An intelligent progress control method and system for information engineering supervision

Abstract

The present invention discloses an intelligent progress control method and system for information engineering supervision, which relates to the field of information engineering supervision, wherein the method comprises: loading basic project information; generating multiple groups of construction chain lists; matching construction chain nodes and node planned schedules; generating schedule deviation parameters; generating construction status classification results; and performing multi-factor status scoring based on the results to generate construction status multi-factor scoring results; generating deviation factor feature vectors based on the construction status multi-factor scoring results and schedule deviation parameters, setting the feature vectors as progress control reference data and sending them to supervision staff. The method solves the technical problem of low accuracy of progress control for information engineering supervision in the prior art, resulting in poor progress control effects. The method achieves technical effects such as improving the accuracy and precision of progress control for information engineering supervision, and improving the effect and quality of progress control.

Description

Technical Field

[0001] The present invention relates to the field of information engineering supervision, and in particular to an intelligent progress control method and system for information engineering supervision. Background Art

[0002] With the continuous development of science and technology, information technology has been widely applied to all aspects of people's production and life. At the same time, people's demand for information technology has also continued to increase. This has led to the integration of traditional civil engineering construction with information technology, introducing information engineering construction supervision, or information engineering supervision. Progress control is a crucial component of information engineering supervision and one of its main areas of focus. The primary goal of progress control is to ensure that the project is completed and delivered within the contractually stipulated timeframe. Progress control permeates the entire process of information engineering supervision and is of vital importance to it.

[0003] In the prior art, there is a technical problem that the accuracy of progress control for information engineering supervision is not high, resulting in poor progress control effects. Summary of the Invention

[0004] The present application provides an intelligent progress control method and system for information engineering supervision, which solves the technical problem in the prior art that the progress control accuracy of information engineering supervision is not high, resulting in poor progress control effect.

[0005] In view of the above problems, the present application provides an intelligent progress control method and system for information engineering supervision.

[0006] In the first aspect, the present application provides an intelligent progress control method for information engineering supervision, wherein the method is applied to an intelligent progress control system for information engineering supervision, and the method includes: loading basic project information, wherein the basic project information includes progress record data and construction status information; decomposing the construction process of the project according to the planned progress information, and generating multiple groups of construction chain lists; inputting the basic project information into the multiple groups of construction chain lists, matching the construction chain nodes and the node planned progress; generating progress deviation parameters based on the matching and comparison of the progress record data and the node planned progress; classifying according to the construction status information and the construction chain nodes, and generating a construction status classification result; traversing the construction status classification result to perform multi-factor status scoring, and generating a construction status multi-factor scoring result; generating a deviation factor feature vector based on the construction status multi-factor scoring result and the progress deviation parameter, and setting the deviation factor feature vector as progress control reference data and sending it to the supervision staff.

[0007] In the second aspect, the present application also provides an intelligent progress control system for information engineering supervision, wherein the system includes: an information loading module, the information loading module is used to load basic project information, wherein the basic project information includes progress record data and construction status information; a disassembly module, the disassembly module is used to disassemble the construction process of the project according to the planned progress information, and generate multiple groups of construction chain lists; a matching module, the matching module is used to input the basic project information into the multiple groups of construction chain lists, match the construction chain nodes and the node planned progress; a comparison module, the comparison module is used to match and compare the progress record data with the node planned progress, and generate a progress deviation parameter; a classification module, the classification module is used to classify according to the construction status information and the construction chain nodes, and generate a construction status classification result; a scoring module, the scoring module is used to traverse the construction status classification result to perform multi-factor status scoring, and generate a construction status multi-factor scoring result; a control module, the control module is used to generate a deviation factor feature vector based on the construction status multi-factor scoring result and the progress deviation parameter, and set the deviation factor feature vector as progress control reference data and send it to the supervision staff.

[0008] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0009] By loading the basic project information, progress record data and construction status information are obtained. The planned progress information is used to disassemble the project construction process and generate multiple sets of construction chain lists. The basic project information is input into the multiple sets of construction chain lists, and the construction chain nodes and node planned progress are matched to obtain the progress deviation parameters and construction status classification results. The construction status classification results are traversed to perform multi-factor status scoring, and the construction status multi-factor scoring results are generated. Combined with the progress deviation parameters, the deviation factor feature vector is generated and set as the progress control reference data and sent to the supervision staff. The results achieve the goal of improving the precision and accuracy of the progress control of information engineering supervision, improving the effectiveness and quality of progress control; at the same time, a progress control method for optimizing information engineering supervision is designed to effectively improve the efficiency of progress control; reduce the cost of progress control, and avoid the waste of human, material and other resources during progress control. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] Figure 1 A flowchart of an intelligent progress control method for information engineering supervision is provided for this application;

[0011] Figure 2 This is a flowchart of generating multiple construction chain lists in an intelligent progress control method for information engineering supervision in this application;

[0012] Figure 3This is a flowchart of generating a multi-factor scoring result of a construction status in an intelligent progress control method for information engineering supervision;

[0013] Figure 4 This application is a structural diagram of an intelligent progress control system for information engineering supervision.

[0014] Explanation of the accompanying drawings: information loading module 11, disassembly module 12, matching module 13, comparison module 14, classification module 15, scoring module 16, management and control module 17. DETAILED DESCRIPTION

[0015] This application provides an intelligent progress control method and system for information engineering supervision. It solves the technical problem in the prior art that the progress control accuracy of information engineering supervision is not high, resulting in poor progress control effect. It achieves the goal of improving the precision and accuracy of progress control in information engineering supervision, and improving the effect and quality of progress control. At the same time, it designs a progress control method for optimizing information engineering supervision, effectively improving the efficiency of progress control, reducing the cost of progress control, and avoiding the waste of human, material and other resources during progress control.

[0016] Example 1

[0017] Please see the attached Figure 1 The present application provides an intelligent progress control method for information engineering supervision, wherein the method is applied to an intelligent progress control system for information engineering supervision, and the method specifically comprises the following steps:

[0018] Step S100: loading basic project information, wherein the basic project information includes progress record data and construction status information;

[0019] Specifically, the basic information of the project is automatically loaded by the information loading module of the intelligent progress control system for information engineering supervision. The project is any project that uses the intelligent progress control system for information engineering supervision of the present application for progress control. For example, the project can be a railway station renovation project, a subway construction project, a scenic area construction project, etc. The basic information of the project includes progress record data and construction status information. The progress record data includes data information such as project location, project type, project plan, construction unit, contractor, supervision record data, etc. The construction status information includes data information such as time, weather, construction content, and construction progress completion status. The technical effect of loading the basic information of the project, determining the basic information of the project, and providing data support for the subsequent generation of multiple groups of construction chain lists is achieved.

[0020] Step S200: Decomposing the construction process of the project according to the planned progress information and generating multiple construction chain lists;

[0021] Further, as attached Figure 2 As shown, step S200 of this application also includes:

[0022] Step S210: extracting the basic information of the project and generating construction areas and construction tasks, wherein the construction areas and the construction tasks correspond one to one;

[0023] Step S220: traverse the construction area, match the work types according to the construction tasks, and generate construction work type information and construction sequence information;

[0024] Step S230: Sort the construction work type information according to the construction sequence information to generate a first-level construction chain list;

[0025] Step S240: Initializing the first-level construction chain list according to the planned progress information to generate a second-level construction chain list;

[0026] Step S250: setting the secondary construction chain list as the multiple groups of construction chain lists.

[0027] Specifically, the obtained basic project information contains a large amount of data and is relatively complex. This data is then extracted to obtain construction areas and construction tasks. Based on this data, work type matching is performed to obtain construction work type information and construction sequence information. Furthermore, the construction work type information is sorted using the construction sequence information to obtain a first-level construction chain list, which is initialized using the planned progress information to generate a second-level construction chain list, which is the multiple construction chain lists. The construction area includes data such as the construction location and construction area. The construction task includes data such as the specific construction project and construction plan corresponding to the construction area. Furthermore, there is a one-to-one correspondence between the construction area and the construction task. The construction work type information includes data such as the type and number of workers required for the construction area. The construction sequence information represents data such as the construction sequence and steps for the construction area. Different work types in the construction work type information are performed in the same construction area according to the construction sequence information, forming a construction chain. Traversing all construction areas results in multiple construction chains, which form the first-level construction chain list. The second-level construction chain list is obtained by adjusting the obtained first-level construction chain list according to the planned progress information. The planned progress information is the specific ideal construction progress information for each construction phase divided during construction budgeting. This achieves the technical effect of obtaining multiple sets of construction chain lists with high accuracy and adaptability, laying the foundation for subsequent matching of construction chain nodes with their planned progress.

[0028] Step S300: inputting the basic information of the project into the multiple sets of construction chain lists, and matching the construction chain nodes and the node plan schedules;

[0029] Step S400: Generate a progress deviation parameter based on the progress record data and the node plan progress match comparison;

[0030] Step S500: Classify the construction status information and the construction chain nodes to generate a construction status classification result;

[0031] Specifically, the obtained project basic information is input as input into the multiple construction chain lists to match construction chain nodes with the node planned schedules. On the one hand, a progress deviation parameter is obtained by matching and comparing the progress record data in the project basic information with the node planned schedules. On the other hand, a construction status classification result is obtained by classifying the construction status information in the project basic information and the construction chain nodes. The node planned schedule is a parameter representing the planned schedule information corresponding to the progress record data. The progress record data has a one-to-one correspondence with the node planned schedule. The progress deviation parameter is data information representing the progress deviation between the progress record data and the node planned schedule. The construction chain node is data information representing the node corresponding to the construction status information in the multiple construction chain lists. The construction status classification result includes data information such as the manpower status information, material status information, and financial status information corresponding to the construction status information. This achieves the technical effect of inputting the project basic information into the multiple construction chain lists to obtain relatively accurate progress deviation parameters and construction status classification results, providing data support for subsequent intelligent progress control.

[0032] Step S600: traversing the construction status classification results to perform multi-factor status scoring and generate a construction status multi-factor scoring result;

[0033] Further, as attached Figure 3 As shown, step S600 of this application also includes:

[0034] Step S610: Split the construction status classification results to generate human resource status classification results, material resource status classification results, and capital status classification results;

[0035] Specifically, the intelligent progress control system for information engineering supervision automatically adaptively splits the obtained construction status classification results to obtain human status classification results, material status classification results and financial status classification results. Among them, the human status classification results include data information such as construction efficiency and construction quality. The material status classification results include data information such as construction material quality and construction material supply efficiency. The financial status classification results include data information such as the amount of disposable funds during the construction period and the construction cost budget amount. The technical effect of determining the human status classification results, material status classification results and financial status classification results is achieved, and providing data support for the subsequent scoring using the random forest status scoring model.

[0036] Step S620: Obtain a random forest status scoring model, score the human resource status classification results, material resource status classification results, and financial status classification results respectively, and generate a human resource status scoring list, a material resource status scoring list, and a financial status scoring list;

[0037] Furthermore, step S620 of this application also includes:

[0038] Step S621: The random forest status scoring model includes a human resource status scoring sub-model, a material resource status scoring sub-model, and a financial status scoring sub-model;

[0039] Step S622: inputting the human status classification result into the human status scoring sub-model to generate a human status scoring list;

[0040] Step S623: inputting the physical state classification result into the physical state scoring sub-model to generate a physical state scoring list;

[0041] Step S624: input the fund status classification result into the fund status scoring sub-model to generate a fund status scoring list.

[0042] Specifically, the human status classification result is used as input information and input into the human status scoring sub-model to obtain a human status scoring list. Furthermore, the physical status classification result is used as input information and input into the physical status scoring sub-model to obtain a physical status scoring list. Subsequently, the financial status classification result is used as input information and input into the financial status scoring sub-model to obtain a financial status scoring list. The human status scoring sub-model is trained using a large amount of data and information related to the human status classification results, and has the functions of intelligently scoring the input human status classification results and automatically generating a human status scoring list. The human status scoring list is data information used to represent the scores corresponding to the human status classification results. Similarly, the physical status scoring sub-model is trained using a large amount of data and information related to the physical status classification results, and has the functions of intelligently scoring the input physical status classification results and automatically generating a physical status scoring list. The physical status scoring list is data information used to represent the scores corresponding to the physical status classification results. The fund status scoring sub-model is obtained through training with a large amount of data information related to the fund status classification results, and has the functions of intelligently scoring the input fund status classification results and automatically generating a fund status scoring list. The fund status scoring list is data information used to characterize the score corresponding to the fund status classification result. The human status scoring sub-model, the material status scoring sub-model and the fund status scoring sub-model are all included in the random forest state scoring model. After jointly training the human status scoring sub-model, the material status scoring sub-model and the fund status scoring sub-model through the random forest method, the random forest state scoring model is obtained. The randomness of the random forest method is reflected in the random selection of sample features and random selection of samples, and has the advantages of fast training speed, simplicity, strong generalization ability, and easy implementation. The technical effect of obtaining a human status scoring list, a material status scoring list and a fund status scoring list with high accuracy through the random forest state scoring model is achieved, laying the foundation for the subsequent generation of scoring screening results.

[0043] Step S630: Traversing the human resource status scoring threshold, material resource status scoring threshold, and financial resource status scoring threshold corresponding to the construction chain nodes to filter the human resource status scoring list, the material resource status scoring list, and the financial resource status scoring list to generate a scoring filtering result;

[0044] Furthermore, step S630 of this application also includes:

[0045] Step S631: When the manpower status score of the same node meets the manpower status score threshold, the material status score meets the material status score threshold, and the capital status score meets the capital status score threshold, the node is added to the score screening result;

[0046] Step S632: When the manpower status score of the same node does not meet the manpower status score threshold, or the material status score does not meet the material status score threshold, or the capital status score does not meet the capital status score threshold, a status deviation factor is added;

[0047] Step S633: constructing a single factor feature vector according to the state deviation factor and the progress deviation parameter of the corresponding node, and expanding the deviation factor feature vector.

[0048] Specifically, the human status score list, the material status score list, and the financial status score list are screened based on the human status score threshold, material status score threshold, and financial status score threshold corresponding to the construction chain node to obtain a score screening result. If the human status score, material status score, and financial status score of the same node (i.e., the same construction chain node) all meet the corresponding human status score threshold, material status score threshold, and financial status score threshold, then the human status score, material status score, and financial status score of that node are added to the score screening result. If the human status score of the same node (i.e., the same construction chain node) does not meet the human status score threshold, then the human status score of that node is added to the state deviation factor. Similarly, if the material status score of the same node (i.e., the same construction chain node) does not meet the material status score threshold, then the material status score of that node is added to the state deviation factor. If the financial status score of the same node (i.e., the same construction chain node) does not meet the financial status score threshold, then the financial status score of that node is added to the state deviation factor. Further, a single factor feature vector is constructed, and the deviation factor feature vector is expanded based on the single factor feature vector. The human resource status scoring threshold, the material resource status scoring threshold, and the financial resource status scoring threshold can be pre-set and determined by an intelligent progress control system for information engineering supervision after comprehensively analyzing the key points and difficulties of the progress control process, or can be adaptively set based on actual progress control needs. The scoring screening results include a human resource status score, a material resource status score, and a financial resource status score. Furthermore, the human resource status score, the material resource status score, and the financial resource status score in the scoring screening results all meet the corresponding human resource status scoring threshold, the material resource status score threshold, and the financial resource status score threshold. The status deviation factor includes a human resource status score that does not meet the human resource status scoring threshold, a material resource status score that does not meet the material resource status scoring threshold, and a financial resource status score that does not meet the financial resource status scoring threshold. The single-factor feature vector is a vector composed of the state deviation factor and the progress deviation parameter of the corresponding node. This achieves the technical effect of obtaining a highly reliable scoring screening result, providing data support for the subsequent multi-factor scoring results of the construction status. Simultaneously, a single-factor feature vector is constructed to improve the accuracy of the subsequently obtained deviation factor feature vector.

[0049] Step S640: Score the construction chain nodes according to the score screening result to generate the construction status multi-factor score result.

[0050] Furthermore, step S640 of this application also includes:

[0051] Step S641: sorting the human resource status score, the material resource status score, and the financial status score of the same node in the score screening result from highest to lowest according to the scores, and generating a score sorting result;

[0052] Step S642: inputting the score ranking result into the weight allocation table for weight matching to generate a weight allocation result;

[0053] Step S643: performing weighted summation according to the weight distribution results to generate the multi-factor scoring result of the construction status.

[0054] Specifically, the human resource status score, the material resource status score, and the financial status score of the same node (i.e., the same construction chain node) in the scoring screening results are sorted from largest to smallest to generate a scoring ranking result, which is then input into a weight allocation table for weight matching to obtain a weight allocation result. Furthermore, the weight allocation results are weighted and summed to obtain the multi-factor scoring result of the construction status. The scoring ranking result is data information used to characterize the relative scores among the human resource status score, the material resource status score, and the financial status score of the same node (i.e., the same construction chain node) in the scoring screening results. The higher the score, the higher the ranking. The weight allocation table can be custom constructed by a weight allocation expert group based on experience and is used to weight match the scoring ranking results, thereby ensuring that the final multi-factor scoring result of the construction status can characterize the coordination of the three aspects of human resource status, material resource status, and financial status. The multi-factor scoring result of the construction status is the comprehensive scoring data information of the construction status after sorting, weight matching, and weighted summing the human resource status score, the material resource status score, and the financial status score in the scoring screening results. The technical effect of obtaining a multi-factor scoring result of construction status with high rationality and accuracy, and providing data support for the subsequent acquisition of the deviation factor characteristic vector, was achieved.

[0055] Step S700: Generate a deviation factor characteristic vector based on the construction status multi-factor scoring result and the progress deviation parameter, and set the deviation factor characteristic vector as progress control reference data and send it to the supervision staff.

[0056] Furthermore, step S700 of this application also includes:

[0057] Step S710: determining whether the construction status multi-factor scoring result meets the multi-factor scoring threshold;

[0058] Step S720: If not satisfied, construct a multi-factor feature vector based on the construction status multi-factor scoring result and the progress deviation parameter of the corresponding node, and add it to the deviation factor feature vector.

[0059] Specifically, it is judged whether the multi-factor scoring result of the construction status meets the multi-factor scoring threshold. If the multi-factor scoring result of the construction status meets the multi-factor scoring threshold, it indicates that the overall coordination of the construction status is good and the deviation of each node of the construction status is within the allowable range. If the multi-factor scoring result of the construction status does not meet the multi-factor scoring threshold, it indicates that the overall construction status is not coordinated and in-depth progress control is required. Based on this, a multi-factor characteristic vector is constructed, which is the deviation factor characteristic vector, and it is set as the progress control reference data and sent to the supervision staff, and then the specific nodes and locations of the progress control are determined, the scope of investigation is narrowed, and the efficiency of progress control is improved. Among them, the multi-factor scoring threshold can be automatically set and determined by the intelligent progress control system of the information engineering supervision. The deviation factor characteristic vector is a vector composed of the multi-factor scoring result of the construction status and the progress deviation parameter. The technical effect of obtaining a more accurate deviation factor characteristic vector and improving the quality and efficiency of progress control is achieved.

[0060] In summary, the intelligent progress control method for information engineering supervision provided by this application has the following technical effects:

[0061] By loading the basic project information, progress record data and construction status information are obtained. The planned progress information is used to disassemble the project construction process and generate multiple sets of construction chain lists. The basic project information is input into the multiple sets of construction chain lists, and the construction chain nodes and node planned progress are matched to obtain the progress deviation parameters and construction status classification results. The construction status classification results are traversed to perform multi-factor status scoring, and the construction status multi-factor scoring results are generated. Combined with the progress deviation parameters, the deviation factor feature vector is generated and set as the progress control reference data and sent to the supervision staff. The results achieve the goal of improving the precision and accuracy of the progress control of information engineering supervision, improving the effectiveness and quality of progress control; at the same time, a progress control method for optimizing information engineering supervision is designed to effectively improve the efficiency of progress control; reduce the cost of progress control, and avoid the waste of human, material and other resources during progress control.

[0062] Example 2

[0063] Based on the same inventive concept as the intelligent progress control method for information engineering supervision in the aforementioned embodiment, the present invention also provides an intelligent progress control system for information engineering supervision, please refer to the attached Figure 4 , the system comprising:

[0064] An information loading module 11 is used to load basic project information, wherein the basic project information includes progress record data and construction status information;

[0065] A disassembly module 12 is used to disassemble the construction process of the project according to the planned progress information and generate multiple groups of construction chain lists;

[0066] A matching module 13 is configured to input the basic project information into the multiple construction chain lists and match the construction chain nodes with the node plan schedules;

[0067] A comparison module 14 is configured to generate a progress deviation parameter based on a comparison between the progress record data and the node plan progress;

[0068] A classification module 15 is configured to classify the construction status information and the construction chain nodes to generate a construction status classification result;

[0069] A scoring module 16 is configured to perform multi-factor scoring on the construction status classification results to generate a multi-factor scoring result of the construction status;

[0070] The control module 17 is used to generate a deviation factor characteristic vector based on the multi-factor scoring result of the construction status and the progress deviation parameter, and set the deviation factor characteristic vector as progress control reference data to send to the supervision staff.

[0071] This application provides an intelligent progress control method for information engineering supervision, wherein the method is applied to an intelligent progress control system for information engineering supervision. The method comprises: loading basic project information to obtain progress record data and construction status information. Using the planned progress information, the project construction process is decomposed to generate multiple construction chain lists. The basic project information is input into the multiple construction chain lists, and the construction chain nodes are matched with the node planned progress to obtain progress deviation parameters and construction status classification results. The construction status classification results are traversed to perform a multi-factor status score, generating a multi-factor construction status score result. Combined with the progress deviation parameters, a deviation factor feature vector is generated, which is used as reference data for progress control and sent to supervision personnel. This method solves the technical problem of low precision and poor progress control effectiveness in the prior art for information engineering supervision. The method improves the precision and accuracy of progress control in information engineering supervision, and enhances the effectiveness and quality of progress control. Furthermore, a method for optimizing progress control in information engineering supervision is designed, effectively improving progress control efficiency, reducing progress control costs, and avoiding the waste of human, material, and other resources during progress control.

[0072] This specification and drawings are merely exemplary illustrations of the present application. If modifications and variations of the present invention fall within the scope of the present invention and its equivalents, the present invention is intended to include these modifications and variations.

Claims

1. An intelligent progress control method for information engineering supervision, characterized in that: The method is applied to an intelligent progress control system for information engineering supervision, and the method includes: Loading basic project information, wherein the basic project information includes progress record data and construction status information; Decompose the project construction process according to the planned progress information and generate multiple construction chain lists; Inputting the basic project information into the multiple construction chain lists, and matching the construction chain nodes with the node plan schedules; Generate a progress deviation parameter based on the matching and comparison between the progress record data and the node plan progress; Classify the construction status information and the construction chain nodes to generate a construction status classification result; Traversing the construction status classification results to perform multi-factor status scoring and generate a construction status multi-factor scoring result; Generate a deviation factor characteristic vector based on the construction status multi-factor scoring result and the progress deviation parameter, and set the deviation factor characteristic vector as progress control reference data and send it to the supervision staff; The project construction process is disassembled according to the planned progress information to generate multiple sets of construction chain lists, including: Extracting the basic information of the project to generate construction areas and construction tasks, wherein the construction areas and the construction tasks correspond one to one; Traversing the construction area, matching work types according to the construction tasks, and generating construction work type information and construction sequence information; Sort the construction work type information according to the construction sequence information to generate a first-level construction chain list; Initializing the first-level construction chain list according to the planned progress information to generate a second-level construction chain list; Setting the secondary construction chain list to the multiple groups of construction chain lists; The traversing the construction status classification results to perform multi-factor status scoring and generate a construction status multi-factor scoring result includes: Splitting the construction status classification results to generate a human resource status classification result, a material resource status classification result, and a capital status classification result; Obtaining a random forest status scoring model, scoring the human resource status classification results, material resource status classification results, and financial status classification results respectively, and generating a human resource status scoring list, a material resource status scoring list, and a financial status scoring list; Traversing the human resource status scoring threshold, the material resource status scoring threshold, and the financial status scoring threshold corresponding to the construction chain nodes one by one to filter the human resource status scoring list, the material resource status scoring list, and the financial status scoring list to generate a scoring screening result; The scores of the construction chain nodes are summed according to the score screening results to generate the construction status multi-factor score result.

2. The method according to claim 1, wherein The random forest status scoring model is obtained, and the human resource status classification results, material resource status classification results, and financial status classification results are scored respectively to generate a human resource status scoring list, a material resource status scoring list, and a financial status scoring list, including: The random forest status scoring model includes a human status scoring sub-model, a material status scoring sub-model and a financial status scoring sub-model; Inputting the human status classification result into the human status scoring sub-model to generate a human status scoring list; Inputting the physical state classification result into the physical state scoring sub-model to generate a physical state scoring list; The fund status classification result is input into the fund status scoring sub-model to generate a fund status scoring list.

3. The method according to claim 1, wherein The traversal of the human resource status scoring threshold, the material resource status scoring threshold, and the financial resource status scoring threshold corresponding to the construction chain nodes one by one to filter the human resource status scoring list, the material resource status scoring list, and the financial resource status scoring list to generate a scoring screening result further includes: When the manpower status score of the same node meets the manpower status score threshold, the material status score meets the material status score threshold, and the capital status score meets the capital status score threshold, the node is added to the score screening result; When the manpower status score of the same node does not meet the manpower status score threshold, or the material status score does not meet the material status score threshold, or the capital status score does not meet the capital status score threshold, a status deviation factor is added; A single factor feature vector is constructed according to the state deviation factor and the progress deviation parameter of the corresponding node, and the deviation factor feature vector is expanded.

4. The method according to claim 1, wherein The step of summing up the scores of the construction chain nodes according to the score screening results to generate the construction status multi-factor score result includes: sorting the human resource status score, the material resource status score, and the financial status score of the same node in the score screening result according to the scores from largest to smallest, and generating a score sorting result; Input the score ranking results into the weight allocation table for weight matching to generate a weight allocation result; A weighted sum is performed according to the weight distribution result to generate the multi-factor scoring result of the construction status.

5. The method according to claim 1, wherein Generating a deviation factor characteristic vector according to the construction status multi-factor scoring result and the progress deviation parameter includes: Determining whether the construction status multi-factor scoring result meets the multi-factor scoring threshold; If not, a multi-factor feature vector is constructed based on the multi-factor scoring result of the construction status and the progress deviation parameter of the corresponding node, and added to the deviation factor feature vector.

6. An intelligent progress control system for information engineering supervision, characterized in that: An intelligent progress control method for information engineering supervision according to any one of claims 1 to 5, the system comprising: An information loading module, the information loading module is used to load basic project information, wherein the basic project information includes progress record data and construction status information; A disassembly module, which is used to disassemble the construction process of the project according to the planned progress information and generate multiple groups of construction chain lists; A matching module, the matching module is used to input the basic information of the project into the multiple groups of construction chain lists, and match the construction chain nodes and the node plan schedule; A comparison module, configured to generate a progress deviation parameter based on a comparison between the progress record data and the node plan progress; A classification module, the classification module is used to classify the construction status information and the construction chain nodes to generate a construction status classification result; A scoring module, wherein the scoring module is used to traverse the construction status classification results to perform multi-factor status scoring and generate a construction status multi-factor scoring result; The control module is used to generate a deviation factor characteristic vector based on the multi-factor scoring result of the construction status and the progress deviation parameter, and set the deviation factor characteristic vector as progress control reference data and send it to the supervision staff.

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