WO2024065776A1

WO2024065776A1 - Method for data processing, apparatus for data processing, electronic device, and storage medium

Info

Publication number: WO2024065776A1
Application number: PCT/CN2022/123514
Authority: WO
Inventors: Zhen Zhang; Bangping YU
Original assignee: Siemens Aktiengesellschaft; Siemens Ltd., China
Priority date: 2022-09-30
Filing date: 2022-09-30
Publication date: 2024-04-04

Abstract

A method for data processing, an apparatus for data processing, an electronic device, and a storage medium are provided. The method includes: acquiring first enterprise information of a target enterprise; analyzing a content of answers to a questionnaire obtained for the target enterprise, to obtain a first assessment result of the target enterprise of at least one intelligent manufacturing capability component, where the first assessment result is used for indicating a capability intensity of the target enterprise of the corresponding intelligent manufacturing capability component; and determining a diagnostic opinion for the target enterprise from a pre-established database based on the first enterprise information and the first assessment result, where the diagnostic opinion is used for indicating at least one of an advantage, a disadvantage, or an upgrade recommendation of the target enterprise in a current intelligent manufacturing capability.

Description

METHOD FOR DATA PROCESSING, APPARATUS FOR DATA PROCESSING, ELECTRONIC DEVICE, AND STORAGE MEDIUM

TECHNICAL FIELD

The present disclosure relates to the technical field of computers, and particularly relates to a method for data processing, an apparatus for data processing, an electronic device, and a storage medium.

BACKGROUND

Nowadays, increasingly more enterprises choose digital upgrade. In order to implement the digital upgrade of an enterprise in a more targeted manner, the first step is generally assessing and diagnosing a current intelligent manufacturing capability of the enterprise, and obtaining an improvement recommendation.

In related technologies, an approach of assessing and diagnosing the current intelligent manufacturing capability of the enterprise is to rely on expert surveys, where experts can learn about a current status of the enterprise by on-site visits to the enterprise, thereby giving a diagnostic report and an improvement recommendation about the current intelligent manufacturing capability of the enterprise. However, this approach is relatively inefficient, and is easily limited to the influence of external factors, such as time, space, and epidemic situation. Therefore, a new technical solution is required to improve the efficiency in assessing and diagnosing the current intelligent manufacturing capability of the enterprise.

SUMMARY

In order to at least partially solve the above technical problem, embodiments of the present disclosure provide a method for data processing, an apparatus for data processing, an electronic device, and a storage medium.

According to a first aspect of the embodiments of the present disclosure, an embodiment of the present disclosure provides a method for data processing, including:

acquiring first enterprise information of a target enterprise;

analyzing a content of answers to a questionnaire obtained for the target enterprise, to obtain a first assessment result of the target enterprise of at least one intelligent manufacturing capability component, where the first assessment result is used for indicating a capability intensity of the target enterprise of the corresponding intelligent manufacturing capability component; and

determining a diagnostic opinion for the target enterprise from a pre-established database based on the first enterprise information and the first assessment result, wherein the diagnostic opinion is used for indicating at least one of an advantage, a disadvantage, or an upgrade recommendation of the target enterprise in a current intelligent manufacturing capability.

According to a second aspect of the embodiments of the present disclosure, an embodiment of the present disclosure provides an apparatus for data processing, including:

an acquiring module configured to acquire first enterprise information of a target enterprise;

an analyzing module configured to analyze a content of answers obtained in a questionnaire for the target enterprise, to obtain a first assessment result of the target enterprise of at least one intelligent manufacturing capability component, wherein the first assessment result is used for indicating a capability intensity of the target enterprise of the corresponding intelligent manufacturing capability component; and

a determining module configured to determine a diagnostic opinion for the target enterprise from a pre-established database based on the first enterprise information and the first assessment result, where the diagnostic opinion is used for indicating at least one of an advantage, a disadvantage, or an upgrade recommendation of the target enterprise in a current intelligent manufacturing capability.

According to a third aspect of the embodiments of the present disclosure, an embodiment of the present disclosure provides a data processing method. The method acquires enterprise information of a target enterprise. The enterprise information of the target enterprise includes at least one of type, scale, location, staff and output of the target enterprise. The method also includes obtaining an assessment result of the target enterprise of at least one manufacturing capability component based on a questionnaire for the target enterprise, wherein the at least one manufacturing capability is obtained based on the industrial standard associated with the target enterprise; calculating a similarity score between the target enterprise and reference enterprises in a preset database based on the enterprise information and the assessment result; generating a diagnostic result of the target enterprise based on a historical diagnostic report of a reference enterprise in the preset database when the similarity score between the target enterprise and the reference enterprise is greater than a preset threshold, wherein the preset database includes historical diagnostic reports of the reference enterprises; and generating a diagnostic report of the target enterprise based on the enterprise information, assessment result of the at least one manufacturing capability component, and diagnostic result of the target enterprise.

According to a forth aspect of the embodiments of the present disclosure, an embodiment of the present disclosure provides An apparatus for data processing. The apparatus includes an enterprise-information acquiring module. an assessment result obtaining module, a similarity calculation module, an diagnostic result generating module, and a diagnostic report generating module. The enterprise-information acquiring module is configured to acquire enterprise information of a target enterprise, wherein the enterprise information of the target enterprise includes at least one of type, scale, location, staff and output of the target enterprise. The assessment result obtaining module is configured to obtain an assessment result of the target enterprise of at least one manufacturing capability component based on a questionnaire for the target enterprise, wherein the at least one manufacturing capability is obtained based on the industrial standard associated with the target enterprise. The similarity calculation module is configured to calculate a similarity score between the target enterprise and reference enterprises in a preset database based on the enterprise information and the assessment result. The diagnostic result generating module is configured to generate a diagnostic result of the target enterprise based on a historical diagnostic report of a reference enterprise in the preset database when the similarity score between the target enterprise and the reference enterprise is greater than a preset threshold, wherein the preset database includes historical diagnostic reports of the reference enterprises. The diagnostic report generating module is configured to generating a diagnostic report of the target enterprise based on the enterprise information, assessment result of the at least one manufacturing capability component, and diagnostic result of the target enterprise.

According to a fifth aspect of the embodiments of the present disclosure, an embodiment of the present disclosure provides an electronic device, including: a processor, a communication interface, a memory, and a communication bus, where the processor, the communication interface, and the memory complete communication with each other through the communication bus; and the memory is configured to store at least one executable instruction, where the executable instruction causes the processor to execute operations corresponding to the method for data processing provided in the first aspect.

According to a sixth aspect of the embodiments of the present disclosure, an embodiment of the present disclosure provides a computer readable storage medium. The computer readable storage medium stores computer instructions thereon, where the computer instructions, when executed by a processor, cause the processor to execute the method for data processing provided in the first aspect.

According to a seventh aspect of the embodiments of the present disclosure, an embodiment of the present disclosure provides a computer program product. The computer program product is tangibly stored on a computer readable medium, and includes computer executable instructions, where the computer executable instructions, when executed, cause at least one processor to execute the method for data processing provided in the first aspect.

The method for data processing in the embodiments of the present disclosure may acquire first enterprise information of a target enterprise, analyze a content of answers to a questionnaire obtained for the target enterprise, to obtain a first assessment result of the target enterprise of at least one intelligent manufacturing capability component, wherein the first assessment result is used for indicating a capability intensity of the target enterprise of the corresponding intelligent manufacturing capability component; and finally determine a diagnostic opinion for the target enterprise from a pre-established database based on the first enterprise information and the first assessment result, where the diagnostic opinion is used for indicating at least one of an advantage, a disadvantage, or an upgrade recommendation of the target enterprise in a current intelligent manufacturing capability. With the method for data processing in the present disclosure, a staff member may input the first enterprise information and the content of the answers, and then accurately and quickly obtain the diagnostic opinion for the target enterprise, such that the current intelligent manufacturing capability of the target enterprise may be assessed and diagnosed to provide the diagnostic opinion without relying on a diagnostic report and an improvement recommendation given by an expert for the target enterprise, and without being easily limited to the influence of external factors, such as time, space and epidemic situation, thereby effectively improving the efficiency in assessing and diagnosing the current intelligent manufacturing capability of the enterprise, and giving a reasonable diagnostic recommendation. Thus, the target enterprise may refer to the diagnostic recommendation for digital upgrade of the target enterprise, thereby better satisfying the diagnostic requirements of the target enterprise.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings below are only intended to give schematic illustrations and explanations on the present disclosure, and do not impose any limitation on the scope of the present disclosure.

Fig. 1 shows an alternative flowchart of a method for data processing according to the first aspect of the present disclosure.

Fig. 2 shows an alternative flowchart of the method for data processing according to the first aspect of the present disclosure.

Fig. 3 shows an alternative block diagram of an apparatus for data processing according to the second aspect of the present disclosure.

Fig. 4 shows another flowchart of a method for data processing according to the third aspect of the present disclosure.

Fig. 5 shows an alternative flowchart of the method for data processing according to the third aspect of the present disclosure.

Fig. 6 shows an alternative block diagram of an apparatus for data processing according to the fourth aspect of the present disclosure.

Fig. 7 shows an alternative schematic structural diagram of an electronic device in an embodiment of the present disclosure.

DETAILED DESCRIPTION OF THE EMBODIMENTS

To enable those skilled in the art to better understand the technical solutions in embodiments of the present disclosure, the technical solutions in the embodiments of the present disclosure will be clearly and completely described below with reference to the drawings in the embodiments of the present disclosure. Apparently, the described embodiments are merely a part, instead of all, of the embodiments of the present disclosure. All other embodiments obtained by those of ordinary skills in the art based on some embodiments among the embodiments of the present disclosure shall fall within the scope of protection of the embodiments of the present disclosure.

It should be noted that the method for data processing in the embodiments of the present disclosure may be executed by an apparatus for data processing, the apparatus for data processing may be a computer device capable of data processing, and the computer device may include one or more processing units, such as a CPU, a MCU, or a PLC, or, the apparatus for data processing may also execute the method for data processing based on a cloud system, an edge computing system, or a software platform. It should be understood that no limitation is imposed on this in the embodiments of the present disclosure.

Fig. 1 shows an alternative flowchart of a method for data processing according to the first aspect of the present disclosure. According to a first aspect of the embodiments of the present disclosure, referring to the flowchart in Fig. 1, the method for data processing provided in an embodiment of the present disclosure includes step S101, step S102, step S103, and step S103 as follows:

Step S101: acquiring first enterprise information of a target enterprise.

In the present disclosure, the first enterprise information of the target enterprise may include at least one of enterprise name, industry, enterprise nature, location, enterprise profile, enterprise products, staff composition information, production and operation information of the target enterprise, which are all basic information related to the target enterprise.

A specific resource of the first enterprise information of the target enterprise is not limited in the present disclosure. For example, in some embodiments, the first enterprise information may be manually inputted into a preset enterprise information template by a staff member (who may be a staff member performing a diagnosis task, or may be a staff member of the target enterprise) , and acquired by an apparatus for data processing through an inputted enterprise information template. This is not limited here.

In an example that the enterprise information of the target enterprise is obtained through the enterprise information template, the content of the enterprise information template may be set as required, and then may be stored in a template database to facilitate access. Taking the apparatus for data processing being a computer as an example, a staff member (who may be a staff member performing a diagnosis task or an enterprise staff member) may select an appropriate enterprise information template from a template database storing a plurality of enterprise information templates as required. The staff member fills the corresponding first enterprise information into the enterprise information template through a UI of the computer (i.e., the apparatus for data processing) . The apparatus for data processing of the present disclosure can obtain the first enterprise information of the target enterprise through the filled enterprise information template. Generally speaking, the more detailed and abundant the first enterprise information is, the better it is, and the more conducive it is to the subsequent determination of the diagnostic opinion of the target enterprise.

The staff member may select the enterprise information template from the plurality of enterprise information templates and inputs the first enterprise information, such that the first enterprise information of the target enterprise can be acquired in a more targeted manner in the present disclosure, and such that a more targeted and reliable diagnostic opinion on the target enterprise can be subsequently obtained using the method for data processing of the present disclosure, thereby contributing to satisfying different requirements of different enterprises.

Step S102: analyzing a content of answers to a questionnaire obtained for the target enterprise to obtain a first assessment result of the target enterprise of at least one intelligent manufacturing capability component, where the first assessment result is used for indicating a capability intensity of the target enterprise of the corresponding intelligent manufacturing capability component. Optionally, the questionnaire may involve/relates to one or more current intelligent manufacturing capability components of the target enterprise.

The intelligent manufacturing capability in the present disclosure is the extent to which a factory manages the enhancement and comprehensive utilization of staff, technology, resources, manufacturing, etc. to achieve the goal of intelligent manufacturing. Optionally, a capability sub-domain may work as a manufacturing capability component/dimension and different capability sub-domains work as different manufacturing capability components. The capability sub-domains include: organizational strategy, skills of the staff, data, integration, information security, equipment, networks, product design, industrial design, procurement, planning and scheduling, production operations, equipment management, safety and environmental protection, warehousing and distribution, energy management, logistics, sales, customer service, and product service. Reference may be specifically made to the national standards GB/T39116-2020 Maturity model of intelligent manufacturing capability.

The content of the questionnaire may be set as required (for example, different contents of the questionnaire may be set in accordance with industries of different enterprises) , and then may be stored in a template database to facilitate access. For example, a preset questionnaire template may be selected for use as the questionnaire. The questionnaire template is constructed based on the standard maturity model of intelligent manufacturing capability, and the content of the questionnaire may be set and modified in accordance with different industries associated with the target enterprises.

Alternatively, the method for data processing of the present disclosure may further include: obtaining, in response to a selection instruction for selection for a questionnaire template among a plurality of preset questionnaire templates, the questionnaire template from the template database, and using the questionnaire template as the questionnaire.

In the example where a computer operates as the data processing apparatus, a staff member (who may be a staff member performing a diagnosis task or an enterprise staff member) may select an appropriate questionnaire template from the plurality of questionnaire templates as needed, and then fills the questionnaire. The staff member may fill the questionnaire with the content of answers through the UI of the computer (i.e., the apparatus for data processing) . The data processing apparatus of the present disclosure may obtain the content of the answers through the filled questionnaire. Generally speaking, the more detailed and abundant the content of the questionnaire is, the better the quality of the content of the answers is, and the more conducive it is to the subsequent determination of the diagnostic opinion of the target enterprise.

Obviously, the staff member selects a questionnaire template from the plurality of questionnaire templates for use as the questionnaire, and inputs the content of the answers, such that the capability intensity of the target enterprise of the corresponding intelligent manufacturing capability component can be assessed in a more targeted manner in the present disclosure, thereby obtaining a reasonable first assessment result, and such that a more targeted and reliable diagnostic opinion on the target enterprise can be subsequently obtained using the method for data processing of the present disclosure, thereby contributing more to satisfying different requirements of different enterprises.

In the present disclosure, the first assessment result may include at least one score of the target enterprise of the intelligent manufacturing capability component obtained by analysis based on the content of the questionnaire answer, and can effectively indicate the intensity of the target enterprise of the corresponding intelligent manufacturing capability component. On this basis, in some alternative embodiments, the step S102 specifically includes: analyzing the content of the answers based on a preset maturity model of intelligent manufacturing capability, to obtain the first assessment result of the target enterprise of the intelligent manufacturing capability component.

The analyzing the content of the answers based on the maturity model of intelligent manufacturing capability is performed in accordance with a mature technology in national standards, specifically with reference to the national standards GB/T39116-2020 Maturity model of intelligent manufacturing capability and GB/T39117-2020 Maturity assessment method of intelligent manufacturing capability. The description will not be repeated here. Optionally, each capability sub-domain may work as one intelligent manufacturing capability component for the Maturity model of intelligent manufacturing capability. Different capability sub-domains may work as different components of the intelligent manufacturing capability. The present disclosure analyzes the content of the obtained answers using the preset maturity model of intelligent manufacturing capability, thereby more objectively obtaining the first assessment result of the target enterprise of the at least one intelligent manufacturing capability component.

Alternatively, the above enterprise information template may be a part of the questionnaire template, i.e., the selected questionnaire template (i.e., the questionnaire) may be filled to obtain the enterprise information of the target enterprise and the content of the answers together, thereby facilitating subsequent data processing. This is not limited here in the present disclosure.

Step S103: determining a diagnostic opinion for the target enterprise from a pre-established database based on the first enterprise information and the first assessment result.

In the present disclosure, the diagnostic opinion may be used for indicating at least one of an advantage, a disadvantage, or an upgrade recommendation of the target enterprise in a current intelligent manufacturing capability, to obtain a reasonable diagnostic opinion, and provide reliable reference for the digital upgrade of the target enterprise. Specifically, the diagnostic opinion, for example, may include at least one of summary of enterprise researches, recommendation on intelligent reconstruction, recommendation on intelligent reconstruction embodiments, analysis on intelligent reconstruction benefits, or the like.

The method for data processing in the present disclosure may acquire first enterprise information of a target enterprise, analyze a content of answers to a questionnaire obtained for the target enterprise, to obtain the first assessment result of the target enterprise of at least one intelligent manufacturing capability component, wherein the first assessment result is indicative of an intensity/strength of the target enterprise of the corresponding intelligent manufacturing capability component; and finally determine a diagnostic opinion for the target enterprise from a pre-established database based on the first enterprise information and the first assessment result, where the diagnostic opinion is used for indicating at least one of an advantage, a disadvantage, or an upgrade recommendation of the target enterprise in a current intelligent manufacturing capability. With the method for data processing in the present disclosure, a staff member may input the first enterprise information and the content of the answers, and then accurately and quickly obtain the diagnostic opinion for the target enterprise, such that the current intelligent manufacturing capability of the target enterprise may be assessed and diagnosed to provide the diagnostic opinion without relying on a diagnostic report and an improvement recommendation given by an expert for the target enterprise, and without being easily limited to the influence of external factors, such as time, space and epidemic situation, thereby effectively improving the efficiency in assessing and diagnosing the current intelligent manufacturing capability of the enterprise, and giving a reasonable diagnostic recommendation. Thus, the target enterprise may refer to the diagnostic recommendation for digital upgrade of the target enterprise, thereby better satisfying the diagnostic requirements of the target enterprise.

Specific implementations of S103 are not limited in the present disclosure, as long as the requirements can be satisfied. In some alternative embodiments, the database includes a knowledge graph library, where a plurality of knowledge graphs is pre-stored in the knowledge graph library, the plurality of knowledge graphs is generated based on different historical diagnostic reports respectively, one or more historical diagnostic reports among the historical diagnostic reports are for a given enterprise, and the historical diagnostic reports include second enterprise information of a targeted enterprise thereof, a second assessment result of the at least one intelligent manufacturing capability component, and a historical diagnostic opinion for the enterprise; and on this basis, the step S103 specifically includes: determining at least one knowledge graph from the knowledge graph library based on the first enterprise information and the first assessment result, and determining the diagnostic opinion for the target enterprise based on a historical diagnostic report corresponding to the at least one knowledge graph.

The historical diagnostic report may be a diagnostic report obtained before a current method for data processing is executed. The historical diagnostic report includes, but is not limited to, a prior diagnostic report provided by an expert, and a prior diagnostic report file obtained using the method for data processing, and the like. These historical diagnostic reports may be historical diagnostic reports obtained for different enterprises, each historical diagnostic report may be targeted for a different enterprise, or a plurality of historical diagnostic reports may be targeted for one enterprise, or the like. For example, taking a knowledge graph generated from 4 historical diagnostic reports (denoted as reports 1, 2, 3, and 4 respectively) in total as an example, the reports 1, 2, 3, and 4 may be targeted for 4 different enterprises, respectively; or, the report 1 is targeted for an enterprise 1, the reports 2 and 3 are targeted for an enterprise 2, and the report 4 is targeted for an enterprise 3; or, the reports 1 and 2 are targeted for the enterprise 1, and the reports 3 and 4 are targeted for the enterprise 2, and so on. This is not limited here. It should be understood that the enterprises targeted by the historical diagnostic reports may also include the target enterprise.

The present disclosure determines at least one knowledge graph generated based on the historical diagnostic report from the knowledge graph library based on the first enterprise information and the first assessment result, determines the diagnostic opinion of the target enterprise based on a historical diagnostic report corresponding to the at least one knowledge graph, and may determine an effective diagnostic opinion for the target enterprise with reference to a prior historical diagnostic report, to satisfy the diagnostic requirements of the target enterprise.

In addition, compared with simply computing an intelligent manufacturing capability level of the target enterprise and giving a formatted report, the present disclosure determines the diagnostic opinion for the target enterprise based on the knowledge graph, thereby contributing more to embody the diagnostic opinion, and reducing the burden of subsequent generation of the diagnostic opinion report file.

In the present disclosure, the knowledge graph is generated based on the historical diagnostic opinion, and alternatively, the knowledge graph may be generated by the following approach: extracting, from a text of the historical diagnostic report, a plurality of entity objects, a relationship between the plurality of entity objects, and a plurality of diagnostic opinion text segments; and generating the knowledge graph based on the plurality of entity objects, the relationship between the plurality of entity objects, and the plurality of diagnostic opinion text segments. In the present disclosure, word segmentation and semantic analysis may be performed on the text of the historical diagnostic report using a natural language processing algorithm, to extract the plurality of entity objects, the relationship between the plurality of entity objects, and the plurality of diagnostic opinion text segments. Entity objects may exist as entities when constituting the knowledge graph, and may be some objectively existing and mutually distinguishable objects or abstract concepts or connections of an enterprise, e.g., may include the name, location, products, staff, and the like of the enterprise. The relationship between the plurality of entity objects may represent a connection between two entity objects, and may exist as a side when constituting the knowledge graph. For example, when the two entity objects are "XX company" and "Mr. A” respectively, the relationship between the two entity objects may be "general manager, " i.e., the general manager of the XX company is Mr. A; for another example, when the entity objects are "XX company" and "Beijing" respectively, the relationship between the two entity objects may be "location of the headquarters" , i.e., the XX company is headquartered in Beijing; and so on. The diagnostic opinion text segment may be a text segment recording a specific diagnostic opinion in the text of the historical diagnostic report, for example, a text segment of summary of enterprise researches, recommendation on intelligent reconstruction, recommendation on intelligent reconstruction embodiments, or analysis on intelligent reconstruction benefits. Different diagnostic opinion text segments are classified and are associated with entity objects and relationships. The knowledge graph is generated based on the plurality of entity objects, the relationship between the plurality of entity objects, and the plurality of diagnostic opinion text segments and is stored in the knowledge graph library.

It should be understood that during analysis, for different words representing a given entity object, words with close semantic meanings may be unified into one word during analysis and extraction. For example, words, such as Beijing, BEIJING, and the capital of China, have the same actual meaning, and may be unified as Beijing. It should be understood that this example is not used as any limitation on the present disclosure.

In some alternative embodiments, the “determining at least one knowledge graph from the knowledge graph library based on the first enterprise information and the first assessment result, and determining the diagnostic opinion for the target enterprise based on the historical diagnostic report corresponding to the at least one knowledge graph” includes: computing, for each knowledge graph in the knowledge graph library, a first similarity between the first enterprise information and the second enterprise information of a historical diagnostic report corresponding to the knowledge graph, and a second similarity between the first assessment result and second assessment result of the historical diagnostic report corresponding to the knowledge graph respectively, to determine a third similarity based on the first similarity and the second similarity, where the third similarity is used for indicating a similarity between the target enterprise and an enterprise targeted by the historical diagnostic report corresponding to the knowledge graph; determining whether a knowledge graph in the knowledge graph library has a third similarity greater than a preset similarity threshold; and determining, when a knowledge graph in the knowledge graph library has a similarity greater than the preset similarity threshold, the diagnostic opinion for the target enterprise based on a historical diagnostic report corresponding to a knowledge graph with a largest similarity.

Alternatively, the first similarity, the second similarity, and the third similarity may be computed by a preset similarity algorithm. The first similarity degree and the second similarity degree can reflect a similarity relationship between the target enterprise and the enterprise corresponding to the knowledge graph in terms of attributes. The attributes may include enterprise type, enterprise scale, enterprise location, enterprise staff, enterprise output, assessment result (i.e., the assessment result may be regarded as an attribute alone) , and the like. The third similarity may be analyzed to comprehensively judge whether the target enterprise and the enterprise targeted by the historical diagnostic report corresponding to the knowledge graph are enterprises of the same type. Specifically, when the third similarity is greater than the preset similarity threshold, it means that a knowledge graph in the knowledge graph library is generated based on the historical diagnostic report of the enterprise of the same type as the target enterprise, and therefore, the diagnostic opinion for the target enterprise may be determined based on a historical diagnostic report corresponding to a knowledge graph with a highest third similarity. Alternatively, Jaccard similarity algorithm may be used as the similarity algorithm.

Alternatively, the third similarity may be obtained by weighting and summing the first similarity and the second similarity in accordance with a predetermined weight parameter, where the weight parameter may be set as required. This is not limited in here in the present disclosure.

In the present disclosure, the diagnostic opinion for the target enterprise is determined based on the historical diagnostic report corresponding to the knowledge graph with the largest third similarity, so that the diagnostic opinion for the target enterprise can be determined with reference to the historical diagnostic report of the enterprise with the highest third similarity to the target enterprise, the diagnostic opinion of the target enterprise can be more efficiently determined without the need of repeatedly giving a reusable diagnostic opinion in the historical diagnostic report, thereby improving the utilization rate of the historical diagnostic report on the basis of accurately determining the diagnostic opinion for the target enterprise.

In the present disclosure, the determining the diagnostic opinion for the target enterprise based on the historical diagnostic report corresponding to the knowledge graph with the largest third similarity may be extracting a diagnostic opinion text segment from the historical diagnostic report based on the knowledge graph, and then determining the diagnostic opinion for the target enterprise based on the extracted diagnostic opinion text segment. For example, at least a part of content of the extracted diagnostic opinion text segment may be used as a part of the diagnostic opinion for the target enterprise. For another example, at least a part of content of the extracted diagnostic opinion text segment may be used as at least one recommended diagnostic opinion, and then the apparatus for data processing determines, in response to a selection instruction for selection for the at least one recommended diagnostic opinion, the diagnostic opinion for the target enterprise from the at least one recommended diagnostic opinion. Alternatively, after the diagnostic opinion text segment is extracted, the diagnostic opinion text segment may be modified as required, to facilitate subsequently obtaining the diagnostic opinion for the target enterprise. Alternatively, the historical diagnostic report may be subsequently used as a report template, and a diagnostic opinion text of the target enterprise may be filled into the report template.

The preset similarity threshold may be different for different target enterprises, and may be set as required. This is not particularly limited here.

In some alternative embodiments, the “determining at least one knowledge graph from the knowledge graph library based on the first enterprise information and the first assessment result, and determining the diagnostic opinion for the target enterprise based on the historical diagnostic report corresponding to the at least one knowledge graph” further includes: searching in the knowledge graph library based on at least one keyword, when no knowledge graph in the knowledge graph library has a third similarity greater than the preset similarity threshold, to determine at least one knowledge graph matching each keyword, and obtain at least one diagnostic opinion text segment matching the keyword based on the obtained at least one knowledge graph; and determining the diagnostic opinion for the target enterprise based on the extracted at least one diagnostic opinion text segment.

In the present disclosure, in this way, the diagnostic opinion for the target enterprise can be quickly determined based on the historical diagnostic report corresponding to the knowledge graph, and the diagnostic opinion for the target enterprise can be determined with reference to the historical diagnostic report, so that the diagnostic opinion of the target enterprise can be more efficiently determined without the need of repeatedly giving a reusable diagnostic opinion in the historical diagnostic report, thereby improving the utilization rate of the historical diagnostic report on the basis of accurately determining the diagnostic opinion for the target enterprise.

Whether a keyword matches the knowledge graph may be determined by searching whether the knowledge graph includes a word with close or identical semantic meaning to the keyword, and determining that the keyword matches the knowledge graph if the knowledge graph includes a word with close or identical semantic meanings to the keyword. For example, a semantic similarity between two words may be computed in accordance with a predetermined algorithm; if the semantic similarity is greater than a predetermined value (which may be set as required, e.g., 80%or 90%) , the two words are considered to have close semantic meanings; and if the semantic similarity is identical, the two words are considered to have identical semantic meanings. It should be understood that this is only an example, and does not constitute any limitation to the present disclosure.

A diagnostic opinion text segment that matches a keyword may be a diagnostic opinion text segment comprising a word with close or identical semantic meanings to the keyword. The diagnostic opinion text segment is extracted, and then used as reference for determining the diagnostic opinion for the target enterprise. For example, at least a part of content of the extracted diagnostic opinion text segment may be used as a part of the diagnostic opinion for the target enterprise. For another example, at least a part of content of the extracted diagnostic opinion text segment may be used as at least one recommended diagnostic opinion, and then the apparatus for data processing determines, in response to a selection instruction for selection for the at least one recommended diagnostic opinion, the diagnostic opinion for the target enterprise from the at least one recommended diagnostic opinion. Alternatively, after the diagnostic opinion text segment is extracted, the diagnostic opinion text segment may be modified as required, to facilitate subsequently obtaining the diagnostic opinion for the target enterprise.

Alternatively, the searching in the knowledge graph library based on the keyword may be implemented by ELK search, to obtain a matching knowledge graph, and to obtain at least one diagnostic opinion text segment matching the keyword based on the obtained at least one knowledge graph, or may be implemented by other search algorithms. This is not limited here.

In some alternative embodiments, referring to the flowchart of steps in Fig. 2, the method for data processing in the present disclosure further includes: step S104: generating a diagnostic report with respect to the target enterprise based on the first enterprise information, the content of the answers, and the diagnostic opinion with respect to the target enterprise.

Specifically, in the present disclosure, information, such as the first enterprise information, the questionnaire, the content of the answers, and the diagnostic opinion with respect to the target enterprise, may be summarized and analyzed to generate a customized diagnostic report file for the target enterprise, and an enterprise staff member may view the diagnostic result of the current intelligent manufacturing capability of the target enterprise through the diagnostic report file, thereby providing reasonable reference for the staff member of the target enterprise to digitally upgrade the target enterprise.

In the present disclosure, the diagnostic report file may be generated based on the first enterprise information, the content of the answers, and the diagnostic opinion with respect to the target enterprise by any approach. The specific generation approach is not limited here. For example, in some embodiments, step S104 may be inputting the first enterprise information, the content of the answers, and the diagnostic opinion for the target enterprise into a preset diagnostic report generating model to generate the diagnostic report file. The diagnostic report file is automatically generated by the preset diagnostic report generating model, thereby improving the accuracy and efficiency in generating the diagnostic report file.

The file format of the diagnostic report text is not limited in the present disclosure, and may be selected as required. For example, the file format of the diagnostic report file may be a word file (e.g., doc or docx) or a PDF file, or may be, in some other embodiments, e.g., an excel, PPT, or txt file.

The diagnostic report file in the present disclosure is allowed to be modified in accordance with a modification instruction of the staff member. Specifically, the method for data processing may further include: adjusting, in response to a modification instruction of a user, a content recorded in the diagnostic report file, and updating the diagnostic report file. The modification instruction may be targeted for any content in the diagnostic report file. This is not limited here, as long as the requirements can be satisfied. The content recorded in the diagnostic report file is allowed to be modified, so that the finally generated diagnostic report file contributes more to satisfying the digital upgrade requirements of the target enterprise.

Alternatively, while the diagnostic report file is generated, an assessment result for a maturity level of the current intelligent manufacturing capability and a prediction result for a maturity level of a future intelligent manufacturing capability of the target enterprise may also be outputted (specifically, the maturity level of the intelligent manufacturing capability may be divided into five levels, of which, the first level is a planning level, the second level is a normative level, the third level is an integration level, the fourth level is an optimization level, and the fifth level is a leading level, specifically with reference to the national standard GB/T39117-2020 Maturity assessment method of intelligent manufacturing capability) , to provide reference for the digital upgrade of the target enterprise.

It is understandable that the above contents are only some example explanations of the method for data processing in the embodiments of the present disclosure, and are not used as any limitation on the embodiments of the present disclosure.

Fig. 3 shows an alternative block diagram of an apparatus for data processing according to the second aspect of the present disclosure. Referring to Fig. 3, a second aspect of the present disclosure provides an apparatus 300 for data processing, including:

an acquiring model 301 configured to acquire first enterprise information of a target enterprise;

an analyzing model 302 configured to analyze a content of answers to a questionnaire obtained for the target enterprise, to obtain a first assessment result of the target enterprise of at least one intelligent manufacturing capability component, where the first assessment result is used for indicating a capability intensity of the target enterprise of the corresponding intelligent manufacturing capability component; and

a determining model 303 configured to determine a diagnostic opinion for the target enterprise from a pre-established database based on the first enterprise information and the first assessment result, where the diagnostic opinion is used for indicating at least one of an advantage, a disadvantage, or an upgrade recommendation of the target enterprise in the current intelligent manufacturing capability.

The apparatus 300 for data processing of the present disclosure is based on the same inventive concept as the above method for data processing according to the first aspect of the present disclosure, and is the apparatus for data processing mentioned in the above method for data processing according to the first aspect of the present disclosure. Relevant contents of the apparatus may be understood with reference to the above embodiments of the method for data processing. The description will not be repeated here.

Fig. 4 shows another flowchart of a method for data processing according to the third aspect of the present disclosure. With reference to Fig. 4, the method includes:

Step S401: acquiring enterprise information of a target enterprise.

Specifically, the enterprise information of the target enterprise may be at least one of type, scale, location, staff and output of the target enterprise.

A specific resource of the enterprise information of the target enterprise is not limited in the present disclosure. For example, in some embodiments, the enterprise information may be manually inputted into a preset enterprise information template by a staff member (who may be a staff member performing a diagnosis task, or may be a staff member of the target enterprise) , and acquired by an apparatus for data processing through an inputted enterprise information template. This is not limited here.

In an example that the enterprise information of the target enterprise is obtained through the enterprise information template, the content of the enterprise information template may be set as required, and then may be stored in a template database to facilitate access. Taking the apparatus for data processing being a computer as an example, a staff member (who may be a staff member performing a diagnosis task or an enterprise staff member) may select an appropriate enterprise information template from a template database storing a plurality of enterprise information templates as required. The staff member fills the corresponding enterprise information into the enterprise information template through a UI of the computer (i.e., the apparatus for data processing) . The apparatus for data processing of the present disclosure can obtain the enterprise information of the target enterprise through the filled enterprise information template. Generally speaking, the more detailed and abundant the enterprise information is, the better it is, and the more conducive it is to the subsequent determination of the diagnostic opinion of the target enterprise.

Step S402: obtaining an assessment result of the target enterprise of at least one manufacturing capability component based on a questionnaire for the target enterprise, wherein the at least one manufacturing capability is obtained based on the industrial standard associated with the target enterprise.

Alternatively, the content (such as questions and options for questions) of the questionnaire in the present disclosure may be set with reference to a standard maturity model of intelligent manufacturing capability.

A national standard may serve as an alternative to the industrial standard for the target enterprise. Embodiments of the present disclosure are not limited to the industrial standard and national standard.

Optionally, the manufacturing capability in the embodiment may be intelligent manufacturing capability, and the manufacturing capability component may be intelligent manufacturing capability component.

Alternatively, the content of the questionnaire may involve one or more components of the intelligent manufacturing capability of the enterprise among a current digital strategy, system integration, product design, manufacturing planning, device type or status, production line type or status, service and maintenance, quality management, IT security, and human resource.

Alternatively, the data processing method of the present disclosure may further include: obtaining, in response to a selection instruction for selection for a questionnaire template among a plurality of preset questionnaire templates, the questionnaire template from the template database, and using the questionnaire template as the questionnaire.

Obviously, the staff member selects a questionnaire template from the plurality of questionnaire templates for use as the questionnaire, and inputs the content of the answers, such that the capability intensity of the target enterprise of the corresponding intelligent manufacturing capability component can be assessed in a more targeted manner in the present disclosure, thereby obtaining a reasonable assessment result, and such that a more targeted and reliable diagnostic opinion on the target enterprise can be subsequently obtained using the method for data processing of the present disclosure, thereby contributing more to satisfying different requirements of different enterprises.

In the present disclosure, the assessment result may include at least one assessment score of the target enterprise of the intelligent manufacturing capability component obtained by analysis based on the content of the questionnaire answer, and can effectively indicate the intensity of the target enterprise of the corresponding intelligent manufacturing capability component.

Thus, in some optional embodiments, step S 402 specifically includes: analyzing the content of the answers based on a preset maturity model of the intelligent manufacturing capability, to obtain the assessment result of the target enterprise of the at least one intelligent manufacturing capability component.

Analysis based on the preset maturity model of the intelligent manufacturing capability is mature technology based on national standards. Reference may be specifically made to national standard GB/T39116-2020 Maturity model of intelligent manufacturing capability and national standard GB/T39117-2020 Maturity assessment method of intelligent manufacturing capability. No more details on how to perform the analysis will be discussed herein. Optionally, a capability sub-domain may work as a manufacturing capability component/dimension and different capability sub-domains work as different manufacturing capability components. Analysis is performed on the contents of the answers to the questionnaire based on the preset maturity model of the intelligent manufacturing capability. In this way, objective assessment results of at least one manufacturing capability component may be obtained.

Optionally, the enterprise information template may be part of the questionnaire template. Enterprise information of the target enterprise and answers to the questionnaire may be obtained together from a filled questionnaire template (i.e., the questionnaire) which is selected for data processing subsequently performed.

Step S403: calculating a similarity score between the target enterprise and reference enterprises in a preset database based on the enterprise information and the assessment result.

The preset database may include the historical diagnostic reports of the reference enterprises. The historical diagnostic reports of the reference enterprises may be historical diagnostic reports made for enterprises of various industrial sectors, or historical diagnostic reports for the target enterprise at different development stage.

The number, composition, industrial sectors of the reference enterprises may be configured or set according to the needs of a user in an embodiment of the present disclosure.

The historical diagnostic reports of the reference enterprises in the preset database may be updated periodically. The updating interval may be set by the user.

A targeted diagnostic result may be generated for the target enterprise by way of managing the historical diagnostic reports of the reference enterprises in the preset database.

Step S404: generating a diagnostic result of the target enterprise based on a historical diagnostic report of a reference enterprise when the similarity score between the target enterprise and the reference enterprise is greater than a preset threshold.

In an embodiment of the present disclosure, the diagnostic result of the target enterprise may indicate at least one of the advantage, disadvantage, or an upgrade recommendation of the target enterprise. Specifically, the diagnostic result includes at least one of a report of studies on the target enterprise, an improvement recommendation about the intelligent manufacturing capability of the enterprise, and implementation solution for improving the intelligent manufacturing capability of the enterprise, and profitability analysis of the improvement on intelligent manufacturing capability of the enterprise.

In an embodiment of the present disclosure, a diagnostic result of the target enterprise is generated based on the historical diagnostic report of the reference enterprises in the preset database whose similarity score with the target enterprise is greater than a preset threshold. Thus, the diagnostic result can meet the needs of the target enterprise.

The preset threshold may be set by a skilled person in the art based on the needs of the target enterprise.

Step S405: generating a diagnostic report of the target enterprise based on the enterprise information, assessment result of the at least one manufacturing capability component, and diagnostic result of the target enterprise.

Specifically, a model may be created based on the enterprise information, assessment result of the at least one manufacturing capability component, and diagnostic result of the target enterprise to generate the diagnostic report of the target enterprise.

The model for generating the diagnostic report of the target enterprise may automatically process the enterprise information, assessment result of the at least one manufacturing capability component, and diagnostic result of the target enterprise and generate the diagnostic report of the target enterprise.

The diagnostic reports of the target enterprise generated in the embodiment of the present disclosure may be automatically incorporated and updated in the preset database so that the diagnostic reports of the target enterprise may serve as historical diagnostic reports of the reference enterprises.

The diagnostic result of the target enterprise is generated by way of searching for a reference enterprise similar to the target enterprise among the historical diagnostic reports of reference enterprises in the preset database, and then the diagnostic report of the target enterprise is generated in one or more embodiment of the present disclosure. The diagnostic report of the target enterprise includes not only the enterprise information, and the assessment result of the at least one manufacturing capability component, but also the diagnostic result of the target enterprise. The generation of the diagnostic report is not influenced by external factors including time, space, novel coronavirus and expert availability, and can meet the diagnostic needs of the target enterprise sufficiently.

In an embodiment of the present disclosure, the preset database includes expert diagnostic opinions corresponding to searchable keywords. NLP data analysis may be performed on the description of expert diagnosis to obtain expert diagnostic opinions corresponding to the search keyword input by the user. In this way, the user may obtain an expert diagnostic opinion with input keywords.

The expert diagnostic opinions corresponding to searchable keywords are pre-stored in the preset database so that a user may obtain an expert diagnostic opinion by inputting a search keyword.

The searchable expert diagnostic opinions contain searchable keyword for elastic search.

With reference to Fig. 5, the method may further include:

Step S406: obtaining an expert diagnostic opinion based on a keyword input by a user when no reference enterprise exists in the preset database whose similarity score with the target enterprise is greater than the preset threshold.

When no reference enterprise exists in the preset database whose similarity score with the target enterprise is greater than the preset threshold, an expert diagnostic opinion may be obtained based on the keyword input by the user. Thus, the diagnostic result can be generated, and then the diagnostic report can be generated even in the case that no reference enterprise exists in the preset database whose similarity score with the target enterprise is greater than the preset threshold.

In another embodiment of the present disclosure, Step S406 may also include: obtaining the expert diagnostic opinion based on the search keyword input by a user; and generating the diagnostic result of the target enterprise based on the expert diagnostic opinion and historical diagnostic reports of the reference enterprise in the preset database.

The expert diagnostic opinion corresponding to the keyword input by the user is added in the diagnostic result of the target enterprise which is generated based on the historical diagnostic reports of the reference enterprise. In this way, a more comprehensive diagnostic report may be provided to meet the diagnostic needs of the target enterprise.

Fig. 6 shows an alternative block diagram of an apparatus for data processing according to the fourth aspect of the present disclosure. The apparatus 600 for data processing according to the fourth aspect of the present disclosure includes:

an enterprise-information acquiring module 601, configured to acquire enterprise information of a target enterprise, wherein the enterprise information of the target enterprise includes at least one of type, scale, location, staff and output of the target enterprise;

an assessment result obtaining module 602, configured to obtain an assessment result of the target enterprise of at least one manufacturing capability component based on a questionnaire for the target enterprise, wherein the at least one manufacturing capability is obtained based on the industrial standard associated with the target enterprise;

a similarity calculation module 603, configured to calculate a similarity score between the target enterprise and reference enterprises in a preset database based on the enterprise information and the assessment result;

an diagnostic result generating module 604, configured to generate a diagnostic result of the target enterprise based on a historical diagnostic report of a reference enterprise in the preset database when the similarity score between the target enterprise and the reference enterprise is greater than a preset threshold, wherein the preset database includes historical diagnostic reports of the reference enterprises; and

a diagnostic report generating module 605, configured to generating a diagnostic report of the target enterprise based on the enterprise information, assessment result of the at least one manufacturing capability component, and diagnostic result of the target enterprise.

The apparatus 600 for data processing of the present disclosure is based on the same inventive concept as the above method for data processing according to the first aspect of the present disclosure, and is the apparatus for data processing mentioned in the above method for data processing according to the first aspect of the present disclosure. Relevant contents of the apparatus may be understood with reference to the above embodiments of the method for data processing. The description will not be repeated here.

Fig. 7 shows a schematic structural diagram of an alternative electronic device according to an embodiment of the present disclosure. The embodiment of the present disclosure does not impose any limitation on specific implementations of the electronic device 700. As an example, referring to Fig. 7, the electronic device 700 provided in a third aspect of the embodiments of the present disclosure includes: a processor 702, a communication interface 704, a memory 706, and a communication bus 708. In the figure:

The processor 702, the communication interface 704, and the memory 706 complete communication with each other through the communication bus 708.

The communication interface 704 is configured to communicate with other electronic devices or servers.

The processor 702 is configured to execute a program 710, and specifically may execute relevant steps in any one of the above embodiments of the method for data processing.

Specifically, the program 710 may include a program code. The program code includes computer operation instructions.

The processor 702 may be a central processing unit (CPU) , or an Application Specific Integrated Circuit (ASIC) , or one or more integrated circuits configured to implement the embodiments of the present disclosure. One or more processors included in a smart device may be processors of a given type, e.g., one or more CPUs; or may be processors of different types, e.g., one or more CPUs and one or more ASICs.

The memory 706 is configured to store the program 710. The memory 706 may include a high-speed RAM memory, and may further include a non-volatile memory, e.g., at least one disk memory.

The program 710 may specifically be used for causing the processor 702 to execute the method for data processing in any one of the above embodiments.

Corresponding description in the corresponding steps and units in any one of the above embodiments of the method for data processing may be referred to for specific implementations of each step in the program 710. The description will not be repeated here. Those skilled in the art can clearly understand that, for convenience and simplicity of description, the description of corresponding processes in the above method embodiments may be referred to for specific working processes of the above described device and modules. The description will not be repeated here.

According to a fourth aspect of the embodiments of the present disclosure, an embodiment of the present disclosure provides a computer storage medium storing instructions for causing a machine to execute the method for data processing as described herein.

According to a fifth aspect of the embodiments of the present disclosure, an embodiment of the present disclosure further provides a computer program product. The computer program product is tangibly stored on a computer readable medium, and includes computer executable instructions, where the computer executable instructions, when executed, cause at least one processor to execute the method for data processing provided in the above embodiments. It should be understood that each solution in the present embodiment has corresponding technical effects in the above method embodiments. The description will not be repeated here.

Relevant contents and beneficial effects in the embodiments of the apparatus 300 for data processing/data processing apparatus 600/electronic device 400/computer storage medium/computer program product in the present disclosure are substantially similar to the relevant contents and beneficial effects in the methods for data processing provided in the first and third aspects. Therefore, the description here is relatively brief, and may be understood in accordance with the above embodiments of the method for data processing.

It should be understood that the steps disclosed in the method embodiments of the present disclosure may be executed in different sequences and/or may be executed in parallel. Expressions similar to "first" and "second" used in the embodiments of the present disclosure may embellish various components irrespective of sequence and/or importance, but these expressions do not impose any limitation on the corresponding components. The above expressions are only configured for the purpose of distinguishing the components from other components.

Finally, it should be noted that: the above embodiments are merely used to illustrate the technical solutions of the embodiments of the present disclosure, instead of imposing any limitation on the technical solutions. While the present disclosure is described in detail with reference to the above embodiments, those of ordinary skills in the art should understand that: the technical solutions disclosed in the above embodiments may still be modified or a part or all of the technical features therein may be replaced equivalently. These modifications and replacements are not intended to make the essence of corresponding technical solutions depart from the spirit and scope of the technical solutions of the embodiments of the present disclosure.

Claims

A method for data processing, comprising:

acquiring a first enterprise information of a target enterprise;

analyzing a content of answers to a questionnaire obtained for the target enterprise, to obtain a first assessment result of the target enterprise of at least one intelligent manufacturing capability component, wherein the first assessment result is used for indicating a capability intensity of the target enterprise of the intelligent manufacturing capability component; and

determining a diagnostic opinion for the target enterprise from a pre-established database based on the first enterprise information and the first assessment result, wherein the diagnostic opinion is used for indicating at least one of an advantage, a disadvantage, or an upgrade recommendation of the target enterprise in a current intelligent manufacturing capability.
The method according to claim 1, wherein the database comprises a knowledge graph library, wherein a plurality of knowledge graphs is pre-stored in the knowledge graph library, the plurality of knowledge graphs is generated based on different historical diagnostic reports respectively, one or more historical diagnostic reports among the historical diagnostic reports are for a given enterprise, and the historical diagnostic reports include second enterprise information of a targeted enterprise thereof, a second assessment result of at least one intelligent manufacturing capability component, and a historical diagnostic opinion for the enterprise; and

the determining the diagnostic opinion for the target enterprise from the pre-established database based on the first enterprise information and the first assessment result comprises:

determining at least one knowledge graph from the knowledge graph library based on the first enterprise information and the first assessment result, and determining the diagnostic opinion for the target enterprise based on a historical diagnostic report corresponding to the at least one knowledge graph.
The method according to claim 2, wherein the determining the at least one knowledge graph from the knowledge graph library based on the first enterprise information and the first assessment result, and determining the diagnostic opinion for the target enterprise based on the historical diagnostic report corresponding to the at least one knowledge graph comprises:

computing, for each knowledge graph in the knowledge graph library, a first similarity between the first enterprise information and the second enterprise information of a historical diagnostic report corresponding to the each knowledge graph, and a second similarity between the first assessment result and second assessment result of the historical diagnostic report corresponding to the each knowledge graph respectively, to determine a third similarity based on the first similarity and the second similarity, wherein the third similarity is used for indicating a similarity between the target enterprise and an enterprise targeted by the historical diagnostic report corresponding to the each knowledge graph;

determining whether a knowledge graph in the knowledge graph library has a third similarity greater than a preset similarity threshold; and

determining, when a knowledge graph in the knowledge graph library has a third similarity greater than the preset similarity threshold, the diagnostic opinion for the target enterprise based on a historical diagnostic report corresponding to a knowledge graph with a largest third similarity.
The method according to claim 3, wherein the determining the at least one knowledge graph from the knowledge graph library based on the first enterprise information and the first assessment result, and determining the diagnostic opinion for the target enterprise based on the historical diagnostic report corresponding to the at least one knowledge graph further comprises:

searching in the knowledge graph library based on at least one keyword, when no knowledge graph in the knowledge graph library has a third similarity greater than the preset similarity threshold, to determine at least one knowledge graph matching each keyword, and obtain at least one diagnostic opinion text segment matching the each keyword based on the obtained at least one knowledge graph; and

determining the diagnostic opinion for the target enterprise based on the extracted at least one diagnostic opinion text segment.
The method according to claim 1, wherein the method further comprises: generating a diagnostic report file for the target enterprise based on the first enterprise information, the content of the answers, and the diagnostic opinion for the target enterprise.
The method according to claim 5, wherein the generating the diagnostic report file for the target enterprise based on the first enterprise information, the content of the answers, and the diagnostic opinion for the target enterprise comprises:

inputting the first enterprise information, the content of the answers, and the diagnostic opinion for the target enterprise into a preset diagnostic report generating model to generate the diagnostic report file.
An apparatus (300) for data processing, comprising:

an acquiring module (301) , configured to acquire a first enterprise information of a target enterprise;

an analyzing module (302) , configured to analyze a content of answers to a questionnaire obtained for the target enterprise, to obtain a first assessment result of the target enterprise of at least one intelligent manufacturing capability component, wherein the first assessment result is used for indicating a capability intensity of the target enterprise of the intelligent manufacturing capability component; and

a determining module (303) , configured to determine a diagnostic opinion for the target enterprise from a pre-established database based on the first enterprise information and the first assessment result, wherein the diagnostic opinion is used for indicating at least one of an advantage, a disadvantage, or an upgrade recommendation of the target enterprise in a current intelligent manufacturing capability.
A method for data processing, characterizing in that the method comprising:

acquiring enterprise information of a target enterprise, wherein the enterprise information of the target enterprise includes at least one of type, scale, location, staff and output of the target enterprise;

obtaining an assessment result of the target enterprise of at least one manufacturing capability component based on a questionnaire for the target enterprise, wherein the at least one manufacturing capability is obtained based on the industrial standard associated with the target enterprise;

calculating a similarity score between the target enterprise and reference enterprises in a preset database based on the enterprise information and the assessment result;

generating a diagnostic result of the target enterprise based on a historical diagnostic report of a reference enterprise in the preset database when the similarity score between the target enterprise and the reference enterprise is greater than a preset threshold, wherein the preset database includes historical diagnostic reports of the reference enterprises; and

generating a diagnostic report of the target enterprise based on the enterprise information, assessment result of the at least one manufacturing capability component, and diagnostic result of the target enterprise.
The method according to claim 8, characterizing in that the preset database includes expert diagnostic opinions corresponding to searchable keywords, the method further comprising: obtaining an expert diagnostic opinion based on a keyword input by a user when no reference enterprise exists in the preset database whose similarity score with the target enterprise is greater than the preset threshold.
The method according to claim 9, characterizing in that the generating the diagnostic result of the target enterprise based on a diagnostic report of a reference enterprise in the preset database, further comprising:

obtaining the expert diagnostic opinion based on the search keyword input by a user; and

generating the diagnostic result of the target enterprise based on the expert diagnostic opinion and historical diagnostic report of the reference enterprise in the preset database.
The method according to claims 9 or 10, characterizing in that the method comprising: receiving description of expert diagnosis and perform NLP data analysis with respect to the description of expert diagnosis, and obtaining expert diagnostic opinions corresponding to the search keyword input by the user.
The method according to claim 8, characterizing in that the method comprising: Obtaining an industrial maturity model based on the industrial standard associated with the target enterprise, and generating the questionnaire based on the industrial maturity model.
An apparatus (600) for data processing, comprising:

an enterprise-information acquiring module (601) , configured to acquire enterprise information of a target enterprise, wherein the enterprise information of the target enterprise includes at least one of type, scale, location, staff and output of the target enterprise;

an assessment result obtaining module (602) , configured to obtain an assessment result of the target enterprise of at least one manufacturing capability component based on a questionnaire for the target enterprise, wherein the at least one manufacturing capability is obtained based on the industrial standard associated with the target enterprise;

a similarity calculation module (603) , configured to calculate a similarity score between the target enterprise and reference enterprises in a preset database based on the enterprise information and the assessment result;

an diagnostic result generating module (604) , configured to generate a diagnostic result of the target enterprise based on a historical diagnostic report of a reference enterprise in the preset database when the similarity score between the target enterprise and the reference enterprise is greater than a preset threshold, wherein the preset database includes historical diagnostic reports of the reference enterprises; and

a diagnostic report generating module (605) , configured to generating a diagnostic report of the target enterprise based on the enterprise information, assessment result of the at least one manufacturing capability component, and diagnostic result of the target enterprise.
An electronic device (700) , comprising: a processor (702) , a communication interface (704) , a memory (706) , and a bus (708) , wherein the processor (702) , the communication interface (704) and the memory (706) complete communication with each other through the bus 708) ; and

the memory (706) is configured to store at least one executable instruction, wherein the executable instruction causes the processor (702) to execute operations corresponding to the method according to any one of claims 1-6 or 8-12.
A computer readable storage medium, storing computer instructions thereon, wherein the computer instructions, when executed by a processor, cause the processor to execute the method according to any one of claims 1-6 or 8-12.
A computer program product, being tangibly stored on a computer readable medium and comprising computer executable instructions, wherein the computer executable instructions, when executed, cause at least one processor to execute the method according to any one of claims 1-6 or 8-12.