CN111798123A - Compliance evaluation method, device, equipment and medium based on artificial intelligence - Google Patents
Compliance evaluation method, device, equipment and medium based on artificial intelligence Download PDFInfo
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Abstract
The utility model relates to the technical field of artificial intelligence, and provides a compliance evaluation method, a device, equipment and a medium based on artificial intelligence, the method can identify texts of text data to obtain data to be analyzed, an index system comprising primary indexes is constructed based on an analytic hierarchy process, each primary index comprises secondary indexes, a target field corresponding to each secondary index and a function script corresponding to each target field are obtained, target parameters matched with each target field are obtained from the data to be analyzed, each target parameter is added to the corresponding function script for operation, the score of each secondary index is output, the score of each secondary index is input to a scoring model, the compliance score is output, and the compliance of an enterprise or a mechanism is accurately and quantitatively and automatically evaluated. The method can be applied to intelligent government affairs, so that the construction of the intelligent city is promoted. In addition, the invention also relates to a block chain technology, and the compliance score can be stored in the block chain node.
Description
Technical Field
The invention relates to the technical field of artificial intelligence, in particular to a compliance evaluation method, a compliance evaluation device, compliance evaluation equipment and compliance evaluation media based on artificial intelligence.
Background
In order to promote the central enterprise to comprehensively strengthen the compliance management, accelerate the promotion of the compliance management level, make a law to control the central enterprise and guarantee the continuous healthy development of the enterprise, the compliance quantitative evaluation is promoted according to the relevant law and regulation regulations, such as the national common Bureau of the people's republic of China, the national asset Law of the national common Bureau of China, and the like.
The conventional technology cannot quantize legal compliance data, and mass text data and the quantity of the text data cannot be directly quantized and cannot directly reflect the legal compliance. But rather, a summary opinion needs to be manually analyzed and derived from different dimensions through extensive review of existing relevant material.
However, with the development of technology and the demand of information service, users find out that the legal compliance risk of enterprises can be identified rapidly, and the health condition of enterprises under the state resource committee can be mastered dynamically in real time. The state resource commission needs to evaluate the legal compliance of enterprises in real time, and based on the need, the risk and the compliance of the enterprises need to be evaluated quickly.
Disclosure of Invention
In view of the foregoing, it is desirable to provide a compliance evaluation method, device, apparatus and medium based on artificial intelligence, which can automatically evaluate compliance of an enterprise or an organization accurately and quantitatively based on artificial intelligence technology.
An artificial intelligence based compliance assessment method, comprising:
crawling original data from a specified platform by adopting a breadth-first traversal strategy;
preprocessing the original data to obtain text data;
performing text recognition on the text data to obtain data to be analyzed;
constructing an index system based on an analytic hierarchy process, wherein the index system comprises at least one first-level index, and each first-level index comprises at least one second-level index;
acquiring a target field corresponding to each secondary index and a function script corresponding to each target field, acquiring a target parameter matched with each target field from the data to be analyzed, adding each target parameter to the corresponding function script for operation, and outputting a score of each secondary index;
and inputting the score of each secondary index into a pre-constructed scoring model for processing, and outputting a compliance score.
According to a preferred embodiment of the present invention, the preprocessing the original data to obtain text data includes:
when the original data is of a picture type, converting the original data into an initial text, filtering and cleaning the initial text to obtain a filtered text, and coding the filtered text based on a UTF-8 coding algorithm to obtain the text data; or
And when the original data is of a text type, filtering and cleaning the original data to obtain a filtered text, and coding the filtered text based on a UTF-8 coding algorithm to obtain the text data.
According to a preferred embodiment of the present invention, the performing text recognition on the text data to obtain data to be analyzed includes:
carrying out Word Embedding processing on the data to be analyzed to generate Word vectors;
performing convolution operation on the word vector to output a feature map;
performing maximum pooling treatment on the feature map to obtain a plurality of pooling features;
splicing the plurality of pooling characteristics, inputting the spliced pooling characteristics to a softmax layer, and outputting the data to be analyzed.
According to a preferred embodiment of the present invention, the construction of the index system based on the analytic hierarchy process comprises:
acquiring data to be processed;
performing clustering and layering processing on the data to be processed based on an analytic hierarchy process to obtain a plurality of initial first-level indexes and at least one second-level index included in each initial first-level index;
determining the importance of each initial primary index configured in advance;
comparing the importance of every two initial primary indexes to construct a judgment matrix;
calculating the weight of each initial primary index according to the judgment matrix;
acquiring an initial primary index with the weight greater than or equal to a preset weight as the at least one primary index;
and constructing the index system by using the at least one primary index and the at least one secondary index corresponding to each primary index.
According to a preferred embodiment of the present invention, the obtaining the target parameter matched with each target field from the data to be analyzed includes:
acquiring a pre-configured knowledge graph;
calculating the similarity between each target field and the entity on each node in the knowledge graph;
acquiring at least one entity with the similarity greater than or equal to a preset similarity from each node as a candidate field of each target field;
integrating each target field and the corresponding candidate field to construct an extension field of each target field;
traversing the data to be analyzed by utilizing the extension field of each target field;
and determining the traversed data as target parameters matched with each target field.
According to a preferred embodiment of the present invention, the method for evaluating compliance based on artificial intelligence further comprises:
when the index system is detected to be updated, acquiring the score of the updated index;
and correcting the scoring model by adopting a back propagation algorithm according to the updated index value.
According to a preferred embodiment of the present invention, the modifying the scoring model by using a back propagation algorithm according to the updated score of the index comprises:
performing compression estimation on the value of the updated index by adopting a Lasso algorithm to obtain at least one first index value;
performing time series prediction analysis on the at least one first index value by adopting an LSTM algorithm to obtain at least one second index value;
inputting the at least one second index value into the scoring model, and calculating a return error;
and updating the weight of the scoring model according to the return error until the return error is less than or equal to a preset error, and stopping correction.
An artificial intelligence based compliance assessment device, comprising:
the crawling unit is used for crawling original data from a specified platform by adopting a breadth-first traversal strategy;
the preprocessing unit is used for preprocessing the original data to obtain text data;
the identification unit is used for carrying out text identification on the text data to obtain data to be analyzed;
the system comprises a construction unit, a data processing unit and a data processing unit, wherein the construction unit is used for constructing an index system based on an analytic hierarchy process, the index system comprises at least one primary index, and each primary index comprises at least one secondary index;
the operation unit is used for acquiring a target field corresponding to each secondary index and a function script corresponding to each target field, acquiring a target parameter matched with each target field from the data to be analyzed, adding each target parameter to the corresponding function script for operation, and outputting a score of each secondary index;
and the input unit is used for inputting the score of each secondary index into a pre-constructed scoring model for processing and outputting the compliance score.
An electronic device, the electronic device comprising:
a memory storing at least one instruction; and
a processor executing instructions stored in the memory to implement the artificial intelligence based compliance assessment methodology.
A computer-readable storage medium having stored therein at least one instruction for execution by a processor in an electronic device to implement the artificial intelligence based compliance assessment method.
According to the technical scheme, the invention can adopt a breadth-first traversal strategy to crawl original data from a specified platform, preprocessing the original data to obtain text data, performing text recognition on the text data to obtain data to be analyzed, constructing an index system based on an analytic hierarchy process, the index system comprises at least one primary index, each primary index comprises at least one secondary index, a target field corresponding to each secondary index and a function script corresponding to each target field are obtained, a target parameter matched with each target field is obtained from the data to be analyzed, and each target parameter is added to the corresponding function script for operation, the score of each secondary index is output, the score of each secondary index is input to a pre-constructed scoring model for processing, and a compliance score is output, so that the compliance of an enterprise or an organization can be accurately and quantitatively automatically evaluated based on an artificial intelligence technology.
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FIG. 1 is a flow chart of a preferred embodiment of the artificial intelligence based compliance assessment method of the present invention.
FIG. 2 is a functional block diagram of a preferred embodiment of the artificial intelligence based compliance assessment device of the present invention.
Fig. 3 is a schematic structural diagram of an electronic device implementing a compliance evaluation method based on artificial intelligence according to a preferred embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be described in detail with reference to the accompanying drawings and specific embodiments.
FIG. 1 is a flow chart of a preferred embodiment of the compliance assessment method based on artificial intelligence of the present invention. The order of the steps in the flow chart may be changed and some steps may be omitted according to different needs.
The compliance evaluation method based on artificial intelligence is applied to one or more electronic devices, which are devices capable of automatically performing numerical calculation and/or information processing according to preset or stored instructions, and the hardware thereof includes, but is not limited to, a microprocessor, an Application Specific Integrated Circuit (ASIC), a Programmable Gate Array (FPGA), a Digital Signal Processor (DSP), an embedded device, and the like.
The electronic device may be any electronic product capable of performing human-computer interaction with a user, for example, a Personal computer, a tablet computer, a smart phone, a Personal Digital Assistant (PDA), a game machine, an interactive Internet Protocol Television (IPTV), an intelligent wearable device, and the like.
The electronic device may also include a network device and/or a user device. The network device includes, but is not limited to, a single network server, a server group consisting of a plurality of network servers, or a cloud computing (cloud computing) based cloud consisting of a large number of hosts or network servers.
The Network where the electronic device is located includes, but is not limited to, the internet, a wide area Network, a metropolitan area Network, a local area Network, a Virtual Private Network (VPN), and the like.
And S10, adopting a breadth first traversal (Procedure Breath-first-search) strategy to crawl the original data from the specified platform.
The crawled original data are important indexes for compliance evaluation of enterprises, and the condition of each enterprise also influences economic development, so that the original data can be used as important indexes/indexes for macroscopic economic research and analysis and can be used for enterprise risk analysis.
For example: the raw data may include macro-economic data or the like.
In at least one embodiment of the invention, the specified platform may be a platform that deploys complete macro-economic data, the data on the platform having real reliability.
It should be noted that, the data on the specified platform may be stored in a blockchain, which further improves the security of the data.
In the embodiment, the original data is crawled from the specified platform by adopting a breadth-first traversal strategy, and the breadth-first traversal strategy is traversed according to the hierarchy, so that the found first feasible solution is generally the optimal solution, and the data can be found more quickly compared with a common depth traversal mode without backtracking, that is, as long as the traversed problem has a solution, the solution can be always obtained, and the solution is the solution of the shortest path, so that the traversal efficiency is higher.
And S11, preprocessing the original data to obtain text data.
In at least one embodiment of the present invention, the preprocessing the original data to obtain text data includes:
when the original data is of a picture type, converting the original data into an initial text, filtering and cleaning the initial text to obtain a filtered text, and coding the filtered text based on a UTF-8 coding algorithm to obtain the text data; or
And when the original data is of a text type, filtering and cleaning the original data to obtain a filtered text, and coding the filtered text based on a UTF-8 coding algorithm to obtain the text data.
Specifically, the original data may be converted into the initial text using an OCR (Optical Character Recognition) algorithm.
Meanwhile, the filtered text is coded through the UTF-8 coding algorithm, full-angle and half-angle symbol conversion, messy code removal and other operations can be performed on the filtered text, and coding unification is finally achieved.
Through the implementation mode, the original data can be filtered and cleaned to eliminate interference information, and further the original data is converted into a uniform text format, so that the uniformity of the data format is realized, and the preprocessed text data can be recognized and processed by a machine.
And S12, performing text recognition on the text data to obtain data to be analyzed.
In at least one embodiment of the present invention, the performing text recognition on the text data to obtain data to be analyzed includes:
carrying out Word Embedding processing on the data to be analyzed to generate Word vectors;
performing convolution operation on the word vector to output a feature map;
performing maximum pooling treatment on the feature map to obtain a plurality of pooling features;
splicing the plurality of pooling characteristics, inputting the spliced pooling characteristics to a softmax layer, and outputting the data to be analyzed.
In the above embodiment, the Word Embedding process is used to digitize the natural language, so that the subsequent process is facilitated, and the recognition mode is simpler and faster than that of the conventional CNN network.
S13, constructing an index system based on the analytic hierarchy process, wherein the index system comprises at least one primary index, and each primary index comprises at least one secondary index.
In at least one embodiment of the present invention, the construction of the index system based on the analytic hierarchy process comprises:
acquiring data to be processed;
performing clustering and layering processing on the data to be processed based on an analytic hierarchy process to obtain a plurality of initial first-level indexes and at least one second-level index included in each initial first-level index;
determining the importance of each initial primary index configured in advance;
comparing the importance of every two initial primary indexes to construct a judgment matrix;
calculating the weight of each initial primary index according to the judgment matrix;
acquiring an initial primary index with the weight greater than or equal to a preset weight as the at least one primary index;
and constructing the index system by using the at least one primary index and the at least one secondary index corresponding to each primary index.
The importance of each initial primary index may be evaluated by an expert, or may be obtained by analyzing historical data, which is not limited herein.
And S14, acquiring a target field corresponding to each secondary index and a function script corresponding to each target field, acquiring a target parameter matched with each target field from the data to be analyzed, adding each target parameter to the corresponding function script for operation, and outputting the score of each secondary index.
For example: when the evaluation factors of the secondary index A are as follows: and the evaluation factors belong to the jurisdiction scope of the 'anti-commercial bribery', so that the 'anti-commercial briy' can be used as a target field of the secondary index A, and all data related to the 'anti-commercial briy' can be acquired as an evaluation basis of the secondary index A.
In at least one embodiment of the present invention, the obtaining target parameters matching with each target field from the data to be analyzed includes:
acquiring a pre-configured knowledge graph;
calculating the similarity between each target field and the entity on each node in the knowledge graph;
acquiring at least one entity with the similarity greater than or equal to a preset similarity from each node as a candidate field of each target field;
integrating each target field and the corresponding candidate field to construct an extension field of each target field;
traversing the data to be analyzed by utilizing the extension field of each target field;
and determining the traversed data as target parameters matched with each target field.
According to the embodiment, each target field is expanded by using the knowledge graph and then traversed, so that the traversed target parameters are better and more comprehensive, calculation errors caused by parameter omission are avoided, and the accuracy of the data analysis result is influenced.
And S15, inputting the score of each secondary index into a pre-constructed scoring model for processing, and outputting a compliance score.
In this embodiment, a threshold may be set, and when the compliance score is higher than the threshold, it is determined that compliance is better, and when the compliance score is lower than or equal to the threshold, it is determined that compliance is worse.
For example: when the compliance score is 99 minutes, which is higher than the threshold 85, it can be determined that the compliance of the corresponding enterprise or institution is better and the risk is lower.
By the aid of the method, full-automatic analysis of the data to be analyzed is realized, the analysis efficiency is high, the compliance of an enterprise or an organization can be comprehensively evaluated, the risk of the enterprise or the organization can be further evaluated, and the macro-economic data can be analyzed.
In at least one embodiment of the present invention, the artificial intelligence based compliance assessment method further comprises:
acquiring a training sample, and splitting the training sample into a training set and a verification set;
inputting the training set into a neural network for learning to obtain an initial model;
validating the initial model using the validation set;
and when the initial model passes the verification, stopping training to obtain the scoring model.
Through the implementation mode, the scoring model can be trained, so that full-automatic scoring can be performed by using the trained scoring model, and the processing efficiency is improved.
In at least one embodiment of the present invention, the artificial intelligence based compliance assessment method further comprises:
when the index system is detected to be updated, acquiring the score of the updated index;
and correcting the scoring model by adopting a back propagation algorithm according to the updated index value.
By continuously updating and correcting the scoring model, the coverage of the model can be wider, the model is continuously suitable for new evaluation strategies, and the practicability is higher.
Specifically, the modifying the scoring model by using a back propagation algorithm according to the updated score of the index includes:
performing compression estimation on the updated index value by using a Lasso (last absolute shrinkage and selection operator) algorithm to obtain at least one first index value;
performing time series prediction analysis on the at least one first index value by using an LSTM (Long Short-Term Memory network) algorithm to obtain at least one second index value;
inputting the at least one second index value into the scoring model, and calculating a return error;
and updating the weight of the scoring model according to the return error until the return error is less than or equal to a preset error, and stopping correction.
Through the implementation mode, the scoring model can be further optimized, so that the accuracy of the evaluation on the regularity is improved.
In this embodiment, in order to further ensure the security and privacy of the data, the index system and the compliance score may also be stored in the blockchain, so as to effectively prevent the data from being maliciously tampered.
According to the technical scheme, the invention can adopt a breadth-first traversal strategy to crawl original data from a specified platform, preprocessing the original data to obtain text data, performing text recognition on the text data to obtain data to be analyzed, constructing an index system based on an analytic hierarchy process, the index system comprises at least one primary index, each primary index comprises at least one secondary index, a target field corresponding to each secondary index and a function script corresponding to each target field are obtained, a target parameter matched with each target field is obtained from the data to be analyzed, and each target parameter is added to the corresponding function script for operation, the score of each secondary index is output, the score of each secondary index is input to a pre-constructed scoring model for processing, and a compliance score is output, so that the compliance of an enterprise or an organization can be accurately and quantitatively automatically evaluated based on an artificial intelligence technology. In addition, the invention can also be applied to intelligent government affairs, thereby promoting the construction of intelligent cities. The invention also relates to blockchain techniques, and the compliance scores may be stored in blockchain nodes.
Fig. 2 is a functional block diagram of a preferred embodiment of the compliance assessment device based on artificial intelligence according to the present invention. The compliance evaluation device 11 based on artificial intelligence comprises a crawling unit 110, a preprocessing unit 111, an identifying unit 112, a constructing unit 113, an arithmetic unit 114, an input unit 115, an acquiring unit 116 and a correcting unit 117. The module/unit referred to in the present invention refers to a series of computer program segments that can be executed by the processor 13 and that can perform a fixed function, and that are stored in the memory 12. In the present embodiment, the functions of the modules/units will be described in detail in the following embodiments.
The crawling unit 110 crawls the raw data from the specified platform using a breadth first traversal (Procedure _ first _ search) strategy.
The crawled original data are important indexes for compliance evaluation of enterprises, and the condition of each enterprise also influences economic development, so that the original data can be used as important indexes/indexes for macroscopic economic research and analysis and can be used for enterprise risk analysis.
For example: the raw data may include macro-economic data or the like.
In at least one embodiment of the invention, the specified platform may be a platform that deploys complete macro-economic data, the data on the platform having real reliability.
It should be noted that, the data on the specified platform may be stored in a blockchain, which further improves the security of the data.
In the embodiment, the original data is crawled from the specified platform by adopting a breadth-first traversal strategy, and the breadth-first traversal strategy is traversed according to the hierarchy, so that the found first feasible solution is generally the optimal solution, and the data can be found more quickly compared with a common depth traversal mode without backtracking, that is, as long as the traversed problem has a solution, the solution can be always obtained, and the solution is the solution of the shortest path, so that the traversal efficiency is higher.
The preprocessing unit 111 preprocesses the original data to obtain text data.
In at least one embodiment of the present invention, the preprocessing unit 111 performs preprocessing on the raw data to obtain text data, where the preprocessing includes:
when the original data is of a picture type, converting the original data into an initial text, filtering and cleaning the initial text to obtain a filtered text, and coding the filtered text based on a UTF-8 coding algorithm to obtain the text data; or
And when the original data is of a text type, filtering and cleaning the original data to obtain a filtered text, and coding the filtered text based on a UTF-8 coding algorithm to obtain the text data.
Specifically, the original data may be converted into the initial text using an OCR (Optical Character Recognition) algorithm.
Meanwhile, the filtered text is coded through the UTF-8 coding algorithm, full-angle and half-angle symbol conversion, messy code removal and other operations can be performed on the filtered text, and coding unification is finally achieved.
Through the implementation mode, the original data can be filtered and cleaned to eliminate interference information, and further the original data is converted into a uniform text format, so that the uniformity of the data format is realized, and the preprocessed text data can be recognized and processed by a machine.
The recognition unit 112 performs text recognition on the text data to obtain data to be analyzed.
In at least one embodiment of the present invention, the recognizing unit 112 performs text recognition on the text data, and obtaining the data to be analyzed includes:
carrying out Word Embedding processing on the data to be analyzed to generate Word vectors;
performing convolution operation on the word vector to output a feature map;
performing maximum pooling treatment on the feature map to obtain a plurality of pooling features;
splicing the plurality of pooling characteristics, inputting the spliced pooling characteristics to a softmax layer, and outputting the data to be analyzed.
In the above embodiment, the Word Embedding process is used to digitize the natural language, so that the subsequent process is facilitated, and the recognition mode is simpler and faster than that of the conventional CNN network.
The construction unit 113 constructs an index system based on an analytic hierarchy process, the index system comprising at least one primary index, each primary index comprising at least one secondary index.
In at least one embodiment of the present invention, the constructing unit 113 constructs the index system based on the analytic hierarchy process including:
acquiring data to be processed;
performing clustering and layering processing on the data to be processed based on an analytic hierarchy process to obtain a plurality of initial first-level indexes and at least one second-level index included in each initial first-level index;
determining the importance of each initial primary index configured in advance;
comparing the importance of every two initial primary indexes to construct a judgment matrix;
calculating the weight of each initial primary index according to the judgment matrix;
acquiring an initial primary index with the weight greater than or equal to a preset weight as the at least one primary index;
and constructing the index system by using the at least one primary index and the at least one secondary index corresponding to each primary index.
The importance of each initial primary index may be evaluated by an expert, or may be obtained by analyzing historical data, which is not limited herein.
The operation unit 114 obtains a target field corresponding to each secondary index and a function script corresponding to each target field, obtains a target parameter matched with each target field from the data to be analyzed, adds each target parameter to the corresponding function script for operation, and outputs a score of each secondary index.
For example: when the evaluation factors of the secondary index A are as follows: and the evaluation factors belong to the jurisdiction scope of the 'anti-commercial bribery', so that the 'anti-commercial briy' can be used as a target field of the secondary index A, and all data related to the 'anti-commercial briy' can be acquired as an evaluation basis of the secondary index A.
In at least one embodiment of the present invention, the obtaining, by the arithmetic unit 114, the target parameter matched with each target field from the data to be analyzed includes:
acquiring a pre-configured knowledge graph;
calculating the similarity between each target field and the entity on each node in the knowledge graph;
acquiring at least one entity with the similarity greater than or equal to a preset similarity from each node as a candidate field of each target field;
integrating each target field and the corresponding candidate field to construct an extension field of each target field;
traversing the data to be analyzed by utilizing the extension field of each target field;
and determining the traversed data as target parameters matched with each target field.
According to the embodiment, each target field is expanded by using the knowledge graph and then traversed, so that the traversed target parameters are better and more comprehensive, calculation errors caused by parameter omission are avoided, and the accuracy of the data analysis result is influenced.
The input unit 115 inputs the score of each secondary index into a pre-constructed scoring model for processing, and outputs a compliance score.
In this embodiment, a threshold may be set, and when the compliance score is higher than the threshold, it is determined that compliance is better, and when the compliance score is lower than or equal to the threshold, it is determined that compliance is worse.
For example: when the compliance score is 99 minutes, which is higher than the threshold 85, it can be determined that the compliance of the corresponding enterprise or institution is better and the risk is lower.
By the aid of the method, full-automatic analysis of the data to be analyzed is realized, the analysis efficiency is high, the compliance of an enterprise or an organization can be comprehensively evaluated, the risk of the enterprise or the organization can be further evaluated, and the macro-economic data can be analyzed.
In at least one embodiment of the present invention, a training sample is obtained, and the training sample is split into a training set and a verification set;
inputting the training set into a neural network for learning to obtain an initial model;
validating the initial model using the validation set;
and when the initial model passes the verification, stopping training to obtain the scoring model.
Through the implementation mode, the scoring model can be trained, so that full-automatic scoring can be performed by using the trained scoring model, and the processing efficiency is improved.
In at least one embodiment of the present invention, when it is detected that the index system is updated, the obtaining unit 116 obtains a score of the updated index;
according to the score of the updated index, the modification unit 117 modifies the scoring model using a back propagation algorithm.
By continuously updating and correcting the scoring model, the coverage of the model can be wider, the model is continuously suitable for new evaluation strategies, and the practicability is higher.
Specifically, the modifying unit 117, according to the score of the updated indicator, modifying the scoring model by using a back propagation algorithm includes:
performing compression estimation on the updated index value by using a Lasso (last absolute shrinkage and selection operator) algorithm to obtain at least one first index value;
performing time series prediction analysis on the at least one first index value by using an LSTM (Long Short-Term Memory network) algorithm to obtain at least one second index value;
inputting the at least one second index value into the scoring model, and calculating a return error;
and updating the weight of the scoring model according to the return error until the return error is less than or equal to a preset error, and stopping correction.
Through the implementation mode, the scoring model can be further optimized, so that the accuracy of the evaluation on the regularity is improved.
In this embodiment, in order to further ensure the security and privacy of the data, the index system and the compliance score may also be stored in the blockchain, so as to effectively prevent the data from being maliciously tampered.
According to the technical scheme, the invention can adopt a breadth-first traversal strategy to crawl original data from a specified platform, preprocessing the original data to obtain text data, performing text recognition on the text data to obtain data to be analyzed, constructing an index system based on an analytic hierarchy process, the index system comprises at least one primary index, each primary index comprises at least one secondary index, a target field corresponding to each secondary index and a function script corresponding to each target field are obtained, a target parameter matched with each target field is obtained from the data to be analyzed, and each target parameter is added to the corresponding function script for operation, the score of each secondary index is output, the score of each secondary index is input to a pre-constructed scoring model for processing, and a compliance score is output, so that the compliance of an enterprise or an organization can be accurately and quantitatively automatically evaluated based on an artificial intelligence technology. In addition, the invention can also be applied to intelligent government affairs, thereby promoting the construction of intelligent cities. The invention also relates to blockchain techniques, and the compliance scores may be stored in blockchain nodes.
Fig. 3 is a schematic structural diagram of an electronic device according to a preferred embodiment of the method for evaluating compliance based on artificial intelligence according to the present invention.
The electronic device 1 may comprise a memory 12, a processor 13 and a bus, and may further comprise a computer program, such as an artificial intelligence based compliance assessment program, stored in the memory 12 and executable on the processor 13.
It will be understood by those skilled in the art that the schematic diagram is merely an example of the electronic device 1, and does not constitute a limitation to the electronic device 1, the electronic device 1 may have a bus-type structure or a star-type structure, the electronic device 1 may further include more or less hardware or software than those shown in the figures, or different component arrangements, for example, the electronic device 1 may further include an input and output device, a network access device, and the like.
It should be noted that the electronic device 1 is only an example, and other existing or future electronic products, such as those that can be adapted to the present invention, should also be included in the scope of the present invention, and are included herein by reference.
The memory 12 includes at least one type of readable storage medium, which includes flash memory, removable hard disks, multimedia cards, card-type memory (e.g., SD or DX memory, etc.), magnetic memory, magnetic disks, optical disks, etc. The memory 12 may in some embodiments be an internal storage unit of the electronic device 1, for example a removable hard disk of the electronic device 1. The memory 12 may also be an external storage device of the electronic device 1 in other embodiments, such as a plug-in mobile hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like provided on the electronic device 1. Further, the memory 12 may also include both an internal storage unit and an external storage device of the electronic device 1. The memory 12 may be used not only to store application software installed in the electronic device 1 and various types of data, such as codes of a compliance evaluation program based on artificial intelligence, etc., but also to temporarily store data that has been output or is to be output.
The processor 13 may be composed of an integrated circuit in some embodiments, for example, a single packaged integrated circuit, or may be composed of a plurality of integrated circuits packaged with the same or different functions, including one or more Central Processing Units (CPUs), microprocessors, digital Processing chips, graphics processors, and combinations of various control chips. The processor 13 is a Control Unit (Control Unit) of the electronic device 1, connects various components of the electronic device 1 by various interfaces and lines, and executes various functions and processes data of the electronic device 1 by running or executing programs or modules stored in the memory 12 (for example, executing a compliance evaluation program based on artificial intelligence, etc.), and calling data stored in the memory 12.
The processor 13 executes an operating system of the electronic device 1 and various installed application programs. The processor 13 executes the application program to implement the steps in the various artificial intelligence based compliance assessment method embodiments described above, such as the steps shown in FIG. 1.
Illustratively, the computer program may be divided into one or more modules/units, which are stored in the memory 12 and executed by the processor 13 to accomplish the present invention. The one or more modules/units may be a series of computer program instruction segments capable of performing specific functions, which are used to describe the execution process of the computer program in the electronic device 1. For example, the computer program may be divided into a crawling unit 110, a preprocessing unit 111, a recognition unit 112, a construction unit 113, an operation unit 114, an input unit 115, an acquisition unit 116, and a modification unit 117.
Alternatively, the processor 13, when executing the computer program, implements the functions of the modules/units in the above device embodiments, for example:
crawling original data from a specified platform by adopting a breadth-first traversal strategy;
preprocessing the original data to obtain text data;
performing text recognition on the text data to obtain data to be analyzed;
constructing an index system based on an analytic hierarchy process, wherein the index system comprises at least one first-level index, and each first-level index comprises at least one second-level index;
acquiring a target field corresponding to each secondary index and a function script corresponding to each target field, acquiring a target parameter matched with each target field from the data to be analyzed, adding each target parameter to the corresponding function script for operation, and outputting a score of each secondary index;
and inputting the score of each secondary index into a pre-constructed scoring model for processing, and outputting a compliance score.
The integrated unit implemented in the form of a software functional module may be stored in a computer-readable storage medium. The software functional module is stored in a storage medium and includes several instructions to enable a computer device (which may be a personal computer, a computer device, or a network device) or a processor (processor) to execute the parts of the compliance evaluation method based on artificial intelligence according to the embodiments of the present invention.
The integrated modules/units of the electronic device 1 may be stored in a computer-readable storage medium if they are implemented in the form of software functional units and sold or used as separate products. Based on such understanding, all or part of the flow of the method according to the embodiments of the present invention may be implemented by a computer program, which may be stored in a computer-readable storage medium, and when the computer program is executed by a processor, the steps of the method embodiments may be implemented.
Wherein the computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying said computer program code, recording medium, U-disk, removable hard disk, magnetic disk, optical disk, computer Memory, Read-Only Memory (ROM).
Further, the computer-usable storage medium may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function, and the like; the storage data area may store data created according to the use of the blockchain node, and the like.
The block chain is a novel application mode of computer technologies such as distributed data storage, point-to-point transmission, a consensus mechanism, an encryption algorithm and the like. A block chain (Blockchain), which is essentially a decentralized database, is a series of data blocks associated by using a cryptographic method, and each data block contains information of a batch of network transactions, so as to verify the validity (anti-counterfeiting) of the information and generate a next block. The blockchain may include a blockchain underlying platform, a platform product service layer, an application service layer, and the like.
The bus may be a Peripheral Component Interconnect (PCI) bus, an Extended Industry Standard Architecture (EISA) bus, or the like. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of illustration, only one arrow is shown in FIG. 3, but this does not indicate only one bus or one type of bus. The bus is arranged to enable connection communication between the memory 12 and at least one processor 13 or the like.
Although not shown, the electronic device 1 may further include a power supply (such as a battery) for supplying power to each component, and preferably, the power supply may be logically connected to the at least one processor 13 through a power management device, so as to implement functions of charge management, discharge management, power consumption management, and the like through the power management device. The power supply may also include any component of one or more dc or ac power sources, recharging devices, power failure detection circuitry, power converters or inverters, power status indicators, and the like. The electronic device 1 may further include various sensors, a bluetooth module, a Wi-Fi module, and the like, which are not described herein again.
Further, the electronic device 1 may further include a network interface, and optionally, the network interface may include a wired interface and/or a wireless interface (such as a WI-FI interface, a bluetooth interface, etc.), which are generally used for establishing a communication connection between the electronic device 1 and other electronic devices.
Optionally, the electronic device 1 may further comprise a user interface, which may be a Display (Display), an input unit (such as a Keyboard), and optionally a standard wired interface, a wireless interface. Alternatively, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, an OLED (Organic Light-Emitting Diode) touch device, or the like. The display, which may also be referred to as a display screen or display unit, is suitable for displaying information processed in the electronic device 1 and for displaying a visualized user interface, among other things.
It is to be understood that the described embodiments are for purposes of illustration only and that the scope of the appended claims is not limited to such structures.
Fig. 3 only shows the electronic device 1 with components 12-13, and it will be understood by a person skilled in the art that the structure shown in fig. 3 does not constitute a limitation of the electronic device 1, and may comprise fewer or more components than shown, or a combination of certain components, or a different arrangement of components.
With reference to fig. 1, the memory 12 of the electronic device 1 stores a plurality of instructions to implement an artificial intelligence based compliance assessment method, and the processor 13 executes the plurality of instructions to implement:
crawling original data from a specified platform by adopting a breadth-first traversal strategy;
preprocessing the original data to obtain text data;
performing text recognition on the text data to obtain data to be analyzed;
constructing an index system based on an analytic hierarchy process, wherein the index system comprises at least one first-level index, and each first-level index comprises at least one second-level index;
acquiring a target field corresponding to each secondary index and a function script corresponding to each target field, acquiring a target parameter matched with each target field from the data to be analyzed, adding each target parameter to the corresponding function script for operation, and outputting a score of each secondary index;
and inputting the score of each secondary index into a pre-constructed scoring model for processing, and outputting a compliance score.
Specifically, the processor 13 may refer to the description of the relevant steps in the embodiment corresponding to fig. 1 for a specific implementation method of the instruction, which is not described herein again.
In the embodiments provided in the present invention, it should be understood that the disclosed system, apparatus and method may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the modules is only one logical functional division, and other divisions may be realized in practice.
The modules described as separate parts may or may not be physically separate, and parts displayed as modules may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment.
In addition, functional modules in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, or in a form of hardware plus a software functional module.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential attributes thereof.
The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference signs in the claims shall not be construed as limiting the claim concerned.
Furthermore, it is obvious that the word "comprising" does not exclude other elements or steps, and the singular does not exclude the plural. A plurality of units or means recited in the system claims may also be implemented by one unit or means in software or hardware. The terms second, etc. are used to denote names, but not any particular order.
Finally, it should be noted that the above embodiments are only for illustrating the technical solutions of the present invention and not for limiting, and although the present invention is described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications or equivalent substitutions may be made on the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.
Claims (10)
1. An artificial intelligence based compliance assessment method, characterized in that the artificial intelligence based compliance assessment method comprises:
crawling original data from a specified platform by adopting a breadth-first traversal strategy;
preprocessing the original data to obtain text data;
performing text recognition on the text data to obtain data to be analyzed;
constructing an index system based on an analytic hierarchy process, wherein the index system comprises at least one first-level index, and each first-level index comprises at least one second-level index;
acquiring a target field corresponding to each secondary index and a function script corresponding to each target field, acquiring a target parameter matched with each target field from the data to be analyzed, adding each target parameter to the corresponding function script for operation, and outputting a score of each secondary index;
and inputting the score of each secondary index into a pre-constructed scoring model for processing, and outputting a compliance score.
2. The artificial intelligence based compliance assessment method of claim 1, wherein said pre-processing said raw data to obtain text data comprises:
when the original data is of a picture type, converting the original data into an initial text, filtering and cleaning the initial text to obtain a filtered text, and coding the filtered text based on a UTF-8 coding algorithm to obtain the text data; or
And when the original data is of a text type, filtering and cleaning the original data to obtain a filtered text, and coding the filtered text based on a UTF-8 coding algorithm to obtain the text data.
3. The artificial intelligence based compliance assessment method according to claim 1, wherein said performing text recognition on said text data to obtain data to be analyzed comprises:
carrying out Word Embedding processing on the data to be analyzed to generate Word vectors;
performing convolution operation on the word vector to output a feature map;
performing maximum pooling treatment on the feature map to obtain a plurality of pooling features;
splicing the plurality of pooling characteristics, inputting the spliced pooling characteristics to a softmax layer, and outputting the data to be analyzed.
4. The artificial intelligence-based compliance assessment method of claim 1, wherein said building an indicator system based on an analytic hierarchy process comprises:
acquiring data to be processed;
performing clustering and layering processing on the data to be processed based on an analytic hierarchy process to obtain a plurality of initial first-level indexes and at least one second-level index included in each initial first-level index;
determining the importance of each initial primary index configured in advance;
comparing the importance of every two initial primary indexes to construct a judgment matrix;
calculating the weight of each initial primary index according to the judgment matrix;
acquiring an initial primary index with the weight greater than or equal to a preset weight as the at least one primary index;
and constructing the index system by using the at least one primary index and the at least one secondary index corresponding to each primary index.
5. The artificial intelligence based compliance assessment method according to claim 1, wherein said obtaining target parameters matching each target field from said data to be analyzed comprises:
acquiring a pre-configured knowledge graph;
calculating the similarity between each target field and the entity on each node in the knowledge graph;
acquiring at least one entity with the similarity greater than or equal to a preset similarity from each node as a candidate field of each target field;
integrating each target field and the corresponding candidate field to construct an extension field of each target field;
traversing the data to be analyzed by utilizing the extension field of each target field;
and determining the traversed data as target parameters matched with each target field.
6. The artificial intelligence based compliance assessment method of claim 1, wherein said artificial intelligence based compliance assessment method further comprises:
when the index system is detected to be updated, acquiring the score of the updated index;
and correcting the scoring model by adopting a back propagation algorithm according to the updated index value.
7. The artificial intelligence-based compliance assessment method according to claim 6, wherein said adapting said scoring model according to the score of said updated indicator using a back propagation algorithm comprises:
performing compression estimation on the value of the updated index by adopting a Lasso algorithm to obtain at least one first index value;
performing time series prediction analysis on the at least one first index value by adopting an LSTM algorithm to obtain at least one second index value;
inputting the at least one second index value into the scoring model, and calculating a return error;
and updating the weight of the scoring model according to the return error until the return error is less than or equal to a preset error, and stopping correction.
8. An artificial intelligence based compliance assessment device, comprising:
the crawling unit is used for crawling original data from a specified platform by adopting a breadth-first traversal strategy;
the preprocessing unit is used for preprocessing the original data to obtain text data;
the identification unit is used for carrying out text identification on the text data to obtain data to be analyzed;
the system comprises a construction unit, a data processing unit and a data processing unit, wherein the construction unit is used for constructing an index system based on an analytic hierarchy process, the index system comprises at least one primary index, and each primary index comprises at least one secondary index;
the operation unit is used for acquiring a target field corresponding to each secondary index and a function script corresponding to each target field, acquiring a target parameter matched with each target field from the data to be analyzed, adding each target parameter to the corresponding function script for operation, and outputting a score of each secondary index;
and the input unit is used for inputting the score of each secondary index into a pre-constructed scoring model for processing and outputting the compliance score.
9. An electronic device, characterized in that the electronic device comprises:
a memory storing at least one instruction; and
a processor executing instructions stored in the memory to implement the artificial intelligence based compliance assessment method of any one of claims 1 to 7.
10. A computer-readable storage medium characterized by: the computer-readable storage medium has stored therein at least one instruction that is executable by a processor in an electronic device to implement the artificial intelligence based compliance assessment method of any one of claims 1 to 7.
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