercise the title are the same as the names of the application lists of the new house titles, and the citizen identification numbers of the identification cards are the same as the identification numbers of the application lists of the new house titles. Analyzing a plurality of historical success case data of newly developed building (group) title house number certificate transaction items, and acquiring each material element of government affairs materials declaring the newly developed building (group) title house number certificate transaction items, and the corresponding logical relationship of each material element and the logical rule of the material element. The government affair materials of the newly developed (group) title house license transaction items comprise immortal property licenses, identity cards, a newly developed house license application form and the like, and the government affair material elements of the newly developed (group) title house license transaction items comprise names, birth dates, identity card numbers and the like, immortal property licenses, identity cards and the relations among the material elements of the newly developed (group) title house license transaction items, such as whether the names of the immortal property licenses are the same as the names of the identity cards or whether the application dates of the newly developed house license application form are smaller than the effective dates of the identity cards or not. And (3) training the material elements of the government affairs materials of the newly developed building (group) title house license transaction items and the logic rules corresponding to the material elements by using a machine learning algorithm (such as a binary tree, a K-adjacent algorithm and the like) to obtain a material element rule model. The government affair materials successfully approved for the newly developed house title ticket transaction items submitted by the prior application users are input into the material element rule model to obtain the logical relations among the material elements of the newly developed house title ticket transaction items and the probability values of the logical relations, and when the probability values are larger than 0.85, the logical rules corresponding to the logical relations are stored in a central rule base of the government affair approval system, for example, the stored logical rules comprise that the name of a righter with an immotile title is the same as that of a newly developed house title ticket application table, and the name of an identity card (page 1) is the same as that of the newly developed house title ticket application table. The auditor manually rechecks the logic stored in the central rule base of the government affair approval system, removes the logic rules which do not meet the requirements, and retains the logic rules which meet the requirements to form the rule base.
When an applicant user selects a personal or enterprise login interface according to the identity of the applicant user, after the application user logs in, the applicant user fills corresponding information according to the item application setting of the item transaction items of the title of a newly developed building (group), and uploads the application material information required by the item transaction items of the title of the newly developed building (group), as shown in fig. 3, when the applicant user submits the item transaction items of the title of the newly developed building (group), the application materials such as a real estate title 1, an identity card 1, a new house title application form 1 and the like need to be submitted, if the applicant user submits other government items before, the information of the real estate title 1, the identity card 1 and the new house title application form 1 is uploaded, and the real estate title 1, the identity card 1 and the new house title application form 1 of the applicant user are stored in an application material library, at this time, the application user does not need to upload the related materials again.
The newly developed building (group) title house license transaction system can determine the application elements of the newly developed building (group) title house license transaction according to the newly developed building (group) title house license application setting information and the application material of the newly developed building (group) title house license, and as shown in fig. 4, the application elements of the newly developed building (group) title house license application table 1 include name, stamp, identity number, contact way and planned house license address. The applicable elements of the immobilization title 1 are valid period (deadline), sitting, righter, and real property number. The application requirements of the identity card 1 include a citizen identity number, a name and an expiration date. If the application user only uploaded in the property right 1 before, ID card 1, newly handling house number plate application form 1 information one or two kinds, at this moment, when the application user carried out the operation next step, newly developed building (crowd) property title house number plate system automatic verification application material data submission is incomplete, and the suggestion check-up fails, as shown in FIG. 5, the suggestion application user submitted application material data incomplete, the application user uploaded the required application material of building (crowd) property name plate item of new development according to the suggestion, newly developed building (crowd) property name plate system adopted OCR technical recognition application material's field information uploaded, and convert the application material picture information uploaded into text information, according to the application setting information with the field confirms the application element of application item. The system automatically examines and approves according to the logic rules which are obtained by machine learning training and stored in the central rule base of the government approval system, for example, the name of the person entitled to the unmoved property right-1 is the same as the name of the new house license application table-1, the name of the ID card (page 1) -1 is the same as the name of the new house license whole application table-1, and the like, when the application material data of the newly developed building (group) property right house license transaction system uploaded by the application user are complete and the next operation is carried out, the newly developed building (group) property right house license transaction system automatically judges whether the material elements of the government materials submitted by the application user meet the requirements according to the stored logic rules, such as the name of the person entitled to the unmoved property right-1 is the same as the name of the new house license application table-1, the name of the ID card (page 1) -1 is the same as the name of the new house license whole application table-1, and the like, if the requirement is met, the application user submits the newly developed building (group) title house number license items to an examination and approval end for examination and approval after the examination and approval of the manual rule pass, the application user submits the newly developed building (group) title house number license items to the examination and approval end for examination and approval, the newly developed building (group) title house number license handling system automatically distributes the list to corresponding examination and approval personnel for automatically and uniformly accepting tasks according to the scheduling sequence of the examination and approval personnel in the scheduling setting, the examination and approval personnel can accept the newly developed building (group) title house number license application items through mobile end APP (such as mobile phone end APP and Ipad end APP) and enter an acceptance interface to examine and check the government affair materials of the newly developed building (group) title house number license application items, at the moment, the examination and approval personnel only need to simply check whether text information submitted by the application user and automatically identified and converted through OCR is consistent with the content on the application material picture or not, and the logical judgment of the approval logical rule is not needed. When the upper and lower consistence of the application elements is correct, the approver submitted to the next approver manually inspects whether the government affair materials (such as very thick government affair materials, qualification contents, environment evaluation reports and the like submitted by the application user) meet the approval standard, when the manual inspection has no problem, the approver at the approver stores the government affair materials submitted by the application user in an application material library, the newly developed building (group) title house certificate transaction system informs the application user that newly developed building (group) title house certificate transaction items have been accepted, the approver at the approver re-inspects, inspects and issues the newly developed building (group) title house certificate transaction items, and otherwise, the newly developed building (group) title house certificate transaction system returns to the enterprise end or the personal end.
According to the government affair approval method based on machine learning, the material elements of government affair materials and the logic relation among the material elements are obtained; training the material elements of the government affairs materials and the logic relation between the material elements by using a machine learning algorithm to obtain a material element rule model; inputting the government materials into the material element rule model, and outputting logical relations between the material elements of the government materials and probabilities of the logical relations; when the probability of the logical relation corresponding to the material element is larger than a preset threshold value, storing the logical relation into a rule base of the government affair approval system, manually checking the rule, and reserving the logical rules meeting the requirements to form the rule base; uploading government affair application materials, and carrying out intelligent examination and approval on material elements of the government affair application materials by utilizing the logic relation stored in the rule base of the government affair examination and approval system. Through the government affair approval rule based on machine learning training, the number of input items of a user is reduced, and the approval speed of electronic government affair application items is increased.
Fig. 6 illustrates a machine learning based government approval system according to an embodiment of the present disclosure. As shown in fig. 6, the government affairs approval system includes:
the rule model training module is used for acquiring material elements of government affair materials and logic relations among the material elements; training the material elements of the government affairs materials and the logic relation between the material elements by using a machine learning algorithm to obtain a material element rule model;
a comparison module for inputting the government affairs materials into the material element rule model and outputting the logic relationship between the material elements of the government affairs materials and the probability of the logic relationship; when the probability of the logical relation corresponding to the material element is larger than a preset threshold value, storing the logical relation into a rule base of the government affair approval system, manually checking the rule, and reserving the logical rules meeting the requirements to form the rule base;
and the intelligent approval module is used for uploading government affair application materials and intelligently approving the material elements of the government affair application materials by utilizing the logical relation stored in the rule base of the government affair approval system.
Fig. 7 is a block diagram illustrating a government approval apparatus 800 for machine learning, according to an example embodiment. For example, the device 800 may be a newly developed device for handling a house title, a device for approval of registration of a construction crane, a device for approval of pre-sale approval of a commercial housing, a device for tertiary qualification approval of a real estate development enterprise, or the like.
Referring to fig. 7, the apparatus 800 may include one or more of the following components: processing component 802, memory 804, power component 806, multimedia component 808, audio component 810, input/output (I/O) interface 812, sensor component 814, and communication component 816.
The processing component 802 generally controls overall operation of the device 800, such as operations associated with display, telephone calls, data communications, camera operations, and recording operations. The processing components 802 may include one or more processors 820 to execute instructions to perform all or a portion of the steps of the methods described above. Further, the processing component 802 can include one or more modules that facilitate interaction between the processing component 802 and other components. For example, the processing component 802 can include a multimedia module to facilitate interaction between the multimedia component 808 and the processing component 802.
The memory 804 is configured to store various types of data to support operations at the apparatus 800. Examples of such data include instructions for any application or method operating on device 800, contact data, phonebook data, messages, pictures, videos, and so forth. The memory 804 may be implemented by any type or combination of volatile or non-volatile memory devices such as Static Random Access Memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic or optical disks.
The multimedia component 808 includes a screen that provides an output interface between the device 800 and a user. In some embodiments, the screen may include a Liquid Crystal Display (LCD) and a Touch Panel (TP). If the screen includes a touch panel, the screen may be implemented as a touch screen to receive an input signal from a user. The touch panel includes one or more touch sensors to sense touch, slide, and gestures on the touch panel. The touch sensor may not only sense the boundary of a touch or slide action, but also detect the duration and pressure associated with the touch or slide operation. In some embodiments, the multimedia component 808 includes a front facing camera and/or a rear facing camera. The front camera and/or the rear camera may receive external multimedia data when the device 800 is in an operating mode, such as a shooting mode or a video mode. Each front camera and rear camera may be a fixed optical lens system or have a focal length and optical zoom capability.
The audio component 810 is configured to output and/or input audio signals. For example, the audio component 810 includes a Microphone (MIC) configured to receive external audio signals when the apparatus 800 is in an operational mode, such as a call mode, a recording mode, and a voice recognition mode. The received audio signals may further be stored in the memory 804 or transmitted via the communication component 816. In some embodiments, audio component 810 also includes a speaker for outputting audio signals.
The I/O interface 812 provides an interface between the processing component 802 and peripheral interface modules, which may be keyboards, click wheels, buttons, etc. These buttons may include, but are not limited to: a home button, a volume button, a start button, and a lock button.
The sensor assembly 814 includes one or more sensors for providing various aspects of state assessment for the device 800. For example, the sensor assembly 814 may detect the open/closed status of the device 800, the relative positioning of components, such as a display and keypad of the device 800, the sensor assembly 814 may also detect a change in the position of the device 800 or a component of the device 800, the presence or absence of user contact with the device 800, the orientation or acceleration/deceleration of the device 800, and a change in the temperature of the device 800. Sensor assembly 814 may include a proximity sensor configured to detect the presence of a nearby object without any physical contact. The sensor assembly 814 may also include a light sensor, such as a CMOS or CCD image sensor, for use in imaging applications. In some embodiments, the sensor assembly 814 may also include an acceleration sensor, a gyroscope sensor, a magnetic sensor, a pressure sensor, or a temperature sensor.
The communication component 816 is configured to facilitate communications between the apparatus 800 and other devices in a wired or wireless manner. The device 800 may access a wireless network based on a communication standard, such as WiFi, 2G or 3G, or a combination thereof. In an exemplary embodiment, the communication component 816 receives a broadcast signal or broadcast related information from an external broadcast management system via a broadcast channel. In an exemplary embodiment, the communication component 816 further includes a Near Field Communication (NFC) module to facilitate short-range communications. For example, the NFC module may be implemented based on Radio Frequency Identification (RFID) technology, infrared data association (IrDA) technology, Ultra Wideband (UWB) technology, Bluetooth (BT) technology, and other technologies.
In an exemplary embodiment, the apparatus 800 may be implemented by one or more Application Specific Integrated Circuits (ASICs), Digital Signal Processors (DSPs), Digital Signal Processing Devices (DSPDs), Programmable Logic Devices (PLDs), Field Programmable Gate Arrays (FPGAs), controllers, micro-controllers, microprocessors or other electronic components for performing the above-described methods.
In an exemplary embodiment, a non-transitory computer-readable storage medium, such as the memory 804, is also provided that includes computer program instructions executable by the processor 820 of the device 800 to perform the above-described methods.
Fig. 8 is a block diagram illustrating a device 1900 for machine learning-based government approval according to an example embodiment. For example, the apparatus 1900 may be provided as a server. Referring to FIG. 8, the device 1900 includes a processing component 1922 further including one or more processors and memory resources, represented by memory 1932, for storing instructions, e.g., applications, executable by the processing component 1922. The application programs stored in memory 1932 may include one or more modules that each correspond to a set of instructions. Further, the processing component 1922 is configured to execute instructions to perform the above-described method.
The device 1900 may also include a power component 1926 configured to perform power management of the device 1900, a wired or wireless network interface 1950 configured to connect the device 1900 to a network, and an input/output (I/O) interface 1958. The device 1900 may operate based on an operating system stored in memory 1932, such as Windows Server, Mac OS XTM, UnixTM, LinuxTM, FreeBSDTM, or the like.
In an exemplary embodiment, a non-transitory computer readable storage medium, such as the memory 1932, is also provided that includes computer program instructions executable by the processing component 1922 of the apparatus 1900 to perform the above-described methods.
The present disclosure may be systems, methods, and/or computer program products. The computer program product may include a computer-readable storage medium having computer-readable program instructions embodied thereon for causing a processor to implement various aspects of the present disclosure.
The computer readable storage medium may be a tangible device that can hold and store the instructions for use by the instruction execution device. The computer readable storage medium may be, for example, but not limited to, an electronic memory device, a magnetic memory device, an optical memory device, an electromagnetic memory device, a semiconductor memory device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a Static Random Access Memory (SRAM), a portable compact disc read-only memory (CD-ROM), a Digital Versatile Disc (DVD), a memory stick, a floppy disk, a mechanical coding device, such as punch cards or in-groove projection structures having instructions stored thereon, and any suitable combination of the foregoing. Computer-readable storage media as used herein is not to be construed as transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission medium (e.g., optical pulses through a fiber optic cable), or electrical signals transmitted through electrical wires.
The computer-readable program instructions described herein may be downloaded from a computer-readable storage medium to a respective computing/processing device, or to an external computer or external storage device via a network, such as the internet, a local area network, a wide area network, and/or a wireless network. The network may include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers and/or edge servers. The network adapter card or network interface in each computing/processing device receives computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in a computer-readable storage medium in the respective computing/processing device.
The computer program instructions for carrying out operations of the present disclosure may be assembler instructions, Instruction Set Architecture (ISA) instructions, machine-related instructions, microcode, firmware instructions, state setting data, or source or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The computer-readable program instructions may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider). In some embodiments, the electronic circuitry that can execute the computer-readable program instructions implements aspects of the present disclosure by utilizing the state information of the computer-readable program instructions to personalize the electronic circuitry, such as a programmable logic circuit, a Field Programmable Gate Array (FPGA), or a Programmable Logic Array (PLA).
Various aspects of the present disclosure are described herein with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the disclosure. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer-readable program instructions.
These computer-readable program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks. These computer-readable program instructions may also be stored in a computer-readable storage medium that can direct a computer, programmable data processing apparatus, and/or other devices to function in a particular manner, such that the computer-readable medium storing the instructions comprises an article of manufacture including instructions which implement the function/act specified in the flowchart and/or block diagram block or blocks.
The computer readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other devices to cause a series of operational steps to be performed on the computer, other programmable apparatus or other devices to produce a computer implemented process such that the instructions which execute on the computer, other programmable apparatus or other devices implement the functions/acts specified in the flowchart and/or block diagram block or blocks.
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of instructions, which comprises one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
Having described embodiments of the present disclosure, the foregoing description is intended to be exemplary, not exhaustive, and not limited to the disclosed embodiments. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The terminology used herein is chosen in order to best explain the principles of the embodiments, the practical application, or improvements made to the technology in the marketplace, or to enable others of ordinary skill in the art to understand the embodiments disclosed herein.
Claims (7)
1. A method for government affairs approval based on machine learning, the method comprising:
acquiring material elements of government affair materials and logic relations among the material elements; training the material elements of the government affairs materials and the logic relation between the material elements by using a machine learning algorithm to obtain a material element rule model;
inputting the government materials into the material element rule model, and outputting logical relations between the material elements of the government materials and probabilities of the logical relations; when the probability of the logical relation corresponding to the material element is larger than a preset threshold value, storing the logical relation into a rule base of the government affair approval system, manually checking the rule, and reserving the logical rules meeting the requirements to form the rule base;
uploading government affair application materials, and carrying out intelligent examination and approval on material elements of the government affair application materials by utilizing the logic relation stored in the rule base of the government affair examination and approval system.
2. A government approval method according to claim 1, wherein said material elements include a text type element, a number type element, an amount type element and a date type element.
3. A government approval method according to claim 1 wherein said approval rule logic comprises: equal to, not equal to, greater than, less than, greater than or equal to, less than or equal to, including, not including, intersecting.
4. The government approval method according to claim 1, further comprising:
and after the intelligent examination and approval is passed, sending the order to an examination and approval person for rechecking government affair material elements, storing the application material into a material library after the rechecking is passed, simultaneously or after the rechecking of the application elements, carrying out examination and approval of the manual rules, and after the approval of the manual rules is passed, informing an application user that the item application is accepted, otherwise, returning to the application user.
5. A machine learning based government affairs approval system, the system comprising:
the rule model training module is used for acquiring material elements of government affair materials and logic relations among the material elements; training the material elements of the government affairs materials and the logic relation between the material elements by using a machine learning algorithm to obtain a material element rule model;
a comparison module for inputting the government affairs materials into the material element rule model and outputting the logic relationship between the material elements of the government affairs materials and the probability of the logic relationship; when the probability of the logical relationship corresponding to the material element is larger than a preset threshold value, storing the logical relationship into a rule base of the government affair approval system;
and the intelligent approval module is used for uploading government affair application materials and intelligently approving the material elements of the government affair application materials by utilizing the logical relation stored in the rule base of the government affair approval system.
6. A government affairs approval apparatus based on machine learning, comprising:
a processor;
a memory for storing processor-executable instructions;
wherein the processor is configured to implement the method of any of claims 1-4 when executing the instructions.
7. A non-transitory computer readable storage medium having computer program instructions stored thereon, wherein the computer program instructions, when executed by a processor, implement the method of any of claims 1-4.
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