Method, apparatus, device and medium for determining target evaluation data

Abstract

The present disclosure provides a method for determining target evaluation data, which can be applied to the field of big data technology. The method includes: determining a target object to be evaluated according to the input statement information, where the target object includes a business department for executing production tasks, and the statement information includes text information for describing the target object; determining target evaluation data from the full-scale evaluation data according to the identification information of the target object, where the target evaluation data is used to evaluate the target object; in the case where it is determined that the target evaluation data does not include an evaluation method, determining a target evaluation method corresponding to the target evaluation data according to the statement information and the full-scale evaluation data; and updating the target evaluation data based on the target evaluation method. The present disclosure also provides a device, equipment, storage medium and program product for determining target evaluation data.

Description

Technical Field

[0001] The present disclosure relates to the field of big data technology, and in particular, to a method, apparatus, electronic device, medium, and program product for determining target evaluation data. Background Art

[0002] Currently, enterprise auditors rely on their own experience to determine audit data. For example, auditors determine the evaluation data of the object to be evaluated based on their own experience, or rely on their own experience to determine relevant enterprise terms, and then determine the evaluation data after searching.

[0003] In the case of quarterly or annual audits of multiple business departments, auditors need to spend a large amount of labor costs and time costs to determine the evaluation data of each business department, resulting in a high cumulative labor cost. Moreover, for each audit task, auditors determine the evaluation data according to the complete process, resulting in a cumbersome process for determining the evaluation data, waste of resources, and affecting the evaluation efficiency.

[0004] In addition, in related technologies, before operating on a new audit task, it is necessary to determine an evaluation method. However, the determination of the evaluation method requires multi-level approval, resulting in a complex and cumbersome process for determining the evaluation method and affecting the evaluation efficiency. Summary of the Invention `

[0005] In view of the above problems, the present disclosure provides a method, apparatus, device, medium, and program product for determining target evaluation data.

[0006] According to a first aspect of the present disclosure, there is provided a method for determining target evaluation data, including: determining a target object to be evaluated according to input statement information, where the target object includes a business department for executing a production task, and the statement information includes text information for describing the target object;

[0007] Determining target evaluation data from the full-scale evaluation data according to the identification information of the target object, where the target evaluation data is used to evaluate the target object;

[0008] In the case where it is determined that the target evaluation data does not include an evaluation method, determining a target evaluation method corresponding to the target evaluation data according to the statement information and the full-scale evaluation data; and

[0009] Updating the target evaluation data based on the target evaluation method.

[0010] According to an embodiment of the present disclosure, the full-scale evaluation data and the target evaluation data both include first clause information and second clause information. The first clause information includes the content of the system clause for the business department and the first clause number, and the second clause information includes the content of the general standard clause and the second clause number.

[0011] According to an embodiment of the present disclosure, wherein the target evaluation data includes N pieces of evaluation data for evaluating a target object; determining the target evaluation data from the full amount of evaluation data according to the identification information of the target object includes:

[0012] Extracting M pieces of evaluation data corresponding to the target object from the hash data table according to the identification information of the target object, wherein the hash data table includes the full amount of evaluation data, and M is greater than or equal to 1; and

[0013] Screening out N pieces of evaluation data from the M pieces of evaluation data according to the first clause number and the second clause number, wherein N is greater than or equal to 1 and N is less than or equal to M.

[0014] According to an embodiment of the present disclosure, wherein the first clause number includes a directory label number for distinguishing multiple first clauses; screening out N pieces of evaluation data from the M pieces of evaluation data according to the first clause number and the second clause number includes:

[0015] In the case where it is determined that there are evaluation data with the same second clause number among the M pieces of evaluation data, randomly screening out one piece of evaluation data from the evaluation data with the same second clause number and deleting the other evaluation data with the same second clause number to obtain P pieces of evaluation data, wherein P is greater than or equal to N and P is less than or equal to M;

[0016] In the case where it is determined that there are evaluation data with the same directory label number among the P pieces of evaluation data, randomly screening out one piece of evaluation data from the evaluation data with the same directory label number and deleting the other evaluation data with the same directory label number to obtain N pieces of evaluation data; and

[0017] Wherein, in the process of deleting the other evaluation data with the same directory label number, record the second clause number of the evaluation data with the same directory label number.

[0018] According to an embodiment of the present disclosure, wherein updating the target evaluation data based on the target evaluation method includes:

[0019] Obtaining the target evaluation method, the first clause number and the second clause number corresponding to each of the N pieces of evaluation data in the target evaluation data, and updating the target evaluation data, wherein each of the N pieces of evaluation data in the target evaluation data has a unique first clause information and a target evaluation method, and at least one second clause information corresponding to the first clause information.

[0020] According to an embodiment of the present disclosure, wherein in the case where it is determined that the target evaluation data does not include an evaluation method, determining the target evaluation method corresponding to the evaluation data according to the statement information and the full amount of evaluation data includes:

[0021] Calculating the similarity between the statement information and the content of the first clause in the full amount of evaluation data;

[0022] When there is a historical evaluation method for the first clause content with the highest similarity, obtain the historical evaluation method; and

[0023] Generate a target evaluation method corresponding to the target object according to the historical evaluation method and the target object.

[0024] According to an embodiment of the present disclosure, it further includes:

[0025] Based on the target object, the first clause information, and the second clause information, classify the full - volume evaluation data respectively to obtain the first evaluation data corresponding to the first clause information, the second evaluation data corresponding to the second clause information, and the third evaluation data corresponding to the target object, and generate the first evaluation data graph, the second evaluation data graph, and the third evaluation data graph.

[0026] A second aspect of the present disclosure provides a device for determining target evaluation data, including: a first determination module, configured to determine a target object to be evaluated according to the input statement information, where the target object includes a business department for executing production tasks, and the statement information includes text information for describing the target object;

[0027] A second determination module, configured to determine target evaluation data from the full - volume evaluation data according to the identification information of the target object, where the target evaluation data is used to evaluate the target object;

[0028] A third determination module, configured to determine a target evaluation method corresponding to the target evaluation data according to the statement information and the full - volume evaluation data when it is determined that the target evaluation data does not include an evaluation method; and

[0029] A fourth determination module, configured to update the target evaluation data based on the target evaluation method.

[0030] A third aspect of the present disclosure provides an electronic device, including: one or more processors; a memory for storing one or more programs, where when the one or more programs are executed by the one or more processors, the one or more processors execute the above - mentioned method for determining target evaluation data.

[0031] A fourth aspect of the present disclosure further provides a computer - readable storage medium, on which executable instructions are stored, and when the instructions are executed by a processor, the processor executes the above - mentioned method for determining target evaluation data.

[0032] A fifth aspect of the present disclosure further provides a computer program product, including a computer program, and when the computer program is executed by a processor, the above - mentioned method for determining target evaluation data is implemented. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Through the following description of the embodiments of the present disclosure with reference to the accompanying drawings, the above content and other objects, features and advantages of the present disclosure will become clearer. In the drawings:

[0034] Figure 1 Schematically shows an application scenario of a method for determining target evaluation data according to an embodiment of the present disclosure;

[0035] Figure 2 Schematically shows a flowchart of a method for determining target evaluation data according to an embodiment of the present disclosure;

[0036] Figure 3 Schematically shows a flowchart of a method for determining target evaluation data from all evaluation data according to an embodiment of the present disclosure;

[0037] Figure 4 Schematically shows a flowchart of a method for screening evaluation data according to a first clause number and a second clause number according to an embodiment of the present disclosure;

[0038] Figure 5 Schematically shows a flowchart of a method for determining a target evaluation method according to an embodiment of the present disclosure;

[0039] Figure 6 Schematically shows a schematic diagram of an interface for determining target evaluation data according to an embodiment of the present disclosure;

[0040] Figure 7 Schematically shows a structural block diagram of a device for determining target evaluation data according to an embodiment of the present disclosure; and

[0041] Figure 8 Schematically shows a block diagram of an electronic device suitable for a method for determining target evaluation data according to an embodiment of the present disclosure. Detailed implementation manners

[0042] Hereinafter, embodiments of the present disclosure will be described with reference to the accompanying drawings. However, it should be understood that these descriptions are merely exemplary and are not intended to limit the scope of the present disclosure. In the following detailed description, for the sake of explanation, many specific details are set forth to provide a comprehensive understanding of the embodiments of the present disclosure. However, it is obvious that one or more embodiments can also be implemented without these specific details. In addition, in the following description, descriptions of well-known structures and technologies are omitted to avoid unnecessarily confusing the concepts of the present disclosure.

[0043] The terms used herein are merely for describing specific embodiments and are not intended to limit the present disclosure. The terms "including", "comprising", etc. used herein indicate the presence of the described features, steps, operations and / or components, but do not exclude the presence or addition of one or more other features, steps, operations or components.

[0044] All terms used herein (including technical and scientific terms) have the meanings commonly understood by those of ordinary skill in the art, unless otherwise defined. It should be noted that the terms used herein should be interpreted as having a meaning consistent with the context of this specification, and should not be interpreted in an idealized or overly rigid manner.

[0045] In cases where expressions similar to "at least one of A, B, and C, etc." are used, generally, it should be interpreted according to the meaning commonly understood by those of ordinary skill in the art (for example, "a system having at least one of A, B, and C" should include, but not be limited to, a system having only A, only B, only C, having A and B, having A and C, having B and C, and / or having A, B, and C, etc.).

[0046] In the technical solutions of the present disclosure, the processing of the collection, storage, use, processing, transmission, provision, disclosure, and application, etc. of the user's personal information involved all comply with the provisions of relevant laws and regulations, take necessary confidentiality measures, and do not violate public order and good customs.

[0047] In the technical solutions of the present disclosure, before obtaining or collecting the user's personal information, the authorization or consent of the user has been obtained.

[0048] Embodiments of the present disclosure provide a method for determining target evaluation data. According to the input statement information, a target object to be evaluated is determined. The target object includes a business department for executing production tasks, and the statement information includes text information for describing the target object. According to the identification information of the target object, target evaluation data is determined from the full-scale evaluation data. The target evaluation data is used to evaluate the target object. In the case where it is determined that the target evaluation data does not include an evaluation method, according to the statement information and the full-scale evaluation data, a target evaluation method corresponding to the target evaluation data is determined. And based on the target evaluation method, the target evaluation data is updated.

[0049] Figure 1 Schematically shows an application scenario for the determination of target evaluation data according to an embodiment of the present disclosure.

[0050] As Figure 1 shown, the application scenario 100 according to this embodiment may include terminal devices 101, 102, 103, a network 104, and a server 105. The network 104 is used to provide a medium for communication links between the terminal devices 101, 102, 103 and the server 105. The network 104 may include various connection types, such as wired, wireless communication links, or fiber optic cables, etc.

[0051] Users can use terminal devices 101, 102, and 103 to interact with server 105 via network 104 to receive or send messages, etc. Application software for auditing tasks can be installed on terminal devices 101, 102, and 103 so that users can determine target evaluation data and perform auditing tasks through the above application software; or users can log in to the web version for auditing tasks on terminal devices 101, 102, and 103 to determine target evaluation data and perform auditing tasks.

[0052] Terminal devices 101, 102, and 103 can be various electronic devices with a display screen and supporting web browsing, including but not limited to smartphones, tablets, laptop computers, and desktop computers, etc.

[0053] Server 105 can be a server that provides various services, such as a background management server (only as an example) that supports the websites browsed by users using terminal devices 101, 102, and 103. The background management server can analyze and process data such as user requests received, and feedback the processing results (such as web pages, information, or data obtained or generated according to user requests, etc.) to the terminal devices.

[0054] It should be noted that the method for determining target evaluation data provided by the embodiments of the present disclosure can generally be executed by server 105. Correspondingly, the device for determining target evaluation data provided by the embodiments of the present disclosure can generally be set in server 105. The method for determining target evaluation data provided by the embodiments of the present disclosure can also be executed by a server or a server cluster different from server 105 and capable of communicating with terminal devices 101, 102, 103 and / or server 105. Correspondingly, the device for determining target evaluation data provided by the embodiments of the present disclosure can also be set in a server or a server cluster different from server 105 and capable of communicating with terminal devices 101, 102, 103 and / or server 105.

[0055] It should be understood that Figure 1 the numbers of terminal devices, networks, and servers in

[0056] are merely illustrative. According to the implementation requirements, there can be any number of terminal devices, networks, and servers.

[0056] Based on the Figure 1 scenario described below, the method for determining target evaluation data of the disclosed embodiments will be described in detail through Figures 2 to 7 the following.

[0057] Figure 2 Schematically shows a flowchart of the method for determining target evaluation data according to an embodiment of the present disclosure.

[0058] As shown in Figure 2As shown, the method includes operations S210 to S240.

[0059] In operation S210 , a target object to be evaluated is determined based on the input sentence information. The target object includes a business department for executing a production task. The sentence information includes text information for describing the target object.

[0060] According to an embodiment of the present disclosure, the sentence information includes text information describing the target object, such as text information describing functional characteristics of the target object.

[0061] After obtaining the input sentence information, the sentence information can be segmented to obtain keywords used to describe functional characteristics. The obtained keywords are input into a pre-built knowledge graph to determine at least one target object that matches the keyword.

[0062] For example, the input sentence is "department responsible for inspecting the safety of the computer room." After word segmentation, the keywords obtained include "inspection," "computer room," and "safety." Entering the identified keywords into the knowledge graph, the target objects are determined to be "Equipment Department 1" and "Equipment Department 2."

[0063] According to an embodiment of the present disclosure, the input statement information also includes relevant terms set by the enterprise, and the relevant terms include the target object. After obtaining a keyword based on the statement information, the keyword is compared with the name of the target object stored in the database. If it is determined that there is an object name in the database that is identical to the keyword, the keyword is determined to be the target object.

[0064] When it is determined that there is no object name in the database with the same name as the keyword, the keyword is input into the knowledge graph to determine the target object that matches the keyword.

[0065] For example, if the sentence is "Equipment Department 1 should establish a machine room inspection mechanism," after word segmentation, the keywords obtained include "Equipment Department 1." Since an object named "Equipment Department 1" exists in the database, "Equipment Department 1" can be determined as the target object.

[0066] According to an embodiment of the present disclosure, the input information may also be other types of information, such as picture information, audio information, and video information, etc. Through information conversion technology, the input information of other types is converted into text information for describing the target object.

[0067] In operation S220 , target evaluation data is determined from the full amount of evaluation data according to identification information of the target object, and the target evaluation data is used to evaluate the target object.

[0068] According to an embodiment of the present disclosure, the identification information of the target object includes specific name information. After determining the target object, the target evaluation data corresponding to the target object can be determined from the full-scale evaluation data through the specific name information of the target object.

[0069] Since there may be multiple units with similar names under the business department of an enterprise, as another embodiment of the present disclosure, the identification information of the target object includes an identifier uniquely corresponding to the target object. For example, an MD5 identifier or a user-defined unique identifier. After determining the target object, the corresponding identifier is determined from the database according to the specific name information of the target object, and the target evaluation data corresponding to the target object is determined from the full-scale evaluation data according to the identifier.

[0070] According to an embodiment of the present disclosure, as another embodiment, after determining the target object and before determining the target evaluation data according to the specific name information of the target object, the identifier of the target object can be obtained, and it can be verified whether the identifier of the target object matches the specific name information of the target object. In the case where the identifier matches the specific name information, the specific name information or the identifier is used to determine the target evaluation data.

[0071] According to an embodiment of the present disclosure, the full-scale evaluation data includes an evaluation method and evaluation terms, where the evaluation terms include a first term and a second term. The evaluation method is used to determine how to evaluate the target object, and the evaluation terms are used to support the evaluation method and are presented to the target object as the basis for the evaluation method.

[0072] In operation S230, in the case where it is determined that the target evaluation data does not include an evaluation method, according to the statement information and the full-scale evaluation data, the target evaluation method corresponding to the target evaluation data is determined.

[0073] According to an embodiment of the present disclosure, after obtaining the target evaluation data from the full-scale evaluation data, it is determined whether the evaluation method field of the target evaluation data is empty. The empty evaluation method field indicates that the target evaluation data does not include an evaluation method.

[0074] In the case where it is determined that the target evaluation data does not include an evaluation method, alternative objects similar to the target object are determined according to the statement information. Then, according to the identification information of the alternative objects, the evaluation method corresponding to the alternative objects is determined from the full-scale evaluation data. According to the evaluation method of the alternative objects, the target evaluation method corresponding to the target object is generated.

[0075] Specifically, one or more alternative objects similar to the target object can be determined according to the statement information. According to the evaluation methods of the one or more alternative objects, the target evaluation method corresponding to the target object is determined.

[0076] In operation S240, update the target evaluation data based on the target evaluation method.

[0077] According to an embodiment of the present disclosure, after determining the target evaluation method, update the target evaluation data by using the determined target evaluation method. Specifically, the content of the determined target evaluation method can be added to the evaluation method field, and the abbreviation of the target evaluation method or the location information of the evaluation method can also be added to the evaluation method field.

[0078] Among them, the location information of the evaluation method can be stored in the evaluation method table. For example, in the process of determining the target evaluation data from the full-scale evaluation data, the target evaluation method can be obtained from the evaluation method table according to the identification information of the target object.

[0079] The present disclosure finds that in the related art, experienced auditors can determine the evaluation method and evaluation terms of the business department to be evaluated through their own experience; inexperienced auditors determine the department to be evaluated, as well as the evaluation method and evaluation terms of the department to be evaluated by looking up relevant enterprise terms. This leads to an increase in the labor cost and time cost of completing the audit work. Moreover, when communicating among auditors, due to the different capabilities of auditors, it will also lead to unsmooth communication and affect the evaluation efficiency.

[0080] In addition, with the development of large enterprises, the number of business departments will gradually increase. When adding new business departments, corresponding evaluation methods need to be added. Since the newly added evaluation methods of large enterprises need to go through multiple levels of approval and multiple transfers before they can be applied, it results in low evaluation efficiency and waste of internal evaluation process resources.

[0081] The present disclosure determines the target object by using the input statement information, and obtains the target evaluation data according to the identification information of the target object. There is no need for auditors to determine the target evaluation data based on their own experience or by looking up relevant enterprise terms, realizing the automatic determination of the target evaluation data, reducing the labor cost of determining the target evaluation data, shortening the time for determining the target evaluation data, and improving the evaluation efficiency. Moreover, through the above method, it is also possible to avoid the misselection and omission of the target evaluation data caused by auditors.

[0082] The present disclosure also determines whether the target evaluation data includes an evaluation method. In the case where the target evaluation data does not include an evaluation method, determine the target evaluation method according to the statement information and the full-scale evaluation data. Without going through a complex application process, the target evaluation method can be determined, improving the evaluation efficiency and reducing resource waste.

[0083] According to an embodiment of the present disclosure, both the full-scale evaluation data and the target evaluation data include first clause information and second clause information. The first clause information includes the institutional clause content for the business department and the first clause number, and the second clause information includes the general standard clause content and the second clause number.

[0084] According to an embodiment of the present disclosure, the full-scale evaluation data can split the clause content according to the clause number to obtain a hash data table corresponding to the clause number and the clause content. Specifically, according to the first clause number and the second clause number, a first hash table corresponding to the first clause information and a second hash table corresponding to the second clause information are established, and the first hash table and the second hash table are stored in the database.

[0085] According to an embodiment of the present disclosure, the second clause information includes the general standard clause content and the second clause number. The second clause information is obtained according to a preset time period. When it is determined that the second clause information is updated, the obtained updated second clause information is split according to the second clause number, and the data obtained after splitting is updated to the second hash table.

[0086] Figure 3 The flowchart of a method for determining target evaluation data from full-scale evaluation data according to an embodiment of the present disclosure is schematically shown.

[0087] As Figure 3 shown, the method of this embodiment includes operation S321 to operation S322, and can be a specific embodiment of operation S220.

[0088] In operation S321, according to the identification information of the target object, M evaluation data corresponding to the target object are extracted from the hash data table.

[0089] According to an embodiment of the present disclosure, the hash data table includes full-scale evaluation data. Specifically, the full-scale evaluation data is stored in the form of a hash data table. The first hash table is used to store the full-scale first clauses. The first hash table also includes the association relationship between the first clause and the object to be evaluated. For example, by analyzing the full-scale first clauses, the association relationship between each object to be evaluated appearing in the first clause content and the first clause is established. Similarly, the second hash table is used to store the full-scale second clause information. The first hash table also includes the association relationship between the second clause information and the first clause information.

[0090] According to an embodiment of the present disclosure, after determining the target object, multiple first clause information corresponding to the target object is determined according to the association relationship between the target object and the first clause information. Then, according to the association relationship between the first clause information and the second clause information, multiple second clause information corresponding to the multiple first clause information is determined, and M evaluation data is obtained. Wherein, each evaluation data is a unique combination of the first clause information and the second clause information.

[0091] For example, the first clause numbers matching the target object are A1.1 and A1.2, the second clause numbers corresponding to the first clause number A1.1 are B1.2 and B2.1, and the second clause numbers corresponding to the first clause number A1.2 are B1.2 and B2.2, obtaining 4 evaluation data corresponding to the target object, A1.1 - B1.2, A1.1 - B2.1, A1.2 - B1.2, A1.2 - B2.2.

[0092] In operation S322, N evaluation data is screened out from the M evaluation data according to the first clause number and the second clause number.

[0093] According to an embodiment of the present disclosure, since the target object can correspond to multiple first clause information, and each first clause information can correspond to multiple second clause information objects, the target object corresponds to multiple duplicate first clause information and second clause information. Thus, after determining the M evaluation data corresponding to the target object, according to the first clause number and the second clause number, the M evaluation data is screened, and the duplicate first clause information and second clause information are deleted to reduce the amount of evaluation data.

[0094] Figure 4 The flowchart of the method for screening evaluation data according to the first clause number and the second clause number according to an embodiment of the present disclosure is schematically shown.

[0095] As Figure 4 shown, the method for screening evaluation data in this embodiment includes operation S4321 to operation S4322, which can be a specific embodiment of operation S322.

[0096] In operation S4321, in the case where there is evaluation data with the same second clause number among the M evaluation data, one evaluation data is randomly screened out from the evaluation data with the same second clause number, and the other evaluation data with the same second clause number is deleted, obtaining P evaluation data.

[0097] According to an embodiment of the present disclosure, the second clause information is used to describe industry - general standard clauses. When conducting an audit, at least one evaluation data under this clause can be retained to ensure compliance with industry - general standard clauses. Specifically, after obtaining M evaluation data, the evaluation data with the same second clause number are determined from the M evaluation data. When it is determined that there are evaluation data with the same second clause number among the M evaluation data, the evaluation data with the same second clause number are screened out. After screening out the evaluation data with the same second clause number, one evaluation data is randomly selected from them, and the other evaluation data with the same second clause number are deleted to obtain P evaluation data.

[0098] According to the actual situation, there can be multiple second clause numbers at the same time, and there is at least one evaluation data under each first clause number.

[0099] For example, still taking the first clause numbers A1.1 and A1.2 that match the target object as an example, the 4 obtained evaluation data are A1.1 - B1.2, A1.1 - B2.1, A1.2 - B1.2, and A1.2 - B2.2 respectively. For the second clause number B1.2, there are two evaluation data with the same second clause number, A1.1 - B1.2 and A1.2 - B1.2. After screening according to the second clause number, 3 evaluation data are obtained: A1.1 - B1.2, A1.1 - B2.1, A1.2 - B2.2; or A1.1 - B2.1, A1.2 - B1.2, A1.2 - B2.2.

[0100] In operation S4322, when it is determined that there are evaluation data with the same catalog label among the P evaluation data, one evaluation data is randomly selected from the evaluation data with the same catalog label, and the other evaluation data with the same catalog label are deleted to obtain N evaluation data; among them, during the process of deleting the other evaluation data with the same catalog label, the second clause number of the evaluation data with the same catalog label is recorded.

[0101] According to an embodiment of the present disclosure, the first clause number includes a catalog label, and the catalog label is used to distinguish multiple first clauses. For example, for the first clause numbers A1.1 and A1.2, they belong to clause 1 and clause 2 under the catalog label A1 respectively.

[0102] According to an embodiment of the present disclosure, after determining the P evaluation data, one evaluation data is randomly selected from the evaluation data with the same catalog label, and the other evaluation data with the same catalog label are deleted to obtain N evaluation data. And during the process of deleting the other evaluation data with the same catalog label, the second clause number of the evaluation data with the same catalog label is recorded.

[0103] For example, still taking the first clause numbers A1.1 and A1.2 that match the target object as an example, after screening according to the second clause number, 3 evaluation data are obtained: A1.1 - B1.2, A1.1 - B2.1, A1.2 - B2.2. The catalog numbers of the above 3 evaluation data are all A1. Therefore, an evaluation data A1.1 - B1.2 is randomly selected from the above evaluation data as the evaluation data under the first clause number A1. At the same time, the second clause numbers B2.1 and B2.2 are recorded as the second clause information related to A1.1.

[0104] According to an embodiment of the present disclosure, after screening to obtain N evaluation data, the obtained N evaluation data can also be updated to obtain a corresponding relationship that only includes the target label. For example, for A1.1 - B1.2, the updated evaluation data is A1 - B1, and the second clause information related to A1 is B2.

[0105] According to an embodiment of the present disclosure, after obtaining the target evaluation method, the first clause number, and the second clause number corresponding to each of the N evaluation data, the target evaluation data is updated, where there is a unique first clause information and a target evaluation method for each of the N evaluation data in the target evaluation data, and at least one second clause information corresponding to the first clause information.

[0106] Figure 5 A flowchart showing the determination of the target evaluation method according to an embodiment of the present disclosure is schematically illustrated.

[0107] As Figure 5 shown, the determination of the target evaluation method in this embodiment includes operations S531 to S533, and can be a specific embodiment of operation S230.

[0108] In operation S531, the similarity between the statement information and the first clause content in the full - volume evaluation data is calculated.

[0109] According to an embodiment of the present disclosure, when the field of the evaluation method in the target evaluation data is empty, the text similarity between the statement information and the first clause content in the full - volume evaluation data is calculated. Specifically, the feature vector obtained after performing word - segmentation processing on the statement information is X[1, 1, 1, 1, 1, 1, 1, 1]. Among them, X[1, 1, 1, 1, 1, 1, 1, 1] means that keywords 1, 2, 3, 4, 5, 6, 7, 8 all appear in the standard statement. The feature vector of the first clause content Y is Y[1, 1, 0, 0, 0, 1, 1, 1], which means that keywords 1, 2, 6, 7, 8 appear in the first clause content.

[0110] Calculating the text similarity between the statement information and the first clause content in the full - volume evaluation data satisfies:

[0111]

[0112] Among them, represents the average value of the N-dimensional feature vector of the statement information, represents the average value of the N-dimensional feature vector of the content of the first clause, Xi represents the i-th dimension feature of the statement information, Yi represents the i-th dimension feature of the content of the first clause, P(X, Y) represents the Pearson correlation coefficient between the statement information and the content of the first clause, and W is a preset weight.

[0113] According to the embodiments of the present disclosure, by setting a preset weight, the similarity between the statement information and the content of the first clause is amplified, and the difference in similarity is increased.

[0114] Similarly, to establish the association relationship between the information of the first clause and the information of the second clause, the Pearson correlation coefficient between the content of the first clause and the content of the second clause can also be calculated. For each first clause, calculate the text similarity between the content of all second clauses and the content of this first clause, and use the second clause with the highest similarity as the second clause corresponding to this first clause. During the process of calculating the text similarity, the text similarity can also be amplified by a preset weight.

[0115] In operation S532, when it is determined that there is a historical evaluation method for the content of the first clause with the highest similarity, obtain the historical evaluation method.

[0116] According to the embodiments of the present disclosure, after the content of the first clause with the highest similarity to the statement information, use the clause number of this first clause content to determine the corresponding historical evaluation method from the hash data table. The evaluation method can be stored in the first hash table, and the corresponding evaluation method is determined according to the first clause number.

[0117] Specifically, a hash array can be formed between the evaluation method and the information of the first clause. When the first clause number is determined, it is determined whether there is a historical evaluation method from the hash array according to the first clause number. When it is determined that there is a historical evaluation method, obtain this evaluation method.

[0118] According to the embodiments of the present disclosure, there is a common evaluation method among multiple business departments, and there is a dedicated evaluation method within each department. The evaluation method is related to the information of the first clause. After obtaining the target evaluation data from the full-scale evaluation data, the target evaluation data can be respectively stored in the dedicated evaluation table and the common evaluation table according to the type of the evaluation method.

[0119] The common evaluation table and the dedicated evaluation table include multiple fields. For example, ID serial number, standard name of the first clause, first clause number, first clause name, content of the first clause, corresponding second clause name, second clause number, content of the second clause, applicable department, similarity, inspection method, etc.

[0120] In operation S533, a target evaluation method corresponding to the target object is generated according to the historical evaluation method and the target object.

[0121] According to an embodiment of the present disclosure, when there is a historical evaluation method for the first clause content with the highest similarity, the historical evaluation method is obtained, and a target evaluation method is generated according to the historical evaluation method and the target object.

[0122] For example, the input statement information is "How to conduct a computer room safety inspection for 3 devices". After tokenizing the statement information, the obtained keyword is "3 devices", and the keyword "3 devices" is used as the target object. The first clause content with the highest similarity to the statement information is "1 device should establish a computer room inspection mechanism", that is, the alternative object is "1 device". After obtaining the historical evaluation method of "1 device" (review the inspection logs of 1 device in the first quarter), the target evaluation method "review the inspection logs of 3 devices in the first quarter" is generated according to the target object.

[0123] When there is no historical evaluation method for the first clause content with the highest similarity, the first clause content with the second highest similarity is obtained, and it is determined whether the corresponding first clause has a historical evaluation method until a historical evaluation method is obtained.

[0124] According to an embodiment of the present disclosure, based on the target object, the first clause information, and the second clause information, the full amount of evaluation data is classified respectively to obtain the first evaluation data corresponding to the first clause information, the second evaluation data corresponding to the second clause information, and the third evaluation data corresponding to the target object, and a first evaluation data graph, a second evaluation data graph, and a third evaluation data graph are generated.

[0125] As an embodiment, a big data calculation model is established to calculate multi-dimensional data indicators, and multiple JavaScript big data views are used to display the first evaluation data, the second evaluation data, and the third evaluation data in a multi-dimensional and multi-perspective manner, and a first evaluation data graph, a second evaluation data graph, and a third evaluation data graph are generated. For example, after classifying the full amount of evaluation data, the full amount of first clause information, the full amount of second clause information, and the full amount of target objects are obtained respectively. For the full amount of first clause information and the full amount of second clause data, first evaluation data and second evaluation data including information such as quantity, comparison relationship, similarity, etc. are formed and displayed in one or more views.

[0126] Figure 6 A schematic diagram of an interface for determining target evaluation data according to an embodiment of the present disclosure is schematically shown.

[0127] As Figure 6As shown, the interface 600 includes sub-display windows 601-604, 612, 615-621, and operation windows 605-611, 613-614. Among them, the sub-display window 601 is used to display the "display evaluation flag" of the evaluation system, and the sub-display windows 602 and 603 are used to display the name and navigation information of the evaluation system.

[0128] The operation windows 604 and 605 are respectively used to obtain the first clause and the second clause. Specifically, the first clause and the second clause can be obtained through the input operation and / or click operation of the first clause and / or the second clause. The operation windows 604 and 605 are also used to split the first clause and the second clause, and store the first clause in the first hash table and the second clause in the second hash table.

[0129] The operation window 606 is used to calculate the similarity between the first clause and the second clause in response to the user's click operation, and mark the association relationship between the first clause and the second clause. The operation window 607 is used to split the statement information input by the user, determine the applicable department, and determine the corresponding inspection method according to the applicable department in response to the user's click operation. Among them, the process of determining the applicable department is the process of determining the target object.

[0130] After obtaining the target evaluation data of the applicable department, according to the type of evaluation method, the target evaluation data is divided into general evaluation data and special evaluation data, and stored in the general evaluation table and the special evaluation table respectively. The operation window 608 is used to obtain the general evaluation table in response to the user's click operation. Similarly, the operation window 609 is used to obtain the special evaluation table in response to the user's click operation.

[0131] The operation window 610 is used for data reset or parameter reset. Specifically, it can be reset all at once in response to the user's one operation, or partially reset. The operation window 611 is used to implement data analysis, including generating the first evaluation data graph, the second evaluation data graph, and the third evaluation data graph.

[0132] The sub-display window 612 is used to display the application name identifier or prompt information. The operation window 613 is used to obtain and display the keywords and statement information input by the user. The operation window 614 is used to process the input information in the operation window 613 in response to the user's click operation.

[0133] The sub-display window 615 is used to display the applicable enterprises or the evaluation items of the current application. For example, it displays a simple operation guide. The sub-display window 616 is used to display one or more pieces of evaluation data in the general evaluation table. The sub-display window 617 is used to analyze the data in the general evaluation table. For example, it analyzes the quantity, distribution, etc. of the evaluation data in the table.

[0134] The sub-display window 618 is used to display one or more pieces of evaluation data in the dedicated evaluation form. The sub-display windows 619-621 are used to display multiple matching applicable departments, for example, Department 1, Department 2, and Department 3. Specifically, multiple applicable departments can be displayed on the interface 600 at the same time.

[0135] Figure 7 The structural block diagram of the determination device for target evaluation data according to an embodiment of the present disclosure is schematically shown.

[0136] As Figure 7 shown, the determination device 700 for target evaluation data in this embodiment includes a first determination module 710, a second determination module 720, a third determination module 730, and a fourth determination module 740.

[0137] The first determination module 710 is used to determine a target object to be evaluated according to the input statement information. The target object includes a business department for executing production tasks, and the statement information includes text information for describing the target object. In one embodiment, the first determination module 710 can be used to perform the operation S210 described above, which will not be elaborated here.

[0138] The second determination module 720 is used to determine target evaluation data from the full amount of evaluation data according to the identification information of the target object. The target evaluation data is used to evaluate the target object. In one embodiment, the second determination module 720 can be used to perform the operation S220 described above, which will not be elaborated here.

[0139] The third determination module 730 is used to determine a target evaluation method corresponding to the target evaluation data according to the statement information and the full amount of evaluation data when it is determined that the target evaluation data does not include an evaluation method. In one embodiment, the third determination module 730 can be used to perform the operation S230 described above, which will not be elaborated here.

[0140] The fourth determination module 740 is used to update the target evaluation data based on the target evaluation method. In one embodiment, the fourth determination module 740 can be used to perform the operation S240 described above, which will not be elaborated here.

[0141] According to an embodiment of the present disclosure, the second determination module 720 includes a first determination unit and a second determination unit.

[0142] The first determination unit is used to extract M pieces of evaluation data corresponding to the target object from the hash data table according to the identification information of the target object, where the hash data table includes the full amount of evaluation data, and M is greater than or equal to 1. In one embodiment, the first determination unit can be used to perform the operation S321 described above, which will not be elaborated here.

[0143] The second determination unit is configured to screen out N evaluation data from M evaluation data according to the first clause number and the second clause number, where N is greater than or equal to 1 and less than or equal to M. In one embodiment, the second determination unit may be configured to perform the operation S322 described above, which will not be elaborated herein.

[0144] According to an embodiment of the present disclosure, the second determination unit includes a first determination subunit and a second determination subunit.

[0145] The first determination subunit is configured to, when it is determined that there is evaluation data with the same second clause number among the M evaluation data, randomly screen out one evaluation data from the evaluation data with the same second clause number, and delete the other evaluation data with the same second clause number, to obtain P evaluation data, where P is greater than or equal to N and less than or equal to M. In one embodiment, the first determination subunit may be configured to perform the operation S4321 described above, which will not be elaborated herein.

[0146] The second determination subunit is configured to, when it is determined that there is evaluation data with the same directory label among the P evaluation data, randomly screen out one evaluation data from the evaluation data with the same directory label, and delete the other evaluation data with the same directory label, to obtain N evaluation data; and during the process of deleting the other evaluation data with the same directory label, record the second clause number of the evaluation data with the same directory label. In one embodiment, the second determination subunit may be configured to perform the operation S4322 described above, which will not be elaborated herein.

[0147] According to an embodiment of the present disclosure, the third determination module 730 includes a third determination unit, a fourth determination unit, and a fifth determination unit.

[0148] The third determination unit is configured to calculate the similarity between the statement information and the first clause content in the full amount of evaluation data. In one embodiment, the third determination unit may be configured to perform the operation S531 described above, which will not be elaborated herein.

[0149] The fourth determination unit is configured to obtain the historical evaluation method when it is determined that there is a historical evaluation method for the first clause content with the highest similarity. In one embodiment, the fourth determination unit may be configured to perform the operation S532 described above, which will not be elaborated herein.

[0150] The fifth determination unit is configured to generate a target evaluation method corresponding to the target object according to the historical evaluation method and the target object. In one embodiment, the fifth determination unit may be configured to perform the operation S533 described above, which will not be elaborated herein.

[0151] Figure 8 A block diagram of an electronic device suitable for the method for determining target evaluation data according to an embodiment of the present disclosure is schematically shown.

[0152] As Figure 8 shown, the electronic device 800 according to an embodiment of the present disclosure includes a processor 801, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 802 or a program loaded from a storage section 808 into a random access memory (RAM) 803. The processor 801 can include, for example, a general-purpose microprocessor (e.g., CPU), an instruction set processor, and / or a related chipset, and / or a dedicated microprocessor (e.g., an application-specific integrated circuit (ASIC)), etc. The processor 801 can also include on-board memory for caching purposes. The processor 801 can include a single processing unit or multiple processing units for performing different actions of the method flow according to an embodiment of the present disclosure.

[0153] In the RAM 803, various programs and data required for the operation of the electronic device 800 are stored. The processor 801, the ROM 802, and the RAM 803 are connected to each other via a bus 804. The processor 801 performs various operations of the method flow according to an embodiment of the present disclosure by executing the programs in the ROM 802 and / or the RAM 803. It should be noted that the program can also be stored in one or more memories other than the ROM 802 and the RAM 803. The processor 801 can also perform various operations of the method flow according to an embodiment of the present disclosure by executing the programs stored in the one or more memories.

[0154] According to an embodiment of the present disclosure, the electronic device 800 can further include an input / output (I / O) interface 805, and the input / output (I / O) interface 805 is also connected to the bus 804. The electronic device 800 can further include one or more of the following components connected to the I / O interface 805: an input section 806 including a keyboard, a mouse, etc.; an output section 807 including, for example, a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker, etc.; a storage section 808 including a hard disk, etc.; and a communication section 809 including a network interface card such as a LAN card, a modem, etc. The communication section 809 performs communication processing via a network such as the Internet. A drive 810 is also connected to the I / O interface 805 as needed. A removable medium 811, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 810 as needed so that a computer program read from it can be installed into the storage section 808 as needed.

[0155] The present disclosure also provides a computer-readable storage medium, which may be included in the device / apparatus / system described in the above embodiments; or may exist separately without being assembled into the device / apparatus / system. The above computer-readable storage medium carries one or more programs, and when the above one or more programs are executed, the methods according to the embodiments of the present disclosure are implemented.

[0156] According to an embodiment of the present disclosure, the computer-readable storage medium may be a non-volatile computer-readable storage medium, and may include, for example, but not limited to: portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the above. In the present disclosure, the computer-readable storage medium may be any tangible medium that contains or stores a program, and the program may be used by or in combination with an instruction execution system, apparatus, or device. For example, according to an embodiment of the present disclosure, the computer-readable storage medium may include the above-described ROM 802 and / or RAM 803 and / or one or more memories other than ROM 802 and RAM 803.

[0157] Embodiments of the present disclosure further include a computer program product, which includes a computer program that contains program code for executing the method shown in the flowchart. When the computer program product runs on a computer system, the program code is used to cause the computer system to implement the method for determining target evaluation data provided by the embodiments of the present disclosure.

[0158] When the computer program is executed by the processor 801, the above functions defined in the system / apparatus of the embodiments of the present disclosure are executed. According to an embodiment of the present disclosure, the above-described systems, apparatuses, modules, units, etc. may be implemented by computer program modules.

[0159] In one embodiment, the computer program may rely on tangible storage media such as optical storage devices and magnetic storage devices. In another embodiment, the computer program may also be transmitted and distributed in the form of a signal on a network medium, and downloaded and installed through the communication part 809, and / or installed from the removable medium 811. The program code included in the computer program may be transmitted using any suitable network medium, including but not limited to: wireless, wired, etc., or any suitable combination of the above.

[0160] In such an embodiment, the computer program can be downloaded and installed from a network through the communication section 809, and / or installed from the removable medium 811. When the computer program is executed by the processor 801, the above functions defined in the system of the embodiments of the present disclosure are executed. According to the embodiments of the present disclosure, the above-described systems, devices, apparatuses, modules, units, etc. can be implemented by computer program modules.

[0161] According to the embodiments of the present disclosure, the program code for executing the computer program provided by the embodiments of the present disclosure can be written in any combination of one or more programming languages. Specifically, these computing programs can be implemented using high-level procedures and / or object-oriented programming languages, and / or assembly / machine languages. Programming languages include, but are not limited to, such as Java, C++, Python, the "C" language, or similar programming languages. The program code can be executed entirely on the user computing device, partially on the user device, partially on a remote computing device, or entirely on a remote computing device or server. In the case of a remote computing device, the remote computing device can be connected to the user computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computing device (for example, by using an Internet service provider to connect through the Internet).

[0162] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architectures, functions, and operations 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 diagram may represent a module, a program segment, or a part of code, and the above module, program segment, or part of code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than marked in the accompanying drawings. For example, two consecutive blocks shown may actually be executed substantially in parallel, and they may sometimes be executed in the reverse order, depending on the functions involved. It should also be noted that each block in the block diagram or flowchart, and the combinations of blocks in the block diagram or flowchart, can be implemented by a dedicated hardware-based system for performing the specified functions or operations, or can be implemented by a combination of dedicated hardware and computer instructions.

[0163] Those skilled in the art can understand that the features recited in the various embodiments and / or claims of the present disclosure can be combined or / and combined in various ways, even if such combinations or combinations are not explicitly recited in the present disclosure. In particular, without departing from the spirit and teachings of the present disclosure, the features recited in the various embodiments and / or claims of the present disclosure can be combined and / or combined in various ways. All such combinations and / or combinations fall within the scope of the present disclosure.

[0164] The specific embodiments described above further elaborate on the objectives, technical solutions, and beneficial effects of the present disclosure. It should be understood that the above are only specific embodiments of the present disclosure and are not intended to limit the present disclosure. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present disclosure shall be included within the protection scope of the present disclosure.

Claims

1. A method for determining target evaluation data, comprising: Determining a target object to be evaluated according to the input statement information, where the target object includes a business department for performing production tasks, and the statement information includes text information for describing the target object; Determining target evaluation data from the full - volume evaluation data according to the identification information of the target object, where the target evaluation data is used to evaluate the target object. Herein, both the full - volume evaluation data and the target evaluation data include first clause information and second clause information. The first clause information includes system clause content and a first clause number for the business department, and the second clause information includes general standard clause content and a second clause number; In the case where it is determined that the target evaluation data does not include an evaluation method, determining a target evaluation method corresponding to the target evaluation data according to the statement information and the full - volume evaluation data; and Updating the target evaluation data based on the target evaluation method; The step of, in the case where it is determined that the target evaluation data does not include an evaluation method, determining a target evaluation method corresponding to the evaluation data according to the statement information and the full - volume evaluation data includes: Calculating the similarity between the statement information and the first clause content in the full - volume evaluation data; In the case where there is a historical evaluation method for the first clause content with the highest similarity, obtaining the historical evaluation method; and Generating a target evaluation method corresponding to the target object according to the historical evaluation method and the target object.

2. The method according to claim 1, wherein The target evaluation data includes N evaluation data for evaluating the target object; determining target evaluation data from the full - volume evaluation data according to the identification information of the target object includes: Extracting M evaluation data corresponding to the target object from the hash data table according to the identification information of the target object, where the hash data table includes the full - volume evaluation data and M is greater than or equal to 1; and Selecting N evaluation data from the M evaluation data according to the first clause number and the second clause number, where N is greater than or equal to 1 and N is less than or equal to M.

3. The method according to claim 2, wherein, The first clause number includes a directory label for distinguishing multiple first clauses; Selecting N evaluation data from the M evaluation data according to the first clause number and the second clause number includes: In the case where there are evaluation data with the same second clause number among the M evaluation data, randomly selecting one evaluation data from the evaluation data with the same second clause number and deleting the other evaluation data with the same second clause number to obtain P evaluation data, where P is greater than or equal to N and P is less than or equal to M; In the case where there are evaluation data with the same directory label among the P evaluation data, randomly selecting one evaluation data from the evaluation data with the same directory label and deleting the other evaluation data with the same directory label to obtain N evaluation data; and Wherein, in the process of deleting the other evaluation data with the same directory label, recording the second clause number of the evaluation data with the same directory label.

4. The method according to claim 3, wherein Updating the target evaluation data based on the target evaluation method includes: Obtaining the target evaluation method, the first clause number, and the second clause number corresponding to each of the N evaluation data, and updating the target evaluation data, where there is a unique first clause information and the target evaluation method for each of the N evaluation data in the target evaluation data, and at least one second clause information corresponding to the first clause information.

5. The method according to claim 1, further comprising: Classifying the full evaluation data respectively based on the target object, the first clause information, and the second clause information to obtain first evaluation data corresponding to the first clause information, second evaluation data corresponding to the second clause information, and third evaluation data corresponding to the target object, and generating a first evaluation data graph, a second evaluation data graph, and a third evaluation data graph.

6. An apparatus for determining target evaluation data, comprising: A first determination module, configured to determine a target object to be evaluated according to input statement information, where the target object includes a business department for performing a production task, and the statement information includes text information for describing the target object; A second determination module, configured to determine target evaluation data from the full evaluation data according to the identification information of the target object, where the target evaluation data is used to evaluate the target object, and both the full evaluation data and the target evaluation data include first clause information and second clause information, the first clause information includes institutional clause content and a first clause number for the business department, and the second clause information includes general standard clause content and a second clause number; A third determination module, configured to determine a target evaluation method corresponding to the target evaluation data according to the statement information and the full evaluation data when it is determined that the target evaluation data does not include an evaluation method; and A fourth determination module, configured to update the target evaluation data based on the target evaluation method; The third determination module is further configured to: calculate the similarity between the statement information and the first clause content in the full evaluation data; obtain the historical evaluation method when it is determined that there is a historical evaluation method for the first clause content with the highest similarity; and generate a target evaluation method corresponding to the target object according to the historical evaluation method and the target object.

7. An electronic device, comprising: One or more processors; A storage device for storing one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors are caused to execute the method according to any one of claims 1 to 6.

8. A computer-readable storage medium, having executable instructions stored thereon, which when executed by a processor cause the processor to execute the method according to any one of claims 1 to 6.

9. A computer program product, comprising a computer program, which when executed by a processor implements the method according to any one of claims 1 to 6.

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