Data processing method, device, electronic device and storage medium

Abstract

The embodiment of the present application provides a data processing method, device, electronic device and storage medium. The method includes: obtaining first enterprise information of a target enterprise; analyzing the questionnaire answers obtained in the questionnaire for the target enterprise to obtain a first evaluation result of the target enterprise in at least one intelligent manufacturing capability dimension, wherein the first evaluation result is used to indicate the strength of the target enterprise in the corresponding intelligent manufacturing capability dimension; according to the first enterprise information and at least one first evaluation result, determining a diagnostic opinion for the target enterprise from a pre-established database, wherein the diagnostic opinion is used to indicate at least one of the advantages, defects and upgrade suggestions in the current intelligent manufacturing capability of the target enterprise.

Description

Technical Field

[0001] The present application relates to the field of computer technology, and in particular to a data processing method, device, electronic device and storage medium. Background Art

[0002] Nowadays, more and more companies are choosing to upgrade digitally. In order to make the digital upgrade of enterprises more targeted, the first step is generally to evaluate and diagnose the current intelligent manufacturing capabilities of the enterprise and obtain improvement suggestions.

[0003] In related technologies, one way to evaluate and diagnose the current intelligent manufacturing capabilities of an enterprise is to rely on expert surveys. Experts can understand the current status of the enterprise by visiting the enterprise on site, and then provide a diagnostic report and improvement suggestions on the current intelligent manufacturing capabilities of the enterprise. However, this method is relatively inefficient and is easily affected by external factors such as time, space, and epidemics. Therefore, a new technical solution is needed to improve the efficiency of evaluating and diagnosing the current intelligent manufacturing capabilities of enterprises. Summary of the invention

[0004] In order to at least partially solve the above technical problems, the embodiments of the present application provide a data processing method, device, electronic device and storage medium.

[0005] According to a first aspect of the embodiments of the present application, the embodiments of the present application provide a data processing method, which includes:

[0006] Obtain the first enterprise information of the target enterprise;

[0007] Analyze the answers to the questionnaires for the target enterprise to obtain a first evaluation result of the target enterprise in at least one intelligent manufacturing capability dimension, wherein the first evaluation result is used to indicate the strength of the target enterprise in the corresponding intelligent manufacturing capability dimension;

[0008] Based on the first enterprise information and at least one first evaluation result, a diagnostic opinion for the target enterprise is determined from a pre-established database, wherein the diagnostic opinion is used to indicate at least one of the advantages, disadvantages and upgrade suggestions in the current intelligent manufacturing capabilities of the target enterprise.

[0009] According to a second aspect of the embodiments of the present application, the embodiments of the present application provide a data processing device, including:

[0010] An acquisition module, used to acquire first enterprise information of a target enterprise;

[0011] An analysis module, used to analyze the answers to the questionnaires obtained from the target enterprise, and obtain a first evaluation result of the target enterprise in at least one intelligent manufacturing capability dimension, wherein the first evaluation result is used to indicate the strength of the target enterprise in the corresponding intelligent manufacturing capability dimension;

[0012] A determination module is used to determine a diagnostic opinion for a target enterprise from a pre-established database based on first enterprise information and at least one first evaluation result, wherein the diagnostic opinion is used to indicate at least one of the advantages, defects and upgrade suggestions in the current intelligent manufacturing capabilities of the target enterprise.

[0013] According to a third aspect of the embodiments of the present application, the embodiments of the present application provide a data processing method, including:

[0014] Acquire enterprise information of the target enterprise, wherein the enterprise information includes at least one of the following: type, scale, location, employees, and output;

[0015] Obtaining, based on the questionnaire of the target enterprise, an evaluation result of the target enterprise in at least one manufacturing capability dimension, wherein the at least one manufacturing capability dimension is obtained based on the industry standard of the target enterprise;

[0016] Calculate the similarity between the target enterprise and the template enterprise in a preset database according to the enterprise information and the evaluation result;

[0017] If there is a template enterprise in the preset database whose similarity with the target enterprise is greater than a preset threshold, a diagnosis result of the target enterprise is generated according to the diagnosis and evaluation report of the template enterprise, wherein the preset database includes: a historical diagnosis and evaluation report of the template enterprise;

[0018] A diagnostic assessment report for the target enterprise is generated based on the enterprise information of the target enterprise, the assessment result of the target enterprise in at least one manufacturing capability dimension, and the diagnosis result of the target enterprise.

[0019] According to a fourth aspect of the embodiments of the present application, the embodiments of the present application provide a data processing device, including:

[0020] An enterprise information acquisition module, used to acquire enterprise information of a target enterprise, wherein the enterprise information includes at least one of the following: type, scale, location, employees, and output;

[0021] An evaluation result acquisition module, used to obtain an evaluation result of the target enterprise in at least one manufacturing capability dimension according to the questionnaire of the target enterprise, wherein the at least one manufacturing capability dimension is obtained according to the industry standard of the target enterprise;

[0022] A similarity calculation module, used to calculate the similarity between the target enterprise and the template enterprise in the preset database according to the enterprise information and the evaluation result;

[0023] A diagnosis result generating module, for generating a diagnosis result of the target enterprise according to the diagnosis evaluation report of the template enterprise if there is a template enterprise in the preset database whose similarity with the target enterprise is greater than a preset threshold, wherein the preset database includes: a historical diagnosis evaluation report of the template enterprise;

[0024] The evaluation report generation module is used to generate a diagnostic evaluation report for the target enterprise based on the enterprise information of the target enterprise, the evaluation results of the target enterprise in at least one manufacturing capability dimension, and the diagnosis results of the target enterprise.

[0025] According to the fifth aspect of the embodiments of the present application, the embodiments of the present application provide an electronic device, including: a processor, a communication interface, a memory and a communication bus, the processor, the communication interface and the memory communicate with each other through the communication bus; the memory is used to store at least one executable instruction, and the executable instruction enables the processor to perform the operation corresponding to any one of the data processing methods provided in the first aspect above.

[0026] According to the sixth aspect of the embodiments of the present application, the embodiments of the present application provide a computer-readable storage medium, on which computer instructions are stored. When the computer instructions are executed by a processor, the processor executes any one of the data processing methods provided in the first aspect above.

[0027] According to the seventh aspect of the embodiments of the present application, the embodiments of the present application provide a computer program product, which is tangibly stored on a computer-readable medium and includes computer-executable instructions, which, when executed, enable at least one processor to perform any one of the data processing methods provided in the first aspect above.

[0028] The data processing method of the embodiment of the present application can obtain the first enterprise information of the target enterprise, analyze the questionnaire answers obtained in the questionnaire for the target enterprise, and obtain the first evaluation result of the target enterprise in at least one intelligent manufacturing capability dimension. The first evaluation result is used to indicate the strength of the target enterprise in the corresponding intelligent manufacturing capability dimension. Finally, based on the first enterprise information and at least one first evaluation result, a diagnostic opinion for the target enterprise is determined from a pre-established database. The diagnostic opinion is used to indicate at least one of the advantages, defects and upgrade suggestions in the current intelligent manufacturing capabilities of the target enterprise. Therefore, through the data processing method in the present application, the staff can enter the first enterprise information and the questionnaire answers, and then accurately and quickly obtain the diagnostic opinion for the target enterprise. In this way, when evaluating and diagnosing the current intelligent manufacturing capabilities of the target enterprise, there is no need to rely on experts to give diagnostic reports and improvement suggestions for the target enterprise, and it is not easily limited by external factors such as time, space, and epidemics. It can effectively improve the efficiency of evaluating and diagnosing the current intelligent manufacturing capabilities of the enterprise, and give reasonable diagnostic suggestions, so that the target enterprise can refer to the diagnostic suggestions to carry out the digital upgrade of the target enterprise, and better meet the diagnostic needs of the target enterprise. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] The following drawings are only intended to illustrate and explain the present application, and do not limit the scope of the present application.

[0030] Figure 1 An optional flowchart of the data processing method provided in the first aspect of an embodiment of the present application is shown.

[0031] Figure 2 Another optional flowchart of the data processing method provided in the first aspect of an embodiment of the present application is shown.

[0032] Figure 3 An optional block diagram of a data processing device provided in the second aspect of an embodiment of the present application is shown.

[0033] Figure 4 An optional flowchart of the data processing method provided in the third aspect of an embodiment of the present application is shown.

[0034] Figure 5 Another optional flowchart of the data processing method provided in the third aspect of an embodiment of the present application is shown.

[0035] Figure 6 An optional block diagram of a data processing device provided in the fourth aspect of an embodiment of the present application is shown.

[0036] Figure 7 An optional structural schematic diagram of an electronic device according to an embodiment of the present application is shown. DETAILED DESCRIPTION

[0037] In order to enable those skilled in the art to better understand the technical solutions in the embodiments of the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments in the embodiments of the present application should fall within the scope of protection of the embodiments of the present application.

[0038] It should be noted that the data processing method in the embodiments of the present application can be executed by a data processing device, which can be a computer device capable of performing data processing. The computer device can include one or more processing units, such as a CPU, MCU, PLC, etc. Alternatively, the data processing device can also execute the data processing method based on a cloud system, an edge computing system, a software platform, etc. It should be understood that there is no limitation on this in the embodiments of the present application.

[0039] Figure 1 is an optional flow chart of the data processing method provided by the first aspect of the embodiment of the present application. According to the first aspect of the embodiment of the present application, refer to Figure 1 The data processing method provided in the embodiment of the present application includes the following steps S101, S102 and S103:

[0040] Step S101: Acquire the first enterprise information of the target enterprise.

[0041] In the present application, the first enterprise information of the target enterprise may include at least one of the target enterprise's name, industry, nature of the enterprise, location, company profile, company products, personnel composition information, production and operation information, etc., which are all basic information related to the target enterprise.

[0042] The specific source of the first enterprise information of the target enterprise is not limited in this application. For example, in some embodiments, the first enterprise information can be manually entered into a preset enterprise information template by a staff member (who can be a staff member performing a diagnosis task, or a staff member of the target enterprise, etc.), and the data processing device obtains the first enterprise information through the entered enterprise information template, which is not limited here.

[0043] Taking the example of obtaining the target enterprise's enterprise information through an enterprise information template, the content of the enterprise information template can be set as needed, and after setting, it can be stored in a template database for easy access. Taking the data processing device as an example of a computer, a staff member (who can be a staff member performing a diagnostic task, or an enterprise staff member) can select a suitable enterprise information template from a template database storing multiple enterprise information templates as needed, and the staff member fills the corresponding first enterprise information into the enterprise information template through the UI interface of the computer (i.e., the data processing device). The data processing device of the present application can obtain the first enterprise information of the target enterprise through the filled-in enterprise information template. Generally speaking, the more detailed and rich the first enterprise information is, the better, and it is more conducive to the subsequent determination of the diagnostic opinion of the target enterprise.

[0044] By having the staff select an enterprise information template from multiple enterprise information templates and enter the first enterprise information, the first enterprise information of the target enterprise can be obtained more specifically in this application, so that the diagnostic opinions on the target enterprise subsequently obtained by the data processing method of this application are more targeted and reliable, which is convenient to meet the different needs of different enterprises.

[0045] Step S102: Analyze the answers to the questionnaires for the target enterprise to obtain a first evaluation result of the target enterprise in at least one intelligent manufacturing capability dimension, wherein the first evaluation result is used to indicate the strength of the target enterprise in the corresponding intelligent manufacturing capability dimension.

[0046] Optionally, the content of the questionnaire in this application (such as questions, options, etc.) can be set in combination with a standard intelligent manufacturing capability maturity model. Optionally, the content of the questionnaire can involve one or more current intelligent manufacturing capability dimensions of the enterprise.

[0047] In this application, intelligent manufacturing capability refers to the degree to which an enterprise manages, improves and comprehensively applies personnel, technology, resources, manufacturing, etc. in order to achieve the goal of intelligent manufacturing. Optionally, a capability subdomain can be used as a dimension of intelligent manufacturing capability, and different capability subdomains can be used as different dimensions of intelligent manufacturing capability. Capability subdomains include: organizational strategy, personnel skills, data, integration, information security, equipment, network, 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, product services. For details, please refer to the national standard "GB / T39116-2020 Intelligent Manufacturing Capability Maturity Model".

[0048] The content of the questionnaire can be set as needed (for example, different questionnaire contents can be set according to the industry in which different enterprises are located), and after setting, it can be stored in a template database for easy access. For example, a preset questionnaire template can be selected as a questionnaire. The questionnaire template is built based on the standard intelligent manufacturing capability maturity model, and the content of the questionnaire can be set and modified according to the different industries in which the target enterprises are located.

[0049] Optionally, the data processing method of the present application may further include: in response to a selection instruction for selecting a questionnaire template from a plurality of preset questionnaire templates, obtaining the questionnaire template from a template database, and using the questionnaire template as the questionnaire.

[0050] Taking the data processing device as an example of a computer, a staff member (who may be a staff member performing a diagnostic task or an enterprise staff member) can select a suitable questionnaire template from multiple questionnaire templates as a questionnaire as needed, and then fill in the questionnaire template. The staff member can fill the corresponding questionnaire answer content into the questionnaire template through the UI interface of the computer (i.e., the data processing device). The data processing device of this application can obtain the questionnaire answer content through the filled-in questionnaire template. Generally speaking, the more detailed and rich the questionnaire answer content is, the better it is. The more detailed and rich the questionnaire answer content is, the more conducive it is to the subsequent determination of the diagnostic opinions of the target enterprise.

[0051] Obviously, by having the staff select a questionnaire template from multiple questionnaire templates as the questionnaire and enter the questionnaire answers, the present application can more specifically evaluate the target enterprise's capabilities in the corresponding intelligent manufacturing capability dimension, thereby obtaining a reasonable first evaluation result, so that the diagnostic opinions on the target enterprise subsequently obtained by the data processing method of the present application are more targeted and reliable, and more convenient to meet the different needs of different enterprises.

[0052] In the present application, the first evaluation result may include a score of the target enterprise in at least one intelligent manufacturing capability dimension obtained by analyzing the questionnaire answers, which can effectively indicate the target enterprise's capability strength in the corresponding intelligent manufacturing capability dimension. Based on this, in some optional embodiments, step S102 specifically includes: analyzing the questionnaire answers based on a preset intelligent manufacturing capability maturity model to obtain a first evaluation result of the target enterprise in at least one intelligent manufacturing capability dimension.

[0053] Analysis based on the intelligent manufacturing capability maturity model is a mature technology based on national standards. For details, please refer to the national standards "GB / T39116-2020 Intelligent Manufacturing Capability Maturity Model" and "GB / T39117-2020 Intelligent Manufacturing Capability Maturity Evaluation Method", which will not be elaborated here. Optionally, for the intelligent manufacturing capability maturity model, each capability subdomain can be used as an intelligent manufacturing capability dimension, and different capability subdomains can be used as different intelligent manufacturing capability dimensions. This application analyzes the obtained questionnaire answers through a preset intelligent manufacturing capability maturity model, and can more objectively obtain the first evaluation result of the target enterprise in at least one intelligent manufacturing capability dimension.

[0054] Optionally, the aforementioned enterprise information template may be a part of a questionnaire template, that is, by filling in the selected questionnaire template (i.e., questionnaire), the enterprise information of the target enterprise and the questionnaire answer content may be obtained at the same time, so as to facilitate subsequent data processing. This application does not impose any restrictions here.

[0055] Step S103: Determine a diagnosis opinion for the target enterprise from a pre-established database according to the first enterprise information and at least one first evaluation result.

[0056] In this application, the diagnostic opinion can be used to indicate at least one of the advantages, defects and upgrade suggestions of the current intelligent manufacturing capabilities of the target enterprise, and a reasonable diagnostic opinion can provide a reliable reference for the digital upgrade of the target enterprise. Specifically, the diagnostic opinion can include at least one of the following contents: enterprise research summary, intelligent transformation suggestions, intelligent transformation implementation plan suggestions, intelligent transformation benefit analysis, etc.

[0057] The data processing method in the present application can obtain the first enterprise information of the target enterprise, analyze the questionnaire answers obtained in the questionnaire for the target enterprise, and obtain the first evaluation result of the target enterprise in at least one intelligent manufacturing capability dimension. The first evaluation result is used to indicate the strength of the target enterprise in the corresponding intelligent manufacturing capability dimension. Finally, based on the first enterprise information and at least one first evaluation result, a diagnostic opinion for the target enterprise is determined from a pre-established database. The diagnostic opinion is used to indicate at least one of the advantages, defects and upgrade suggestions in the current intelligent manufacturing capabilities of the target enterprise. Therefore, through the data processing method in the present application, the staff can enter the first enterprise information and the questionnaire answers, and then accurately and quickly obtain the diagnostic opinion for the target enterprise. In this way, when evaluating and diagnosing the current intelligent manufacturing capabilities of the target enterprise, there is no need to rely on experts to give diagnostic reports and improvement suggestions for the target enterprise, and it is not easily limited by external factors such as time, space, and epidemics. It can effectively improve the efficiency of evaluating and diagnosing the current intelligent manufacturing capabilities of the enterprise, and give reasonable diagnostic suggestions, so that the target enterprise can refer to the diagnostic suggestions to carry out the digital upgrade of the target enterprise, and better meet the diagnostic needs of the target enterprise.

[0058] The specific implementation of S103 is not limited in this application, as long as it can meet the needs. In some optional embodiments, the database includes a knowledge graph library, and the knowledge graph library pre-stores multiple knowledge graphs, and the multiple knowledge graphs are generated based on different historical diagnostic reports. One or more historical diagnostic reports in each historical diagnostic report are for the same enterprise, and the historical diagnostic report includes the second enterprise information of the enterprise it is targeted at, the second evaluation result in at least one intelligent manufacturing capability dimension, and the historical diagnostic opinion for the enterprise; on this basis, step S103 specifically includes: according to the first enterprise information and at least one first evaluation result, determine at least one knowledge graph from the knowledge graph library, and determine the diagnostic opinion for the target enterprise according to the historical diagnostic report corresponding to the at least one knowledge graph.

[0059] The historical diagnostic report may be a diagnostic report obtained before the current data processing method is executed, and the historical diagnostic report includes but is not limited to diagnostic reports previously provided by experts, diagnostic report files previously obtained by data processing methods, etc. These historical diagnostic reports may be historical diagnostic reports obtained for different enterprises, each historical diagnostic report may be for a different enterprise, multiple historical diagnostic reports may be for one enterprise, and so on. For example, taking a knowledge graph generated by a total of 4 historical diagnostic reports (respectively recorded as reports 1, 2, 3, and 4) as an example, reports 1, 2, 3, and 4 may be for 4 different enterprises respectively; or, report 1 is for enterprise 1, reports 2 and 3 are for enterprise 2, and report 4 is for enterprise 3; or, reports 1 and 2 are for enterprise 1, reports 3 and 4 are for enterprise 2, and the rest may be deduced in the same way, without limitation here. It should be understood that the enterprises targeted by the historical diagnostic reports may also include target enterprises.

[0060] The present application determines at least one knowledge graph generated based on historical diagnostic reports from a knowledge graph library according to first enterprise information and at least one evaluation result, and determines a diagnostic opinion for the target enterprise according to the historical diagnostic report corresponding to the at least one knowledge graph. An effective diagnostic opinion for the target enterprise can be determined by referring to previous historical diagnostic reports to meet the diagnostic needs of the target enterprise.

[0061] In addition, compared with simply calculating the intelligent manufacturing capability level of the target enterprise and giving a formatted report, the present application determines the diagnostic opinions for the target enterprise through a knowledge graph, which makes it easier to concretize the diagnostic opinions and reduces the burden of subsequent generation of diagnostic opinion report files.

[0062] In the present application, the knowledge graph is generated based on historical diagnostic opinions. Optionally, the knowledge graph can be generated in the following manner: extract multiple entity objects, the relationship between multiple entity objects, and multiple diagnostic opinion text segments from the text of the historical diagnostic report; generate the knowledge graph based on multiple entity objects, the relationship between multiple entity objects, and multiple diagnostic opinion text segments. In the present application, the text of the historical diagnostic report can be segmented and semantically analyzed by a natural language processing algorithm to extract multiple entity objects, the relationship between multiple entity objects, and multiple diagnostic opinion text segments. Entity objects can exist as entities when constituting a knowledge graph, and can be some objective and mutually distinguishable physical objects or abstract concepts or connections of an enterprise, such as the name, location, products, employees, etc. of the enterprise. The relationship between multiple entity objects can represent a certain connection between two entity objects, and can exist as an edge when constituting a knowledge graph. For example, if two entity objects are "XX Company" and "Zhang San", the relationship between the two entity objects can be "General Manager", that is, the general manager of XX Company is Zhang San; for another example, if the entity objects are "XX Company" and "Beijing", the relationship between the two entity objects can be "Headquarters Location", that is, the headquarters location of XX Company is Beijing; and so on. The diagnostic opinion text segment can be a text segment that records specific diagnostic opinions in the text of the historical diagnostic report, such as a text segment of enterprise research summary, intelligent transformation suggestions, intelligent transformation implementation plan suggestions, intelligent transformation benefit analysis, etc. Different diagnostic opinion text segments are classified and associated with entity objects and relationships. A knowledge graph is generated based on multiple entity objects, the relationships between multiple entity objects, and multiple diagnostic opinion text segments and stored in the knowledge graph library.

[0063] It should be understood that when analyzing, for different words representing the same entity object, words with similar semantics can be unified into one word during analysis and extraction. For example, Beijing, BEIJING, and China's capital all have the same actual meaning, so they can be unified into Beijing. It should be understood that this example does not serve as any limitation to the present application.

[0064] In some optional embodiments, "based on the first enterprise information and at least one first evaluation result, at least one knowledge graph is determined from the knowledge graph library, and based on the historical diagnosis report corresponding to the at least one knowledge graph, a diagnostic opinion for the target enterprise is determined", including: for each knowledge graph in the knowledge graph library, a first similarity between the first enterprise information and the second enterprise information of the historical diagnosis report corresponding to the knowledge graph, and a second similarity between the first evaluation result and at least one second evaluation result of the historical diagnosis report corresponding to the knowledge graph are calculated respectively, and a third similarity is determined based on the first similarity and the second similarity, the third similarity being used to indicate the degree of similarity between the target enterprise and the enterprise targeted by the historical diagnosis report corresponding to the knowledge graph; determining whether there is a knowledge graph in the knowledge graph library whose third similarity is greater than a preset similarity threshold; wherein, if there is a knowledge graph whose similarity is greater than the preset similarity threshold, a diagnostic opinion for the target enterprise is determined based on the historical diagnosis report corresponding to the knowledge graph with the greatest similarity.

[0065] Optionally, the first similarity, the second similarity, and the third similarity can be calculated by a preset similarity algorithm. The first similarity and the second similarity can reflect the 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 employees, enterprise output, and evaluation results (i.e., the evaluation results can be used as an attribute alone), etc. By analyzing the third similarity, it can be comprehensively judged whether the target enterprise and the enterprise targeted by the historical diagnostic report corresponding to the knowledge graph are the same type of enterprises. Specifically, when the third similarity is greater than the preset similarity threshold, it means that there is a knowledge graph generated based on the historical diagnostic report of the same type of enterprise as the target enterprise in the knowledge graph library. Therefore, the diagnostic opinion for the target enterprise can be determined based on the historical diagnostic report corresponding to the knowledge graph with the highest third similarity. Optionally, the similarity algorithm can use the Jaccard similarity algorithm.

[0066] Optionally, the third similarity may be obtained by weighted summing the first similarity and the second similarity according to a predetermined weight parameter. The weight parameter may be set as required, and the present application does not limit this.

[0067] In the present application, the diagnostic opinion for the target enterprise is determined based on the historical diagnostic report corresponding to the knowledge graph with the greatest 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, making the determination of the diagnostic opinion for the target enterprise more efficient, and the diagnostic opinions that can be reused in the historical diagnostic reports do not need to be given repeatedly, which improves the utilization rate of historical diagnostic reports while being able to accurately determine the diagnostic opinions for the target enterprise.

[0068] In this application, according to the historical diagnostic report corresponding to the knowledge graph with the largest third similarity, the diagnostic opinion for the target enterprise is determined. It can be that the diagnostic opinion text segment in the historical diagnostic report is extracted according to the knowledge graph, and then the diagnostic opinion for the target enterprise is determined according to the extracted diagnostic opinion text segment. For example, at least part of the extracted diagnostic opinion text segment can be used as part of the diagnostic opinion for the target enterprise. For another example, at least part of the extracted diagnostic opinion text segment can be used as at least one recommended diagnostic opinion, and then the data processing device responds to the selection instruction for selecting at least one recommended diagnostic opinion, and determines the diagnostic opinion for the target enterprise from at least one recommended diagnostic opinion. Optionally, after extracting the diagnostic opinion text segment, the diagnostic opinion text segment can be modified as needed to obtain the diagnostic opinion for the target enterprise later. Optionally, the historical diagnostic report can also be used as a report template afterwards, and the text of the diagnostic opinion of the target enterprise is filled into the report template.

[0069] The preset similarity threshold may be different for different target enterprises and can be set as needed, and is not particularly limited here.

[0070] In some optional embodiments, "determine at least one knowledge graph from the knowledge graph library based on the first enterprise information and at least one first evaluation result, and determine the diagnostic opinion for the target enterprise based on the historical diagnostic report corresponding to the at least one knowledge graph", and also include: if there is no knowledge graph with a third similarity greater than a preset similarity threshold, search the knowledge graph library based on at least one keyword to determine at least one knowledge graph that matches each keyword, and obtain at least one diagnostic opinion text segment that matches the keyword based on the at least one knowledge graph obtained; determine the diagnostic opinion for the target enterprise based on the extracted at least one diagnostic opinion text segment.

[0071] In this way, the present application can quickly determine the diagnostic opinion for the target enterprise based on the historical diagnostic report corresponding to the knowledge graph, and use the historical diagnostic report as a reference to determine the diagnostic opinion for the target enterprise, which makes the determination of the diagnostic opinion for the target enterprise more efficient, and the diagnostic opinions that can be reused in the historical diagnostic report do not need to be given repeatedly. On the basis of being able to accurately determine the diagnostic opinions for the target enterprise, the utilization rate of the historical diagnostic report is improved.

[0072] To determine whether a keyword matches a knowledge graph, the knowledge graph may be searched to see whether it includes words that are semantically similar or semantically identical to the keyword. If so, the keyword is determined to match the knowledge graph. Exemplarily, the semantic similarity between two words may be calculated according to a predetermined algorithm. If the semantic similarity is greater than a predetermined value (which may be set as needed, such as 80%, 90%, etc.), the words are considered to be semantically similar. If the semantic similarity is the same, the words are considered to be semantically identical. It should be understood that this is merely an example and does not constitute any limitation to the present application.

[0073] The diagnostic opinion text segment that matches the keyword can be a diagnostic opinion text segment that contains words that are semantically close to or semantically identical to the keyword. The diagnostic opinion text segment is extracted and then used as a reference to determine the diagnostic opinion for the target enterprise. For example, at least part of the extracted diagnostic opinion text segment can be used as part of the diagnostic opinion for the target enterprise. For another example, at least part of the extracted diagnostic opinion text segment can be used as at least one recommended diagnostic opinion, and then the data processing device responds to a selection instruction for selecting at least one recommended diagnostic opinion, and determines the diagnostic opinion for the target enterprise from at least one recommended diagnostic opinion. Optionally, after extracting the diagnostic opinion text segment, the diagnostic opinion text segment can be modified as needed to facilitate obtaining the diagnostic opinion for the target enterprise later.

[0074] Optionally, a matching knowledge graph can be searched from a knowledge graph library according to a keyword by means of an ELK search, and at least one diagnostic opinion text segment matching the keyword can be obtained according to the obtained at least one knowledge graph. Alternatively, it can be implemented by other search algorithms, which are not limited here.

[0075] In some optional embodiments, referring to Figure 2 The data processing method in the present application further includes: Step S104: generating a diagnostic report file for the target enterprise based on the first enterprise information, the questionnaire answer content and the diagnostic opinion for the target enterprise.

[0076] Specifically, in this application, the first enterprise information, questionnaire, questionnaire answer content, diagnostic opinions on the target enterprise and other information can be summarized and analyzed to generate a customized diagnostic report file for the target enterprise. Enterprise staff can view the diagnostic results of the target enterprise's current intelligent manufacturing capabilities through the diagnostic report file, thereby providing the target enterprise's staff with a reasonable reference for the digital upgrade of the target enterprise.

[0077] In the present application, a diagnostic report file for the target enterprise may be generated in any manner according to the first enterprise information, the questionnaire answer content, and the diagnostic opinion for the target enterprise. The specific generation method is not limited here. For example, in some embodiments, step S104 may be inputting the first enterprise information, the questionnaire answer content, and the diagnostic opinion for the target enterprise into a preset diagnostic report generation model to generate a diagnostic report file. The diagnostic report file is automatically generated by the preset diagnostic report generation model, which improves the accuracy and efficiency of generating the diagnostic report file.

[0078] The file format of the diagnosis report text is not limited in this application and can be selected as needed. For example, the file format of the diagnosis report file can be a word file (such as doc, docx, etc.) or a pdf file, and in other embodiments, it can also be an excel, PPT, txt file, etc.

[0079] The diagnostic report file in this application allows modification according to the modification instructions of the staff. Specifically, the data processing method may also include: in response to the user's modification instructions, adjusting the recorded content in the diagnostic report file, and updating the diagnostic report file. The modification instructions can be for any content in the diagnostic report file, and are not limited here, as long as they can meet the requirements. The recorded content in the diagnostic report file allows modification, so that the diagnostic report file finally generated is more conducive to meeting the needs of the target enterprise's digital upgrade.

[0080] Optionally, while generating the diagnostic report file, it is also possible to output the evaluation results of the target enterprise's current intelligent manufacturing capability maturity level and the predicted results of its future intelligent manufacturing capability maturity level (specifically, the intelligent manufacturing capability maturity level can be divided into five levels, among which the first level is the planning level, the second level is the specification level, the third level is the integration level, the fourth level is the optimization level, and the fifth level is the leading level. For details, please refer to the national standard "GB / T39117-2020 Intelligent Manufacturing Capability Maturity Assessment Method"), which can provide reference for the target enterprise's digital upgrade.

[0081] It should be understood that the above contents are merely some exemplary explanations of the data processing method in the embodiments of the present application, and are not intended to be any limitation to the embodiments of the present application.

[0082] Figure 3 An optional block diagram of a data processing device provided in the second aspect of an embodiment of the present application is shown. Figure 3 The second aspect of the present application provides a data processing device 300, which includes:

[0083] An acquisition module 301 is used to acquire first enterprise information of a target enterprise;

[0084] An analysis module 302 is used to analyze the answers to the questionnaires for the target enterprise to obtain a first evaluation result of the target enterprise in at least one intelligent manufacturing capability dimension, wherein the first evaluation result is used to indicate the strength of the target enterprise in the corresponding intelligent manufacturing capability dimension;

[0085] The determination module 303 is used to determine a diagnostic opinion for the target enterprise from a pre-established database based on the first enterprise information and the at least one first evaluation result, wherein the diagnostic opinion is used to indicate at least one of the advantages, disadvantages and upgrade suggestions in the current intelligent manufacturing capabilities of the target enterprise.

[0086] The data processing device 300 of the present application and the data processing method provided in the first aspect are based on the same inventive concept, that is, the data processing device mentioned in the data processing method provided in the first aspect. The relevant content can be understood by referring to the various embodiments of the above-mentioned data processing method and will not be elaborated here.

[0087] According to the third aspect of the embodiment of the present application, the embodiment of the present application provides a data processing method, see Figure 4 , methods include:

[0088] Step S401: Obtain the enterprise information of the target enterprise.

[0089] Specifically, the enterprise information includes at least one of the following: type, size, location, employees, and output.

[0090] The specific source of the target enterprise's enterprise information is not limited in this application. For example, in some embodiments, the enterprise information can be manually entered into a preset enterprise information template by a staff member (who can be a staff member performing a diagnostic task, or a staff member of the target enterprise, etc.), and the data processing device obtains the entered enterprise information template, which is not limited here.

[0091] Taking the example of obtaining the target enterprise's enterprise information through an enterprise information template, the content of the enterprise information template can be set as needed, and after setting, it can be stored in a template database for easy access. Taking the data processing device as an example of a computer, a staff member (who can be a staff member performing a diagnostic task, or an enterprise staff member) can select a suitable enterprise information template from a template database storing multiple enterprise information templates as needed, and the staff member fills the corresponding enterprise information into the enterprise information template through the UI interface of the computer (i.e., the data processing device). The data processing device of this application can obtain the target enterprise's enterprise information through the filled-in enterprise information template. Generally speaking, the more detailed and rich the enterprise information is, the better, and it is more conducive to the subsequent determination of the diagnostic opinion of the target enterprise.

[0092] By having staff select an enterprise information template from multiple enterprise information templates and enter the enterprise information, the enterprise information of the target enterprise can be obtained more specifically in this application, so that the diagnostic opinions on the target enterprise subsequently obtained by the data processing method of this application are more targeted and reliable, which is convenient for meeting the different needs of different enterprises.

[0093] Step S402: obtaining an evaluation result of the target enterprise in at least one manufacturing capability dimension according to the questionnaire of the target enterprise, wherein the at least one manufacturing capability dimension is obtained according to the industry standard of the target enterprise.

[0094] Optionally, the content of the questionnaire in this application (such as questions, options, etc.) can be set in combination with a standard intelligent manufacturing capability maturity model. Optionally, the content of the questionnaire can involve one or more current manufacturing capability dimensions of the enterprise.

[0095] For target enterprises, in addition to industry standards, national standards can also be used as industry standards, and there is no restriction here.

[0096] In this embodiment, optionally, the manufacturing capability may be an intelligent manufacturing capability, and the manufacturing capability dimension may be an intelligent manufacturing capability dimension.

[0097] In this application, intelligent manufacturing capability refers to the degree to which an enterprise manages, improves and comprehensively applies personnel, technology, resources, manufacturing, etc. in order to achieve the goal of intelligent manufacturing. Optionally, a capability subdomain can be used as a manufacturing capability dimension, and different capability subdomains can be used as different manufacturing capability dimensions. Capability subdomains include: organizational strategy, personnel skills, data, integration, information security, equipment, network, 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, product services. For details, please refer to the national standard "GB / T39116-2020 Intelligent Manufacturing Capability Maturity Model".

[0098] The content of the questionnaire can be set as needed (for example, different questionnaire contents can be set according to the industry in which different enterprises are located), and after setting, it can be stored in a template database for easy access. For example, a preset questionnaire template can be selected as a questionnaire. The questionnaire template is built based on the standard intelligent manufacturing capability maturity model, and the content of the questionnaire can be set and modified according to the different industries in which the target enterprises are located.

[0099] Optionally, the data processing method of the present application may further include: in response to a selection instruction for selecting a questionnaire template from a plurality of preset questionnaire templates, obtaining the questionnaire template from a template database, and using the questionnaire template as the questionnaire.

[0100] Taking the data processing device as an example of a computer, a staff member (who may be a staff member performing a diagnostic task or an enterprise staff member) can select a suitable questionnaire template from multiple questionnaire templates as a questionnaire as needed, and then fill in the questionnaire template. The staff member can fill the corresponding questionnaire answer content into the questionnaire template through the UI interface of the computer (i.e., the data processing device). The data processing device of this application can obtain the questionnaire answer content through the filled-in questionnaire template. Generally speaking, the more detailed and rich the questionnaire answer content is, the better it is. The more detailed and rich the questionnaire answer content is, the more conducive it is to the subsequent determination of the diagnostic opinions of the target enterprise.

[0101] Obviously, by having the staff select a questionnaire template from multiple questionnaire templates as the questionnaire and enter the questionnaire answers, the present application can more specifically evaluate the target enterprise's capabilities in the corresponding manufacturing capability dimension, thereby obtaining a reasonable evaluation result, making the subsequent diagnostic opinions on the target enterprise obtained by the data processing method of the present application more targeted and reliable, and more convenient to meet the different needs of different enterprises.

[0102] In the present application, the evaluation results may include a score of the target enterprise on at least one manufacturing capability dimension obtained by analyzing the questionnaire answers, which can effectively indicate the strength of the target enterprise in the corresponding manufacturing capability dimension.

[0103] Based on this, in some optional embodiments, step S402 specifically includes: analyzing the questionnaire answers based on a preset intelligent manufacturing capability maturity model to obtain an evaluation result of the target enterprise in at least one manufacturing capability dimension.

[0104] Analysis based on the intelligent manufacturing capability maturity model is a mature technology based on national standards. For details, please refer to the national standards "GB / T39116-2020 Intelligent Manufacturing Capability Maturity Model" and "GB / T39117-2020 Intelligent Manufacturing Capability Maturity Evaluation Method", which will not be repeated here. Optionally, for the intelligent manufacturing capability maturity model, each capability subdomain can be used as a manufacturing capability dimension, and different capability subdomains can be used as different manufacturing capability dimensions. This application analyzes the obtained questionnaire answers through a preset intelligent manufacturing capability maturity model, and can more objectively obtain the evaluation results of the target enterprise in at least one manufacturing capability dimension.

[0105] Optionally, the aforementioned enterprise information template may be a part of a questionnaire template, that is, by filling in the selected questionnaire template (i.e., questionnaire), the enterprise information of the target enterprise and the questionnaire answer content may be obtained at the same time, so as to facilitate subsequent data processing. This application does not impose any restrictions here.

[0106] Step S403: Calculate the similarity between the target enterprise and the template enterprise in the preset database based on the enterprise information and the evaluation result.

[0107] Specifically, the preset database in the embodiment of the present application includes: historical diagnostic evaluation reports of template enterprises. The historical diagnostic evaluation reports of template enterprises can be diagnostic evaluation reports conducted on enterprises in various industries in history, and can also be diagnostic evaluation reports conducted on the same enterprise at different stages.

[0108] In the embodiment of the present application, the user can set the number of template enterprises, the composition of template enterprises, and the industries corresponding to the template enterprises as needed.

[0109] The historical diagnosis and evaluation reports of the template enterprises in the preset database can be updated periodically, and the user can further set the update cycle of the historical diagnosis and evaluation reports of the template enterprises.

[0110] The embodiment of the present application can provide more targeted evaluation and diagnosis results to the target enterprise by managing the historical diagnosis and evaluation reports of the template enterprise in the preset database.

[0111] Step S404: If there is a template enterprise in the preset database whose similarity with the target enterprise is greater than a preset threshold, a diagnosis result of the target enterprise is generated according to the diagnosis evaluation report of the template enterprise.

[0112] In the embodiment of the present application, the diagnosis result can be used to indicate at least one of the advantages, disadvantages and upgrade suggestions of the target enterprise. Specifically, the diagnosis result can include at least one of the following contents: enterprise research summary, intelligent transformation suggestion, intelligent transformation implementation plan suggestion, intelligent transformation benefit analysis, etc.

[0113] The embodiment of the present application generates a diagnosis result for the target enterprise based on a diagnosis evaluation report of a template enterprise whose similarity to the target enterprise is greater than a preset threshold, thereby providing the target enterprise with a diagnosis result that meets the needs of the target enterprise.

[0114] The preset threshold in the embodiment of the present application represents the reference situation of the diagnostic evaluation report between the template enterprise and the target enterprise.

[0115] Those skilled in the art can set the preset threshold as needed.

[0116] Step S405: Generate a diagnostic assessment report for the target enterprise based on the enterprise information of the target enterprise, the assessment result of the target enterprise in at least one manufacturing capability dimension, and the diagnosis result of the target enterprise.

[0117] Specifically, the embodiment of the present application inputs the enterprise information of the target enterprise, the evaluation results of the target enterprise in at least one manufacturing capability dimension, and the diagnosis results of the target enterprise into a diagnosis result generation model to obtain a diagnosis evaluation report of the target enterprise.

[0118] The diagnosis result generation model in the embodiment of the present application can automatically process the enterprise information of the target enterprise, the evaluation results of the target enterprise in at least one manufacturing capability dimension, and the diagnosis results of the target enterprise to generate a diagnosis evaluation report for the target enterprise.

[0119] The diagnostic assessment report of the target enterprise in the embodiment of the present application can be automatically updated to the preset database as the diagnostic assessment report of the template enterprise.

[0120] The embodiment of the present application searches for the diagnostic evaluation report of a template enterprise similar to the target enterprise in the diagnostic evaluation report of the template enterprise in the preset database, thereby generating the diagnostic result of the target enterprise, and further generating the diagnostic report of the target enterprise. The diagnostic report of the target enterprise in the embodiment of the present application not only contains the enterprise information of the target enterprise and the evaluation results of the target enterprise in at least one manufacturing capability dimension, but also has the diagnostic results of the target enterprise, which is not easily affected by external factors such as time, space, epidemic situation and specific expert capabilities, and can better meet the diagnostic needs of the target enterprise.

[0121] In another specific implementation of the embodiment of the present application, the preset database also includes: expert diagnosis suggestions corresponding to the search keywords.

[0122] Specifically, the embodiments of the present application also include:

[0123] An expert diagnosis description is received, and NLP data analysis is performed based on the received expert diagnosis description to obtain expert diagnosis suggestions corresponding to the search keywords.

[0124] The preset data of the embodiment of the present application also stores expert diagnostic suggestions corresponding to the search keywords. The expert diagnostic suggestion description can obtain the expert diagnostic suggestions corresponding to the search keywords through NLP data analysis, thereby facilitating obtaining the corresponding expert diagnostic suggestions through the search keywords input by the user.

[0125] In order to increase the accuracy of the search keyword search, the search keyword of this application is an elastic search keyword.

[0126] See also Figure 5 , the method further comprising:

[0127] Step S406: If there is no template enterprise in the preset database whose similarity with the target enterprise is greater than a preset threshold, corresponding expert diagnosis suggestions are obtained according to the search keywords received from the user, and a diagnosis result of the target enterprise is generated according to the expert diagnosis suggestions.

[0128] In the embodiment of the present application, if there is no template enterprise in the preset database whose similarity with the target enterprise is greater than a preset threshold, the corresponding expert diagnosis suggestion is obtained based on the search keyword received by the user, thereby generating a diagnosis result for the target enterprise when there is no diagnostic evaluation report for a template enterprise similar to the target enterprise, and further generating a diagnosis report for the target enterprise.

[0129] In another specific implementation of the embodiment of the present application, step S406 further includes:

[0130] Corresponding expert diagnosis suggestions are obtained according to the search keywords received from the user, and the diagnosis results of the target enterprise are generated according to the expert diagnosis suggestions and the diagnosis evaluation report of the template enterprise.

[0131] The embodiment of the present application can add expert diagnostic suggestions corresponding to the search keywords input by the user to the diagnostic results provided in the diagnostic evaluation report of a template enterprise similar to the target enterprise, thereby providing more comprehensive expert diagnostic suggestions and further meeting the diagnostic needs of the target enterprise.

[0132] Figure 6 An optional block diagram of a data processing device provided in the fourth aspect of an embodiment of the present application is shown. The fourth aspect of the present application provides a data processing device 600, including:

[0133] The enterprise information acquisition module 601 is used to acquire the enterprise information of the target enterprise, wherein the enterprise information includes at least one of the following: type, scale, location, employees, and output;

[0134] An evaluation result acquisition module 602 is used to obtain an evaluation result of the target enterprise in at least one manufacturing capability dimension according to the questionnaire of the target enterprise, wherein the at least one manufacturing capability dimension is obtained according to the industry standard of the target enterprise;

[0135] A similarity calculation module 603, used to calculate the similarity between the target enterprise and the template enterprise in the preset database according to the enterprise information and the evaluation result;

[0136] The diagnosis result generating module 604 is used to generate the diagnosis result of the target enterprise according to the diagnosis evaluation report of the template enterprise if there is a template enterprise in the preset database whose similarity with the target enterprise is greater than a preset threshold, wherein the preset database includes: the historical diagnosis evaluation report of the template enterprise;

[0137] The evaluation report generation module 605 is used to generate a diagnostic evaluation report for the target enterprise based on the enterprise information of the target enterprise, the evaluation result of the target enterprise in at least one manufacturing capability dimension, and the diagnosis result of the target enterprise.

[0138] The data processing device 600 of the present application and the data processing method provided in the third aspect are based on the same inventive concept, that is, the data processing device mentioned in the data processing method provided in the third aspect. The relevant content thereof can be understood by referring to the various embodiments of the above-mentioned data processing method and will not be elaborated here.

[0139] Figure 7 Schematic diagram of the structure of an optional electronic device according to an embodiment of the present application is shown. The embodiment of the present application does not limit the specific implementation of the electronic device 700. As an example, refer to Figure 7 The electronic device 700 provided in the third aspect of the embodiment of the present application includes: a processor 702, a communication interface 704, a memory 706, and a communication bus 708. Among them:

[0140] The processor 702 , the communication interface 704 , and the memory 706 communicate with each other via a communication bus 708 .

[0141] The communication interface 704 is used to communicate with other electronic devices or servers.

[0142] The processor 702 is used to execute the program 710, and specifically can execute the relevant steps in any of the aforementioned data processing method embodiments.

[0143] Specifically, the program 710 may include program codes, which include computer operation instructions.

[0144] 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 application. The one or more processors included in the smart device may be processors of the same type, such as one or more CPUs; or processors of different types, such as one or more CPUs and one or more ASICs.

[0145] The memory 706 is used to store the program 710. The memory 706 may include a high-speed RAM memory, and may also include a non-volatile memory (non-volatile memory), such as at least one disk memory.

[0146] The program 710 may be specifically used to enable the processor 702 to execute the data processing method in any of the aforementioned embodiments.

[0147] The specific implementation of each step in program 710 can refer to the corresponding description of the corresponding steps and units in any of the aforementioned data processing method embodiments, which will not be repeated here. Those skilled in the art can clearly understand that for the convenience and simplicity of description, the specific working process of the above-described devices and modules can refer to the corresponding process description in the aforementioned method embodiments, which will not be repeated here.

[0148] According to a fourth aspect of the embodiments of the present application, the embodiments of the present application provide a computer storage medium storing instructions for causing a machine to execute the data processing method described herein.

[0149] According to the fifth aspect of the embodiments of the present application, the embodiments of the present application further provide a computer program product, which is tangibly stored on a computer-readable medium and includes computer-executable instructions, which, when executed, cause at least one processor to perform the data processing methods provided in the above embodiments. It should be understood that each solution in this embodiment has the corresponding technical effects in the above method embodiments, which will not be repeated here.

[0150] As for the data processing device 300 / data processing device 600 / electronic device 400 / computer storage medium / computer program product embodiments of the present application, the relevant contents and beneficial effects are basically similar to those in the data processing method embodiments provided in the aforementioned first and third aspects, and therefore the description here is relatively brief and can be understood based on the aforementioned data processing method embodiments.

[0151] It should be understood that the various steps described in the method implementation of the present application can be performed in different orders and / or in parallel. The expressions similar to "first" and "second" used in the embodiments of the present application can modify various components regardless of order and / or importance, but these expressions do not limit the corresponding components. The above expressions are only configured for the purpose of distinguishing a component from other components.

[0152] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the embodiments of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A data processing method, characterized in that: include: Obtain the first enterprise information of the target enterprise; Analyze the answers to the questionnaires for the target enterprise to obtain a first evaluation result of the target enterprise in at least one intelligent manufacturing capability dimension, wherein the first evaluation result is used to indicate the strength of the target enterprise in the corresponding intelligent manufacturing capability dimension; Based on the first enterprise information and the at least one first evaluation result, a diagnostic opinion for the target enterprise is determined from a pre-established database, wherein the diagnostic opinion is used to indicate at least one of the advantages, disadvantages and upgrade suggestions in the current intelligent manufacturing capabilities of the target enterprise.

2. The method according to claim 1, characterized in that The database includes a knowledge graph library, in which a plurality of knowledge graphs are pre-stored, the plurality of knowledge graphs are respectively generated based on different historical diagnosis reports, one or more historical diagnosis reports in each historical diagnosis report are for the same enterprise, and the historical diagnosis report includes second enterprise information of the enterprise targeted, a second evaluation result in at least one intelligent manufacturing capability dimension, and a historical diagnosis opinion for the enterprise; Determining a diagnostic opinion for the target enterprise from a pre-established database according to the first enterprise information and the at least one first evaluation result includes: Based on the first enterprise information and the at least one first evaluation result, at least one knowledge graph is determined from the knowledge graph library, and based on the historical diagnosis report corresponding to the at least one knowledge graph, a diagnostic opinion for the target enterprise is determined.

3. The method according to claim 2, characterized in that The determining, based on the first enterprise information and the at least one first evaluation result, at least one knowledge graph from the knowledge graph library, and determining a diagnostic opinion for the target enterprise based on a historical diagnostic report corresponding to the at least one knowledge graph, includes: For each knowledge graph in the knowledge graph library, respectively calculate a first similarity between the first enterprise information and the second enterprise information of the historical diagnosis report corresponding to the knowledge graph, and a second similarity between the first evaluation result and at least one second evaluation result of the historical diagnosis report corresponding to the knowledge graph, and determine a third similarity based on the first similarity and the second similarity, wherein the third similarity is used to indicate the degree of similarity between the target enterprise and the enterprise targeted by the historical diagnosis report corresponding to the knowledge graph; Determine whether there is a knowledge graph in the knowledge graph library whose third similarity is greater than a preset similarity threshold; Among them, if there is a knowledge graph whose third similarity is greater than the preset similarity threshold, the diagnostic opinion for the target enterprise is determined according to the historical diagnostic report corresponding to the knowledge graph with the largest third similarity.

4. The method according to claim 3, characterized in that The determining, based on the first enterprise information and the at least one first evaluation result, at least one knowledge graph from the knowledge graph library, and determining a diagnosis opinion for the target enterprise based on a historical diagnosis report corresponding to the at least one knowledge graph, further includes: If there is no knowledge graph with a third similarity greater than a preset similarity threshold, searching from the knowledge graph library according to at least one keyword to determine at least one knowledge graph matching each keyword, and obtaining at least one diagnostic opinion text segment matching the keyword according to the obtained at least one knowledge graph; A diagnostic opinion for the target enterprise is determined according to the extracted at least one diagnostic opinion text segment.

5. The method according to claim 1, characterized in that The method further includes: generating a diagnosis report file for the target enterprise according to the first enterprise information, the questionnaire answers and the diagnosis opinion for the target enterprise.

6. The method according to claim 5, characterized in that Generate a diagnostic report file for the target enterprise based on the first enterprise information, the questionnaire answers and the diagnostic opinion on the target enterprise, including: The first enterprise information, the questionnaire answer content and the diagnostic opinion for the target enterprise are input into a preset diagnosis report generation model to generate the diagnosis report file.

7. A data processing device (300), characterized in that: include: An acquisition module (301), used to acquire first enterprise information of a target enterprise; An analysis module (302) is used to analyze the answers to the questionnaires obtained from the target enterprise, and obtain a first evaluation result of the target enterprise in at least one intelligent manufacturing capability dimension, wherein the first evaluation result is used to indicate the strength of the target enterprise in the corresponding intelligent manufacturing capability dimension; A determination module (303) is used to determine a diagnostic opinion for a target enterprise from a pre-established database based on the first enterprise information and the at least one first evaluation result, wherein the diagnostic opinion is used to indicate at least one of the advantages, disadvantages and upgrade suggestions in the current intelligent manufacturing capabilities of the target enterprise.

8. A data processing method, characterized in that: include: Acquire enterprise information of the target enterprise, wherein the enterprise information includes at least one of the following: type, scale, location, employees, and output; Obtaining, based on the questionnaire of the target enterprise, an evaluation result of the target enterprise in at least one manufacturing capability dimension, wherein the at least one manufacturing capability dimension is obtained based on the industry standard of the target enterprise; Calculate the similarity between the target enterprise and the template enterprise in a preset database according to the enterprise information and the evaluation result; If there is a template enterprise in the preset database whose similarity with the target enterprise is greater than a preset threshold, a diagnosis result of the target enterprise is generated according to the diagnosis and evaluation report of the template enterprise, wherein the preset database includes: a historical diagnosis and evaluation report of the template enterprise; A diagnostic assessment report for the target enterprise is generated based on the enterprise information of the target enterprise, the assessment result of the target enterprise in at least one manufacturing capability dimension, and the diagnosis result of the target enterprise.

9. The method according to claim 8, characterized in that The preset database also includes: expert diagnosis suggestions corresponding to the search keywords, The method further comprises: If there is no template enterprise in the preset database whose similarity with the target enterprise is greater than a preset threshold, corresponding expert diagnosis suggestions are obtained according to the search keywords received from the user, and a diagnosis result of the target enterprise is generated according to the expert diagnosis suggestions.

10. The method according to claim 9, characterized in that The step of generating the diagnostic result of the target enterprise according to the diagnostic evaluation report of the template enterprise further includes: The corresponding expert diagnosis suggestion is obtained according to the received search keyword input by the user, and the diagnosis result of the target enterprise is generated according to the expert diagnosis suggestion and the diagnosis evaluation report of the template enterprise.

11. The method according to claim 9 or 10, characterized in that: The method further comprises: An expert diagnosis description is received, and NLP data analysis is performed based on the received expert diagnosis description to obtain the expert diagnosis suggestion corresponding to the search keyword.

12. The method according to claim 8, characterized in that The method further comprises: An industry maturity model is obtained according to the industry standard of the target enterprise, and a questionnaire for the target enterprise is generated according to the industry maturity model.

13. A data processing device (600), comprising: An enterprise information acquisition module (601) is used to acquire enterprise information of a target enterprise, wherein the enterprise information includes at least one of the following: type, scale, location, employees, and output; An evaluation result acquisition module (602) is used to obtain an evaluation result of the target enterprise in at least one manufacturing capability dimension according to the questionnaire of the target enterprise, wherein the at least one manufacturing capability dimension is obtained according to the industry standard of the target enterprise; A similarity calculation module (603), used to calculate the similarity between the target enterprise and the template enterprise in the preset database according to the enterprise information and the evaluation result; A diagnosis result generating module (604) is used to generate a diagnosis result of the target enterprise according to the diagnosis evaluation report of the template enterprise if there is a template enterprise in the preset database whose similarity with the target enterprise is greater than a preset threshold, wherein the preset database includes: a historical diagnosis evaluation report of the template enterprise; An evaluation report generation module (605) is used to generate a diagnostic evaluation report for the target enterprise based on the enterprise information of the target enterprise, the evaluation results of the target enterprise in at least one manufacturing capability dimension, and the diagnosis results of the target enterprise.

14. An electronic device (700), characterized in that: include: A processor (702), a communication interface (704), a memory (706) and a communication bus (708), wherein the processor (702), the communication interface (704) and the memory (706) communicate with each other via the communication bus (708); The memory (706) is used to store at least one executable instruction, and the executable instruction enables the processor (702) to perform an operation corresponding to the method as described in any one of claims 1-6 or 8-12.

15. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, which, when executed by a processor, cause the processor to execute the method of any one of claims 1-6 or 8-12.

16. A computer program product, characterized in that The computer program product is tangibly stored on a computer-readable medium and comprises computer-executable instructions which, when executed, cause at least one processor to perform a method according to any one of claims 1-6 or 8-12.

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