A project review business management system

Abstract

The present invention provides a project review business management system, which relates to the technical field of business management, and includes: a project selection module, which is used to select a target project and a responsible unit of the target project in a project database based on a project selection instruction; a data acquisition module, which is used to conduct a first review of the target project, and at the same time, obtain the project data reported by the responsible unit of the target project after the first review is passed, and conduct a second review of the project data; a department preliminary review and expert review module, which is used to conduct a preliminary review of the project data, and distribute the project data to be reviewed to a preset expert group; a final review module, which is used to obtain the review results and the final confirmation results, and conduct a final review of the final confirmation results through the second person in charge of the preset department, and generate the final review results of the project. The present invention can improve the process speed of the post-project review, improve the efficiency of the review, reduce the interference of human factors on the review results, and improve the accuracy and credibility of the project review results.

Description

Technical Field

[0001] The present invention relates to the technical field of business management, and in particular to a project review business management system. Background Art

[0002] In recent years, with the continuous development of science and technology and economy, the number of projects has gradually increased each year, and the disciplines involved in the projects have become more and more diversified. Before the project is delivered, the project responsible unit needs to review the completed project to ensure the quality of the project.

[0003] At present, the review process after the completion of a project usually requires human intervention throughout the process. This kind of review process with full human participation is not only slow and inefficient, and difficult to meet the increasing number of projects, but the review results are also greatly affected by human factors. There may be a phenomenon of human control of the review results, which may lead to the phenomenon that the review results of the project are inconsistent with the quality of project delivery.

[0004] Therefore, the present invention provides a project review business management system. Summary of the invention

[0005] The present invention provides a project review business management system, which is used to increase the process speed of project post-review, improve the efficiency of review, and improve the review accuracy of the actual quality of the project, reduce the interference of human factors, and thus ensure the quality of the project.

[0006] The present invention provides a project review business management system, comprising:

[0007] A project selection module, for selecting and extracting the corresponding target project and the responsible unit of the target project from the project database based on the project selection instruction of the first person in charge of the preset department;

[0008] A data acquisition module, used to conduct a first review of the target project through the target project responsible unit, and at the same time, obtain the project data reported by the target project responsible unit after the first review is passed, and conduct a second review of the project data through the target project responsible unit;

[0009] The department preliminary review and expert review module is used to conduct preliminary review of the project materials that have passed the second review through the preset department, and allocate the project materials that have passed the preliminary review to the preset expert group using the preset material allocation strategy;

[0010] The final review module is used to obtain the review results of the preset expert group and the final confirmation results of the review results by the preset department, and to conduct a final review of the final confirmation results through the second person in charge of the preset department to generate a final review result of the project.

[0011] Preferably, the project selection module includes:

[0012] An instruction acquisition unit, used to acquire a project selection instruction input by the first person in charge of the preset department in combination with a preset project screening condition;

[0013] The project selection unit is used to select a target project and a target project responsible unit that match the project selection instruction in the project database, and send the target project to the corresponding project responsible unit.

[0014] Preferably, the data acquisition module includes:

[0015] A sub-project acquisition unit, used to deconstruct and divide the target project to obtain all sub-projects under the same target project;

[0016] A person-in-charge matching unit, configured to select a third person-in-charge corresponding to the target project and a fourth person-in-charge corresponding to the subproject from a preset project-person-in-charge comparison table based on the input target project and subproject;

[0017] The project review unit is used to conduct a first review of the target project and sub-projects through the target project responsible unit and obtain a first review result.

[0018] Preferably, the data acquisition module further includes:

[0019] A data packet generating unit, configured to obtain the first audit result satisfying the first audit condition and the corresponding target project and sub-project, and generate a data packet to be filled in;

[0020] A data filling unit, used for sending data filling instructions to the third person in charge and the fourth person in charge respectively based on the data packet to be filled in, and writing the filling contents input by the third person in charge and the fourth person in charge into the data packet to be filled in, so as to generate project data;

[0021] The document review unit is used to conduct a second review of the project document through the responsible unit of the target project.

[0022] Preferably, the departmental preliminary review and expert review module includes:

[0023] A material transfer unit, used to mark the project materials that have passed the second review as materials to be reviewed first, and transfer them to the preset department;

[0024] The preliminary review unit is used to conduct preliminary review on the preliminary review materials through the preset department, return the preliminary review materials that fail the preliminary review to the target project responsible unit, and generate a data copy instruction to send to the target project responsible unit to rewrite the data and regenerate the preliminary review materials;

[0025] The expert review unit is used to obtain a preset expert group and a corresponding preset data allocation strategy, and allocate the pending project data that have passed the preliminary review to the corresponding preset expert group for review according to the preset data allocation strategy.

[0026] Preferably, the expert review unit includes:

[0027] An expert information acquisition subunit is used to acquire available expert data from an expert database, and put the available expert data into a blank expert pool to generate an expert pool to be processed;

[0028] A project feature extraction subunit, used to extract feature information of the target project and generate project feature information;

[0029] An evaluation indicator acquisition subunit is used to acquire evaluation indicators matching the target project;

[0030] An expert field matching subunit is used to process the expert data in the expert pool to be processed based on the project feature information and the review index, and eliminate irrelevant expert data;

[0031] The expert level screening subunit is used to obtain the expert level in the patent pool to be processed after the elimination process, and screen out the expert data that does not meet the preset expert level requirements in combination with the review indicators;

[0032] An expert number determination subunit, used to determine the number of the preset expert group based on preset review rules;

[0033] An expert group selection subunit is used to select a plurality of non-overlapping expert groups from the expert pool to be processed based on a preset expert selection strategy;

[0034] The expert group determination subunit is used to obtain the expert group selection instruction of the preset department, determine the preset expert group for the target project review, and at the same time, obtain the evaluation factor corresponding to each expert in combination with the expert level of each expert in the preset expert group;

[0035] A data distribution subunit, configured to distribute corresponding project data to be reviewed to each expert in the preset expert group based on the preset data distribution strategy;

[0036] The expert review subunit is used to obtain the review score and corresponding evaluation of each expert in the preset expert group based on the preset scoring rules, and generate review information corresponding to each expert in combination with the evaluation factor of each expert;

[0037] The comprehensive analysis subunit is used to summarize all the review information from the preset expert group, and process the summarized information in combination with a preset scoring algorithm to comprehensively generate a review result.

[0038] Preferably, the final review module includes:

[0039] An evaluation result verification unit, used to verify the evaluation result in combination with the original evaluation data of the preset expert group;

[0040] A result analysis unit, used to analyze the content of the review results that meet the preset verification requirements, wherein the preset verification requirements include the integrity, consistency, accuracy and credibility of the data;

[0041] An indicator set acquisition unit, used to acquire the indicator information input by the preset department, and aggregate and generate a confirmed indicator set, wherein the confirmed indicator set includes project performance indicators, project experience indicators, and project achievement indicators;

[0042] A content division unit, configured to select a corresponding data division method from a preset indicator-method database in combination with the confirmation indicator set, and divide the parsed content into a plurality of data sets based on the data division method;

[0043] A performance confirmation unit, configured to confirm the performance review score of the project based on the project performance indicator and the corresponding data set, and generate a first confirmation result, wherein the project performance review score includes the review score of the preset expert group on the progress, cost, and delivery quality of the project;

[0044] An experience confirmation unit is used to confirm the experience review score of the project based on the project experience indicator and the corresponding data set, and generate a second confirmation result, wherein the project experience review score includes the review score of the preset expert group on the challenges, problems, corresponding solutions, final solution effects and summary experience encountered during the implementation of the project;

[0045] An achievement confirmation unit is used to confirm the achievement review score of the project based on the project achievement indicator and the corresponding data set, and generate a third confirmation result, wherein the project achievement review score includes the review score of the preset expert group on the actual achievement of the project and the customer's satisfaction;

[0046] A final confirmation unit, configured to perform a comprehensive analysis on the first confirmation result, the second confirmation result and the third confirmation result through the preset department to generate a final confirmation result;

[0047] The final review unit is used to conduct a final review of the final confirmation result through the second person in charge to generate a final review result of the project, and at the same time, generate a final review report in combination with the final review result of the project and the improvement suggestions obtained from multiple parties.

[0048] Preferably, the final confirmation unit comprises:

[0049] The curve determination subunit determines the first scoring data of each expert in different review items, and standardizes the first scoring data to obtain a first scoring curve;

[0050] The interception subunit performs position analysis on the scoring position of each first scoring data in the first scoring curve corresponding to the scoring group, and calculates the first bias of each first scoring data;

[0051] ;in, Represents the value of the i1th scoring data; represents the average value of all the scoring data involved in the corresponding scoring group of the i1th scoring data; ln represents the sign of the logarithmic function; Indicates the scoring position of the i1th scoring data Based on the corresponding scoring group The bias adjustment function of the occurrence position of ; represents the variance of the remaining scoring data in the corresponding scoring group except the i1th scoring data; Represents the variance of all scoring data in the corresponding scoring group; represents the first bias of the i1th first scoring data;

[0052] In chronological order, all the first biases that satisfy The first bias is regarded as the prominent bias, in which Indicates the standard bias comparison value;

[0053] In combination with the scoring time of the first scoring data, determining the number of time series between the first prominent bias and the next prominent bias that is adjacent to the first prominent bias and the total bias vector;

[0054] When the number of time series and the total bias vector meet the interception standard, continue to analyze the next protrusion bias and the next protrusion bias to obtain the boundary bias;

[0055] If satisfied , then the first prominent bias is ignored, where is the comparison coefficient; Indicates the first prominent bias; Indicates the next prominent bias; Indicates the number of corresponding time series;

[0056] If satisfied , then the next prominent bias will be ignored;

[0057] Otherwise, the first protrusion bias and the next protrusion bias are ignored, and the next protrusion bias and the next protrusion bias are analyzed continuously until the boundary bias is obtained;

[0058] The curve position points of the first scoring data corresponding to the boundary bias are regarded as intercepted scoring points to obtain a plurality of intercepted scoring segments;

[0059] A function construction subunit performs a preference analysis on the intercepted scoring segment to construct a first preference threshold value for the corresponding segment;

[0060] ;in, represents the corresponding first preference threshold; Indicates the first bias at the j1th time sequence in the corresponding segment; It represents the average value of the residual bias in the corresponding segment except for the prominent bias; represents the variance of the residual bias in the corresponding segment excluding the prominent bias;

[0061] Determine the preference stability trend of the corresponding expert based on all first preference thresholds and in combination with the value change trend of each intercepted scoring segment;

[0062] ; Among them, Y1 is the preference stable trend function; represents the first preference threshold of the j2-th intercepted scoring segment; represents the line segment fitting coefficient of the j2-th intercepted scoring segment; represents the correlation coefficient of the value change of the j2th intercepted scoring segment;

[0063] According to the first preference threshold and preference stability trend of the corresponding expert in the same review item, the preference correction function of the corresponding expert in the corresponding review item is constructed;

[0064] A score acquisition subunit is used to correct the review score of each review item involved in the performance confirmation unit, the experience confirmation unit, and the achievement confirmation unit according to all preference correction functions;

[0065] The score pair generation subunit is used to establish a scoring node corresponding to each review item, match it with the scoring rule database, obtain the corresponding scoring rule to perform intelligent analysis on the project information in the data set, and associate it with the revised review score under the same review item to generate a score pair;

[0066] The relative scoring subunit is used to calculate the difference between each score pair under the same review item and determine whether the difference meets the consistency condition;

[0067] Calculate the relative score of each review item according to the satisfaction results, and construct the first score pool, the second score pool and the third score pool according to all the relative scores;

[0068] Based on the first score pool, the second score pool and the third score pool, a final confirmation result is calculated.

[0069] The implementation principle and beneficial effects of the present invention are as follows: the present invention can select projects that need to be reviewed in the project database based on the project selection instructions of the first person in charge of the preset department, thereby realizing a one-to-one correspondence between the projects to be reviewed and the responsible persons, and then obtain the project information that has been reviewed by the responsible unit of the target project through the information acquisition module, thereby improving the integrity and consistency of the project information, reducing the occurrence of re-filling of project information due to unqualified project information, and improving the efficiency of project review. Subsequently, the project information is reviewed by the department preliminary review and expert review modules respectively, thereby improving the accuracy of the review results and reducing the influence of human factors on the review results. The review results are then finally confirmed by the preset department, and finally the second person in charge conducts a final review of the final confirmation result to generate the final review result of the project, thereby ensuring the accuracy of the review result.

[0070] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures particularly pointed out in the written description and the accompanying drawings.

[0071] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0072] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0073] Figure 1 The present invention is a framework diagram of a project review business management system in an embodiment of the present invention. DETAILED DESCRIPTION

[0074] The preferred embodiments of the present invention are described below in conjunction with the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0075] The embodiment of the present invention provides a project review business management system, such as Figure 1 As shown, including:

[0076] A project selection module, for selecting and extracting the corresponding target project and the responsible unit of the target project from the project database based on the project selection instruction of the first person in charge of the preset department;

[0077] A data acquisition module is used to conduct a first review of the target project through the responsible unit of the target project, and at the same time, obtain the project data reported by the responsible unit of the target project after the first review is passed, and conduct a second review of the project data through the responsible unit of the target project;

[0078] The department preliminary review and expert review module is used to conduct preliminary review of the project materials that have passed the second review through the preset department, and use the preset material allocation strategy to allocate the project materials that have passed the preliminary review to the preset expert group;

[0079] The final review module is used to obtain the review results of the preset expert group and the final confirmation results of the preset department on the review results, and to conduct a final review of the final confirmation results through the second person in charge of the preset department to generate the final review results of the project.

[0080] In this embodiment, the preset department: that is, the department responsible for the post-project review, which in this invention is the technology department of the group;

[0081] In this embodiment, the first person in charge is the person in charge of selecting the projects to be reviewed in the preset department, and in the present invention, the person in charge of the post-project evaluation of the science and technology department;

[0082] In this embodiment, the project selection instruction: the project selection instruction issued by the first person in charge when selecting the project to be reviewed;

[0083] In this embodiment, the target project: that is, the selected project to be reviewed;

[0084] In this embodiment, the responsible unit of the target project is the responsible unit of the selected project to be reviewed, such as the construction unit, acceptance unit, etc. in the engineering project;

[0085] In this embodiment, the first review: the review process of the target project by the unit responsible for the target project;

[0086] In this embodiment, project information: all information that needs to be reviewed in the target project, which is filled in by the responsible unit of the target project and the subordinate units involved;

[0087] In this embodiment, the second review is a process in which the unit responsible for the target project reviews the submitted project information;

[0088] In this embodiment, the initial review is the process in which the preset department reviews the project materials that have passed the second review;

[0089] In this embodiment, the preset data allocation strategy: the strategy of allocating the project data to each expert of the preset expert group to achieve the purpose of adapting the expert field to the field involved in the project data, so as to obtain the corresponding score of each expert, is preset;

[0090] In this embodiment, the preset expert group: the expert group used to review the project information corresponding to the target project is preset;

[0091] In this embodiment, the project information to be reviewed refers to the project information that has passed the preliminary review by the preset department;

[0092] In this embodiment, the final confirmation result refers to the result obtained after the Ministry of Science and Technology confirms the expert review result;

[0093] In this embodiment, the second person in charge is the person in charge who conducts the final review of the final selection result, and in this invention, the person in charge is the leader of the Ministry of Science and Technology;

[0094] In this embodiment, final review refers to the process of conducting a final evaluation and review of the final confirmation results, which is intended to determine the completion effect of the project and the lessons, experiences and improvement methods gained from the project.

[0095] The working principle and beneficial effects of the above technical solution are as follows: the present invention can select projects that need to be reviewed in the project database based on the project selection instructions of the first person in charge of the preset department, thereby realizing a one-to-one correspondence between the projects to be reviewed and the responsible persons, and then obtain the project information reviewed by the responsible unit of the target project through the information acquisition module, thereby improving the integrity and consistency of the project information, reducing the occurrence of re-filling of project information due to unqualified project information, and improving the efficiency of project review. Subsequently, the project information is preliminarily reviewed and expert reviewed by the departmental preliminary review and expert review modules respectively, thereby improving the accuracy of the review results and reducing the influence of human factors on the review results. The review results are then finally confirmed by the preset department, and finally the final confirmation results are finally reviewed by the second person in charge to generate the final review results of the project, thereby ensuring the accuracy of the review results.

[0096] The embodiment of the present invention provides a project review business management system, a project selection module, including:

[0097] An instruction acquisition unit, used to acquire a project selection instruction input by the first person in charge of a preset department in combination with preset project screening conditions;

[0098] The project selection unit is used to select a target project and a responsible unit of the target project that match the project selection instruction in the project database, and send the target project to the corresponding responsible unit of the project.

[0099] In this embodiment, preset project screening conditions: preset conditions for screening projects in the project database are pre-set, such as the type of project, the amount of work, etc.;

[0100] The working principle and beneficial effects of the above technical solution are as follows: the present invention obtains the names of all projects that have been completed and can be reviewed by combining preset project screening conditions, constructs a project database, narrows the scope of project selection, and reduces the computational complexity of data processing, and then obtains the project selection instruction input by the first person in charge through the instruction acquisition unit, and combines the mapping relationship between the project and the unit to select the target project and the responsible unit of the target project in the project database, and then sends the selected target project to the unit responsible for the target project and its subordinate units, thereby reducing the possibility of mismatching and improving the matching degree between the project and the responsible unit.

[0101] The embodiment of the present invention provides a project review business management system, a data acquisition module, including:

[0102] A sub-project acquisition unit is used to deconstruct and divide the target project to obtain all sub-projects under the same target project;

[0103] A person-in-charge matching unit is used to select a third person-in-charge corresponding to the target project and a fourth person-in-charge corresponding to the sub-project from a preset project-person-in-charge comparison table based on the input target project and sub-project;

[0104] The project review unit is used to conduct a first review of the target project and sub-projects through the target project responsible unit and obtain the first review result.

[0105] In this embodiment, deconstruction and division: that is, the target project is subdivided to obtain multiple sub-projects;

[0106] In this embodiment, sub-project: a project obtained by structurally dividing the target project;

[0107] In this embodiment, the preset project-person-in-charge comparison table: a comparison table containing mapping relationships between all projects and corresponding persons-in-charge, which is preset and used to obtain corresponding project person-in-charge information according to the input project name or feature;

[0108] In this embodiment, the third person in charge is the general person in charge of the target project;

[0109] In this embodiment, the fourth person in charge is the person in charge of the sub-project;

[0110] In this embodiment, the first audit result is the audit result obtained after the responsible unit of the target project audits the target project and its sub-projects. Only projects that pass the audit can proceed to the next step.

[0111] The working principle and beneficial effects of the above technical solution are as follows: the present invention subdivides the target project through the sub-project acquisition unit to obtain multiple sub-projects, and at the same time, obtains the corresponding project leader in combination with the preset project-leader comparison table, thereby realizing a one-to-one correspondence between the project and the responsible person, thereby improving the management efficiency of the project, and through the target project responsible unit and its subordinate units, reviewing the corresponding target projects and sub-projects respectively to determine whether the target projects and sub-projects meet the conditions for submission for review, thereby reducing the subsequent waste of human and material resources due to the project not meeting the conditions for submission for review, and improving the work efficiency of the review business.

[0112] The embodiment of the present invention provides a project review business management system, a data acquisition module, and further includes:

[0113] A data packet generating unit, used to obtain a first audit result satisfying a first audit condition and a corresponding target project and sub-project, and generate a data packet to be filled in;

[0114] A data filling unit is used to send data filling instructions to the third person in charge and the fourth person in charge respectively based on the data packet to be filled in, and write the filling contents input by the third person in charge and the fourth person in charge into the data packet to be filled in, so as to generate project data;

[0115] The document review unit is used to conduct a second review of the project documents through the responsible unit of the target project.

[0116] In this embodiment, the first audit condition is the audit condition used by the responsible unit of the target project to audit the target project and sub-projects, such as the completion rate of the project, the integrity of the documents of each link in the project, etc.;

[0117] In this embodiment, the data package to be filled in is: a data package generated according to the target project and its sub-projects that have passed the first review, and which needs to be filled in by the corresponding person in charge later;

[0118] In this embodiment, the data filling instruction is an instruction generated according to the data packet to be filled in, and is used to fill in the specified content in the data packet to be filled in;

[0119] In this embodiment, the project information is a data packet generated after the content filled in by the third person in charge and the fourth person in charge is written into the designated position in the data packet to be filled in.

[0120] The working principle and beneficial effects of the above technical solution are as follows: the present invention can perform content analysis on the target project and its sub-projects that meet the first review conditions through the data packet generation unit, thereby obtaining the content sections that need to be filled in for the target project and its sub-projects during subsequent reviews, and generating a data packet to be filled in; then, the data filling unit sends a data filling instruction to the person in charge corresponding to the content section, thereby obtaining the project information, and then the responsible unit of the target project conducts a second review of the filled-in content in the project information to ensure the integrity and validity of the project information.

[0121] The embodiment of the present invention provides a project review business management system, a departmental preliminary review and expert review module, including:

[0122] The material transfer unit is used to mark the project materials that have passed the second review as materials to be reviewed first and transfer them to the preset department;

[0123] The preliminary review unit is used to conduct preliminary review of the materials to be reviewed through the preset departments, return the materials to be reviewed that fail the preliminary review to the responsible unit of the target project, and generate a data copy instruction to send to the responsible unit of the target project to rewrite the materials and regenerate the materials to be reviewed;

[0124] The expert review unit is used to obtain the preset expert group and the corresponding preset data allocation strategy, and allocate the project data to be reviewed that have passed the preliminary review to the corresponding preset expert group for review according to the preset data allocation strategy.

[0125] In this embodiment, the materials to be reviewed are project materials that have passed the second review by the responsible unit of the target project;

[0126] In this embodiment, the data copy instruction is an instruction to re-fill in the relevant content sent to the target project responsible unit when the preliminary review result obtained by the preliminary review of the data to be reviewed is unqualified after the preliminary review.

[0127] The working principle and beneficial effects of the above technical solution are as follows: the present invention transfers the materials to be reviewed that have passed the second review of the department responsible for the target project to the preset department through the material transfer unit, and then the preset department conducts a preliminary review on the materials to be reviewed, ensuring that the materials submitted to the preset expert group are accurate, thereby improving the accuracy and consistency of the project materials, and then matching the expert group through the expert review unit, and distributing the project materials to be reviewed to each expert in the preset expert group according to the preset material distribution strategy, thereby reducing the occurrence of mismatches between the fields involved in the project materials and the expert fields, thereby improving the credibility and effectiveness of the expert review scores.

[0128] The embodiment of the present invention provides a project review business management system, an expert review unit, including:

[0129] The expert information acquisition subunit is used to acquire available expert data from the expert database and put the available expert data into a blank expert pool to generate an expert pool to be processed;

[0130] The project feature extraction subunit is used to extract feature information of the target project and generate project feature information;

[0131] An evaluation indicator acquisition subunit is used to acquire evaluation indicators that match the target project;

[0132] The expert field matching subunit is used to process the expert data in the expert pool to be processed based on the project feature information and review indicators, and eliminate irrelevant expert data;

[0133] The expert level screening subunit is used to obtain the expert level in the patent pool to be processed after the elimination process, and screen out the expert data that does not meet the preset expert level requirements in combination with the review indicators;

[0134] An expert number determination subunit is used to determine the number of preset expert groups based on preset review rules;

[0135] An expert group selection subunit is used to select multiple non-repetitive expert groups from the expert pool to be processed based on a preset expert selection strategy;

[0136] The expert group determination subunit is used to obtain the expert group selection instructions of the preset department, determine the preset expert group for the target project review, and at the same time, combine the expert level of each expert in the preset expert group to obtain the evaluation factor corresponding to each expert;

[0137] The data allocation subunit is used to allocate corresponding project data to be reviewed to each expert in the preset expert group based on the preset data allocation strategy;

[0138] The expert review subunit is used to obtain the review score and corresponding evaluation of each expert in the preset expert group based on the preset scoring rules, and generate review information corresponding to each expert in combination with the evaluation factor of each expert;

[0139] The comprehensive analysis subunit is used to summarize all review information from the preset expert group, and process the summarized information in combination with the preset scoring algorithm to comprehensively generate the review results.

[0140] In this embodiment, the expert database: a database containing a large amount of expert information in the field involved in the project, used to screen out expert data that can be used for project review;

[0141] In this embodiment, the available expert data refers to expert data that can currently conduct project review, for example, experts who do not have time conflicts and are suitable for the field;

[0142] In this embodiment, the expert pool to be processed: a data pool generated by placing available expert data into a blank expert pool;

[0143] In this embodiment, the project characteristic information includes information such as the type, field involved, and scale of the project, which is used to screen the expert data;

[0144] In this embodiment, review indicators: various indicators used to review the project, such as target achievement rate, delivery quality, cost performance, etc.;

[0145] In this embodiment, irrelevant expert data refers to expert data that is not related to the current project review, for example, expert data that is not suitable for the field or has time conflicts, etc.;

[0146] In this embodiment, the preset review rules: that is, the review rules and requirements for the project review are pre-set, for example, the level requirements and number requirements of experts, etc.;

[0147] In this embodiment, the preset expert selection strategy: the strategy for selecting experts from the expert pool to be processed is preset;

[0148] In this embodiment, there are no repeated expert groups: there is at least one different expert in each expert group;

[0149] In this embodiment, the expert group selection instruction: the preset department is used to select a selection instruction of a designated expert group from a plurality of non-overlapping expert groups;

[0150] In this embodiment, expert level refers to the level of an expert in a corresponding field. Experts with higher levels have higher evaluation score weights and credibility.

[0151] In this embodiment, the evaluation factor is the value factor for obtaining the evaluation score when the expert conducts the evaluation;

[0152] In this embodiment, the review information includes the expert information and the review score, evaluation, and combination information of the evaluation factors corresponding to the expert information;

[0153] In this embodiment, the preset scoring algorithm: the algorithm used to analyze and calculate all the review information in the summary information and then obtain the review result is preset;

[0154] The working principle and beneficial effects of the above technical solution are as follows: the present invention first selects available expert data from the expert database, and then screens the expert data in combination with the project feature information of the current project and the evaluation indicators to obtain expert data that can participate in the project evaluation, thereby improving the matching degree between experts and projects, and then obtaining multiple expert groups for selection by preset departments through the evaluation rules and preset expert selection strategies of this evaluation, thereby simplifying the process of determining the expert group by the preset department, and at the same time, according to the preset data allocation measurement category, the project data can be accurately assigned to experts in the corresponding field, thereby reducing the scoring error caused by cross-field scoring of experts, thereby improving the accuracy of the evaluation results.

[0155] The embodiment of the present invention provides a project review business management system, a final review module, including:

[0156] The review result verification unit is used to verify the review results in combination with the original review data of the preset expert group;

[0157] A result analysis unit, used to analyze the content of the review results that meet the preset verification requirements, wherein the preset verification requirements include the completeness, consistency, accuracy and credibility of the data;

[0158] The indicator set acquisition unit is used to acquire the indicator information input by the preset department and summarize and generate a confirmed indicator set, wherein the confirmed indicator set includes project performance indicators, project experience indicators, and project achievement indicators;

[0159] A content division unit is used to select a corresponding data division method from a preset indicator-method database in combination with a confirmed indicator set, and divide the parsed content into multiple data sets based on the data division method;

[0160] A performance confirmation unit is used to confirm the performance review score of the project based on the project performance indicator and the corresponding data set, and generate a first confirmation result, wherein the project performance review score includes the review score of the preset expert group on the progress, cost, and delivery quality of the project;

[0161] An experience confirmation unit is used to confirm the experience review score of the project based on the project experience indicators and the corresponding data set, and generate a second confirmation result, wherein the project experience review score includes the review score of the preset expert group on the challenges, problems, corresponding solutions, final solution effects and summary experience encountered during the implementation of the project;

[0162] The result confirmation unit is used to confirm the result review score of the project based on the project result indicators and the corresponding data set, and generate a third confirmation result, wherein the project result review score includes the review score of the preset expert group on the actual results of the project and the customer's satisfaction;

[0163] A final confirmation unit, used to comprehensively analyze the first confirmation result, the second confirmation result and the third confirmation result through a preset department to generate a final confirmation result;

[0164] The final review unit is used to conduct a final review of the final confirmation results through the second person in charge to generate the final review results of the project. At the same time, it combines the final review results of the project and the improvement suggestions obtained from multiple parties to generate a final review report.

[0165] In this embodiment, the original review data refers to the review scores and evaluation data input by the preset expert group during the review process;

[0166] In this embodiment, verification refers to the operation of verifying and validating the review results to reduce the occurrence of misjudgment;

[0167] In this embodiment, content analysis: used to analyze the review results to facilitate subsequent grouping of the review results;

[0168] In this embodiment, indicator information: indicators used to review projects, such as project goal achievement rate, customer satisfaction, etc.;

[0169] In this embodiment, the preset indicator-method database: a database containing the mapping relationship between indicator information and data partitioning methods, which is pre-set and used to filter and obtain the corresponding data partitioning method according to the input indicator information;

[0170] In this embodiment, data set: a data set obtained by dividing the parsed content of the review result according to the data division method;

[0171] In this embodiment, the first confirmation result is the result obtained after the preset department confirms the performance review score of the project;

[0172] In this embodiment, the second confirmation result: that is, the result obtained after the preset department confirms the experience review score of the project;

[0173] In this embodiment, the third confirmation result is the result obtained after the preset department confirms the project achievement review score;

[0174] In this embodiment, the final confirmation result: the confirmation result obtained by the preset department after comprehensive analysis of the first confirmation result, the second confirmation result and the third confirmation result;

[0175] In this embodiment, the multi-party improvement suggestions refer to improvement suggestions from multiple aspects collected from various links of the project.

[0176] The working principle and beneficial effects of the above technical solution are as follows: the present invention first verifies the review results and the original review data through the review result verification unit, thereby ensuring the consistency, accuracy and credibility of the verification results; and then confirms the performance review score, experience review score and achievement review score of the project through the performance confirmation unit, the experience confirmation unit and the achievement confirmation unit, thereby ensuring the consistency and credibility of the review scores; and then makes a final confirmation through the preset department, and at the same time generates a final review report in combination with the final review of the second person in charge and various improvement suggestions collected during the implementation and review of the project, which not only improves the accuracy of the project review results, but also provides valuable experience for future project implementation.

[0177] The embodiment of the present invention provides a project review business management system, a final confirmation unit, including:

[0178] The curve determination subunit determines the first scoring data of each expert in different review items, and standardizes the first scoring data to obtain a first scoring curve;

[0179] The interception subunit performs position analysis on the scoring position of each first scoring data in the first scoring curve corresponding to the scoring group, and calculates the first bias of each first scoring data;

[0180] ;in, Represents the value of the i1th scoring data; represents the average value of all the scoring data involved in the corresponding scoring group of the i1th scoring data; ln represents the sign of the logarithmic function; Indicates the scoring position of the i1th scoring data Based on the corresponding scoring group The bias adjustment function of the occurrence position of ; represents the variance of the remaining scoring data in the corresponding scoring group except the i1th scoring data; Represents the variance of all scoring data in the corresponding scoring group; represents the first bias of the i1th first scoring data;

[0181] In chronological order, all the first biases that satisfy The first bias is regarded as the prominent bias, in which Indicates the standard bias comparison value;

[0182] In combination with the scoring time of the first scoring data, determining the number of time series between the first prominent bias and the next prominent bias that is adjacent to the first prominent bias and the total bias vector;

[0183] When the number of time series and the total bias vector meet the interception criteria, continue to analyze the next protrusion bias and the next protrusion bias to obtain the boundary bias;

[0184] If satisfied , then the first prominent bias is ignored, where is the comparison coefficient; Indicates the first prominent bias; Indicates the next prominent bias; Indicates the number of corresponding time series;

[0185] If satisfied , then the next prominent bias will be ignored;

[0186] Otherwise, the first protrusion bias and the next protrusion bias are ignored, and the next protrusion bias and the next protrusion bias are analyzed continuously until the boundary bias is obtained;

[0187] The curve position points of the first scoring data corresponding to the boundary bias are regarded as intercepted scoring points to obtain a plurality of intercepted scoring segments;

[0188] The function constructs a subunit, performs preference analysis on the intercepted scoring segment, and constructs the first preference threshold of the corresponding segment;

[0189] ;in, represents the corresponding first preference threshold; Indicates the first bias at the j1th time sequence in the corresponding segment; It represents the average value of the residual bias in the corresponding segment except for the prominent bias; represents the variance of the residual bias in the corresponding segment excluding the prominent bias;

[0190] Determine the preference stability trend of the corresponding expert based on all first preference thresholds and in combination with the value change trend of each intercepted scoring segment;

[0191] ; Among them, Y1 is the preference stable trend function; represents the first preference threshold of the j2-th intercepted scoring segment; represents the line segment fitting coefficient of the j2-th intercepted scoring segment; represents the correlation coefficient of the value change of the j2th intercepted scoring segment;

[0192] According to the first preference threshold and preference stability trend of the corresponding expert in the same review item, the preference correction function of the corresponding expert in the corresponding review item is constructed;

[0193] The score acquisition subunit is used to correct the review score of each review item involved in the performance confirmation unit, the experience confirmation unit, and the achievement confirmation unit according to all preference correction functions;

[0194] The score pair generation subunit is used to establish a scoring node corresponding to each review item, match it with the scoring rule database, obtain the corresponding scoring rule to perform intelligent analysis on the project information in the data set, and associate it with the revised review score under the same review item to generate a score pair;

[0195] The relative scoring subunit is used to calculate the difference between each score pair under the same review item and determine whether the difference meets the consistency condition;

[0196] Calculate the relative score of each review item according to the satisfaction results, and construct the first score pool, the second score pool and the third score pool according to all the relative scores;

[0197] Based on the first score pool, the second score pool and the third score pool, a final confirmation result is calculated.

[0198] In this embodiment, review items: items that require experts to evaluate and score during the expert review stage;

[0199] In this embodiment, the first scoring data: namely, the scoring data given by each expert on different scoring items;

[0200] In this embodiment, standardization is an operation used to eliminate the dimension gap between data to make the scoring data analyzable;

[0201] In this embodiment, the first scoring curve is: a scoring curve constructed by standardizing all first scoring data;

[0202] In this embodiment, the scoring position is the position of each first scoring data in all the scoring data in the corresponding scoring group. The position is obtained by sorting the scoring data. For example, 100, 99, 99, 98, and 97 are considered , at this time, 97 is the corresponding expert's score, so the corresponding score position is , the 5th position, that is, the closer the position is to the front and the closer the position is to the back, the greater the corresponding bias possibility is, that is, the corresponding The bigger.

[0203] In this embodiment, the first bias is a coefficient used to characterize the bias of the expert's scoring data, so as to facilitate the subsequent correction of the expert's scoring data;

[0204] In this embodiment, the prominent bias is: that is, the first bias exceeds the standard bias comparison value;

[0205] In this embodiment, the number of time series: that is, the number of corresponding time nodes in the scoring data between two adjacent prominent biases;

[0206] In this embodiment, the total bias vector is: the sum of the bias differences of all first scoring data between adjacent prominent biases;

[0207] In this embodiment, boundary bias: after analyzing all prominent biases under the same review item, two adjacent prominent biases that are not in the ignore state are regarded as boundary biases. For example, there are prominent biases: a1, a2, and a3. At this time, a2 is in the ignore state, and a1 and a3 are not in the ignore state. At this time, a1 and a3 are regarded as boundary biases;

[0208] In this embodiment, the first preference threshold is used to analyze all first biases of the intercepted scoring segments to determine the threshold of the preference trend of the expert when scoring;

[0209] In this embodiment, the preference stability trend is used to characterize the preference trend of the experts in the review process. For example, when some experts are reviewing and scoring, the review process is relatively strict, so the review scores given are usually lower than the average score.

[0210] In this embodiment, the preference correction function is a function used to correct the preference scores of experts, corresponding to each expert one by one;

[0211] In this embodiment, score pair: a score combination generated by associating the score obtained after intelligent analysis under the same review item with the revised review score;

[0212] In this embodiment, relative scoring: the comprehensive scoring score of the review item is calculated based on the difference between each score pair under the same review item;

[0213] In this embodiment, the result is satisfied: that is, the score pair whose difference satisfies the consistency condition;

[0214] In this embodiment, the first score pool: a score pool including scores corresponding to each review item under the performance confirmation unit;

[0215] In this embodiment, the second score pool: a score pool including scores corresponding to each review item under the experience confirmation unit;

[0216] In this embodiment, the third score pool is a score pool including scores corresponding to each review item under the achievement confirmation unit.

[0217] The working principle and beneficial effects of the above technical solution are as follows: the present invention determines the scoring preference of each expert in the project review process by the scoring data given by each expert in different review items, thereby correcting the expert scoring data under each review item, reducing the preference error of the expert's subjective scoring, and at the same time, combined with the scoring rules screened from the scoring database, the project information is intelligently scored through the scoring system, and the expert scoring data and the intelligent scoring data are comprehensively analyzed to obtain the relative score of each review item, which reduces the interference of human factors and improves the fairness and effectiveness of the scoring data.

[0218] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A project review business management system, characterized in that: include: A project selection module, for selecting and extracting the corresponding target project and the responsible unit of the target project from the project database based on the project selection instruction of the first person in charge of the preset department; A data acquisition module, used to conduct a first review of the target project through the target project responsible unit, and at the same time, obtain the project data reported by the target project responsible unit after the first review is passed, and conduct a second review of the project data through the target project responsible unit; The department preliminary review and expert review module is used to conduct preliminary review of the project materials that have passed the second review through the preset department, and allocate the project materials that have passed the preliminary review to the preset expert group using the preset material allocation strategy; A final review module is used to obtain the review results of the preset expert group and the final confirmation results of the preset department on the review results, and to conduct a final review of the final confirmation results through the second person in charge of the preset department to generate a final review result of the project; Wherein, the final review module includes: An evaluation result verification unit, used to verify the evaluation result in combination with the original evaluation data of the preset expert group; A result analysis unit, used to analyze the content of the review results that meet the preset verification requirements, wherein the preset verification requirements include the integrity, consistency, accuracy and credibility of the data; An indicator set acquisition unit, used to acquire the indicator information input by the preset department, and aggregate and generate a confirmed indicator set, wherein the confirmed indicator set includes project performance indicators, project experience indicators, and project achievement indicators; A content division unit, configured to select a corresponding data division method from a preset indicator-method database in combination with the confirmation indicator set, and divide the parsed content into a plurality of data sets based on the data division method; A performance confirmation unit, configured to confirm the performance review score of the project based on the project performance indicator and the corresponding data set, and generate a first confirmation result, wherein the project performance review score includes the review score of the preset expert group on the progress, cost, and delivery quality of the project; An experience confirmation unit is used to confirm the experience review score of the project based on the project experience indicator and the corresponding data set, and generate a second confirmation result, wherein the project experience review score includes the review score of the preset expert group on the challenges, problems, corresponding solutions, final solution effects and summary experience encountered during the implementation of the project; An achievement confirmation unit is used to confirm the achievement review score of the project based on the project achievement indicator and the corresponding data set, and generate a third confirmation result, wherein the project achievement review score includes the review score of the preset expert group on the actual achievement of the project and the customer's satisfaction; A final confirmation unit, configured to perform a comprehensive analysis on the first confirmation result, the second confirmation result and the third confirmation result through the preset department to generate a final confirmation result; A final review unit is used to conduct a final review of the final confirmation result through the second person in charge to generate a final review result of the project, and at the same time, generate a final review report in combination with the final review result of the project and the improvement suggestions obtained from multiple parties; Wherein, the final confirmation unit includes: The curve determination subunit determines the first scoring data of each expert in different review items, and standardizes the first scoring data to obtain a first scoring curve; The interception subunit performs position analysis on the scoring position of each first scoring data in the first scoring curve corresponding to the scoring group, and calculates the first bias of each first scoring data; ;in, Represents the value of the i1th scoring data; represents the average value of all the scoring data involved in the corresponding scoring group of the i1th scoring data; ln represents the sign of the logarithmic function; Indicates the scoring position of the i1th scoring data Based on the corresponding scoring group The bias adjustment function of the occurrence position of ; represents the variance of the remaining scoring data in the corresponding scoring group except the i1th scoring data; Represents the variance of all scoring data in the corresponding scoring group; represents the first bias of the i1th first scoring data; In chronological order, all the first biases that satisfy The first bias is regarded as the prominent bias, in which Indicates the standard bias comparison value; In combination with the scoring time of the first scoring data, determining the number of time series between the first prominent bias and the next prominent bias that is adjacent to the first prominent bias and the total bias vector; When the number of time series and the total bias vector meet the interception standard, continue to analyze the next protrusion bias and the next protrusion bias to obtain the boundary bias; If satisfied , then the first prominent bias is ignored, where is the comparison coefficient; Indicates the first prominent bias; Indicates the next prominent bias; Indicates the number of corresponding time series; If satisfied , then the next prominent bias will be ignored; Otherwise, the first protrusion bias and the next protrusion bias are ignored, and the next protrusion bias and the next protrusion bias are analyzed continuously until the boundary bias is obtained; The curve position points of the first scoring data corresponding to the boundary bias are regarded as intercepted scoring points to obtain a plurality of intercepted scoring segments; A function construction subunit performs a preference analysis on the intercepted scoring segment to construct a first preference threshold value for the corresponding segment; ;in, represents the corresponding first preference threshold; Indicates the first bias at the j1th time sequence in the corresponding segment; It represents the average value of the residual bias in the corresponding segment except for the prominent bias; represents the variance of the residual bias in the corresponding segment excluding the prominent bias; Determine the preference stability trend of the corresponding expert based on all first preference thresholds and in combination with the value change trend of each intercepted scoring segment; ; Among them, Y1 is the preference stable trend function; represents the first preference threshold of the j2-th intercepted scoring segment; represents the line segment fitting coefficient of the j2-th intercepted scoring segment; represents the correlation coefficient of the value change of the j2th intercepted scoring segment; According to the first preference threshold and preference stability trend of the corresponding expert in the same review item, the preference correction function of the corresponding expert in the corresponding review item is constructed; A score acquisition subunit is used to correct the review score of each review item involved in the performance confirmation unit, the experience confirmation unit, and the achievement confirmation unit according to all preference correction functions; The score pair generation subunit is used to establish a scoring node corresponding to each review item, match it with the scoring rule database, obtain the corresponding scoring rule to perform intelligent analysis on the project information in the data set, and associate it with the revised review score under the same review item to generate a score pair; The relative scoring subunit is used to calculate the difference between each score pair under the same review item and determine whether the difference meets the consistency condition; Calculate the relative score of each review item according to the satisfaction results, and construct the first score pool, the second score pool and the third score pool according to all the relative scores; Based on the first score pool, the second score pool and the third score pool, a final confirmation result is calculated.

2. A project review business management system according to claim 1, characterized in that: The project selection module includes: An instruction acquisition unit, used to acquire a project selection instruction input by the first person in charge of the preset department in combination with a preset project screening condition; The project selection unit is used to select a target project and a target project responsible unit that match the project selection instruction in the project database, and send the target project to the corresponding project responsible unit.

3. A project review business management system according to claim 2, characterized in that: The data acquisition module comprises: A sub-project acquisition unit, used to deconstruct and divide the target project to obtain all sub-projects under the same target project; A person-in-charge matching unit, configured to select a third person-in-charge corresponding to the target project and a fourth person-in-charge corresponding to the subproject from a preset project-person-in-charge comparison table based on the input target project and subproject; The project review unit is used to conduct a first review of the target project and sub-projects through the target project responsible unit and obtain a first review result.

4. A project review business management system according to claim 3, characterized in that: The data acquisition module further includes: A data packet generating unit, configured to obtain the first audit result satisfying the first audit condition and the corresponding target project and sub-project, and generate a data packet to be filled in; A data filling unit, used for sending data filling instructions to the third person in charge and the fourth person in charge respectively based on the data packet to be filled in, and writing the filling contents input by the third person in charge and the fourth person in charge into the data packet to be filled in, so as to generate project data; The document review unit is used to conduct a second review of the project document through the responsible unit of the target project.

5. A project review business management system according to claim 1, characterized in that: The departmental preliminary review and expert review modules include: A material transfer unit, used to mark the project materials that have passed the second review as materials to be reviewed first, and transfer them to the preset department; The preliminary review unit is used to conduct preliminary review on the preliminary review materials through the preset department, return the preliminary review materials that fail the preliminary review to the target project responsible unit, and generate a data copy instruction to send to the target project responsible unit to rewrite the data and regenerate the preliminary review materials; The expert review unit is used to obtain a preset expert group and a corresponding preset data allocation strategy, and allocate the pending project data that have passed the preliminary review to the corresponding preset expert group for review according to the preset data allocation strategy.

6. A project review business management system according to claim 5, characterized in that: The expert review unit includes: An expert information acquisition subunit is used to acquire available expert data from an expert database, and put the available expert data into a blank expert pool to generate an expert pool to be processed; A project feature extraction subunit, used to extract feature information of the target project and generate project feature information; An evaluation indicator acquisition subunit is used to acquire evaluation indicators matching the target project; An expert field matching subunit is used to process the expert data in the expert pool to be processed based on the project feature information and the review index, and eliminate irrelevant expert data; The expert level screening subunit is used to obtain the expert level in the patent pool to be processed after the elimination process, and screen out the expert data that does not meet the preset expert level requirements in combination with the review indicators; An expert number determination subunit, used to determine the number of the preset expert group based on preset review rules; An expert group selection subunit is used to select a plurality of non-overlapping expert groups from the expert pool to be processed based on a preset expert selection strategy; The expert group determination subunit is used to obtain the expert group selection instruction of the preset department, determine the preset expert group for the target project review, and at the same time, obtain the evaluation factor corresponding to each expert in combination with the expert level of each expert in the preset expert group; A data distribution subunit, configured to distribute corresponding project data to be reviewed to each expert in the preset expert group based on the preset data distribution strategy; The expert review subunit is used to obtain the review score and corresponding evaluation of each expert in the preset expert group based on the preset scoring rules, and generate review information corresponding to each expert in combination with the evaluation factor of each expert; The comprehensive analysis subunit is used to summarize all the review information from the preset expert group, and process the summarized information in combination with a preset scoring algorithm to comprehensively generate a review result.

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