Method and device for establishing statistical analysis model for petrochemical engineering

Abstract

The invention provides a statistical analysis model establishment method and device and a medium, and relates to the technical field of petrochemical engineering, and the method comprises the steps: obtaining statistical data of an expert database; and based on the statistical data, establishing a statistical analysis model suitable for different roles. Therefore, on one hand, statistical analysis models suitable for different roles are established, so that the different roles can obtain flexible and customizable statistical analysis models, and data support is provided for decision making; and on the other hand, the statistical analysis model corresponds to different roles, so that different user roles can view respective authorized data statistical results, and the security of statistical data is ensured. In addition, the management requirement of the expert database is met, the management level of the expert database is improved, a scientific basis is provided for enterprise management decisions, the efficiency of enterprise management is improved, and the cost of enterprise management is reduced.

Description

Technical Field

[0001] This invention relates to the field of petrochemical engineering technology, and more specifically, to a method, apparatus, and medium for establishing a statistical analysis model. Background Technology

[0002] In recent years, with the development of bidding and tendering, management activities surrounding bidding and tendering have become increasingly detailed and specific. The widespread adoption of various management information systems has led to an explosive growth in all kinds of information. Faced with massive amounts of data, managers have raised higher demands based on office informatization, hoping to obtain flexible and customizable data statistical analysis services. Based on this, statistical analysis models have emerged.

[0003] Statistical analysis models utilize data statistical analysis techniques to analyze various business activity indicators, identify patterns or key issues, and explore ways to improve economic efficiency and management levels. Current technologies cannot achieve customized statistical analysis.

[0004] To address the problems of existing technologies, this invention provides a method, apparatus, and medium for establishing statistical analysis models. Summary of the Invention

[0005] To address the problems of existing technologies, this invention provides a method, apparatus, and medium for establishing a statistical analysis model, the method comprising:

[0006] Obtain statistical data from the expert database;

[0007] Based on the statistical data, establish statistical analysis models applicable to different roles.

[0008] According to one embodiment of the present invention, the statistical data includes: expert data and project data;

[0009] The expert data includes one or more of the following: the total number of times an expert was selected, the number of times the expert participated in the evaluation, the number of times the expert refused to participate, the number of times the expert was selected but did not respond, the expert's assessment score in the historical evaluation period, the expert's assessment score in the current evaluation period, and the corresponding rating star.

[0010] The project data includes one or more of the following: the total number of projects selected, the total number of experts selected, the number of participating experts, the number of experts who refused to participate, the number of experts who were selected but did not respond, the number of experts who requested leave, the attendance rate of the participating experts, and the average execution time of the projects.

[0011] According to one embodiment of the present invention, the role includes: an individual; the statistical analysis model includes: a first statistical analysis model;

[0012] The first statistical analysis model is established through the following steps:

[0013] Based on the expert data and the project data, a first statistical analysis model suitable for the individual is established.

[0014] According to one embodiment of the present invention, the statistical data further includes: expert distribution data, professional data, assessment data, and enterprise data.

[0015] According to one embodiment of the present invention, the expert distribution data includes one or more of the following: total number of experts, gender distribution, age distribution, educational background distribution, professional title distribution, regional distribution, number of experts belonging to the target group company, number of experts not belonging to the target group company, and number of experts corresponding to each subsidiary of the target group company.

[0016] The professional data includes one or more of the following: the total number of professions, the number of experts corresponding to each profession, popular professions, and scarce professions.

[0017] The assessment data includes one or more of the following: the number of experts who deducted points from the assessment, the number of experts who raised objections to the project assessment results, the objection rate of the project assessment results, the proportion of those who accepted objections, the proportion of those who did not accept objections, the total number of people who received indicator warnings in the assessment, and the proportion of those who received indicator warnings in each of the assessments.

[0018] The enterprise data includes one or more of the following: expert distribution data, professional data, project data, performance evaluation data, the number of experts added in the subsidiary, and the number of experts promoted to headquarters experts in the subsidiary.

[0019] According to one embodiment of the present invention, the role further includes: an administrator; the statistical analysis model further includes: a second statistical analysis model;

[0020] The second statistical analysis model is established through the following steps:

[0021] Based on the expert distribution data, the professional data, the project data, the assessment data, and the enterprise data, a second statistical analysis model suitable for the administrator is established.

[0022] According to one embodiment of the present invention, the statistical data further includes: performance data, which includes one or more of the following: the average time for all participating experts to complete the bid evaluation, the standard deviation of the scores given by the participating experts to all bid documents, the number of times the scores given by the participating experts to the bid documents deviate significantly from the average scores given by the participating experts to the bid documents, and the number of times objections were raised to the bid evaluation results of the participating experts.

[0023] According to one embodiment of the present invention, the role further includes: a system administrator; the statistical analysis model further includes: a third statistical analysis model;

[0024] The third statistical analysis model is established through the following steps:

[0025] Based on the expert data, project data, expert distribution data, professional data, assessment data, enterprise data, and performance data, a third statistical analysis model suitable for the system administrator is established.

[0026] According to another aspect of the invention, a storage medium is also provided, comprising a series of instructions for performing the steps of the method as described in any of the preceding claims.

[0027] According to another aspect of the present invention, an apparatus for establishing a statistical analysis model is also provided, which performs the method as described in any of the preceding claims, the apparatus comprising:

[0028] The acquisition module is used to retrieve statistical data from the expert database;

[0029] A module is established to build statistical analysis models suitable for different roles based on the statistical data.

[0030] This invention provides a method, apparatus, and medium for establishing a statistical analysis model, which has the following advantages compared with the prior art:

[0031] This invention establishes statistical analysis models applicable to different roles based on statistical data from an expert database. On the one hand, this allows different roles to access flexible and customizable statistical analysis models, providing data support for decision-making. On the other hand, it ensures that the statistical analysis models correspond to different roles, enabling different user roles to view their authorized statistical data results, thus guaranteeing the security of statistical data. Furthermore, it meets the management needs of the expert database, improves the management level of the expert database, provides a scientific basis for enterprise management decisions, increases enterprise management efficiency, and reduces enterprise management costs.

[0032] Other features and advantages of the invention will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures particularly pointed out in the description, claims, and drawings. Attached Figure Description

[0033] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with the embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings:

[0034] Figure 1 A flowchart illustrating a method for establishing a statistical analysis model according to an embodiment of the present invention is shown;

[0035] Figure 2 A block diagram of an apparatus for establishing a statistical analysis model according to an embodiment of the present invention is shown.

[0036] In the accompanying drawings, the same parts use the same reference numerals. Also, the drawings are not drawn to scale. Detailed Implementation

[0037] To make the objectives, technical solutions, and advantages of the present invention clearer, the embodiments of the present invention will be further described in detail below with reference to the accompanying drawings.

[0038] In view of the above-mentioned deficiencies of the prior art, the present invention provides a method, apparatus and medium for establishing a statistical analysis model. Figure 1 A flowchart illustrating a method for establishing a statistical analysis model according to an embodiment of the present invention is shown. The method includes:

[0039] S101, Obtain statistical data from the expert database;

[0040] S102, Based on statistical data, establish statistical analysis models applicable to different roles.

[0041] For example, the expert database could be a database of experts in petrochemical engineering. Roles are the user's roles. This approach can be applied to petrochemical engineering.

[0042] This invention establishes statistical analysis models applicable to different roles based on statistical data from an expert database. On the one hand, this allows different roles to access flexible and customizable statistical analysis models, providing data support for decision-making. On the other hand, it ensures that the statistical analysis models correspond to different roles, enabling different user roles to view their authorized statistical data results, thus guaranteeing the security of statistical data. Furthermore, it meets the management needs of the expert database, improves the management level of the expert database, provides a scientific basis for enterprise management decisions, increases enterprise management efficiency, and reduces enterprise management costs.

[0043] In one possible embodiment, the statistical data includes: expert data and project data;

[0044] Expert data includes one or more of the following: the total number of times an expert was selected, the number of times an expert participated in the evaluation, the number of times an expert refused to participate, the number of times an expert was selected but did not respond, the expert's assessment score in the historical evaluation period, the expert's assessment score in the current evaluation period, and the corresponding star rating.

[0045] Project data includes one or more of the following: total number of projects selected, total number of experts selected, number of participating experts, number of experts who refused to participate, number of experts selected but not responded, number of experts on leave, attendance rate of participating experts, and average project execution time.

[0046] For example, the attendance rate of the participating experts can be determined using Equation 1:

[0047]

[0048] For example, the average execution time of a project can be determined using Equation 2:

[0049]

[0050] The average execution time of each project reflects the efficiency of the expert database's services. Analyzing expert data provides insights into each expert's performance, supporting expert evaluation. Analyzing project data allows for a comprehensive understanding of the overall operation of the expert database's extraction services, identifying shortcomings and weaknesses, and providing data support for improving service capabilities.

[0051] In one possible embodiment, the roles include: an individual; the statistical analysis model includes: a first statistical analysis model;

[0052] The first statistical analysis model is established through the following steps:

[0053] Based on expert data and project data, establish a first statistical analysis model suitable for individuals.

[0054] Individuals can be expert users. The first statistical analysis model can display statistical analysis results in real time through dashboards, data dashboards, data reports, etc., or in the form of charts such as histograms, trend charts, line charts, and pie charts.

[0055] In this way, based on expert data and project data, a first statistical analysis model suitable for expert users can be flexibly and customized, ensuring that the first statistical analysis model matches the expert users and guaranteeing the security of statistical data.

[0056] In one possible embodiment, the statistical data also includes: expert distribution data, professional data, performance evaluation data, and enterprise data.

[0057] In one possible embodiment, the expert distribution data includes one or more of the following: total number of experts, gender distribution, age distribution, education level distribution, professional title distribution, regional distribution, number of experts belonging to the target group company, number of experts not belonging to the target group company, and number of experts corresponding to each subsidiary of the target group company.

[0058] In this way, statistical analysis of expert distribution data can provide a comprehensive understanding of the expert distribution situation, providing support for optimizing the expert member structure.

[0059] In one possible embodiment, the professional data includes one or more of the following: the total number of professions, the number of experts corresponding to each profession, popular professions, and scarce professions.

[0060] The total number of specialties reflects the coverage of the expert database to national standard specialties; the number of experts for each specialty reflects the ability of each specialty to provide expert extraction services; popular specialties are those that are extracted more than the first target threshold within the target period. Scarce specialties are those that have extraction needs within the target period but cannot be met. For example, the first target threshold can be determined based on the actual application scenario, and this invention does not limit it.

[0061] Professional data can also include the frequency of popular majors and the frequency of scarce majors. The frequency of popular majors and scarce majors can represent the demand for these majors.

[0062] In this way, by analyzing professional data, suggestions can be provided for optimizing the expert structure and professional settings of each specialty.

[0063] In one possible embodiment, the assessment data includes one or more of the following: the number of experts who deducted points from the assessment, the number of experts who raised objections to the project assessment results, the objection rate of the project assessment results, the proportion of those who accepted objections, the proportion of those who did not accept objections, the total number of people who received indicator warnings in the assessment, and the proportion of those who received indicator warnings in each assessment.

[0064] First, the total number of experts evaluating the project can be determined, and then Equation 3 can be used to determine the objection rate of the experts to the project evaluation results:

[0065]

[0066] For example, the number of people who accept project assessment objections and the number of people who do not accept project assessment objections can be determined first, and then the project assessment objection acceptance rate and project assessment objection non-acceptance rate can be determined by Equation 4 and Equation 5 respectively.

[0067]

[0068] For example, we can first determine the number of people who are warned for each indicator in each assessment, and then use Equation 6 to determine the proportion of people who are warned for each indicator in each assessment.

[0069]

[0070] All assessment data can be drilled down and displayed in detail in the form of a data list.

[0071] In this way, by using the assessment data, we can understand the performance of experts in the database during the bid evaluation and review process, identify the shortcomings and weaknesses of the experts in the database in providing bid evaluation and review services, and provide data support for improving the professional capabilities and overall quality of the experts in the database.

[0072] In one possible embodiment, enterprise data includes one or more of the following: expert distribution data belonging to subsidiaries, professional data, project data, performance evaluation data, the number of new experts in subsidiaries, and the number of experts in subsidiaries promoted to headquarters experts.

[0073] In this way, through statistical analysis of enterprise data, we can understand the situation of the enterprises where experts work, as well as the distribution, specialties, projects, and assessment of various experts in each enterprise, providing data support for improving the management and service capabilities of experts in each enterprise.

[0074] In one possible embodiment, the role further includes: an administrator; the statistical analysis model further includes: a second statistical analysis model;

[0075] The second statistical analysis model is established through the following steps:

[0076] Based on expert distribution data, professional data, project data, assessment data, and enterprise data, a second statistical analysis model suitable for administrators is established.

[0077] The administrator can be a subsidiary's enterprise management user. The second statistical analysis model can display the statistical analysis results in real time through dashboards, data dashboards, data reports, etc., or in the form of charts such as histograms, trend charts, line charts, and pie charts.

[0078] In this way, based on expert distribution data, professional data, project data, assessment data, and enterprise data, a customized second statistical analysis model can be established to match the enterprise management users and ensure the security of statistical data.

[0079] In one possible embodiment, the statistical data further includes: performance data, which includes one or more of the following: the average time taken for all participating experts to complete the bid evaluation, the standard deviation of the scores given by the participating experts to all bid documents, the number of times the scores given by the participating experts to the bid documents deviated significantly from the average scores given by the participating experts to all bid documents, and the number of times objections were raised to the bid evaluation results of the participating experts.

[0080] The average time taken by all participating experts to complete the evaluation is an important indicator of their individual proficiency in bid evaluation. For example, the time taken by each expert to complete the evaluation can be calculated first, and then...

[0081] Formula 7 determines the average duration.

[0082]

[0083] First, the score given by the participating experts to each bid document can be determined. Then, the scores can be averaged to obtain the average score given by the participating experts to the bid document. Next, the standard deviation of the scores given by the participating experts to all bid documents can be calculated using Equation 8.

[0084]

[0085] In Equation 8, N refers to the number of tender documents, and S... i μ refers to the score given by the participating experts to the bid documents, σ ​​refers to the average score given by the participating experts to the bid documents, and σ refers to the standard deviation of the scores given by the participating experts to the bid documents.

[0086] When all projects have the same maximum score, the scores given by the participating experts to the tender documents statistically follow a normal distribution.

[0087] Among them, the number of times that the scores given by the participating experts to the bid documents deviate significantly from the average score given by the participating experts to the bid documents refers to the number of times that the difference between the scores given by the participating experts to the bid documents and the average score given by the participating experts to all bid documents is greater than twice the standard deviation (i.e., Or, the number of times the difference between the score given by the participating experts to the bid document and the average score given by the participating experts to all bid documents is less than negative two standard deviations (i.e., ).

[0088] The number of times participating experts raise objections to the evaluation results is an important indicator of the quality of expert evaluation results. For example, the number of objections raised to the evaluation results of each participating expert can be statistically determined.

[0089] In this way, by statistically analyzing the performance data, we can understand the performance of experts in the database during the bidding and evaluation process, identify the shortcomings and weaknesses of experts in the database in providing bidding and evaluation services, and provide data support for improving the professional capabilities and overall quality of experts in the database.

[0090] In one possible embodiment, the role further includes: a system administrator; the statistical analysis model further includes: a third statistical analysis model;

[0091] The third statistical analysis model is established through the following steps:

[0092] Based on expert data, project data, expert distribution data, professional data, assessment data, enterprise data, and performance data, a third statistical analysis model suitable for system administrators is established.

[0093] The system administrator can be the headquarters management user of the target group company. The third statistical analysis model can display the statistical analysis results in real time through dashboards, data dashboards, data reports, etc., or in the form of charts such as histograms, trend charts, line charts, and pie charts.

[0094] In this way, based on expert data, project data, expert distribution data, professional data, assessment data, enterprise data, and performance data, a customized third statistical analysis model suitable for system administrators can be established, ensuring that the third statistical analysis model matches the system administrator and guaranteeing the security of statistical data.

[0095] The statistical analysis model establishment method provided by this invention can also be used in conjunction with a computer-readable storage medium storing a computer program. Executing the computer program runs the statistical analysis model establishment method. The computer program can execute computer instructions, which include computer program code. The computer program code can be in the form of source code, object code, executable file, or some intermediate form.

[0096] Computer-readable storage media can include: any entity or device capable of carrying computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc.

[0097] It should be noted that the contents of computer-readable storage media may be appropriately added to or subtracted from the contents according to the requirements of legislation and patent practice in a jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable storage media may not include electrical carrier signals and telecommunication signals.

[0098] According to another aspect of the present invention, an apparatus for establishing a statistical analysis model is also provided, which performs a method for establishing a statistical analysis model. Figure 2 A block diagram of an apparatus for establishing a statistical analysis model according to an embodiment of the present invention is shown. The apparatus includes:

[0099] Module 510 is used to obtain statistical data from the expert database;

[0100] Module 520 is used to build statistical analysis models suitable for different roles based on statistical data.

[0101] In summary, this invention provides a method, apparatus, and medium for establishing a statistical analysis model, which has the following advantages compared with the prior art:

[0102] This invention establishes statistical analysis models applicable to different roles based on statistical data from an expert database. On the one hand, this allows different roles to access flexible and customizable statistical analysis models, providing data support for decision-making. On the other hand, it ensures that the statistical analysis models correspond to different roles, enabling different user roles to view their authorized statistical data results, thus guaranteeing the security of statistical data. Furthermore, it meets the management needs of the expert database, improves the management level of the expert database, provides a scientific basis for enterprise management decisions, increases enterprise management efficiency, and reduces enterprise management costs.

[0103] It should be understood that the embodiments disclosed herein are not limited to the specific structures, processing steps, or materials disclosed herein, but should be extended to equivalent substitutions of these features as understood by those skilled in the art. It should also be understood that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting.

[0104] In the description of this invention, unless otherwise stated, "a plurality of" means two or more; the terms "upper," "lower," "left," "right," "inner," "outer," "front end," "rear end," "head," "tail," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings, and are only for the convenience of describing the invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the invention. Furthermore, the terms "first," "second," "third," etc., are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0105] In the description of this invention, it should be noted that, unless otherwise explicitly specified and limited, the terms "connected" and "linked" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0106] Certain terms are used throughout this application to refer to specific system components. As those skilled in the art will recognize, the same components may often be referred to by different names, and therefore this application is not intended to distinguish those components that differ only in name and not in function. In this application, the terms “comprise,” “include,” and “have” are used in an open-ended manner and should therefore be interpreted as meaning “including, but not limited to…”. Furthermore, the terms “substantially,” “materially,” or “approximately” as used herein refer to industry-accepted tolerances for the corresponding terms. The term “coupling,” as may be used herein, includes direct coupling and indirect coupling via additional components, elements, circuits, or modules, wherein, for indirect coupling, the intermediate component, element, circuit, or module does not alter the information of the signal but may adjust its current level, voltage level, and / or power level. Inferred coupling (e.g., one element is inferredly coupled to another element) includes direct and indirect coupling between two elements in the same manner as “coupling.”

[0107] The phrase "an embodiment" or "an embodiment" used in this specification means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the invention. Therefore, the phrase "an embodiment" or "an embodiment" appearing in various places throughout the specification does not necessarily refer to the same embodiment.

[0108] The embodiments of the present invention are given for illustrative and descriptive purposes only, and are not intended to be exhaustive or to limit the invention to the forms disclosed. Many modifications and variations will be apparent to those skilled in the art. The embodiments were chosen and described in order to better illustrate the principles and practical application of the invention, and to enable those skilled in the art to understand the invention and to design various embodiments with various modifications suitable for a particular purpose.

[0109] While the embodiments disclosed in this invention are as described above, the content is merely for the purpose of facilitating understanding of the invention and is not intended to limit the invention. Any person skilled in the art to which this invention pertains may make any modifications and variations in form and detail of the implementation without departing from the spirit and scope disclosed herein; however, the scope of patent protection for this invention shall still be determined by the scope defined in the appended claims.

Claims

1. A method of establishing a statistical analysis model, characterized by, The method includes: Obtain statistical data from the expert database; Based on the statistical data, establish statistical analysis models applicable to different roles.

2. The method of claim 1, wherein, The statistical data includes: expert data and project data; The expert data includes one or more of the following: the total number of times an expert was selected, the number of times the expert participated in the evaluation, the number of times the expert refused to participate, the number of times the expert was selected but did not respond, the expert's assessment score in the historical evaluation period, the expert's assessment score in the current evaluation period, and the corresponding rating star. The project data includes one or more of the following: the total number of projects selected, the total number of experts selected, the number of participating experts, the number of experts who refused to participate, the number of experts who were selected but did not respond, the number of experts who requested leave, the attendance rate of the participating experts, and the average execution time of the projects.

3. The method of claim 2, wherein, The role includes: an individual; the statistical analysis model includes: a first statistical analysis model; The first statistical analysis model is established through the following steps: Based on the expert data and the project data, a first statistical analysis model suitable for the individual is established.

4. The method of claim 2 or 3, wherein, The statistical data also includes: expert distribution data, professional data, assessment data, and enterprise data.

5. The method as described in claim 4, characterized in that, The expert distribution data includes one or more of the following: total number of experts, gender distribution, age distribution, education distribution, professional title distribution, regional distribution, number of experts belonging to the target group company, number of experts not belonging to the target group company, and number of experts belonging to each subsidiary of the target group company. The professional data includes one or more of the following: the total number of professions, the number of experts corresponding to each profession, popular professions, and scarce professions. The assessment data includes one or more of the following: the number of experts who deducted points from the assessment, the number of experts who raised objections to the project assessment results, the objection rate of the project assessment results, the proportion of those who accepted objections, the proportion of those who did not accept objections, the total number of people who received indicator warnings in the assessment, and the proportion of those who received indicator warnings in each of the assessments. The enterprise data includes one or more of the following: expert distribution data, professional data, project data, performance evaluation data, the number of new experts in the subsidiary, and the number of experts in the subsidiary who have been promoted to headquarters experts.

6. The method as described in claim 5, characterized in that, The role also includes: administrator; the statistical analysis model also includes: a second statistical analysis model; The second statistical analysis model is established through the following steps: Based on the expert distribution data, the professional data, the project data, the assessment data, and the enterprise data, a second statistical analysis model suitable for the administrator is established.

7. The method as described in claim 6, characterized in that, The statistical data also includes: performance data, which includes one or more of the following: the average time taken for all participating experts to complete the bid evaluation, the standard deviation of the scores given by the participating experts to all bid documents, the number of times the scores given by the participating experts to the bid documents deviated significantly from the average scores given by the participating experts to the bid documents, and the number of times objections were raised to the bid evaluation results of the participating experts.

8. The method as described in claim 7, characterized in that, The roles also include: system administrator; the statistical analysis model also includes: third statistical analysis model; The third statistical analysis model is established through the following steps: Based on the expert data, project data, expert distribution data, professional data, assessment data, enterprise data, and performance data, a third statistical analysis model suitable for the system administrator is established.

9. A storage medium, characterized in that, It includes a series of instructions for performing the method steps as described in any one of claims 1-8.

10. A device for establishing a statistical analysis model, characterized in that, The apparatus for performing the method as described in any one of claims 1-8 comprises: The acquisition module is used to retrieve statistical data from the expert database; A module is established to build statistical analysis models suitable for different roles based on the statistical data.

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