CN111639161A - System information processing method, apparatus, computer system and medium - Google Patents
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Abstract
The disclosure provides a manufacturing degree information processing method applied to a computer system. The method comprises the following steps: system information of a specified enterprise is obtained, and a first feature vector for representing the system information is constructed. And acquiring a second feature vector for representing the supervision requirement information of the area where the specified enterprise is located. And determining the matching degree between the supervision requirement information and the system information based on the first feature vector and the second feature vector. And when the matching degree between the two is lower than a preset threshold value, prompting information indicating that the institutional formulation of the specified enterprise does not meet the supervision requirement of the area where the specified enterprise is located is pushed to the terminal of the specified enterprise. The present disclosure also provides a degree information processing apparatus, a computer system, and a medium.
Description
Technical Field
The present disclosure relates to the field of computer technologies, and in particular, to a method, an apparatus, a computer system, and a medium for processing scheduling information.
Background
Typically, an enterprise may publish and manage system information. The region to which the enterprise belongs can be supervised according to the system information of the enterprise based on the supervision requirement information. The system information of the enterprise determines the implementation standard of the actual business of the enterprise. In the face of a large number of enterprises, the consistency of the supervision requirements of the regions where the system information of the enterprises belongs is very important to supervise. In most cases, the consistency between the system information of the enterprise and the regional supervision requirements needs to be artificially compared, and the method has low efficiency, poor timeliness and low accuracy.
Disclosure of Invention
One aspect of the disclosure provides a method for processing scheduling information, which is applied to a computer system. The method comprises the following steps: system information of a specified enterprise is obtained, and a first feature vector for representing the system information is constructed. And acquiring a second feature vector for representing the supervision requirement information of the area where the specified enterprise is located. And determining the matching degree between the supervision requirement information and the system information based on the first feature vector and the second feature vector. And when the matching degree between the two is lower than a preset threshold value, prompting information indicating that the institutional formulation of the specified enterprise does not meet the supervision requirement of the area where the specified enterprise is located is pushed to the terminal of the specified enterprise.
Optionally, the method further includes: before the first characteristic vector used for representing the system information is constructed, whether the language of the system information is simplified Chinese is determined. If not, the system information language is converted into simplified Chinese.
Optionally, the constructing a first feature vector for characterizing system information includes: and processing system information by using a pre-constructed word frequency-inverse document frequency model to obtain a first feature vector.
Optionally, the processing the system information by using the pre-constructed word frequency-inverse document frequency model to obtain the first feature vector includes: inputting system information into the word frequency-inverse document frequency model to execute the following operations by the word frequency-inverse document frequency model: and performing word segmentation processing on the system information to obtain a plurality of word segmentation results. And counting the word frequency of each word segmentation result in the system information. And determining the word frequency-inverse document frequency characteristic of each word segmentation result based on the word frequency of each word segmentation result and a preset corpus. Then, a first feature vector is constructed based on the respective word frequency-inverse document frequency features of the multiple word segmentation results.
Optionally, the constructing a first feature vector for characterizing the system information includes: and representing system information as a unique heat vector by using a pre-constructed word set model to serve as a first characteristic vector.
Optionally, the obtaining of the second feature vector for characterizing the regulatory requirement information of the area where the specified enterprise is located includes: it is determined whether a second feature vector of the regulatory requirement information is present in a predetermined memory area of the computer system. If so, the second feature vector is read from the predetermined storage area. If not, capturing the supervision requirement information from the specified webpage by using a web crawler, wherein the specified webpage is used for displaying the supervision requirement information, then constructing a second feature vector for representing the supervision requirement information, and storing the second feature vector to a preset storage area.
Optionally, the method further includes: and monitoring the specified webpage. And when the updating event of the specified webpage is monitored, capturing updated supervision requirement information from the specified webpage by using a web crawler. And then constructing a second feature vector for representing the updated supervision requirement information, and storing the second feature vector to a preset storage area.
Optionally, the constructing a second feature vector for characterizing the regulatory requirement information and the constructing a second feature vector for characterizing the updated regulatory requirement information include: and processing the supervision requirement information by utilizing a pre-constructed word frequency-inverse document frequency model to obtain a second feature vector. Or, the supervision requirement information is expressed as a one-hot vector by utilizing a pre-constructed word set model to serve as a second feature vector.
Optionally, the determining the degree of matching between the regulatory requirement information and the system information based on the first feature vector and the second feature vector includes at least one of: and calculating a matching coefficient between the first feature vector and the second feature vector, and determining the matching degree according to the matching coefficient between the first feature vector and the second feature vector. And calculating cosine similarity between the first feature vector and the second feature vector, and determining matching degree according to the cosine similarity between the first feature vector and the second feature vector. And calculating the Minkowski distance between the first feature vector and the second feature vector, and determining the matching degree according to the Minkowski distance between the first feature vector and the second feature vector.
Another aspect of the present disclosure provides a production information processing apparatus applied to a computer system. The device includes: the system comprises a first acquisition module, a construction module, a second acquisition module, a matching module and a supervision processing module. The first acquisition module is used for acquiring system information of a specified enterprise. The construction module is used for constructing a first feature vector for representing system information. The second obtaining module is used for obtaining a second feature vector used for representing the supervision requirement information of the area where the specified enterprise is located. The matching module is used for determining the matching degree between the supervision requirement information and the system information based on the first feature vector and the second feature vector. And the supervision processing module is used for pushing prompt information indicating that the institutional formulation of the specified enterprise does not meet the supervision requirement of the area where the specified enterprise is located to the terminal of the specified enterprise when the matching degree between the two is lower than a preset threshold value.
Another aspect of the present disclosure provides a computer system comprising: a memory, a processor and a computer program stored on the memory and executable on the processor, the processor when executing the computer program for performing the method as described above.
Another aspect of the present disclosure provides a computer-readable storage medium storing computer-executable instructions for implementing the method as described above when executed.
Another aspect of the disclosure provides a computer program comprising computer executable instructions for implementing the method as described above when executed.
According to the embodiment of the disclosure, the system information processing method can automatically acquire the system information of an enterprise and the supervision requirement information of the area where the enterprise is located, and match the system information with the supervision requirement information to determine whether the system information of the enterprise meets the supervision requirement of the area where the enterprise belongs. Specifically, a first feature vector representing the system information and a second feature vector representing the supervision requirement information can be determined by using the processing model, and then the matching degree between the system information and the supervision requirement information can be determined based on the first feature vector and the second feature vector. The benchmarking between the enterprise system and the regional supervision requirements can be quickly and accurately carried out according to actual needs, the manual comparison process is omitted, and the efficiency and the accuracy are improved. And under the condition that the enterprise system is determined not to meet the requirements of prefecture supervision, the computer system can intelligently feed back to the enterprise or regional supervision platform so as to promote the correction of the enterprise system. According to the embodiment of the disclosure, the process can be rapidly completed for each enterprise in each region, so that the normal and ordered operation of the enterprises in each region is maintained.
Drawings
For a more complete understanding of the present disclosure and the advantages thereof, reference is now made to the following descriptions taken in conjunction with the accompanying drawings, in which:
FIG. 1 schematically illustrates an exemplary system architecture for application regime information processing methods and apparatus according to embodiments of the present disclosure;
FIG. 2 schematically shows a flow chart of a regimen information processing method according to an embodiment of the present disclosure;
FIG. 3 schematically illustrates an architectural example diagram of an intelligent targeting tool in accordance with an embodiment of the present disclosure;
FIG. 4 schematically illustrates an example diagram of the regulatory requirement grasping module 330 of FIG. 3, in accordance with an embodiment of the present disclosure;
FIG. 5 schematically illustrates an example diagram of the text translation conversion module 340 of FIG. 3, in accordance with an embodiment of the present disclosure;
FIG. 6 schematically illustrates an example diagram of the mathematical modeling module 350 of FIG. 3 in accordance with an embodiment of the present disclosure;
FIG. 7 schematically illustrates an example diagram of the similarity determination and benchmarking module 360 of FIG. 3, according to an embodiment of the present disclosure;
FIG. 8 schematically shows a block diagram of a system information processing apparatus according to an embodiment of the present disclosure; and
FIG. 9 schematically shows a block diagram of a computer system according to an embodiment of the disclosure.
Detailed Description
Hereinafter, embodiments of the present disclosure will be described with reference to the accompanying drawings. It should be understood that the description is illustrative only and is not intended to limit the scope of the present disclosure. In the following detailed description, for purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of the embodiments of the disclosure. It may be evident, however, that one or more embodiments may be practiced without these specific details. Moreover, in the following description, descriptions of well-known structures and techniques are omitted so as to not unnecessarily obscure the concepts of the present disclosure.
The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the disclosure. The terms "comprises," "comprising," and the like, as used herein, specify the presence of stated features, steps, operations, and/or components, but do not preclude the presence or addition of one or more other features, steps, operations, or components.
All terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art unless otherwise defined. It is noted that the terms used herein should be interpreted as having a meaning that is consistent with the context of this specification and should not be interpreted in an idealized or overly formal sense.
Where a convention analogous to "at least one of A, B and C, etc." is used, in general such a construction is intended in the sense one having skill in the art would understand the convention (e.g., "a system having at least one of A, B and C" would include but not be limited to systems that have a alone, B alone, C alone, a and B together, a and C together, B and C together, and/or A, B, C together, etc.). Where a convention analogous to "A, B or at least one of C, etc." is used, in general such a construction is intended in the sense one having skill in the art would understand the convention (e.g., "a system having at least one of A, B or C" would include but not be limited to systems that have a alone, B alone, C alone, a and B together, a and C together, B and C together, and/or A, B, C together, etc.).
One aspect of the disclosure provides a method and device for processing scheduling information, which is applied to a computer system. The system information processing method can comprise the following steps: the method comprises a first acquisition process, a construction process, a second acquisition process, a matching process and a supervision processing process. In the first acquisition process, system information of a specified enterprise is acquired, and in the construction process, a first feature vector for representing the system information is constructed. And in the second acquisition process, acquiring a second characteristic vector for representing the supervision requirement information of the area where the specified enterprise is located. Then, a matching process can be carried out, and the matching degree between the supervision requirement information and the system information is determined based on the first characteristic vector and the second characteristic vector. And then, a supervision processing process can be carried out according to the matching degree, and when the matching degree between the two is lower than a preset threshold value, prompt information indicating that the system of the specified enterprise does not meet the supervision requirement of the area where the specified enterprise is located is pushed to the terminal of the specified enterprise, so that the specified enterprise corrects the system information of the specified enterprise.
FIG. l schematically illustrates an exemplary system architecture 100 to which the institutional information processing methods and apparatus may be applied, according to embodiments of the present disclosure. It should be noted that fig. 1 is only an example of a system architecture to which the embodiments of the present disclosure may be applied to help those skilled in the art understand the technical content of the present disclosure, and does not mean that the embodiments of the present disclosure may not be applied to other devices, systems, environments or scenarios.
As shown in fig. 1, the system architecture 100 according to this embodiment may include: a system management platform 101 for a plurality of enterprises, a computer system 102, and a supervisory platform 103 for a plurality of regions. The system management platform 101 of a plurality of enterprises includes: an enterprise A system management platform, an enterprise B system management platform and an enterprise C system management platform. The regulatory platform 103 for multiple regions includes: a supervision platform for region 1, a supervision platform for region 2, and a supervision platform for region 3. In other embodiments, multiple regions may share one monitoring platform, which is not limited herein.
The system management platform 101 of any enterprise can run a full-life-cycle system management tool, for example, to implement functions such as making system information inside the enterprise, issuing system information, adding, revising and abolishing system information, performing classification management display of system information, searching system information, performing scenario display of system information, and self-testing the use condition of system information. The system information may be various regulations established by the enterprise in combination with the actual production and operation conditions, such as program files, operation instruction books, and the like. The system management platform 101 is composed of a terminal device and a server which can communicate with each other, for example, a client (such as an application client, a web page client, etc.) having a system management tool installed in the terminal device, and a server having a system management tool installed in the server accordingly.
The monitoring platform 103 of any region is used to implement functions of the corresponding region, such as preparation of monitoring requirement information for an enterprise system, release of the monitoring requirement information, addition, revision and abolishment of the monitoring requirement information, and the like. The regulatory requirement information of a region includes, for example, laws and regulations for enterprise systems, regulatory requirement files, and the like of the region. The administration platform 103 is composed of a terminal device and a server, which can communicate with each other, for example, a client (such as an application client, a web page client, etc.) with an administration tool installed in the terminal device, and a server with an administration tool installed in the server.
The computer system 102 may be any electronic device with certain computing capabilities, such as a mainframe, a server, or a cluster of servers, without limitation. The system information processing method and device according to the embodiment of the disclosure can be executed by the computer system 102. The computer system 102 can obtain institutional information from any enterprise's institutional management platform 101 on the one hand, and regulatory requirements information from any regulatory platform 103 on the other hand. For example, computer system 102 obtains institutional information for Enterprise A from a client of the institutional management tool for Enterprise A. And the computer system 102 also obtains regulatory requirement information for region 2 from a client of a regulatory tool for the region to which enterprise a belongs (e.g., region 2). The computer system 102 may compare the system information of enterprise a with the regulatory requirement information of region 2 to determine whether the system information of enterprise a meets the regulatory requirement of the region to which it belongs. In other embodiments, the computer system 102 may be deployed in the institutional management platform 101 of each enterprise, or the computer system 102 may also be deployed in the administration platform 103 of each region, without limitation.
It should be understood that the number and type of institutional management platforms, computer systems, and administration platforms of FIG. 1 are illustrative only. There may be any number of any type of institutional management platform, computer system, and supervisory platform, as desired for implementation.
The system information of the enterprise determines the implementation standard of the actual business of the enterprise. In the face of a large number of enterprises, the consistency of the supervision requirements of the regions where the system information of the enterprises belongs is very important to supervise. In most cases, the consistency between the system information of the enterprise and the regional supervision requirements needs to be artificially compared, and the method has low efficiency, poor timeliness and low accuracy.
According to an embodiment of the present disclosure, a method for processing degree information is provided, which is exemplarily described below with reference to the drawings. It should be noted that the system information processing method provided by the embodiment of the present disclosure can be executed by the computer system. Accordingly, the system information processing device provided by the embodiment of the present disclosure can be disposed in the computer system. It should be noted that the sequence numbers of the respective operations in the following methods are merely used as representations of the operations for description, and should not be construed as representing the execution order of the respective operations. The method need not be performed in the exact order shown, unless explicitly stated.
Fig. 2 schematically shows a flowchart of a system information processing method according to an embodiment of the present disclosure, which may be applied to a computer system, and the computer system may be used as a server or a terminal device to implement automatic system information and regional supervision requirement alignment of an enterprise.
As shown in fig. 2, the system information processing method may include operations S210 to S250.
In operation S210, system information of a specified enterprise is acquired.
Illustratively, the institutional information for a given enterprise may correspond to, for example, a textual representation of at least one institutional file established by the given enterprise in connection with its actual production operations. This operation S210 may obtain corresponding institutional information from the institutional management platform of the specified enterprise, for example, as described above. The system information should be the latest published system information of a given enterprise.
In operation S220, a first feature vector for characterizing the regime information is constructed.
Illustratively, the first feature vector may include a plurality of feature values that are used to describe the institutional information from a plurality of dimensions to embody various surface text (surface text) features, various semantic (semantic) text features, etc. of the institutional information. For example, the process of constructing the first feature vector may be performed using a pre-constructed process model in a computer system.
In operation S230, a second feature vector for characterizing regulatory requirement information of a region where a specific enterprise is located is obtained.
For example, the present operation S230 may obtain the regulatory requirement information and the second feature vector thereof locally from the computer system, or may obtain the regulatory requirement information and the second feature vector thereof from other devices or platforms based on the network. For example, if the enterprise is designated as enterprise a and the region to which enterprise a belongs is designated as region 2, the operation may obtain the regulatory requirement information from the regulatory platform of region 2 and determine the second characteristic information based on the regulatory requirement information. Wherein the obtained regulatory requirement information should be the latest version of the regulatory requirement information of region 2. The second feature vector may include a plurality of feature values, which are used to describe the regulatory requirement information from a plurality of dimensions to embody various surface text features, various semantic text features, and the like of the regulatory requirement information. For example, the process of obtaining the second feature vector of the regulatory requirement information may also be performed using a pre-constructed process model in the computer system. The regulatory requirement information may be, for example, textual representations of various regulatory requirement files.
In operation S240, a degree of matching between the regulatory requirement information and the system information is determined based on the first feature vector and the second feature vector.
By using the above example, the higher the matching degree between the system information of the enterprise a and the regulatory requirement information of the region 2, the higher the consistency between the system information of the enterprise a and the regulatory requirement information of the region 2 is indicated, that is, the more the system information of the enterprise a meets the regulatory requirement of the region 2.
In operation S250, when the matching degree between the two is lower than the predetermined threshold, a prompt message indicating that the institutional formulation of the specified enterprise does not meet the regulatory requirement of the area where the specified enterprise is located is pushed to the terminal of the specified enterprise.
Continuing with the above example, when the degree of matching between the system information of enterprise a and the regulatory requirement information of region 2 is lower than the predetermined threshold, it indicates that the system information of enterprise a is inconsistent with the regulatory requirement of region 2, i.e., that the system information of enterprise a does not comply with the regulatory requirement of region 2. Therefore, at this time, in operation S250, prompt information needs to be pushed to the system management platform (for example, terminal device or server) of the enterprise a, where the prompt information is used to indicate that the system establishment of the enterprise a does not meet the monitoring requirement of the area where the enterprise a is located, so that the enterprise a can correct the system information in time to achieve the monitoring purpose. In other embodiments, when it is determined that the system information of the enterprise a does not meet the regulatory requirement of the area 2, alarm information may be pushed to a regulatory platform (e.g., a terminal device or a server) of the area 2, where the alarm information is used to indicate that the system formulation of the enterprise a does not meet the regulatory requirement of the area where the enterprise a is located, so that the regulatory platform of the area 2 may manage the enterprise a in time through other channels, so that the enterprise a corrects the system information thereof, thereby achieving the purpose of regulation.
Those skilled in the art can understand that the system information processing method according to the embodiment of the present disclosure can automatically acquire the system information of an enterprise and the regulatory requirement information of the area where the enterprise is located, and match the system information and the regulatory requirement information to determine whether the system information of the enterprise meets the regulatory requirement of the area where the enterprise belongs. Specifically, a first feature vector representing the system information and a second feature vector representing the supervision requirement information can be determined by using the processing model, and then the matching degree between the system information and the supervision requirement information can be determined based on the first feature vector and the second feature vector. The benchmarking between the enterprise system and the regional supervision requirements can be quickly and accurately carried out according to actual needs, the manual comparison process is omitted, and the efficiency and the accuracy are improved. And under the condition that the enterprise system is determined not to meet the requirements of prefecture supervision, the computer system can intelligently feed back to the enterprise or regional supervision platform so as to promote the correction of the enterprise system. According to the embodiment of the disclosure, the process can be rapidly completed for each enterprise in each region, so that the normal and ordered operation of the enterprises in each region is maintained.
Illustratively, the system information processing method according to the embodiment of the present disclosure may be implemented by an intelligent benchmarking tool, which may be run in the computer system. FIG. 3 schematically illustrates an architectural example diagram of an intelligent targeting tool according to an embodiment of the present disclosure.
As shown in fig. 3, the intelligent targeting tool 300 may include, for example: an interface module 310, a system information capture module 320, a regulatory requirement capture module 330, a text translation conversion module 340, a mathematical modeling module 350, and a similarity determination and benchmarking module 360.
Illustratively, the interface module 310 is used to present a user interaction interface of the intelligent targeting tool. The system information grasping module 320 is used for acquiring system information of each enterprise. When the intelligent benchmarking tool is deployed in a system management platform of an enterprise, the system information capture module can directly use locally stored system information. The regulatory requirement capturing module 330 is used for acquiring regulatory requirement information of each region. The word translation conversion module 340 is used for performing machine translation on system information and supervision requirement information, and the system information and the supervision requirement information are expressed by texts in the same language. The mathematical modeling module 350 is used to pre-build a process model, for example, a process model that can build a first feature vector based on institutional information and can build a second feature vector based on regulatory requirement information. The similarity judgment and benchmarking analysis module 360 is configured to determine a matching degree between the institutional information and the regulatory requirement information based on the first feature vector and the second feature vector, thereby determining a benchmarking result therebetween, and performing subsequent policy handling based on the benchmarking result.
According to an embodiment of the present disclosure, the process of obtaining the second feature vector of the regulatory requirement information for characterizing the area where the designated enterprise is located may be performed by using the regulatory requirement capturing module 330 shown in fig. 3. This process may include, for example: it is determined whether a second feature vector of the regulatory requirement information is present in a predetermined memory area of the computer system. If so, the second feature vector is read from the predetermined storage area. And if not, capturing the supervision requirement information from a specified webpage by using the web crawler, wherein the specified webpage is used for displaying the supervision requirement information. And then constructing a second feature vector for representing the supervision requirement information, and storing the second feature vector to a preset storage area. It can be understood that, in the case that the regulatory requirement information of a region is not changed, if the regulatory requirement information of the region is obtained once and the second feature vector is constructed based on the regulatory requirement information, since the computer system stores the second feature vector obtained each time, the second feature vector can be found from the local predetermined storage area of the computer system without reconstruction. If the regulatory requirement information of the region has not been acquired, the regulatory requirement information needs to be crawled from a specified webpage showing the regulatory requirement information.
Further, in one embodiment of the present disclosure, even though the computer system locally already stores the regulatory requirement information of a region and the second feature vector characterizing the regulatory requirement information, if the regulatory requirement information of the region is updated, the regulatory requirement information stored locally by the computer system is caused to be no longer the latest regulatory requirement information of the region. In this case, in order to ensure timeliness of the benchmarking result, the updated supervision requirement information can still be acquired again by the network. Illustratively, the system information processing method according to the embodiment of the present disclosure may further include: and monitoring a specified webpage of the region for displaying the supervision requirement information aiming at any region. And when the updating event of the specified webpage is monitored, capturing updated supervision requirement information from the specified webpage by using a web crawler. And then reconstructing a second feature vector for representing the supervision requirement information based on the updated supervision requirement information, and storing the second feature vector to a predetermined storage area.
Fig. 4 schematically illustrates an example diagram of the regulatory requirement grasping module 330 in fig. 3, according to an embodiment of the disclosure. As shown in fig. 4, the regulatory requirement crawling module 330 may include a home regulatory requirement website maintenance unit 331, a regulatory requirement network crawling unit 332, and a regulatory content reading unit 333.
Illustratively, the home administrative requirement website maintenance unit 331 is configured to maintain a mapping table, in which address information of the administrative requirement websites of the respective regions is recorded, for example, the address information of the administrative requirement website of the region 1 is www.locationlxxx.com, and the address can be directed to a specified web page of the region 1 for displaying the administrative requirement information. Other regions are the same, and are not described herein again. The mapping table is updated along with the change of the address information of the supervision requirement website of each region. The regulatory requirement network crawling unit 332 crawls the relevant content of the regulatory requirement information of the corresponding region from the regulatory requirement website of the region by using the web crawler technology, with the mapping table maintained by the local regulatory requirement website maintenance unit 331 as an index. For example, when processing a business system information belonging to region 1, the relevant content of the regulatory requirement information of region l is extracted from the website indicated by www.locationlxxx.com. And reads out the text content of the monitoring requirement information from the captured mass content by using the monitoring content reading unit 333 and stores the text content in the local database of the computer system.
According to the embodiment of the present disclosure, the above system information processing process may be performed, for example, for system information and regulatory requirement information expressed in simplified chinese. Before constructing the first feature vector for representing the system information, the text translation module 340 may be used to determine whether the language of the system information is simplified Chinese. If not, the system information language is converted into simplified Chinese. Similarly, before the second feature vector representing the regulatory requirement information needs to be constructed, the above-mentioned text translation and conversion module 340 may also be used to determine whether the language of the regulatory requirement information is simplified chinese, and convert the language of the regulatory requirement information into simplified chinese in the case of non-simplified chinese. So that the subsequent process of representing the subject system information and the supervision requirement information in the same language. In other embodiments, the above-mentioned system information processing process may also be performed on texts in other languages, for example, in english, the texts need to be uniformly converted into english texts before constructing the feature vectors, which is not described herein again.
Fig. 5 schematically illustrates an example diagram of the text translation conversion module 340 in fig. 3 according to an embodiment of the disclosure. As shown in fig. 5, the text translation conversion module 340 may include a sorting unit 341, a machine translation unit 342, and a structuring processing unit 343.
For example, after the regulatory requirement information of a region is acquired, the arrangement unit 341 may perform preprocessing processes such as data cleaning and language identification on the regulatory requirement information. And when it is determined that the regulatory requirement information is a non-chinese simplified, the regulatory requirement information is converted to simplified chinese text using the machine translation unit 342. For the chinese text, due to the continuity of the chinese text, the structured processing unit 343 may also be utilized to perform natural language processing on the regulatory requirement information, so as to facilitate the subsequent construction process of the feature vector. Similarly, the text processing procedure may also be performed according to the system information of the enterprise, and is not described herein again.
According to an embodiment of the present disclosure, the processing model pre-constructed by the mathematical modeling module 350 shown in fig. 3 may be, for example, a Term Frequency-Inverse Document Frequency (TF-IDF) model. Illustratively, the above process of constructing the first feature vector for characterizing the system information may include: and processing system information by using a pre-constructed word frequency-inverse document frequency model to obtain a first feature vector. The above process of constructing the second feature vector for characterizing the regulatory requirement information may include: and processing the supervision requirement information by utilizing a pre-constructed word frequency-inverse document frequency model to obtain a second feature vector.
FIG. 6 schematically shows an example diagram of the mathematical modeling module 350 of FIG. 3 in accordance with an embodiment of the present disclosure. As shown in fig. 6, the mathematical modeling module 350 may include a regulatory requirement structuring unit 351, a system information structuring unit 352, a model creating unit 353, and a feature vector constructing unit 354.
Illustratively, the mathematical modeling module 350 mainly performs natural language processing on the acquired regulatory requirement information and institutional information by using the regulatory requirement structuring unit 351 and institutional information structuring unit 352, respectively, which may be performed alternatively to the structuring processing performed by the structuring processing unit 343 above, to obtain the regulatory requirement structured data and the enterprise institutional structured data, respectively. The model creation unit 353 creates a mathematical model such as the word frequency-inverse document frequency model mentioned above. The feature vector construction unit 354 determines a first feature vector for characterizing the system information and a second feature vector for characterizing the regulatory requirement information based on the word frequency-inverse document frequency model. The process of constructing the first feature vector is illustrated below.
For example, for the system information of enterprise a, the system information is input into the word frequency-inverse document frequency model, so that the following operations are performed by the word frequency-inverse document frequency model: and performing word segmentation processing on the system information to obtain a plurality of word segmentation results. And counting the word frequency of each word segmentation result in the system information. And determining the word frequency-inverse document frequency characteristic of each word segmentation result based on the word frequency of each word segmentation result and a preset corpus. Then, a first feature vector is constructed based on the respective word frequency-inverse document frequency features of the multiple word segmentation results. The preset corpus can include a plurality of documents.
The process of calculating the word frequency-inverse document frequency characteristic of each word segmentation result may be implemented, for example, as follows. First, for a word segmentation result X (which may be called an entry) in system information (which may be called a document) of an enterprise a, a Term Frequency (TF) in a Term Frequency-inverse document Frequency feature of the word segmentation result X is a ratio of the number of occurrences of a certain word segmentation result X in the system information to the number of occurrences of all word segmentation results in the system information, as shown in formula (1).
Also for the word segmentation result X, the Inverse Document Frequency (IDF) in the word Frequency-Inverse Document Frequency feature of the word segmentation result X is used to illustrate the category distinguishing capability of the word segmentation result X. If the corpus contains fewer documents containing the word segmentation result X, the IDF of the word segmentation result X is larger, which indicates that the word segmentation result X has good category distinguishing capability. The IDF of a certain segmentation result X is calculated by dividing the total number of documents in the corpus by the number of documents containing the segmentation result X, and then taking the logarithm of the obtained quotient, as shown in formula (2). To avoid a denominator of 0, an offset factor of 1 is also added in this example.
Then, by multiplying the TF value of the segmentation result X by the IDF value of the segmentation result X, "TF-IDF weight", that is, the feature value of the word frequency-inverse document frequency feature of the segmentation result X, can be obtained, as shown in formula (3).
TF-IDF＝TF*IDF
Formula (3)
For a plurality of word segmentation results in the system information of the enterprise A, the characteristic values of the word frequency-inverse document frequency characteristics of the plurality of word segmentation results can be obtained through the process. For example, the system information includes the word segmentation result { a1, a 2.., an }, where n is an integer greater than 2. The resulting first feature vector may be, for example, { T }a1，Ta2，...，TanIn which T isa1The word frequency-inverse document frequency characteristic, T, of the word segmentation result a1a2Is the word frequency-inverse document frequency characteristic of the word segmentation result a2anThe word frequency-inverse document frequency characteristic of the word segmentation result an.
Similarly, in the case that the second feature vector needs to be constructed, the feature vector construction unit 354 may perform word segmentation processing on the monitoring requirement information according to the above implementation process by using the word frequency-inverse document frequency model, so as to obtain a plurality of word segmentation results of the monitoring requirement information. And respectively calculating the word frequency-inverse document frequency characteristics of the multiple word segmentation results of the supervision requirement information according to the formulas (1) to (3), and forming a second feature vector for representing the supervision requirement information by the word frequency-inverse document frequency characteristics of the multiple word segmentation results.
According to another embodiment of the present disclosure, the model creation unit 353 may also create other types of mathematical models to perform feature vector conversion of institutional information and regulatory requirement information. Such as a word Set model (Set of Words, SoW). The above-mentioned feature vector construction unit 354 may construct a first feature vector for characterizing the system information, for example, by: and representing the system information as a one-hot vector by using a pre-constructed word set model to serve as a first feature vector. Similarly, the process of constructing the second feature vector may also be to represent the regulatory requirement information as a unique heat vector by using a pre-constructed word set model to serve as the second feature vector.
FIG. 7 schematically illustrates an example diagram of the similarity determination and benchmarking module 360 of FIG. 3, according to an embodiment of the present disclosure. As shown in fig. 7, the similarity determination and benchmarking module 360 may include a similarity calculation unit 361, a matching degree determination unit 362 and a benchmarking analysis unit 363.
The similarity calculation unit 361 calculates the similarity between the first feature vector and the second feature vector. The matching degree determination unit 362 calculates the matching degree between the regulatory requirement information and the system information based on the similarity of the feature vectors. Illustratively, the determining the degree of matching between the regulatory requirement information and the system information based on the first eigenvector and the second eigenvector includes at least one of: calculating a matching coefficient (MatchingCoffective) between the first characteristic vector and the second characteristic vector, and determining a matching degree according to the matching coefficient between the first characteristic vector and the second characteristic vector; calculating cosine similarity (cosine) between the first eigenvector and the second eigenvector, and determining matching degree according to the cosine similarity between the first eigenvector and the second eigenvector; calculating the Minkowski Distance (Minkowski Distance) between the first feature vector and the second feature vector, and determining the matching degree according to the Minkowski Distance between the first feature vector and the second feature vector. The minkowski Distance may include manhattan Distance (manhattan Distance), Euclidean Distance (Euclidean Distance), Chebyshev Distance (Chebyshev Distance), and the like, among others. The method for calculating the Similarity between feature vectors is a Surface Text Similarity (Surface Text Similarity) calculation method, and in other embodiments, a Semantic Similarity (Semantic Similarity) calculation method may be further used to determine the matching degree between the system information and the monitoring requirement information, for example, Semantic matching Based on a Knowledge base (Knowledge-Based), Semantic matching Based on a Corpus (Corpus-Based), and the like, which are not limited herein.
Fig. 8 schematically shows a block diagram of a system information processing apparatus according to an embodiment of the present disclosure, which can be applied to various types of computer systems.
As shown in fig. 8, the system information processing apparatus 800 may include: a first acquisition module 810, a construction module 820, a second acquisition module 830, a matching module 840, and a supervision processing module 850.
The first obtaining module 810 is used for obtaining system information of a specified enterprise.
The second obtaining module 830 is configured to obtain a second feature vector used for representing the regulatory requirement information of the area where the specified enterprise is located.
The matching module 840 is configured to determine a matching degree between the regulatory requirement information and the system information based on the first feature vector and the second feature vector.
The supervision processing module 850 is configured to, when the matching degree between the two is lower than a predetermined threshold, push, to the terminal of the specified enterprise, a prompt message indicating that the institutional formulation of the specified enterprise does not meet the supervision requirement of the region where the specified enterprise is located.
It should be noted that the implementation, solved technical problems, implemented functions, and achieved technical effects of each module/unit/subunit and the like in the apparatus part embodiment are respectively the same as or similar to the implementation, solved technical problems, implemented functions, and achieved technical effects of each corresponding step in the method part embodiment, and are not described herein again.
Any number of modules, sub-modules, units, sub-units, or at least part of the functionality of any number thereof according to embodiments of the present disclosure may be implemented in one module. Any one or more of the modules, sub-modules, units, and sub-units according to the embodiments of the present disclosure may be implemented by being split into a plurality of modules. Any one or more of the modules, sub-modules, units, sub-units according to embodiments of the present disclosure may be implemented at least in part as a hardware circuit, such as a Field Programmable Gate Array (FPGA), a Programmable Logic Array (PLA), a system on a chip, a system on a substrate, a system on a package, an Application Specific Integrated Circuit (ASIC), or may be implemented in any other reasonable manner of hardware or firmware by integrating or packaging a circuit, or in any one of or a suitable combination of software, hardware, and firmware implementations. Alternatively, one or more of the modules, sub-modules, units, sub-units according to embodiments of the disclosure may be at least partially implemented as a computer program module, which when executed may perform the corresponding functions.
For example, any plurality of the first obtaining module 810, the constructing module 820, the second obtaining module 830, the matching module 840 and the supervision processing module 850 may be combined into one module to be implemented, or any one of the modules may be split into a plurality of modules. Alternatively, at least part of the functionality of one or more of these modules may be combined with at least part of the functionality of the other modules and implemented in one module. According to an embodiment of the present disclosure, at least one of the first obtaining module 810, the constructing module 820, the second obtaining module 830, the matching module 840 and the supervision processing module 850 may be implemented at least partially as a hardware circuit, such as a Field Programmable Gate Array (FPGA), a Programmable Logic Array (PLA), a system on a chip, a system on a substrate, a system on a package, an Application Specific Integrated Circuit (ASIC), or may be implemented by hardware or firmware in any other reasonable manner of integrating or packaging a circuit, or implemented by any one of three implementations of software, hardware and firmware, or any suitable combination of any of them. Alternatively, at least one of the first obtaining module 810, the constructing module 820, the second obtaining module 830, the matching module 840 and the supervisory processing module 850 may be at least partially implemented as a computer program module that, when executed, may perform a corresponding function.
FIG. 9 schematically illustrates a block diagram of a computer system suitable for implementing the above-described method according to an embodiment of the present disclosure. The computer system illustrated in FIG. 9 is only one example and should not impose any limitations on the scope of use or functionality of embodiments of the disclosure.
As shown in fig. 9, a computer system 900 according to an embodiment of the present disclosure includes a processor 901 which can perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM)902 or a program loaded from a storage section 908 into a Random Access Memory (RAM) 903. Processor 901 may comprise, for example, a general purpose microprocessor (e.g., a CPU), an instruction set processor and/or associated chipset, and/or a special purpose microprocessor (e.g., an Application Specific Integrated Circuit (ASIC)), among others. The processor 901 may also include on-board memory for caching purposes. The processor 901 may comprise a single processing unit or a plurality of processing units for performing the different actions of the method flows according to embodiments of the present disclosure.
In the RAM 903, various programs and data necessary for the operation of the system 900 are stored. The processor 901, the ROM 902, and the RAM 903 are connected to each other through a bus 904. The processor 901 performs various operations of the method flows according to the embodiments of the present disclosure by executing programs in the ROM 902 and/or the RAM 903. Note that the programs may also be stored in one or more memories other than the ROM 902 and the RAM 903. The processor 901 may also perform various operations of the method flows according to embodiments of the present disclosure by executing programs stored in the one or more memories.
According to embodiments of the present disclosure, method flows according to embodiments of the present disclosure may be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer readable storage medium, the computer program containing program code for performing the method illustrated by the flow chart. In such an embodiment, the computer program may be downloaded and installed from a network through the communication section 909, and/or installed from the removable medium 911. The computer program, when executed by the processor 901, performs the above-described functions defined in the system of the embodiment of the present disclosure. The systems, devices, apparatuses, modules, units, etc. described above may be implemented by computer program modules according to embodiments of the present disclosure.
The present disclosure also provides a computer-readable storage medium, which may be contained in the apparatus/device/system described in the above embodiments; or may exist separately and not be assembled into the device/apparatus/system. The computer-readable storage medium carries one or more programs which, when executed, implement the method according to an embodiment of the disclosure.
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams or flowchart illustration, and combinations of blocks in the block diagrams or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
It will be understood by those skilled in the art that while the present disclosure has been shown and described with reference to certain exemplary embodiments thereof, it will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the present disclosure as defined by the appended claims and their equivalents. Accordingly, the scope of the present disclosure should not be limited to the above-described embodiments, but should be defined not only by the appended claims, but also by equivalents thereof.
Claims (12)
1. A manufacturing information processing method applied to a computer system comprises the following steps:
system information of a specified enterprise is obtained;
constructing a first characteristic vector for representing the system information;
acquiring a second feature vector used for representing the supervision requirement information of the area where the specified enterprise is located;
determining a degree of matching between the regulatory requirement information and the institutional information based on the first feature vector and the second feature vector; and
and when the matching degree is lower than a preset threshold value, pushing prompt information indicating that the institutional formulation of the specified enterprise does not meet the supervision requirement of the area where the specified enterprise is located to the terminal of the specified enterprise.
2. The method of claim 1, further comprising:
before the first characteristic vector used for representing the system information is constructed, determining whether the language of the system information is simplified Chinese or not; and
if not, the system information language is converted into simplified Chinese.
3. The method of claim 1, wherein said constructing a first feature vector for characterizing said regime information comprises:
and processing the system information by utilizing a pre-constructed word frequency-inverse document frequency model to obtain the first characteristic vector.
4. The method of claim 3, wherein the processing the regime information using a pre-constructed word frequency-inverse document frequency model to obtain the first feature vector comprises:
inputting the system information into the word frequency-inverse document frequency model to execute the following operations by the word frequency-inverse document frequency model:
performing word segmentation processing on the system information to obtain a plurality of word segmentation results;
counting the word frequency of each word segmentation result in the system information;
determining the word frequency-inverse document frequency characteristic of each word segmentation result based on the word frequency of each word segmentation result and a preset corpus; and
and constructing the first feature vector based on the respective word frequency-inverse document frequency features of the word segmentation results.
5. The method of claim 1, wherein said constructing a first feature vector for characterizing said regime information comprises:
and representing the system information as a unique heat vector by using a pre-constructed word set model to serve as the first characteristic vector.
6. The method of claim 1, wherein the obtaining a second feature vector that characterizes regulatory requirement information for the area in which the specified enterprise is located comprises:
determining whether a second feature vector of the regulatory requirement information exists in a predetermined storage area of the computer system;
if yes, reading the second feature vector from the preset storage area;
if not, capturing the supervision requirement information from a specified webpage by using a web crawler, wherein the specified webpage is used for displaying the supervision requirement information; and
and constructing a second feature vector for characterizing the regulatory requirement information, and storing the second feature vector to the predetermined storage area.
7. The method of claim 6, further comprising:
monitoring the specified webpage;
when the updating event of the specified webpage is monitored, capturing updated supervision requirement information from the specified webpage by using a web crawler; and
and constructing a second feature vector for representing the supervision requirement information based on the updated supervision requirement information, and storing the second feature vector to the predetermined storage area.
8. The method of claim 6 or 7, wherein said constructing a second feature vector for characterizing the regulatory requirement information comprises:
processing the supervision requirement information by using a pre-constructed word frequency-inverse document frequency model to obtain the second eigenvector; or
And representing the supervision requirement information as a one-hot vector by using a pre-constructed word set model to serve as the second feature vector.
9. The method of claim 1, wherein the determining a degree of match between the regulatory requirement information and the institutional information based on the first eigenvector and the second eigenvector comprises at least one of:
calculating a matching coefficient between the first feature vector and the second feature vector, and determining the matching degree according to the matching coefficient;
calculating cosine similarity between the first feature vector and the second feature vector, and determining the matching degree according to the cosine similarity; and
calculating a Minkowski distance between the first eigenvector and the second eigenvector, and determining the degree of matching according to the Minkowski distance.
10. A manufacturing information processing apparatus applied to a computer system, the apparatus comprising:
the system comprises a first acquisition module, a second acquisition module and a third acquisition module, wherein the first acquisition module is used for acquiring system information of a specified enterprise;
the construction module is used for constructing a first characteristic vector for representing the system information;
the second acquisition module is used for acquiring a second feature vector used for representing the supervision requirement information of the area where the specified enterprise is located;
a matching module, configured to determine a matching degree between the regulatory requirement information and the system information based on the first feature vector and the second feature vector; and
and the supervision processing module is used for pushing prompt information indicating that the institutional formulation of the specified enterprise does not meet the supervision requirement of the area where the specified enterprise is located to the terminal of the specified enterprise when the matching degree is lower than a preset threshold value.
11. A computer system, comprising: memory, processor and computer program stored on the memory and executable on the processor, the processor when executing the computer program for implementing the method according to any of claims 1 to 9.
12. A computer-readable storage medium storing computer-executable instructions for implementing the method of any one of claims 1-9 when executed.
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