CN114971833A - Tax information processing method and related equipment - Google Patents
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Abstract
The application discloses a tax information processing method, which comprises the following steps: acquiring a tax policy file of a preset server by using a robot flow automation technology, and storing the tax policy file as a tax policy text; performing lexical analysis, syntactic analysis and semantic analysis on the tax policy text by using a natural language processing technology to obtain a plurality of key information corresponding to preset tax preferential indexes; the key information and the corresponding preset tax and revenue preferential indexes are stored in a correlated mode; matching and confirming the existing financial index of the target object with a plurality of key information corresponding to the preset tax preferential indexes; and responding to the matching passing, sending the tax policy file to the target object or the specified object, and otherwise, not sending the tax policy file. Related apparatus are also disclosed. Through the scheme, the tax policy file can be inquired in place of manpower, manpower resources are saved, and a company can check and enjoy the tax preferential policy in time.
Description
Technical Field
The application relates to the field of tax, in particular to a tax information processing method and related equipment.
Background
In order to enjoy the tax-preferential policy in time, at present, companies manually go to local tax administration websites and national tax administration websites to inquire about issued tax-preferential policies, and manually judge whether the company can enjoy the tax-preferential policies. The mode of manually inquiring, identifying and matching the tax coupon policy has low efficiency and high labor cost investment, and is mainly embodied in the following aspects:
tax staff need to pay attention to policy issuing of relevant websites in time, otherwise, tax staff may forget to miss time for enjoying tax preferential policies;
the tax staff manually inquires and downloads the tax preferential policy, which is repeated low-value work and is not beneficial to the development and innovation of staff;
the tax staff manually judges whether the company can enjoy the tax-preferential policy, and needs the tax staff to collect the existing information (such as staff number, business income, total amount of assets and the like) of the company and match the enjoying conditions of the tax-preferential policy, so that the tax staff is time-consuming and labor-consuming and is easy to make mistakes, and the tax risk is caused;
the tax preferential policies are not only the tax preferential policies which are uniformly issued nationwide, but also local tax preferential policies which are distributed dispersedly, have higher requirements on the knowledge reserves of tax staff and have larger manpower input.
Disclosure of Invention
The application at least provides a tax information processing method, a tax information processing device, electronic equipment and a storage medium, which can automatically recommend tax information matched by an enterprise to a corresponding receiver, and improve data transmission accuracy and efficiency.
The application provides a tax information processing method in a first aspect, including: acquiring a tax policy file of a preset server by using a robot flow automation technology, and storing the tax policy file as a tax policy text; performing lexical analysis, syntactic analysis and semantic analysis on the tax policy text by using a natural language processing technology to obtain a plurality of key information corresponding to preset tax preferential indexes; the key information and the corresponding preset tax and revenue preferential indexes are stored in a correlated mode; matching and confirming the existing financial index of the target object with a plurality of key information corresponding to the preset tax preferential indexes; and responding to the matching passing, sending the tax policy file to the target object or the specified object, and otherwise, not sending the tax policy file.
According to the scheme, the tax policy file of the preset server is acquired by the robot flow automation technology, manual inquiry of the tax policy file is replaced, manpower resources are saved, a plurality of key information of preset tax preferential indexes are extracted by the natural language processing technology, the tax policy can be matched with the existing financial indexes of the target object quickly and efficiently, and a company can check and enjoy the tax preferential policy in time.
In some embodiments, the performing lexical analysis, syntactic analysis, and semantic analysis on the tax policy text by using a natural language processing technique to obtain a plurality of key information corresponding to preset tax preferential indicators includes: and performing lexical analysis, syntactic analysis and semantic analysis on the tax policy text by using a natural language processing technology to obtain respective key information of corresponding time, semantic roles, tax changes and respondents.
And performing lexical analysis, syntactic analysis and semantic analysis on the tax policy text by using a natural language processing technology to obtain respective key information of corresponding time, semantic roles, tax changes and respondents, thereby realizing text backbone extraction and classification of the tax policy.
In some embodiments, the performing lexical analysis, syntactic analysis, and semantic analysis on the tax policy text using natural language processing techniques includes: performing Chinese word segmentation and part-of-speech tagging on the tax policy text by using a natural language processing technology to obtain a word after word segmentation and part-of-speech tagging thereof; determining a grammatical structure or dependency relationship between the words by using the parts of speech; and carrying out word sense disambiguation and word similarity checking on the basis of the grammar structure or the dependency relationship.
And determining a grammar structure or a dependency relationship among the words by using the parts of speech, and performing word sense disambiguation and word similarity check on the basis of the grammar structure or the dependency relationship, so that the accuracy of text trunk extraction and classification of the tax policy can be improved.
In some embodiments, the obtaining the tax policy file of the provisioning server by using the robotic process automation technology includes: and logging in local tax bureau and national tax bureau websites according to a certain frequency by utilizing a robot flow automation technology, and inquiring and downloading tax preferential policy files released by the local tax bureau and the national tax bureau.
The robot process automation technology is utilized to log in local tax bureau and national tax bureau websites according to a certain frequency, and tax preferential policy files issued by the local tax bureau and the national tax bureau are inquired and downloaded, so that the manpower resources are saved, and the efficiency of inquiring and downloading policies is improved.
In some embodiments, the saving as tax policy text comprises: and saving the tax policy text to a local server.
And storing the tax policy text to a local server so as to rapidly match the tax policy text with a target object.
In some embodiments, the associating and saving the plurality of pieces of key information with the corresponding preset tax and revenue preferential indexes includes: associating the plurality of key information with the corresponding preset tax discount indexes to store the key information as tax discount reminding items, and marking labels on the tax discount reminding items, wherein different labels represent different types of tax discount reminding items; sending the tax policy file to the target object or the specified object comprises: and sending the tax policy file and at least another tax policy file corresponding to the tax preferential reminding item of the same label to the target object or the specified object.
And associating and storing the key information and the preset tax preference index as a tax preference reminding item according to the key information and the preset tax preference index, and marking the same label on tax policy files of the same type to refine tax policy categories, replace manual policy classification and save manpower resources.
In some embodiments, the matching and confirming the existing financial index of the target object and the plurality of key information of the corresponding preset tax and revenue preferential indexes comprises: and counting the matching similarity, and when the matching similarity is smaller than a default threshold, adjusting the default threshold of the matching similarity to increase the passing probability of the matching.
According to the matching similarity of the existing financial index of the target object and the plurality of pieces of key information corresponding to the preset tax preference indexes, the tax policy which accords with the target object can be screened out, and when the matching similarity is smaller than a default threshold, the default value of the matching similarity threshold is adjusted, the situation that the policy file cannot be recommended due to over-high threshold setting is avoided, and the policy file with higher current similarity can be recommended to the target object.
The second aspect of the present application provides a tax information processing apparatus, including: the acquiring module is used for acquiring a tax policy file of a preset server by using a robot flow automation technology and storing the file as a tax policy text; the first processing module is used for performing lexical analysis, syntactic analysis and semantic analysis on the tax policy text by using a natural language processing technology to obtain a plurality of pieces of key information corresponding to preset tax preferential indexes; the second processing module is used for storing the plurality of key information and the corresponding preset tax and revenue preferential indexes in a correlation manner; the third processing module is used for matching and confirming the existing financial index of the target object with a plurality of key information corresponding to the preset tax and revenue preferential indexes; and the sending module is used for responding to the matching passing, sending the tax policy file to the target object or the specified object, and otherwise, not sending the tax policy file.
A third aspect of the present application provides an electronic device, which includes a memory and a processor coupled to each other, where the processor is configured to execute program instructions stored in the memory to implement the method for processing tax information in the first aspect.
A fourth aspect of the present application provides a non-transitory computer-readable storage medium, on which program instructions are stored, and the program instructions, when executed by a processor, implement the method for processing tax information in the first aspect.
According to the scheme, the robot flow automation technology is adopted to replace manual work to timely acquire the tax policies issued by the collection place and the national tax administration, the labor cost is reduced, the companies can timely enjoy the tax coupon policies, semantic analysis is performed through the natural language processing technology, various condition indexes in the tax policies are intelligently identified, then the various condition indexes of the tax coupon policies are accurately matched with the relevant financial indexes and tax information of the companies, after matching is passed, the tax coupon policies are automatically recommended to tax staff, the data transmission accuracy and efficiency are improved, and meanwhile the working efficiency of staff and the accuracy of policy matching are improved.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the application.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the application.
Fig. 1 is a schematic flowchart of an embodiment of a tax information processing method according to the present application.
Fig. 2 is a schematic diagram of a tax policy text parsing application of the present application.
FIG. 3 is a diagram of a tax policy text semantic analysis application of the present application.
Fig. 4 is a schematic diagram of a framework of an embodiment of a tax information processing apparatus according to the present application.
Fig. 5 is a schematic block diagram of an embodiment of an electronic device according to the present application.
FIG. 6 is a block diagram of one embodiment of a non-volatile computer-readable storage medium of the present application.
Detailed Description
The following describes in detail the embodiments of the present application with reference to the drawings attached hereto.
In the following description, for purposes of explanation and not limitation, specific details are set forth such as particular system structures, interfaces, techniques, etc. in order to provide a thorough understanding of the present application.
The term "and/or" herein is merely an association describing an associated object, meaning that three relationships may exist, e.g., a and/or B, may mean: a exists alone, A and B exist simultaneously, and B exists alone. In addition, the character "/" herein generally indicates that the former and latter related objects are in an "or" relationship. Further, the term "plurality" herein means two or more than two. In addition, the term "at least one" herein means any one of a plurality or any combination of at least two of a plurality, for example, including at least one of A, B, C, and may mean including any one or more elements selected from the group consisting of A, B and C.
Referring to fig. 1, fig. 1 is a schematic flow chart illustrating a tax information processing method according to an embodiment of the present application. The main body of the tax information processing method may be the tax information processing apparatus, for example, the tax information processing method may be executed by a terminal device or a server or other processing device, where the terminal device may be a User Equipment (UE), a mobile device, a User terminal, a cellular phone, a cordless phone, a Personal Digital Assistant (PDA), a handheld device, a computing device, a vehicle-mounted device, a wearable device, or the like. In some possible implementations, the tax information processing may be implemented by way of a processor calling computer-readable instructions stored in a memory.
Specifically, as shown in fig. 1, the method may include the steps of:
step S11: and acquiring a tax policy file of a preset server by using a robot process automation technology, and storing the file as a tax policy text.
The robot Process Automation technology (robot Process Automation) has the main function of executing the interaction between the work information and the service according to a Process designed in advance and automatically finishing the interaction between the work information and the service.
For example, after the preset server is determined, the tax policy of the preset server is obtained by using a robot flow automation technology, the preset server refers to a target server storing the tax policy, such as a website server of the national tax administration and a website server of a local tax administration, and after the tax policy file is obtained, the tax file is stored as a tax policy text for subsequent processing.
Step S12: and performing lexical analysis, syntactic analysis and semantic analysis on the tax policy text by using a natural language processing technology to obtain a plurality of pieces of key information corresponding to preset tax preferential indexes.
The Natural Language Processing (NLP) aims to enable a computer/machine to understand the meaning of a natural language text, and enables the computer to extract a plurality of key information in the tax policy text through lexical analysis, syntactic analysis and semantic analysis on the tax policy text.
The preset preferential indexes are preset according to actual conditions and are used for classifying tax policies, and the key information refers to effective information obtained by processing in a preferential policy text, such as: time, semantic role, tax change, etc.
Step S13: and storing the plurality of key information and the corresponding preset tax and revenue preferential indexes in a correlation manner.
And performing association storage on the extracted key information and the corresponding preset tax preference indexes to obtain joint expression of the key information and the preset tax preference indexes of the preference policy so as to classify and screen the tax preference policy.
Step S14: and matching and confirming the existing financial index of the target object with a plurality of pieces of key information corresponding to the preset tax preference index.
The method comprises the steps of matching and confirming an existing financial index of a target object and a plurality of pieces of key information corresponding to preset tax preferential indexes, and screening a tax preferential policy meeting the existing financial index of the target object from the tax preferential policies meeting the preset tax preferential indexes, wherein the target object refers to a taxpayer and/or a tax enterprise, and the existing financial index of the target object refers to various current financial data parameters of the target object, such as staff number, business income, total amount of assets and tax types.
Step S15: and responding to the matching passing, sending the tax policy file to the target object or the specified object, and otherwise, not sending the tax policy file.
The matching passing means that when the similarity between the existing financial index of the target object and a plurality of key information corresponding to the preset tax preference index exceeds a preset threshold, a tax policy file is sent to the target object or the designated object to recommend the tax policy file to the target object or the designated object and remind the target object or the designated object to confirm and check the tax policy file, when the similarity between the existing financial index of the target object and the plurality of key information corresponding to the preset tax preference index does not exceed or reach the preset threshold, the matching fails, the tax policy file is not sent, the setting of the threshold can be selected according to the actual situation, for example, if the view range of the tax policy is desired to be enlarged, a lower threshold can be set to avoid the situation of missing the tax policy, if the view efficiency of the tax policy is desired to be further improved, a higher threshold may be set.
If the technical scheme of the present application relates to personal information, a product applying the technical scheme of the present application clearly informs personal information processing rules and obtains personal self-approval before processing the personal information. If the technical scheme of the application relates to sensitive personal information, a product applying the technical scheme of the application obtains individual consent before processing the personal information, and simultaneously meets the requirement of 'express consent'. For example, at a personal information collection device such as a camera, a clear and significant flag is set to inform that the personal information collection range is entered, the personal information is collected, and if the person voluntarily enters the collection range, the person is considered as agreeing to collect the personal information; or on the device for processing the personal information, under the condition of informing the personal information processing rule by using obvious identification/information, obtaining personal authorization by modes of popping window information or asking a person to upload personal information of the person by himself, and the like; the personal information processing rule may include information such as a personal information processor, a personal information processing purpose, a processing method, and a type of personal information to be processed.
In the embodiment, the robot flow automation technology is adopted to replace manpower to timely acquire tax policies issued by collection places and the national tax administration, the labor cost is reduced, the target object can be timely checked and selected, intelligent semantic analysis is performed through the natural language processing technology, each condition index in the tax policies is intelligently identified, then each condition index of the tax policies is accurately matched with relevant financial indexes and tax information of companies, after matching is passed, the tax policies are automatically recommended to tax staff, the working efficiency of staff is improved, the accuracy of index identification is ensured, and tax risks are avoided.
As described above, the natural language processing technology can be used to perform lexical analysis, syntactic analysis and semantic analysis on the tax policy text to obtain a plurality of key information corresponding to the preset tax preferential indicators.
In some embodiments, performing lexical analysis, syntactic analysis, and semantic analysis on the tax policy text by using a natural language processing technique to obtain a plurality of key information corresponding to the preset tax preferential indicators includes: and performing lexical analysis, syntactic analysis and semantic analysis on the tax policy text by using a natural language processing technology to obtain respective key information corresponding to time, semantic roles and tax changes.
Lexical analysis scans a source program from left to right one by one to generate word symbols, reformulates a text as a character string into a text composed of a word symbol string, and judges a type of a word according to a character next to the word. The syntactic analysis is to analyze the grammatical function of words in a sentence, and the semantic analysis can carry out context-related property review on the preferential policy text and classify the contents with the same property for understanding tax policy text information.
The time refers to the time range applicable to the policy, tax change refers to the change condition of tax rate, the semantic role refers to the role played by the argument in the event pointed by the verb, and mainly comprises the following steps: the actors, respondents, objects, etc., wherein the nouns associated with the verbs are called arguments.
In the embodiment, after the tax policy text is subjected to lexical analysis, syntactic analysis and semantic analysis, the parts of speech of the words in each sentence in the tax policy text can be obtained, the grammatical structure of the sentence or the dependency relationship among the words in the sentence is determined, the key information such as time, semantic roles, tax changes and event recipients can be extracted, and the main content of the tax policy can be accurately represented.
As described above, the method for lexical analysis, syntactic analysis and semantic analysis of the tax policy text by using the natural language processing technology, and in some embodiments, the method for lexical analysis, syntactic analysis and semantic analysis of the tax policy text by using the natural language processing technology includes: performing Chinese word segmentation and part-of-speech tagging on the tax policy text by using a natural language processing technology to obtain a word after word segmentation and part-of-speech tagging thereof; determining a grammatical structure or a dependency relationship between words by utilizing the parts of speech; word sense disambiguation and word similarity checking are performed on the basis of grammatical structures or dependencies.
In the lexical analysis, Chinese word segmentation and part-of-speech tagging are basic modules of natural language processing technology, and different from English, Chinese sentences have no word boundary, when tax policy texts are processed, word segmentation is performed firstly, part-of-speech tagging is performed after word segmentation, words in tax policies can be extracted, and part-of-speech tagging of the words in the preferential policy texts is determined.
Further, after lexical analysis, processing the tax policy text through syntactic analysis to obtain a correct sentence component relationship, and analyzing the stem of the sentence and the relationship among the components, wherein the main method is to analyze the syntactic structure (a main predicate structure) of the sentence and the dependency relationship (parallel, dependent, and the like) among vocabularies, so that the tax policy text with a complex syntactic structure can be clearly shown, and thus the dependency relationship among the vocabularies is determined, at this time, please refer to fig. 2, and fig. 2 is a syntactic analysis application diagram of the tax policy text of the present application.
Furthermore, after Chinese word segmentation, part-of-speech tagging and syntactic analysis processing in lexical analysis, part-of-speech, syntactic structure and dependency relationship among the words in the tax policy text sentence are obtained, semantic analysis of the tax policy is performed on the basis, and the semantic analysis mainly comprises word meaning disambiguation and word similarity check to determine semantic roles in the tax policy text. The purpose of word meaning disambiguation is to give an input tax policy text, judge the meaning of words according to the context of the words, and thus determine the meaning of polysemous words in the tax policy text, wherein word similarity refers to the degree that two words can be replaced and used in different contexts without changing the syntactic semantic structure of the text, and the part of speech, syntactic structure and dependency relationship between words of the obtained tax policy text can be verified through word similarity check.
Further, in semantic analysis, semantic role labeling technology can be used for determining semantic roles in a sentence, and the semantic role labeling technology can analyze the relation between each component in the sentence and a predicate, namely the predicate-argument structure of the sentence, by taking the predicate of the tax policy text as the center, and describe the structural relation by using the semantic roles.
At this time, please refer to fig. 3, fig. 3 is a schematic diagram illustrating the application of semantic analysis of text for tax revenue policy of the present application, as shown in the figure, in a sentence "value-added tax exempt small-scale taxpayer whose monthly sales amount is less than 15 ten thousand yuan (including the actual value) from 4/1/2021 to 12/31/2022/31/2021," including "and" exempt "are determined as predicates through morphological analysis, and" the actual value "and" value-added tax "are nouns directly collocated with verbs, i.e., arguments, so that" value-added tax "and" actual value "are determined as victims, and no subject language appears in the sentence, so that no constructor exists.
In the embodiment, Chinese word segmentation and part-of-speech tagging are performed on the tax policy text by using a natural language processing technology, so that words in the tax policy text can be extracted, and the part-of-speech of the words in the tax policy text is clarified; determining a syntactic structure or dependency relationship among the words by utilizing the part of speech, and clearly representing the tax policy text with complex syntactic structure; word meaning disambiguation and word similarity check are carried out on the basis of a grammatical structure or a dependency relationship, the part of speech, the syntactic structure and the dependency relationship among words of the obtained tax policy text can be verified, and the accuracy of extracting key information of the tax policy text is further improved.
As described above, the method for acquiring a tax policy file of a preset server by using a robot flow automation technology, in some embodiments, the method for acquiring a tax policy file of a preset server by using a robot flow automation technology includes: and logging in local tax bureau and national tax bureau websites according to a certain frequency by utilizing a robot flow automation technology, and inquiring and downloading tax preferential policy files released by the local tax bureau and the national tax bureau.
According to different release frequencies of tax policies of local tax bureau and national tax bureau, different login frequencies can be set by using a robot flow automation technology, and released tax policy files can be inquired and downloaded.
By utilizing the robot process automation technology, different login frequencies are set according to different release frequencies, so that the manpower resource is saved, and the target object is ensured to obtain the tax policy file in time.
After the published tax policy document is queried and downloaded, the tax document is saved as a tax policy text, which in some embodiments includes: and saving the tax policy text to a local server.
The local server stores the tax policy text and the tax information of the target object, and in other embodiments, the tax policy text and the key information obtained after being processed by the natural language processing technology can be input into the internal server for subsequent matching.
In the embodiment, the tax policy text is stored in the local server, so that the tax policy text can be called at any time subsequently, and the tax information of the tax policy text and the target object is processed or analyzed.
After the tax policy text and the plurality of key information which are obtained after the processing of the natural language processing technology are input into the internal server, the plurality of key information and the corresponding preset tax and revenue preferential indexes are stored in an associated mode, and in some embodiments, the storing of the plurality of key information and the corresponding preset tax and revenue preferential indexes in the associated mode comprises the following steps: associating and storing a plurality of key information with corresponding preset tax preferential indexes to obtain tax preferential reminding items, and marking labels on the tax preferential reminding items, wherein different labels represent different types of tax preferential reminding items; sending the tax policy document to the target object or the specified object includes: and sending the tax policy file and at least another tax policy file corresponding to the tax preferential reminding item of the same label to the target object or the specified object.
The method comprises the steps of storing a plurality of key information and corresponding preset tax preferential indexes in an associated manner, storing tax policy files meeting the same preset preferential index into one type and storing the tax policy files as tax preferential reminding items, printing the same label on the tax policy files meeting the same preset preferential index, and representing different types of tax preferential reminding items by different labels, wherein the preferential policy files in different labels respectively meet different preset preferential indexes, namely classifying the tax policy files by different labels, for example, printing different labels on the tax policy files aiming at different tax types, for example, printing different labels on the tax policies issued in different time periods, and setting different labels or label combinations according to actual conditions so as to classify the tax policies.
The preset preferential indexes refer to preset conditions of the target object and are used for classifying and screening the tax policy files with the extracted keywords, the tax preferential reminding items are used for reminding the target object to check the screened same-class policy files, and the labels are used for displaying classification standards and bases of the policy files in the preferential reminding items.
In the embodiment, according to a plurality of key information and corresponding preset tax preference indexes, the key information and the preset tax preference indexes are associated and stored as the tax preference reminding items, the tax policy files of the same type can be marked with the same label, the tax policy categories are refined, manual policy classification is replaced, and manpower resources are saved.
As described above, when the existing financial index of the target object is matched with a plurality of pieces of key information corresponding to the preset tax preferential indexes and the matching confirmation is passed, the tax policy file corresponding to the plurality of pieces of matched key information and at least another tax policy file of the same label are sent to the target object.
In some embodiments, the matching and confirming the existing financial index of the target object and the plurality of pieces of key information corresponding to the preset tax and revenue preferential indexes comprises: and (5) counting the similarity, and when the similarity is smaller than a default threshold, adjusting the similarity threshold of the matching confirmation to increase the probability of passing the matching.
When matching confirmation is carried out on the existing financial indexes of the target object and a plurality of pieces of key information corresponding to preset tax preference indexes, calculating the similarity between the existing financial indexes of the target object and the tax policy files, and when the similarity is smaller than a threshold value, showing that the matching degree of the tax policy files and the existing financial indexes of the target object is low, reducing the preset similarity threshold value, screening the tax policy files meeting the adjusted similarity, and sending the tax policy files to the target object so as to ensure that the target object can receive the policy files when the matching degree of the tax policy files and the existing financial indexes of the target object is low; when the similarity is greater than the threshold value, the matching degree of the tax policy file and the existing financial index of the target object is higher, and at the moment, the tax policy file with the similarity greater than the threshold value is sent to the target object so that the target object can check and select the tax policy file.
Calculating the similarity, inputting a plurality of key information of the existing financial index as a target and the corresponding preset tax preferential index, outputting the similarity of the two documents, and expressing the similarity by using the decimal number in the [0,1] interval, wherein in some embodiments, the similarity is calculated by adopting an unsupervised method, for example: similarity is calculated based on the minimum edit distance, Euclidean distance, cosine distance, Jacard similarity and other methods, and in other embodiments, similarity is calculated by adopting a supervised method, for example, a supervised model such as a naive Bayes classifier is used for judging text similarity or calculating similarity. The specific method and steps for calculating the similarity are not limited herein.
In the embodiment, according to the matching similarity between the existing financial index of the target object and the plurality of pieces of key information corresponding to the preset tax preference indexes, the tax policy conforming to the target object can be screened out, and when the matching similarity is smaller than the default threshold, the default threshold of the matching similarity threshold is adjusted, so that the situation that the policy file cannot be recommended due to over-high threshold setting is avoided, and the policy file with higher current similarity can be recommended to the target object.
It will be understood by those skilled in the art that in the method of the present invention, the order of writing the steps does not imply a strict order of execution and any limitations on the implementation, and the specific order of execution of the steps should be determined by their function and possible inherent logic.
Referring to fig. 4, fig. 4 is a schematic diagram of a tax information processing apparatus 40 according to an embodiment of the present application. The tax information processing device 40 comprises an acquisition module 41, a first processing module 42, a second processing module 43, a third processing module 44 and a sending module 45, wherein the acquisition module 41 is used for acquiring a tax policy file of a preset server by using a robot flow automation technology and storing the tax policy file as a tax policy text; the first processing module 42 is configured to perform lexical analysis, syntactic analysis and semantic analysis on the tax policy text by using a natural language processing technology to obtain a plurality of pieces of key information corresponding to preset tax preferential indicators; the second processing module 43 is configured to store the plurality of key information in association with the corresponding preset tax and revenue preferential indicators; the third processing module 44 is configured to match and confirm the existing financial index of the target object with a plurality of pieces of key information corresponding to the preset tax preferential indexes; and the sending module 45 is used for responding to the matching passing, sending the tax policy file to the target object or the specified object, and otherwise, not sending the tax policy file.
According to the scheme, the tax policy file of the preset server is acquired by the robot flow automation technology, manual inquiry of the tax policy file is replaced, manpower resources are saved, a plurality of key information of preset tax preferential indexes are extracted by the natural language processing technology, the tax policy can be matched with the existing financial indexes of the target object quickly and efficiently, and a company can check and enjoy the tax preferential policy in time.
In some embodiments, performing lexical analysis, syntactic analysis, and semantic analysis on the tax policy text by using a natural language processing technique to obtain a plurality of key information corresponding to the preset tax preferential indicators includes: and performing lexical analysis, syntactic analysis and semantic analysis on the tax policy text by using a natural language processing technology to obtain respective key information of corresponding time, semantic roles, tax changes and the subjects.
In some embodiments, lexical, syntactic, semantic analysis of the tax policy text using natural language processing techniques includes: performing Chinese word segmentation and part-of-speech tagging on the tax policy text by using a natural language processing technology to obtain a word after word segmentation and part-of-speech tagging thereof; determining a grammatical structure or a dependency relationship between words by utilizing the parts of speech; word sense disambiguation and word similarity checking are performed on the basis of grammatical structures or dependencies.
In some embodiments, obtaining the tax policy file of the provisioning server using robotic process automation technology comprises: and logging in local tax bureau and national tax bureau websites according to a certain frequency by using a robot flow automation technology, and inquiring and downloading tax preferential policy files released by the local tax bureau and the national tax bureau.
In some embodiments, saving as tax policy text includes: and saving the tax policy text to a local server.
In some embodiments, the associating and storing the plurality of pieces of key information with the corresponding preset tax and revenue preferential indexes includes: associating and storing a plurality of key information with corresponding preset tax preferential indexes to obtain tax preferential reminding items, and marking labels on the tax preferential reminding items, wherein different labels represent different types of tax preferential reminding items; sending the tax policy document to the target object or the specified object includes: and sending the tax policy file and at least another tax policy file corresponding to the tax preferential reminding item of the same label to the target object or the specified object.
In some embodiments, the matching and confirming the existing financial index of the target object and the plurality of pieces of key information corresponding to the preset tax and revenue preferential indexes comprises: and counting the matching similarity, and adjusting the default threshold of the matching similarity threshold when the matching similarity is smaller than the default threshold so as to increase the passing probability of the matching.
Referring to fig. 5, fig. 5 is a schematic diagram of a frame of an embodiment of an electronic device according to the present application. The electronic device 50 includes a memory 51 and a processor 52 coupled to each other, and the processor 52 is configured to execute program instructions stored in the memory 51 to implement the steps of any one of the embodiments of the tax information processing method described above. In one particular implementation scenario, the electronic device 50 may include, but is not limited to: a microcomputer, a server, and the electronic device 50 may also include a mobile device such as a notebook computer, a tablet computer, and the like, which is not limited herein.
Specifically, the processor 52 is configured to control itself and the memory 51 to implement the steps of any of the embodiments of the tax information processing method described above. Processor 52 may also be referred to as a CPU (Central Processing Unit). Processor 52 may be an integrated circuit chip having signal processing capabilities. The Processor 52 may also be a general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic, discrete hardware components. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. In addition, the processor 52 may be commonly implemented by an integrated circuit chip.
Referring to fig. 6, fig. 6 is a block diagram illustrating an embodiment of a non-volatile computer readable storage medium 60 according to the present application. The non-transitory computer readable storage medium 60 stores program instructions 601 capable of being executed by the processor, the program instructions 601 being used to implement the steps of any one of the embodiments of the tax information processing method described above.
In some embodiments, functions of or modules included in the apparatus provided in the embodiments of the present disclosure may be used to execute the method described in the above method embodiments, and specific implementation thereof may refer to the description of the above method embodiments, and for brevity, will not be described again here.
The foregoing description of the various embodiments is intended to highlight various differences between the embodiments, and the same or similar parts may be referred to each other, and for brevity, will not be described again herein.
In the several embodiments provided in the present application, it should be understood that the disclosed method and apparatus may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, a division of a module or a unit is merely one type of logical division, and an actual implementation may have another division, for example, a unit or a component may be combined or integrated with another system, or some features may be omitted, or not implemented. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection of devices or units through some interfaces, and may be in an electrical, mechanical or other form.
In addition, functional units in the embodiments of the present application 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, and can also be realized in a form of a software functional unit.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solutions of the present application, which are essential or contributing to the prior art, or all or part of the technical solutions may be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, a network device, or the like) or a processor (processor) to execute all or part of the steps of the methods of the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
Claims (10)
1. A tax information processing method is characterized by comprising the following steps:
acquiring a tax policy file of a preset server by using a robot flow automation technology, and storing the tax policy file as a tax policy text;
performing lexical analysis, syntactic analysis and semantic analysis on the tax policy text by using a natural language processing technology to obtain a plurality of key information corresponding to preset tax preferential indexes;
the key information and the corresponding preset tax and revenue preferential indexes are stored in a correlated mode;
matching and confirming the existing financial index of the target object with a plurality of key information corresponding to the preset tax preferential indexes;
and responding to the matching passing, sending the tax policy file to the target object or the specified object, and otherwise, not sending the tax policy file.
2. The method of claim 1,
the obtaining of a plurality of key information corresponding to preset tax preferential indexes by performing lexical analysis, syntactic analysis and semantic analysis on the tax policy text by using a natural language processing technology comprises:
and performing lexical analysis, syntactic analysis and semantic analysis on the tax policy text by using a natural language processing technology to obtain respective key information corresponding to time, semantic roles and tax changes.
3. The method of claim 2,
the lexical analysis, the syntactic analysis and the semantic analysis of the tax policy text by using the natural language processing technology comprise:
performing Chinese word segmentation and part-of-speech tagging on the tax policy text by using a natural language processing technology to obtain a word after word segmentation and part-of-speech tagging thereof;
determining a grammatical structure or dependency relationship between the words by using the parts of speech;
and carrying out word sense disambiguation and word similarity checking on the basis of the grammar structure or the dependency relationship.
4. The method of claim 1,
the method for acquiring the tax policy file of the preset server by using the robot process automation technology comprises the following steps:
and logging in local tax bureau and national tax bureau websites according to a certain frequency by utilizing a robot flow automation technology, and inquiring and downloading tax preferential policy files released by the local tax bureau and the national tax bureau.
5. The method of claim 1,
the text saved as the tax policy includes:
and saving the tax policy text to a local server.
6. The method of claim 1,
the step of associating and storing the plurality of key information and the corresponding preset tax and revenue preferential indexes comprises the following steps:
associating the plurality of key information with the corresponding preset tax discount indexes to store the key information as tax discount reminding items, and marking labels on the tax discount reminding items, wherein different labels represent different types of tax discount reminding items;
sending the tax policy file to the target object or the specified object comprises:
and sending the tax policy file and at least another tax policy file corresponding to the tax preferential reminding item of the same label to the target object or the specified object.
7. The method of claim 1,
the matching and confirming of the existing financial index of the target object and the plurality of key information corresponding to the preset tax and revenue preferential indexes comprises the following steps:
and counting the matching similarity, and when the matching similarity is smaller than a default threshold, adjusting the default threshold of the matching similarity to increase the passing probability of the matching.
8. A tax information processing apparatus, comprising:
the acquiring module is used for acquiring a tax policy file of a preset server by using a robot flow automation technology and storing the file as a tax policy text;
the first processing module is used for performing lexical analysis, syntactic analysis and semantic analysis on the tax policy text by using a natural language processing technology to obtain a plurality of pieces of key information corresponding to preset tax preferential indexes;
the second processing module is used for storing the plurality of pieces of key information and the corresponding preset tax and revenue preferential indexes in a correlation manner;
the third processing module is used for matching and confirming the existing financial index of the target object with a plurality of key information corresponding to the preset tax and revenue preferential indexes;
and the sending module is used for responding to the matching passing, sending the tax policy file to the target object or the specified object, and otherwise, not sending the tax policy file.
9. An electronic device comprising a memory and a processor coupled to each other, wherein the processor is configured to execute program instructions stored in the memory to implement the tax information processing method according to any one of claims 1 to 7.
10. A non-transitory computer-readable storage medium having stored thereon program instructions, wherein the program instructions, when executed by a processor, implement the tax information processing method according to any one of claims 1 to 7.
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| CN202210714419.8A CN114971833A (en) | 2022-06-22 | 2022-06-22 | Tax information processing method and related equipment |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN116384939A (en) * | 2023-04-13 | 2023-07-04 | 华腾建信科技有限公司 | Engineering project safety management method, device, equipment and storage medium |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN116384939A (en) * | 2023-04-13 | 2023-07-04 | 华腾建信科技有限公司 | Engineering project safety management method, device, equipment and storage medium |
| CN116384939B (en) * | 2023-04-13 | 2023-12-01 | 华腾建信科技有限公司 | Engineering project safety management method, device, equipment and storage medium |
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