Engineering project progress information monitoring method and system based on big data

Abstract

The invention is suitable for the technical field of progress information monitoring, and particularly relates to an engineering project progress information monitoring method and system based on big data, and the method comprises the steps: delimiting a construction region of an engineering project, collecting the image data of the construction region, obtaining a construction planning map of the engineering project, and splicing the image data, obtaining a panoramic image of the construction area; and obtaining a construction task of each engineer, wherein the construction task comprises a task area and an operation target. By determining the construction task of each engineer, construction area distribution can be defined, progress management can be refined, the project progress can be dynamically updated by collecting real-time video data, the real-time performance of progress information is ensured, progress information errors are greatly reduced by utilizing a big data comparison model and generating progress logs, and the progress management efficiency is improved. Therefore, the progress bottleneck can be quickly positioned, and decision support is provided for project progress management.

Description

Technical Field

[0001] The present invention relates to the technical field of progress information monitoring, and in particular to a method and system for monitoring progress information of an engineering project based on big data. Background Art

[0002] In the construction of engineering projects, project progress information is often obtained through manual collection and manual recording. However, this method of acquisition is time-consuming and cannot effectively monitor the progress of engineering projects. In addition, progress information needs to be manually evaluated and filled in, which can easily lead to errors or deviations in filling in, affecting decision-making efficiency and causing the project to fail to be completed as scheduled.

[0003] With the popularization of intelligent technology, more and more engineering projects have begun to use smart safety helmets, which integrate cameras, communication modules, batteries and storage devices, etc., which can significantly improve the safety management capabilities of construction sites; therefore, "how to use smart safety helmets to monitor progress information" is the technical problem that the present invention needs to solve. Summary of the invention

[0004] The purpose of the present invention is to provide a method and system for monitoring project progress information based on big data, so as to solve the problem of "how to use smart safety helmets to monitor progress information" raised in the above background technology.

[0005] To achieve the above object, the present invention provides the following technical solutions: A method for monitoring project progress information based on big data, the method comprising: Delineating a construction area of ​​the project, collecting image data of the construction area, obtaining a construction planning map of the project, and splicing the image data to obtain a panoramic image of the construction area; Acquire the construction task of each engineering staff, wherein the construction task includes: a task area and an operation target, and extract a real scene image of the task area from the panoramic image; From each of the task areas, several shooting positions are selected, and electronic fences are constructed respectively, and a trigger mechanism is embedded in the electronic fence, wherein the trigger mechanism is: when the engineer enters the electronic fence, the video shooting device pre-integrated in the safety helmet is activated; Based on the trigger mechanism, real-time video data of the task area is collected, and several snapshots are intercepted, and a big data comparison model is constructed using a preset image difference detection algorithm, and the real scene image and the snapshot are input into the big data comparison model, and a difference map is output, the difference map is integrated, a progress log is generated, and the progress log is pushed to a preset terminal; The panoramic image is updated using the snapshot, and the updated real scene image is integrated into the progress log.

[0006] Furthermore, the steps of demarcating the construction area of ​​the engineering project, collecting image data of the construction area, obtaining a construction planning map of the engineering project, and splicing the image data to obtain a panoramic image of the construction area include: Preprocessing the image data to extract key features; Based on the key features, the image data are aligned and spliced ​​to generate a panoramic image.

[0007] Furthermore, the step of obtaining the construction task of each engineering staff, wherein the construction task includes: a task area and an operation target, and extracting a real scene image of the task area from the panoramic image includes: Integrate the construction task into the progress log, receive feedback data from the preset terminal, and determine whether the operation target is completed; If not, the generation time of the feedback data is recorded, and based on the current time, the unfinished time of the operation target is counted, and the unfinished time is marked in the panoramic image.

[0008] Furthermore, the step of selecting a plurality of shooting positions from each of the task areas, respectively constructing electronic fences, and embedding a trigger mechanism into the electronic fences includes: In the construction area, risk points are identified and an early warning mechanism is established; An electronic fence is constructed with the shooting position as the center and the preset distance as the radius.

[0009] Furthermore, based on the trigger mechanism, the real-time video data of the task area is collected, and several snapshots are captured, and the steps of constructing a big data comparison model using a preset image difference detection algorithm include: Construct an abnormal feature set, traverse the real-time video data, and determine whether there are abnormal features; If yes, activate the pre-built contingency strategy.

[0010] Furthermore, the steps of outputting a difference map, integrating the difference map, generating a progress log, and pushing the progress log to a preset terminal include: Based on a preset time period, the progress logs are integrated to generate a progress form; Insert a timestamp into the progress form, integrate all progress forms, obtain a progress summary table, and embed an index mechanism with time as an element.

[0011] Furthermore, the step of using the snapshot to update the panoramic image and integrating the updated real scene image into the progress log includes: Determine the version of the panoramic image and build a version chain; The identity information of the engineering personnel is obtained, and the identity information is written into the corresponding task area in the version chain.

[0012] Furthermore, the system comprises: The obtaining module is used to delimit the construction area of ​​the engineering project, collect image data of the construction area, obtain a construction planning map of the engineering project, and splice the image data to obtain a panoramic image of the construction area; A capture module is used to obtain the construction task of each engineering staff, wherein the construction task includes: a task area and an operation target, and a real scene image of the task area is captured from the panoramic image; A start module is used to select a number of shooting positions from each of the task areas, respectively construct an electronic fence, and embed a trigger mechanism into the electronic fence, wherein the trigger mechanism is: when the engineer enters the electronic fence, the video shooting device pre-integrated in the safety helmet is started; A push module is used to collect real-time video data of the task area based on the trigger mechanism, and intercept a number of snapshots, use a preset image difference detection algorithm to build a big data comparison model, input the real scene image and the snapshot into the big data comparison model, output a difference map, integrate the difference map, generate a progress log, and push the progress log to a preset terminal; The integration module is used to update the panoramic image using the snapshot and integrate the updated real scene image into the progress log.

[0013] Furthermore, the obtaining module includes: An extraction unit, used for preprocessing the image data to extract key features; The stitching unit is used to align and stitch the image data according to the key features to generate a panoramic image.

[0014] Furthermore, the interception module includes: A receiving unit, used to integrate the construction task into the progress log and receive feedback data from the preset terminal; The marking unit is used to determine whether the operation target is completed. If not, it records the generation time of the feedback data, calculates the unfinished time of the operation target based on the current time, and marks the unfinished time in the panoramic image.

[0015] Compared with the prior art, the present invention has the following beneficial effects: By drawing panoramic images, the progress information of the project can be displayed intuitively and delayed areas can be quickly identified. By determining the construction tasks of each engineering staff member, the allocation of construction areas can be clarified and progress management can be refined. By collecting real-time video data, the project progress can be dynamically updated to ensure the real-time nature of progress information. By utilizing big data comparison models and generating progress logs, the error of progress information is greatly reduced, and progress bottlenecks can be quickly located, thereby providing decision support for project progress management. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 A flowchart of a method for monitoring project progress information based on big data provided by an embodiment of the present invention; Figure 2 A first sub-flow chart of a method for monitoring project progress information based on big data provided by an embodiment of the present invention; Figure 3 A second sub-flow chart of the method for monitoring project progress information based on big data provided by an embodiment of the present invention; Figure 4 A third sub-flow chart of the method for monitoring project progress information based on big data provided by an embodiment of the present invention; Figure 5 A fourth sub-flow chart of the method for monitoring project progress information based on big data provided by an embodiment of the present invention; Figure 6 A fifth sub-flow chart of the method for monitoring project progress information based on big data provided by an embodiment of the present invention; Figure 7 A block diagram of the construction project progress information monitoring system based on big data provided by an embodiment of the present invention; Figure 8 A block diagram of the components of a module in a project progress information monitoring system based on big data provided by an embodiment of the present invention; Fig. 9 A block diagram of the composition of an interception module in a project progress information monitoring system based on big data provided by an embodiment of the present invention; Fig.10 A block diagram of the composition of a startup module in a project progress information monitoring system based on big data provided by an embodiment of the present invention; Fig.11 A block diagram of the composition of a push module in a project progress information monitoring system based on big data provided by an embodiment of the present invention; Fig.12 A block diagram of the composition of the integration module in the engineering project progress information monitoring system based on big data provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0017] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0018] In Example 1, Figure 1 The implementation process of the method for monitoring the progress information of an engineering project based on big data provided by an embodiment of the present invention is shown and described in detail below: S100: Delineating a construction area of ​​a project, collecting image data of the construction area, obtaining a construction planning map of the project, and splicing the image data to obtain a panoramic image of the construction area.

[0019] Delineate the construction area of ​​the project, use handheld high-resolution cameras, drones or laser scanners and other equipment to collect multi-angle images of the construction area, and stitch and correct the multi-angle images according to the spatial position and proportion shown in the construction plan to generate a panoramic image of the construction area; the panoramic image can intuitively show the actual status of the entire construction area.

[0020] S200: Acquire the construction task of each engineering staff member, wherein the construction task includes: a task area and an operation target, and extract a real scene image of the task area from the panoramic image.

[0021] The construction tasks of each engineer are obtained, where the construction tasks include information such as the task area and the work target. The image cropping algorithm is used to extract the real-life image of the task area corresponding to each engineer from the panoramic image.

[0022] S300: Select several shooting positions from each of the task areas, construct electronic fences respectively, and embed a trigger mechanism into the electronic fences, wherein the trigger mechanism is: when the engineer enters the electronic fence, the video shooting device pre-integrated in the safety helmet is activated.

[0023] In each task area, multiple shooting positions are determined, and an electronic fence is constructed with the shooting positions as the center of the circle; specifically, a suitable shooting position is selected from the task area, and the real-time video data of all shooting positions are integrated to obtain a regional panoramic image of the task area, and an RFID signal receiver is installed at the shooting position, and an electronic fence is constructed with the shooting position as the center of the circle and the receiving range of the RFID signal receiver as the radius; an RFID tag is integrated into the engineer's helmet, and when the RFID receiver detects that the engineer has entered the electronic fence, it triggers the start of the video shooting device in the engineer's helmet and collects real-time video data of the task area.

[0024] S400: Based on the trigger mechanism, real-time video data of the task area is collected, and several snapshots are captured. A big data comparison model is constructed using a preset image difference detection algorithm. The real scene image and the snapshot are input into the big data comparison model, and a difference map is output. The difference map is integrated to generate a progress log, and the progress log is pushed to a preset terminal.

[0025] Several snapshots are taken from the real-time video data of each shooting location, and an image difference detection algorithm is selected from the existing technology, and a comparison model is constructed based on the algorithm. Since there are multiple shooting locations and multiple snapshots of each shooting location, the image data processing volume is large. Therefore, the comparison model requires efficient data storage and computing capabilities, which requires the use of big data technology (such as distributed storage) to store snapshots, real-life images, comparison results and progress data, etc., to ensure that the comparison model can quickly output difference images to meet the dynamic update needs of the project progress.

[0026] The real-life image and snapshot are input into the comparison model, and a difference image is output. The progress changes in the task area are shown through the image differences between the real-life image and the snapshot. The difference image is integrated to generate a progress log, and the progress log is pushed to a preset terminal, which is the terminal of the project manager. There is no limit on the step length of the progress log. In other words, a daily progress log can be generated in units of "days", or a monthly progress log can be generated in units of "months".

[0027] S500: Using the snapshot, updating the panoramic image, and integrating the updated real scene image into the progress log.

[0028] Use snapshots to update real-life and panoramic images.

[0029] In this embodiment, the real-life picture of the task area is collected by utilizing the video capture equipment integrated in the engineering personnel's helmet, and the progress of the project is dynamically updated. It should be noted that the real-time progress of the project can also be collected by video surveillance equipment at fixed points, but based on life experience, the progress of the project will not change significantly in a short period of time. Therefore, such real-time monitoring will cause a data processing burden. In this embodiment, the progress in the corresponding task area will be updated only when the engineering personnel enter the shooting position. Therefore, not only can the construction situation in a wider range be obtained and more comprehensive progress data be collected, but also the data processing efficiency can be guaranteed, and the stability of project monitoring can be improved.

[0030] In Example 2, Figure 2The implementation process of the method for monitoring the progress information of an engineering project based on big data provided by an embodiment of the present invention is shown. The steps of demarcating the construction area of ​​the engineering project, collecting image data of the construction area, obtaining a construction planning map of the engineering project, and splicing the image data to obtain a panoramic image of the construction area are described in detail as follows: S101: Preprocess the image data to extract key features.

[0031] The image data is preprocessed, including: removing noise, adjusting brightness and contrast, etc., and extracting key features from it. The key features are features that can distinguish the image data, including but not limited to fire-fighting equipment, piled up building materials, etc.

[0032] S102: Based on the key features, align and stitch the image data to generate a panoramic image.

[0033] Based on key features, different image data are matched, aligned and stitched to generate a panoramic image of the construction area.

[0034] In Example 3, Figure 3 The implementation process of the method for monitoring the progress information of an engineering project based on big data provided by an embodiment of the present invention is shown. The following is a detailed description of the steps of obtaining the construction tasks of each engineering personnel, wherein the construction tasks include: a task area and an operation target, and extracting a real scene image of the task area from the panoramic image, as follows: S201: Integrate the construction task into the progress log, receive feedback data from the preset terminal, and determine whether the operation target is completed.

[0035] The construction task and progress log are sent together to a preset terminal, and "yes" and "no" selection buttons are sent to the preset terminal; the above preset terminal is the engineering project manager terminal, and the engineering project manager determines whether the operation target is completed.

[0036] S202: If not, record the generation time of the feedback data, calculate the unfinished time of the task target based on the current time, and mark the unfinished time in the panoramic image.

[0037] If the result of the judgment is unfinished, the time corresponding to the unfinished is recorded, that is, the generation time; and according to the current time, the unfinished duration of the work target is calculated, and the unfinished duration is marked to the corresponding position of the panoramic image.

[0038] In Example 4, Figure 4The implementation process of the method for monitoring the progress information of an engineering project based on big data provided by an embodiment of the present invention is shown. The steps of selecting a plurality of shooting positions from each of the task areas, constructing electronic fences respectively, and embedding a trigger mechanism into the electronic fences are described in detail as follows: S301: In the construction area, risk points are delineated and an early warning mechanism is established.

[0039] In the construction area, the risk points where accidents are prone to occur are traversed and marked in the panoramic image; an early warning mechanism is created. When engineering personnel enter the risk points, the early warning devices in the construction area are triggered and activated. The early warning devices can be buzzers or flashing lights.

[0040] S302: constructing an electronic fence with the shooting position as the center and the preset distance as the radius.

[0041] An electronic fence is constructed with the shooting position as the center and the preset distance as the radius, wherein the preset distance is the receiving range of the RFID signal receiver or is set by the engineering management personnel.

[0042] In Example 5, Figure 5 The implementation process of the project progress information monitoring method based on big data provided by an embodiment of the present invention is shown. The following is a detailed description of the steps of collecting real-time video data of the task area based on the trigger mechanism, intercepting several snapshots, and building a big data comparison model using a preset image difference detection algorithm, as follows: S401: Construct an abnormal feature set and traverse the real-time video data.

[0043] Construct an abnormal feature set, where the abnormal feature set is the violation features of the construction site; including not wearing a safety helmet and reflective vest, etc., as well as illegal use of electricity and lack of warning signs.

[0044] S402: Determine whether there are abnormal features, and if so, activate the pre-built emergency strategy.

[0045] When abnormal features are detected, an emergency strategy is triggered, which includes: sending the corresponding snapshot to a preset terminal and starting the early warning device, etc.

[0046] In Example 6, Figure 5 The implementation process of the method for monitoring the progress information of an engineering project based on big data provided by an embodiment of the present invention is shown. The steps of outputting a difference map, integrating the difference map, generating a progress log, and pushing the progress log to a preset terminal are described in detail as follows: S403: Integrate the progress logs based on a preset time period to generate a progress form.

[0047] Integrate the progress logs according to preset time periods, such as "day" or "month", and generate a progress form.

[0048] S404: inserting a timestamp into the progress form, integrating all progress forms, obtaining a progress summary table, and embedding an index mechanism with time as an element.

[0049] Insert the timestamp corresponding to the preset time period into the progress form, integrate all the progress forms, and generate a progress summary table; in order to facilitate the backtracking of the project construction process, embed an index mechanism into the progress summary table, so that the project construction process can be quickly traced back with time as the factor.

[0050] In Example 7, Figure 6 The implementation process of the method for monitoring the progress information of an engineering project based on big data provided by an embodiment of the present invention is shown. The steps of using the snapshot to update the panoramic image and integrating the updated real scene image into the progress log are described in detail as follows: S501: Determine the version of the panoramic image and construct a version chain.

[0051] All data of the panoramic image are saved, and the version of the panoramic image is determined using the preset version editing rules. The panoramic image is updated in chronological order, and a version chain is constructed using the updated panoramic image.

[0052] S502: Acquire the identity information of the engineering personnel, and write the identity information into the corresponding task area in the version chain.

[0053] Build a version chain for each task area and identify the engineering staff corresponding to the version chain. The benefit of this is that you can quickly locate the engineering staff corresponding to the task area with delayed progress.

[0054] Figure 7 The following is a structural block diagram of a construction project progress information monitoring system based on big data provided by an embodiment of the present invention. The construction project progress information monitoring system based on big data 1 includes: The obtaining module 11 is used to delimit the construction area of ​​the project, collect image data of the construction area, obtain a construction planning map of the project, and splice the image data to obtain a panoramic image of the construction area; The capture module 12 is used to obtain the construction task of each engineering personnel, wherein the construction task includes: a task area and an operation target, and capture a real scene image of the task area from the panoramic image; The start module 13 is used to select a number of shooting positions from each of the task areas, and respectively construct an electronic fence, and embed a trigger mechanism into the electronic fence, wherein the trigger mechanism is: when the engineer enters the electronic fence, the video shooting device pre-integrated in the safety helmet is started; The push module 14 is used to collect real-time video data of the task area based on the trigger mechanism, and intercept a number of snapshots, use a preset image difference detection algorithm to build a big data comparison model, input the real scene image and the snapshot into the big data comparison model, output a difference map, integrate the difference map, generate a progress log, and push the progress log to a preset terminal; The integration module 15 is used to update the real scene image using the snapshot, and integrate the updated real scene image into the progress log.

[0055] Figure 8 The structure diagram of the construction project progress information monitoring system based on big data provided by an embodiment of the present invention is shown, and the obtaining module 11 includes: An extraction unit 111 is used to pre-process the image data and extract key features; The stitching unit 112 is used to align and stitch the image data according to the key features to generate a panoramic image.

[0056] Fig. 9 The structure diagram of the construction project progress information monitoring system based on big data provided by an embodiment of the present invention is shown, and the interception module 12 includes: The receiving unit 121 is used to integrate the construction task into the progress log and receive feedback data from the preset terminal; The marking unit 122 is used to determine whether the work target is completed. If not, it records the generation time of the feedback data, calculates the unfinished time of the work target based on the current time, and marks the unfinished time in the panoramic image.

[0057] Fig.10 The structure diagram of the construction project progress information monitoring system based on big data provided by an embodiment of the present invention is shown, and the startup module 13 includes: A delineation unit 131 is used to delineate risk points in the construction area and establish an early warning mechanism; The construction unit 132 is used to construct an electronic fence with the shooting position as the center and the preset distance as the radius.

[0058] Fig.11 The structure diagram of the construction project progress information monitoring system based on big data provided by an embodiment of the present invention is shown, and the push module 14 includes: A traversal unit 141 is used to construct an abnormal feature set and traverse the real-time video data; an activation unit 142, for determining whether there are abnormal features, and if so, activating a pre-built emergency strategy; A generating unit 143, configured to integrate the progress logs according to a preset time period and generate a progress form; The obtaining unit 144 is used to insert a timestamp into the progress form, integrate all progress forms, obtain a progress summary table, and embed an index mechanism with time as an element.

[0059] Fig.12 The structure diagram of the construction project progress information monitoring system based on big data provided by an embodiment of the present invention is shown, and the integration module 15 includes: A determination unit 151, configured to determine a version of the panoramic image and construct a version chain; The writing unit 152 is used to obtain the identity information of the engineering personnel and write the identity information into the corresponding task area in the version chain.

[0060] The obtaining module 11 is mainly used to complete step S100, the intercepting module 12 is mainly used to complete step S200, the starting module 13 is mainly used to complete step S300, the pushing module 14 is mainly used to complete step S400, and the integrating module 15 is mainly used to complete step S500; The extraction unit 111 is mainly used to complete step S101, and the splicing unit 112 is mainly used to complete step S102; The receiving unit 121 is mainly used to complete step S201, and the marking unit 122 is mainly used to complete step S202; The delimiting unit 131 is mainly used to complete step S301, and the constructing unit 132 is mainly used to complete step S302; The traversal unit 141 is mainly used to complete step S401, the activation unit 142 is mainly used to complete step S402, the generation unit 143 is mainly used to complete step S403, and the obtaining unit 144 is mainly used to complete step S404; The determining unit 151 is mainly used to complete step S501, and the writing unit 152 is mainly used to complete step S502.

[0061] The technical features of the above-described embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the above-described 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.

[0062] The above-mentioned embodiments only express several implementation methods of the present invention, and the description thereof is relatively specific and detailed, but it cannot be understood as limiting the scope of the patent of the present invention. It should be pointed out that, for ordinary technicians in this field, several variations and improvements can be made without departing from the concept of the present invention, which all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention shall be subject to the attached claims.

[0063] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for monitoring project progress information based on big data, characterized in that: The method comprises: Delineating a construction area of ​​the project, collecting image data of the construction area, obtaining a construction planning map of the project, and splicing the image data to obtain a panoramic image of the construction area; Acquire the construction task of each engineering staff, wherein the construction task includes: a task area and an operation target, and extract a real scene image of the task area from the panoramic image; From each of the task areas, several shooting positions are selected, and electronic fences are constructed respectively, and a trigger mechanism is embedded in the electronic fence, wherein the trigger mechanism is: when the engineer enters the electronic fence, the video shooting device pre-integrated in the safety helmet is activated; Based on the trigger mechanism, real-time video data of the task area is collected, and several snapshots are intercepted, and a big data comparison model is constructed using a preset image difference detection algorithm, and the real scene image and the snapshot are input into the big data comparison model, and a difference map is output, the difference map is integrated, a progress log is generated, and the progress log is pushed to a preset terminal; The panoramic image is updated using the snapshot, and the updated real scene image is integrated into the progress log.

2. The method for monitoring project progress information based on big data according to claim 1 is characterized in that: The steps of demarcating the construction area of ​​the engineering project, collecting image data of the construction area, obtaining a construction planning map of the engineering project, and splicing the image data to obtain a panoramic image of the construction area include: Preprocessing the image data to extract key features; Based on the key features, the image data are aligned and spliced ​​to generate a panoramic image.

3. The method for monitoring project progress information based on big data according to claim 1 is characterized in that: The step of obtaining the construction task of each engineering personnel, wherein the construction task includes: a task area and an operation target, and extracting a real scene image of the task area from the panoramic image includes: Integrate the construction task into the progress log, receive feedback data from the preset terminal, and determine whether the operation target is completed; If not, the generation time of the feedback data is recorded, and based on the current time, the unfinished time of the operation target is counted, and the unfinished time is marked in the panoramic image.

4. The method for monitoring project progress information based on big data according to claim 1, characterized in that: The step of selecting a plurality of shooting positions from each of the task areas, constructing electronic fences respectively, and embedding a trigger mechanism into the electronic fences comprises: In the construction area, risk points are identified and an early warning mechanism is established; An electronic fence is constructed with the shooting position as the center and the preset distance as the radius.

5. The method for monitoring project progress information based on big data according to claim 1 is characterized in that: The steps of collecting real-time video data of the task area based on the trigger mechanism, taking out a number of snapshots, and building a big data comparison model using a preset image difference detection algorithm include: Construct an abnormal feature set, traverse the real-time video data, and determine whether there are abnormal features; If yes, activate the pre-built contingency strategy.

6. The method for monitoring project progress information based on big data according to claim 3 is characterized in that: The steps of outputting a difference map, integrating the difference map, generating a progress log, and pushing the progress log to a preset terminal include: Based on a preset time period, the progress logs are integrated to generate a progress form; Insert a timestamp into the progress form, integrate all progress forms, obtain a progress summary table, and embed an index mechanism with time as an element.

7. The method for monitoring project progress information based on big data according to claim 1, characterized in that: The step of updating the panoramic image by using the snapshot and integrating the updated real scene image into the progress log comprises: Determine the version of the panoramic image and build a version chain; The identity information of the engineering personnel is obtained, and the identity information is written into the corresponding task area in the version chain.

8. A project progress information monitoring system based on big data, characterized in that: The system comprises: The obtaining module is used to delimit the construction area of ​​the engineering project, collect image data of the construction area, obtain a construction planning map of the engineering project, and splice the image data to obtain a panoramic image of the construction area; A capture module is used to obtain the construction task of each engineering staff, wherein the construction task includes: a task area and an operation target, and a real scene image of the task area is captured from the panoramic image; A start module is used to select a number of shooting positions from each of the task areas, respectively construct an electronic fence, and embed a trigger mechanism into the electronic fence, wherein the trigger mechanism is: when the engineer enters the electronic fence, the video shooting device pre-integrated in the safety helmet is started; A push module is used to collect real-time video data of the task area based on the trigger mechanism, and intercept a number of snapshots, use a preset image difference detection algorithm to build a big data comparison model, input the real scene image and the snapshot into the big data comparison model, output a difference map, integrate the difference map, generate a progress log, and push the progress log to a preset terminal; The integration module is used to update the panoramic image using the snapshot and integrate the updated real scene image into the progress log.

9. The engineering project progress information monitoring system based on big data according to claim 8 is characterized in that: The obtaining module comprises: An extraction unit, used for preprocessing the image data to extract key features; The stitching unit is used to align and stitch the image data according to the key features to generate a panoramic image.

10. The engineering project progress information monitoring system based on big data according to claim 8, characterized in that: The interception module comprises: A receiving unit, used to integrate the construction task into the progress log and receive feedback data from the preset terminal; The marking unit is used to determine whether the operation target is completed. If not, it records the generation time of the feedback data, calculates the unfinished time of the operation target based on the current time, and marks the unfinished time in the panoramic image.

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