Data entry system and method for construction project progress management

Abstract

The invention discloses a data entry system and method for constructional engineering progress management, and relates to the technical field of informatization management, the system comprises a mobile terminal and a server, and an image acquisition module in the mobile terminal is used for acquiring an engineering field image; the environmental parameter acquisition module is used for acquiring environmental parameters of an engineering site, wherein the environmental parameters comprise acquisition time, geographic coordinates, environmental temperature and environmental humidity; the engineering data entry interface is issued through the server; an identity verification module in the server is used for verifying the dynamic identity token; the engineering data entry library is used for storing various engineering data entry interfaces; the project progress identification module is used for carrying out image preprocessing and identification on the project field image; according to the method, the complex data entry work becomes simple and efficient, the normalization of original data acquisition and the accuracy of final entry data are ensured, and the real-time updating of the project progress is realized.

Description

Technical Field

[0001] This application relates to the field of information management technology, and in particular to a data entry system and method for construction project progress management. Background Technology

[0002] Construction project schedule management refers to a series of management activities that organize, coordinate, control, and supervise the entire process of a construction project from planning and design to construction completion. Its main objective is to ensure that the construction project can be completed in accordance with the predetermined time, quality, cost, and safety standards, and to meet the project's design requirements and the client's expectations.

[0003] Construction project schedule management is key to the success of construction projects. Traditionally, progress data entry mainly relies on managers manually recording data on-site and then manually entering it into the computer system back in the office.

[0004] Traditional methods have the following significant drawbacks: manual recording and secondary data entry are time-consuming and labor-intensive, resulting in the inability to update progress data in real time, making it impossible for managers to obtain the latest situation on the construction site, leading to insufficient basis for decision-making; the judgment of project progress relies heavily on personal experience, and different personnel have different judgment standards, which can easily lead to errors and ambiguities, resulting in low data reliability; traditional data entry is mostly simple numbers or text, lacking the correlation of multi-dimensional information on site such as images, locations, and environments, making it difficult to trace and verify, and leaving no evidence to refer to in case of disputes. Summary of the Invention

[0005] The purpose of this application is to provide a data entry system and method for construction project progress management, which reduces the burden and error probability of project progress management personnel, and makes complex data entry work simple and efficient; it ensures the standardization of original data collection and the accuracy of final entered data, provides strong evidence for project progress data, and forms an immutable data chain; it solves the problem that managers cannot obtain the latest situation on the construction site, resulting in insufficient decision-making basis, realizes real-time updates of project progress, and improves the efficiency and objectivity of project progress management.

[0006] To achieve the above objectives, this application provides the following solution: Firstly, this application provides a data entry system for construction project progress management, including a mobile terminal and a server. The mobile terminal is used by project progress management personnel to operate on-site, specifically including: The image acquisition module is used to acquire images of the construction site that include markers indicating the progress of the project. An environmental parameter acquisition module is used to collect environmental parameters at the engineering site. The environmental parameters include one or more of the following: acquisition time, geographic coordinates, ambient temperature, and ambient humidity. The engineering data entry interface, distributed through the server, is used to receive revisions and confirmations from engineering progress management personnel regarding the draft engineering progress data. Data transmission module, used for wireless data transmission; The server is used for remote data processing, and wirelessly transmits data to the mobile terminal through the data transmission module. It receives engineering site images acquired by the image acquisition module and environmental parameters acquired by the environmental parameter acquisition module, specifically including: The identity verification module is used to verify dynamic identity tokens; Engineering data entry library, used to store various engineering data entry interfaces; The project progress recognition module is used to preprocess and recognize the project site images; A baseline schedule library is used to store baseline schedule nodes for various engineering projects; The project progress assessment module is used to assess project progress and generate predictive progress reports.

[0007] Secondly, this application provides a data entry method for construction project progress management. Using the aforementioned data entry system for construction project progress management, the data entry method includes the following steps: S1. The server receives and verifies a dynamic identity token from a mobile terminal, which is input by the project progress manager at the project site via a mobile terminal. S2. After the identity verification module is successful, the server selects the appropriate engineering data entry interface configuration information from the engineering data entry database and sends it to the mobile terminal according to the identity and permissions of the engineering progress management personnel. The mobile terminal then loads the corresponding engineering data entry interface. S3. The server receives images of the engineering site captured by the image acquisition module in the mobile terminal; S4. The server calls the project progress recognition module to perform image preprocessing and recognition on the project site images, and extracts the progress feature information in the images; S5. The server generates a draft project progress data based on the extracted progress feature information and the benchmark schedule library. S6. The server sends the draft project progress data to the mobile terminal, and the mobile terminal receives the project progress management personnel's correction and confirmation of the draft project progress data, and generates the final project progress data. S7. The server receives and stores data packets from the mobile terminal through an encrypted link, and the data packets are packaged by the mobile terminal to form the final project progress data and environmental parameters.

[0008] Optionally, the specific process of the image acquisition module in the mobile terminal in S3 acquiring images of the engineering site includes: S301. Overlay and display the outline of the predefined progress marker in the viewfinder preview interface of the image acquisition module; S302. When the mobile terminal is detected to be in a stable posture and the predefined progress marker outline and the actual progress marker outline match a preset matching threshold within a preset time threshold, the on-site image acquisition is automatically triggered. S303. While acquiring images of the engineering site, simultaneously record a video of the site environment for a preset duration.

[0009] Optionally, the project progress recognition module described in S4 is pre-trained before being invoked, and the specific pre-training process includes: S41. Construct a training dataset; the training dataset includes building images labeled with construction component types, locations, and installation status. S42. Construct a dual attention mechanism; embed channel attention modules and spatial attention modules into the convolutional neural network model; S43. Construct multi-task learning; simultaneously train component classification task, bounding box regression task, and state score regression task through multi-task learning.

[0010] Optionally, the specific process of image preprocessing and recognition of the project site images by the project progress recognition module in S4 includes: S401. Preprocess the engineering site image, the preprocessing including brightness correction, geometric distortion correction and noise reduction; S402. Identify and locate construction components in the construction site image using the construction progress recognition model; the construction progress recognition model is a convolutional neural network model based on a dual attention mechanism. S403, The project progress identification model outputs the confidence level of the identified construction component type, the bounding box coordinates, and the status score used to characterize the installation integrity.

[0011] Optionally, the specific process for generating the draft project schedule data as described in S5 includes: S501. Match the extracted progress feature information with the corresponding project's baseline schedule nodes obtained from the baseline schedule library; S502. Automatically fill in the fields in the preset data template according to the matching results to generate a draft of the project progress data; the fields include project stage, construction location, completed workload and current progress percentage, as well as installation status and number of days ahead or behind the planned node.

[0012] Optionally, the specific process by which the mobile terminal receives revisions and confirmations of the draft project progress data from the project progress management personnel, as described in S6, includes: S601. When displaying a draft of project progress data on a mobile terminal, highlight fields with automatically generated confidence levels below a preset threshold. S602: Receive corrections from project progress management personnel for highlighted fields, and fill the corrections into the corresponding fields; S603. Before the project progress management personnel confirm the data, a logical consistency check shall be performed on the corrected data. If a logical conflict is found with the baseline schedule or the data that has been entered, a warning shall be issued to the project progress management personnel.

[0013] Optionally, the specific process by which the data packet described in S7 is formed by packaging the final project progress data and environmental parameters through a mobile terminal includes: S701. Obtain environmental parameters collected by the environmental parameter acquisition module; S702. Associate the environmental parameters, the final project progress data, and the on-site environmental video to generate a unique data sequence number; S703. Using the data sequence number as an initialization vector, the associated data is encrypted and a data packet is formed.

[0014] Optionally, the data entry method for construction project progress management further includes: S8. The server automatically updates the overall project progress based on the data packets and performs project progress evaluation through the project progress evaluation module.

[0015] Optionally, the specific process of evaluating project progress through the project progress evaluation module described in S8 includes: S801. Construct a progress prediction model based on historical project progress data and final project progress data in the data package; S802. Predict the project progress at future time nodes using the progress prediction model, and generate a predictive progress report; S803. Compare the predictive progress report with the baseline progress plan nodes. When a progress risk is predicted, generate an early warning message and push the early warning message to the relevant responsible persons.

[0016] According to the specific embodiments provided in this application, the following technical effects are disclosed: This application provides a data entry system and method for construction project progress management. Through identity verification, the system selects and sends the appropriate engineering data entry interface configuration information from the engineering data entry database to a mobile terminal. The mobile terminal loads the corresponding engineering data entry interface and receives corrections and confirmations from project progress management personnel regarding the draft project progress data. This solves the problems of time-consuming and laborious manual recording and secondary entry, reducing the burden on project progress management personnel and the probability of errors, making complex data entry work simple and efficient. The system also uses an image acquisition module to collect images of the construction site, performs image preprocessing and recognition, and combines this with an environmental parameter acquisition module to collect environmental parameters of the construction site. This addresses the issues of project progress judgment heavily relying on personal experience, inconsistent judgment standards among different personnel, easy errors and ambiguities, low data reliability, and the fact that traditional data entry often consists of simple numbers or text, lacking the correlation of multi-dimensional information such as images, location, and environment, making traceability and verification difficult, and leaving no evidence to refer to in case of disputes. This system ensures the standardization of original data collection and the accuracy of the final entered data, providing strong evidence for project progress data. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 A schematic diagram of functional modules of a data entry system for construction project progress management provided in an embodiment of this application; Figure 2 A flowchart illustrating a data entry method for construction project progress management, provided as an embodiment of this application; Figure 3 This is a flowchart illustrating a data entry method for construction project progress management, provided as another embodiment of this application. Detailed Implementation

[0019] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0020] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0021] The data entry system for construction project progress management provided in this application embodiment, such as Figure 1 As shown, the system includes a mobile terminal and a server. The mobile terminal is used by project progress managers to operate on-site. Specifically, it includes: an image acquisition module for acquiring images of the project site containing project progress markers; an environmental parameter acquisition module for acquiring environmental parameters of the project site, including one or more of acquisition time, geographic coordinates, ambient temperature, and ambient humidity; a project data entry interface, distributed by the server, for receiving corrections and confirmations of draft project progress data from project progress managers; and a data transmission module for wireless data transmission. The server is used for remote data processing, wirelessly transmitting data with the mobile terminal via the data transmission module, and receiving project site images acquired by the image acquisition module and environmental parameters acquired by the environmental parameter acquisition module. Specifically, it includes: an identity verification module for verifying a dynamic identity token; a project data entry library for storing various project data entry interfaces; a project progress recognition module for preprocessing and recognizing the project site images; a baseline schedule library for storing baseline schedule nodes for various project projects; and a project progress evaluation module for evaluating project progress and generating predictive progress reports.

[0022] In one exemplary embodiment, such as Figure 2 As shown, a data entry method for construction project progress management is provided, including the following steps S1 to S7. Wherein: S1. The server receives and verifies a dynamic identity token from a mobile terminal, which is input by the project progress manager at the project site via a mobile terminal. S2. After the identity verification module is successful, the server selects the appropriate engineering data entry interface configuration information from the engineering data entry database and sends it to the mobile terminal according to the identity and permissions of the engineering progress management personnel. The mobile terminal then loads the corresponding engineering data entry interface. S3. The server receives images of the engineering site captured by the image acquisition module in the mobile terminal; S4. The server calls the project progress recognition module to perform image preprocessing and recognition on the project site images, and extracts the progress feature information in the images; S5. The server generates a draft project progress data based on the extracted progress feature information and the benchmark schedule library. S6. The server sends the draft project progress data to the mobile terminal, and the mobile terminal receives the project progress management personnel's correction and confirmation of the draft project progress data, and generates the final project progress data. S7. The server receives and stores data packets from the mobile terminal through an encrypted link, and the data packets are packaged by the mobile terminal to form the final project progress data and environmental parameters.

[0023] Implementing S1 to S7 as described above reduces the burden and error probability of project progress management personnel, making complex data entry work simple and efficient; it ensures the standardization of raw data collection and the accuracy of final data entry, providing strong evidence for project progress data and forming an immutable data chain; and it solves the problem that managers cannot obtain the latest situation on the construction site, resulting in insufficient decision-making basis.

[0024] As an optional implementation, to ensure the standardization of the original data collection for project progress and the accuracy of the final entered data, viewfinder assistance and posture detection are used to automatically trigger image acquisition. The specific process of the image acquisition module in the mobile terminal mentioned in S3 above acquiring images of the project site includes: S301. Overlay and display the outline of the predefined progress marker in the viewfinder preview interface of the image acquisition module; S302. When the mobile terminal is detected to be in a stable posture and the predefined progress marker outline and the actual progress marker outline match a preset matching threshold within a preset time threshold, the on-site image acquisition is automatically triggered. S303. While acquiring images of the engineering site, simultaneously record a video of the site environment for a preset duration.

[0025] Specifically, the image acquisition module predefines various project progress marker outlines. For example, in high-rise building projects, this includes foundation engineering, main structure engineering, masonry engineering, interior and exterior decoration engineering, and equipment installation engineering. Each of these projects can be further subdivided into various construction categories. For instance, main structure engineering includes rebar tying, formwork erection, and concrete pouring. Project progress marker outlines are defined for each construction category and stored in the image acquisition module. When the image acquisition module acquires an image of a specific construction site, it selects and overlays the appropriate project progress marker outline onto the preview interface of the image acquisition module. The mobile terminal integrates attitude detection. When the mobile terminal's attitude is detected to be stable, within a preset time threshold (e.g., three seconds), if the actual progress marker outline in the preview interface matches the predefined progress marker outline with a preset matching threshold (e.g., 80%), the image acquisition function of the image acquisition module is automatically triggered. At the same time, a video of the on-site environment with a preset duration is recorded. The preset duration can be set to 10 to 15 seconds. The on-site environment video is used to form a correlation with the subsequent final project progress data, providing strong evidence for the project progress data and facilitating traceability and verification.

[0026] As an optional implementation, in order to automatically extract key features such as the type, location, and installation status of construction components from on-site images and automatically generate a draft of project progress data accordingly, reducing manual intervention, the project progress recognition module described in S4 above is pre-trained before being called. The specific pre-training process includes: S41. Construct a training dataset; the training dataset includes building images labeled with construction component types, locations, and installation status. S42. Construct a dual attention mechanism; embed channel attention modules and spatial attention modules into the convolutional neural network model; S43. Construct multi-task learning; simultaneously train component classification task, bounding box regression task, and state score regression task through multi-task learning.

[0027] Specifically, the project progress recognition module is based on a convolutional neural network model. Channel attention and spatial attention modules are embedded within this model to enhance its feature extraction capabilities. Specifically, the channel attention module allows the convolutional neural network model to learn the importance of each feature channel; for example, when recognizing a "gray concrete column," the model assigns a higher weight to the "gray" channel. The spatial attention module allows the convolutional neural network model to learn the importance of each spatial location in the building image, ensuring that the model focuses on the "concrete column" itself, rather than the background, regardless of its location within the image. Multi-task learning leverages shared information and correlations among multiple related tasks to enhance the generalization ability and efficiency of convolutional neural network models through joint training. The dual-attention mechanism provides high-quality, task-adaptive features for multi-task learning, dynamically adjusting feature weights for different tasks. For example, when performing a component classification task, the channel attention module and spatial attention module automatically amplify the feature channels and regions most relevant to the construction component type. When performing a bounding box regression task, the spatial attention module focuses more on the boundaries and contours of the construction components. When performing a state scoring regression task, the spatial attention module guides the model to pay attention to subtle features related to the state. Simultaneously, multi-task learning provides richer supervision signals for the channel and spatial attention modules. Ultimately, the trained engineering progress recognition module learns to complete multiple tasks simultaneously and understands which parts of the features and regions of the construction image should be paid attention to during each task, achieving greater intelligence, efficiency, and generalization ability.

[0028] As an optional implementation, in order to improve recognition accuracy and efficiency, the specific process of image preprocessing and recognition of the project site images by the project progress recognition module in S4 above includes: S401. Preprocess the engineering site image, the preprocessing including brightness correction, geometric distortion correction and noise reduction; S402. Identify and locate construction components in the construction site image using the construction progress recognition model; the construction progress recognition model is a convolutional neural network model based on a dual attention mechanism. S403, The project progress identification model outputs the confidence level of the identified construction component type, the bounding box coordinates, and the status score used to characterize the installation integrity.

[0029] Specifically, the type confidence score, as the output of the component classification task in the project progress identification model, is used to determine which component type the construction component belongs to, such as precast beams, steel structure nodes, concrete wall panels, etc., and provides a confidence score between 0% and 100%, indicating the degree of certainty that it belongs to that category; the bounding box coordinates, as the output of the bounding box regression task in the project progress identification model, are used to identify the location of the construction component in the project site image; and the state score, representing the installation integrity, as the output of the state score regression task in the project progress identification model, is used to evaluate the installation status of the construction component, outputting a continuous value, such as 0 to 10, to quantify its integrity.

[0030] As an optional implementation, in order to improve the completeness and accuracy of project progress identification, the specific process for generating the draft project progress data described in S5 above includes: S501. Match the extracted progress feature information with the corresponding project's baseline schedule nodes obtained from the baseline schedule library; S502. Automatically fill in the fields in the preset data template according to the matching results to generate a draft of the project progress data; the fields include project stage, construction location, completed workload and current progress percentage, as well as installation status and number of days ahead or behind the planned node.

[0031] As an optional implementation, in order to reduce the operational burden and error probability of project progress management personnel, and to simplify and streamline complex data entry tasks, making operations more focused, the specific process of the mobile terminal receiving the project progress management personnel's corrections and confirmations of the project progress data draft in S6 above includes: S601. When displaying a draft of project progress data on a mobile terminal, highlight fields with automatically generated confidence levels below a preset threshold. S602: Receive corrections from project progress management personnel for highlighted fields, and fill the corrections into the corresponding fields; S603. Before the project progress management personnel confirm the data, a logical consistency check shall be performed on the corrected data. If a logical conflict is found with the baseline schedule or the data that has been entered, a warning shall be issued to the project progress management personnel.

[0032] Specifically, when the project progress recognition module identifies images of the construction site, it determines the confidence level of a construction component, such as a precast beam, steel structure node, or concrete wall panel, based on its type. The confidence level ranges from 0% to 100%. For example, a 95% confidence level for a precast staircase means the module is 95% certain that the component is indeed a precast staircase. Therefore, a preset threshold for the confidence level is set, such as 80%. When the confidence level of the recognition result falls below 80%, the corresponding field is highlighted to prompt project progress management personnel to make corrections and confirm the information.

[0033] As an optional implementation, in order to form an immutable data chain and provide strong evidence for the project progress data, the specific process of packaging the final project progress data and environmental parameters through a mobile terminal in the data packet described in S7 above includes: S701. Obtain environmental parameters collected by the environmental parameter acquisition module; S702. Associate the environmental parameters, the final project progress data, and the on-site environmental video to generate a unique data sequence number; S703. Using the data sequence number as an initialization vector, the associated data is encrypted and a data packet is formed.

[0034] Specifically, environmental parameters include collection time, geographic coordinates, ambient temperature, and ambient humidity. These parameters are linked to the final project progress data for easy traceability and verification, providing evidence in case of disputes. A Secure Sockets Layer (SSL) connection is established between the mobile terminal and the server. Encrypted data packets are uploaded in chunks via the SSL connection. The server receives and fully assembles the data chunks, decrypts them using the corresponding key, verifies data integrity, and then saves the data.

[0035] In another exemplary embodiment of this application, such as Figure 3 As shown, after S7 above, the method may further include: S8. The server automatically updates the overall project progress based on the data packets and performs project progress evaluation through the project progress evaluation module.

[0036] Specifically, by updating the overall project schedule, the problem of managers being unable to obtain the latest information on the construction site, resulting in insufficient basis for decision-making, is solved. Real-time updates of the project schedule are achieved, making the basis for decision-making more sufficient and improving the efficiency and objectivity of project schedule management.

[0037] As an optional implementation method, in order to provide early warning of future project schedule risks, transform project management from passive response to proactive prevention, and improve overall management efficiency, the specific process of project schedule assessment through the project schedule assessment module described in S8 above includes: S801. Construct a progress prediction model based on historical project progress data and final project progress data in the data package; S802. Predict the project progress at future time nodes using the progress prediction model, and generate a predictive progress report; S803. Compare the predictive progress report with the baseline progress plan nodes. When a progress risk is predicted, generate an early warning message and push the early warning message to the relevant responsible persons.

[0038] Specifically, schedule risk can be the number of days that the actual project progress lags behind the planned project progress. A schedule risk can be defined as an actual project progress lagging behind the planned project progress by five days. By using a schedule prediction model to provide early warning of schedule risks, project management can be transformed from a passive response to a proactive prevention and control mechanism, thereby improving overall management efficiency.

[0039] This application also provides an application scenario in which the above-described data entry method for construction project progress management is applied. Specifically, the data entry method provided in this embodiment can be applied to the two key sub-projects of rebar tying and formwork erection during the main structure construction phase of a building to manage the construction project progress. First, project progress management personnel enter the project site with mobile terminals and input a dynamic identity token through the terminals. After the server verifies the dynamic identity token, it selects the rebar tying and formwork erection data entry interface configuration information from the project data entry database based on the project progress management personnel's identity permissions and project type, and sends it to the mobile terminal. The mobile terminal then loads the corresponding entry interface. Next, the mobile terminal captures on-site images of rebar tying and formwork erection and wirelessly transmits them to the server via the data transmission module. The server calls the project progress recognition module to preprocess and recognize the on-site images of rebar tying and formwork erection, extracting progress feature information from the images, including the number of completed columns and beams and their installation status. Combined with the progress plan nodes of rebar tying and formwork erection, a draft project progress data is generated and sent to the mobile terminal. The project progress management personnel revise and confirm the draft project progress data to generate the final project progress data. The final project progress data, along with environmental parameters, is packaged into a data package and uploaded to the server for storage.

[0040] To further verify the technical effect of this application, a comparative test was conducted, and two control examples were set up: Compare with Example 1: Traditional manual data entry method In this comparative example, the traditional manual data entry method, still widely used in the current construction engineering field, is adopted. The specific process is as follows: When project progress managers arrive at the construction site, they use paper forms and pens to record the construction progress through visual inspection and manual measurement. For example, for the progress of rebar tying, project progress managers need to count the number of columns and beams that have been completed, estimate the percentage of completion, and check or fill in the corresponding positions on the form. Afterwards, project progress managers need to return to the office and manually enter the data from the paper forms into the project management software on the computer. After all the data has been entered, they need to manually summarize and calculate it in order to understand the overall progress and compare and analyze it with the baseline plan.

[0041] Compare with Example 2: Basic Electronic Data Entry Method Based on Mobile Devices This comparative example represents an improved solution already used in the prior art, which uses a mobile terminal for data collection, but its functionality is relatively basic. The specific process is as follows: On-site project progress managers use mobile terminals equipped with a simplified project management app. This app provides electronic forms, requiring them to manually select the construction location (e.g., select "5th Floor, Area A, Column 5" from a dropdown list), manually input or select the completed workload (e.g., input "1"), and manually take photos of the site. The app does not provide any framing assistance or automatic triggering functions when taking photos; it relies entirely on the project progress managers' judgment and operation. After all data (text and photos) is filled in, the project progress manager clicks the "Submit" button. The data is uploaded to the server via a regular network connection. The server is only responsible for storing the data and does not have the functions of automatically recognizing image content, automatically generating project progress data, automatically calculating progress deviations, or predicting risks.

[0042] For the two key sub-projects of rebar tying and formwork erection in the main structure construction stage, a four-week comparative test was organized. The test divided the construction area into two similar areas, A and B. Area A adopted the method described in this application, while area B adopted the mobile-based electronic data entry method described in Comparative Example 2. At the same time, the traditional manual data entry method described in Comparative Example 1 was used as a historical baseline for reference. The main test data comparison is shown in Table 1.

[0043] Table 1 Comparison of Test Data

[0044] Test data analysis explanation: 1. This application (Area A) reduces the time for a single data entry to less than a minute, mainly due to the technical features of automatic identification of project progress and automatic generation of project progress data drafts, which saves a lot of time for manual counting, measurement and manual input. Although Area B has achieved electronic processing, core data still needs to be manually entered, so the efficiency improvement is limited. Area C has the lowest efficiency because it includes physical movement and secondary data entry.

[0045] 2. The extremely low error rate of this application (Area A) directly reflects the technical effects of framing assistance, automatic triggering, high-precision engineering progress recognition, and human-computer interaction verification. These features work together to form a complete quality control chain from the source of data collection to final confirmation. Areas B and C, on the other hand, rely heavily on manual labor, and their error rates are naturally higher.

[0046] 3. This application (Area A) is able to achieve near real-time early warning, which is entirely due to the server's automatic updates of progress and project progress assessment and progress prediction. This changes the management behavior from post-event remediation to in-event control and even pre-event prediction. Areas B and C have significant data processing and aggregation delays, resulting in delayed management response.

[0047] 4. The 100% multidimensional data association in this application (Area A) is guaranteed by multidimensional data packaging and encryption, forming an immutable "data fingerprint", which greatly enhances the evidentiary value and management value of the project progress data.

[0048] In summary, the comparative test data fully demonstrates that this application is not a simple improvement on existing technologies, but rather a collaborative innovation across multiple technical dimensions, including project progress identification, data generation and evaluation, and encrypted transmission and verification. It has constructed a completely new paradigm for project progress data entry and management, achieving a synergistic effect far exceeding that of a single technical improvement and producing significant results.

[0049] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0050] This document uses specific examples to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this application. Furthermore, those skilled in the art will recognize that, based on the ideas of this application, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A data entry system for construction project progress management, characterized in that, The data entry system for construction project progress management includes a mobile terminal and a server; The mobile terminal is used by project progress management personnel to operate on-site, specifically including: The image acquisition module is used to acquire images of the construction site that include markers indicating the progress of the project. An environmental parameter acquisition module is used to collect environmental parameters at the engineering site. The environmental parameters include one or more of the following: acquisition time, geographic coordinates, ambient temperature, and ambient humidity. The engineering data entry interface, distributed through the server, is used to receive revisions and confirmations from engineering progress management personnel regarding the draft engineering progress data. Data transmission module, used for wireless data transmission; The server is used for remote data processing, and wirelessly transmits data to the mobile terminal through the data transmission module. It receives engineering site images acquired by the image acquisition module and environmental parameters acquired by the environmental parameter acquisition module, specifically including: The identity verification module is used to verify dynamic identity tokens; Engineering data entry library, used to store various engineering data entry interfaces; The project progress recognition module is used to preprocess and recognize the project site images; A baseline schedule library is used to store baseline schedule nodes for various engineering projects; The project progress assessment module is used to assess project progress and generate predictive progress reports.

2. A data entry method for construction project progress management, characterized in that, The data entry system for construction project progress management as described in claim 1, wherein the data entry method for construction project progress management includes the following steps: S1. The server receives and verifies a dynamic identity token from a mobile terminal, which is input by the project progress manager at the project site via a mobile terminal. S2. After the identity verification module is successful, the server selects the appropriate engineering data entry interface configuration information from the engineering data entry database and sends it to the mobile terminal according to the identity and permissions of the engineering progress management personnel. The mobile terminal then loads the corresponding engineering data entry interface. S3. The server receives images of the engineering site captured by the image acquisition module in the mobile terminal; S4. The server calls the project progress recognition module to perform image preprocessing and recognition on the project site images, and extracts the progress feature information in the images; S5. The server generates a draft project progress data based on the extracted progress feature information and the benchmark schedule library. S6. The server sends the draft project progress data to the mobile terminal, and the mobile terminal receives the project progress management personnel's correction and confirmation of the draft project progress data, and generates the final project progress data. S7. The server receives and stores data packets from the mobile terminal through an encrypted link, and the data packets are packaged by the mobile terminal to form the final project progress data and environmental parameters.

3. The data entry method for construction project progress management according to claim 2, characterized in that, The specific process by which the image acquisition module in the mobile terminal described in S3 acquires images of the engineering site includes: S301. Overlay and display the outline of the predefined progress marker in the viewfinder preview interface of the image acquisition module; S302. When the mobile terminal is detected to be in a stable posture and the predefined progress marker outline and the actual progress marker outline match a preset matching threshold within a preset time threshold, the on-site image acquisition is automatically triggered. S303. While acquiring images of the engineering site, simultaneously record a video of the site environment for a preset duration.

4. The data entry method for construction project progress management according to claim 2, characterized in that, The project progress recognition module described in S4 undergoes pre-training before being invoked. The specific pre-training process includes: S41. Construct a training dataset; the training dataset includes building images labeled with construction component types, locations, and installation status. S42. Construct a dual attention mechanism; embed channel attention modules and spatial attention modules into the convolutional neural network model; S43. Construct multi-task learning; simultaneously train component classification task, bounding box regression task, and state score regression task through multi-task learning.

5. The data entry method for construction project progress management according to claim 2, characterized in that, The specific process by which the project progress recognition module described in S4 performs image preprocessing and recognition on project site images includes: S401. Preprocess the engineering site image, the preprocessing including brightness correction, geometric distortion correction and noise reduction; S402. Identify and locate construction components in the construction site image using the construction progress recognition model; the construction progress recognition model is a convolutional neural network model based on a dual attention mechanism. S403, The project progress identification model outputs the confidence level of the identified construction component type, the bounding box coordinates, and the status score used to characterize the installation integrity.

6. The data entry method for construction project progress management according to claim 2, characterized in that, The specific process for generating the draft project schedule data as described in S5 includes: S501. Match the extracted progress feature information with the corresponding project's baseline schedule nodes obtained from the baseline schedule library; S502. Automatically fill in the fields in the preset data template according to the matching results to generate a draft of the project progress data; the fields include project stage, construction location, completed workload and current progress percentage, as well as installation status and number of days ahead or behind the planned node.

7. The data entry method for construction project progress management according to claim 2, characterized in that, The specific process by which the mobile terminal receives revisions and confirmations of the draft project progress data from project progress management personnel, as described in S6, includes: S601. When displaying a draft of project progress data on a mobile terminal, highlight fields with automatically generated confidence levels below a preset threshold. S602: Receive corrections from project progress management personnel for highlighted fields, and fill the corrections into the corresponding fields; S603. Before the project progress management personnel confirm the data, a logical consistency check shall be performed on the corrected data. If a logical conflict is found with the baseline schedule or the data that has been entered, a warning shall be issued to the project progress management personnel.

8. The data entry method for construction project progress management according to claim 3, characterized in that, The specific process by which the data packet described in S7 is formed by packaging the final project progress data and environmental parameters through a mobile terminal includes: S701. Obtain environmental parameters collected by the environmental parameter acquisition module; S702. Associate the environmental parameters and final project progress data with the on-site environmental video mentioned in S303 to generate a unique data sequence number; S703. Using the data sequence number as an initialization vector, the associated data is encrypted and a data packet is formed.

9. The data entry method for construction project progress management according to claim 2, characterized in that, The data entry method for construction project progress management also includes: S8. The server automatically updates the overall project progress based on the data packets and performs project progress evaluation through the project progress evaluation module.

10. The data entry method for construction project progress management according to claim 9, characterized in that, The specific process of evaluating project progress through the project progress evaluation module, as described in S8, includes: S801. Construct a progress prediction model based on historical project progress data and final project progress data in the data package; S802. Predict the project progress at future time nodes using the progress prediction model, and generate a predictive progress report; S803. Compare the predictive progress report with the baseline progress plan nodes. When a progress risk is predicted, generate an early warning message and push the early warning message to the relevant responsible persons.

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