Intelligent building progress data control system based on big data calculation

Abstract

The invention discloses an intelligent building progress data control system based on big data calculation, and relates to the field of big data calculation. According to the invention, the comprehensiveness of building progress data monitoring is effectively improved; the method comprises the following steps: setting a progress data set through a progress data summarization module, and collecting a corresponding progress data subset according to the progress data set; generating an area node for the original task area corresponding to the progress data set, constructing a progress data tracking model, obtaining an equipment movement track corresponding to the area node based on the progress data tracking model, and predicting the concrete pouring amount and task completion data; and the data processing module analyzes the equipment movement track, the predicted concrete pouring amount and the task completion data, establishes an abnormal deviation progress data tree, obtains abnormal deviation progress data according to the abnormal deviation progress data tree, sends the abnormal deviation progress data to the response module, and then performs real-time response according to the abnormal deviation progress data.

Description

Technical Field

[0001] The present invention relates to the field of big data computing, and in particular to an intelligent building progress data control system based on big data computing. Background Art

[0002] The technology of big data algorithms covers multiple fields, including distributed storage and processing, data mining, machine learning, graph computing, text mining and natural language processing, recommendation systems, association rule mining, time series analysis, anomaly detection, data compression and dimensionality reduction, network analysis, pattern recognition, etc. The selection of these algorithms depends on the specific application scenarios and problem requirements, and usually requires comprehensive consideration of factors such as algorithm efficiency, accuracy, and scalability. Intelligent buildings have a long history. In terms of basic functions, the intelligence of large public buildings has entered the popularization stage; new office buildings and commercial buildings in major cities across the country are basically intelligent buildings; but there are still some shortcomings and disadvantages in the monitoring of progress data of the construction process, which will lead to problems such as incomplete and untimely monitoring of the construction process; for example, when responding to the progress data of the construction project through a traditional control system, there may be a response delay, which in turn leads to untimely monitoring; therefore, in order to solve the above problems, the present invention provides an intelligent building progress data control system based on big data calculation. Summary of the invention

[0003] In order to solve the above technical problems, the present invention provides an intelligent building progress data control system based on big data calculation; The object of the present invention can be achieved by the following technical solutions: an intelligent building progress data control system based on big data calculation, the system comprising a progress data aggregation module, a progress data processing module, a progress data analysis module and a response module; The progress data aggregation module sets a progress data set and collects a corresponding progress data subset according to the progress data set; The progress data processing module is used to generate regional nodes from the original task area corresponding to the progress data set and construct a progress data tracking model, and obtain the equipment movement trajectory, predicted concrete pouring volume and task completion data corresponding to the regional node based on the progress data tracking model; The progress data analysis module is used to analyze the equipment movement trajectory, the predicted concrete pouring volume and the task completion data and establish an abnormal deviation progress data tree, and obtain abnormal deviation progress data according to the abnormal deviation progress data tree; The response module is used to respond in real time according to the abnormal deviation progress data.

[0004] Furthermore, the progress data aggregation module sets a progress data set, and the process of collecting corresponding progress data subsets according to each progress data set includes: The progress data set includes tasks, task locations, task environments, and task states; a corresponding modal code is set for the progress data set, and a corresponding modal acquisition device is set according to the modal code; Deploy the deployment position of the modal acquisition device corresponding to the task position, task environment and task status at the construction site, and collect the task position subset, task environment subset and task status subset corresponding to the task position, task environment and task status at the deployment position according to the GPS positioning device, Internet of Things sensor device and camera device corresponding to the modal acquisition device; The task location subset includes equipment location and equipment quantity; the task environment subset includes pouring speed, number of concrete vehicles and concrete construction workers; the task status subset includes construction worker images and regional building images.

[0005] Furthermore, the process of obtaining the task corresponding to the progress data set includes: Setting a task progress terminal, wherein the task progress terminal is wirelessly connected to a plurality of mobile terminals corresponding to construction personnel; the task progress terminal comprises a schedule arrangement unit, a resource allocation unit and a task management unit; The original task area, original resource data and original construction personnel data corresponding to the construction site are stored in the resource allocation unit; and the original task area is numbered as i, i=1, 2, ..., n, and n is a positive integer; the original resource data includes the original equipment code, the original equipment location and the original equipment quantity; the original construction personnel data includes the original construction personnel quantity and the original construction personnel personal information; The resource allocation unit arranges the original resource data and the original construction personnel data corresponding to the original task area, generates the corresponding schedule task, sets the execution time period of the schedule task, connects with the schedule task to generate a schedule task set, and then sends it to the schedule unit for storage; and sends the schedule task set to the corresponding mobile terminal for reception through the personal information of each original construction personnel; The various scheduled task sets in the original task area are merged to generate tasks corresponding to the original task area.

[0006] Furthermore, the process of constructing the progress data tracking model by the progress data processing module includes: The regional nodes corresponding to the construction site are connected to generate a regional network of the construction site, and the progress data summary days corresponding to each regional node are set. According to the progress data summary days, the schedule execution period corresponding to each regional node is divided to obtain the corresponding progress data summary time node, and the corresponding progress data set is obtained in real time according to the progress data summary time node; and the regional nodes of the construction site regional network are used as the origin to construct a regional spatial coordinate map, and then a progress data tracking model is constructed.

[0007] Furthermore, the process of obtaining the equipment movement trajectory, predicted concrete pouring volume and task completion data includes: Based on the progress data tracking model, the equipment position corresponding to each progress data summary time node of the regional node is obtained, marked on the corresponding regional spatial coordinate map, and mapped with the original equipment code; with the original equipment position as the starting point, the equipment corresponding to each original equipment code is sequentially connected according to the regional number during the schedule execution period to generate the equipment movement trajectory of the equipment corresponding to the original equipment code during the schedule execution period; According to each regional node, the corresponding pouring unit time is set, and the pouring speed, number of concrete vehicles and concrete construction personnel corresponding to each regional node are obtained, and then the formula is used to calculate the pouring speed, number of concrete vehicles and concrete construction personnel corresponding to each regional node. Get the concrete pouring rate corresponding to each regional node , where j represents the concrete number corresponding to the regional node, j=1, 2, ..., m, and m is a positive integer; V j 、N j and K j They are respectively represented by the pouring speed, number of concrete trucks and concrete construction workers corresponding to the concrete number; By formula Get the predicted concrete pouring volume for each regional node corresponding to the concrete number during the schedule execution period , where t i It represents the schedule execution period corresponding to the regional node; The construction personnel send the construction area and construction duration corresponding to the progress data summary time node to the task management unit through the mobile terminal, and map it with the original construction personnel’s personal information to generate the construction personnel’s corresponding task completion data; The equipment movement trajectory, predicted concrete pouring volume and task completion data are sent to the task management unit for storage.

[0008] Furthermore, the process in which the progress data analysis module analyzes the equipment movement trajectory, predicted concrete pouring volume, and task completion data to establish an abnormal deviation progress data tree includes: According to the original device code, the regional critical position of the corresponding device is set and marked on the regional spatial coordinate map, and then compared with the device position involved in the corresponding device movement trajectory; if the device position exceeds the regional critical position, the corresponding device movement trajectory is marked as an abnormal trajectory; otherwise, it is marked as a normal trajectory; Set the concrete pouring volume corresponding to each regional node during the schedule execution period, and compare it with the predicted concrete pouring volume corresponding to each regional node; if the predicted concrete pouring volume is greater than the concrete pouring volume, the corresponding predicted concrete pouring volume is marked as abnormal concrete pouring volume; otherwise, it is marked as normal concrete pouring volume; By obtaining the images of construction workers corresponding to each regional node, the progress data is obtained, and the actual construction area and actual construction time of the construction workers corresponding to the time node are summarized, and compared with the task completion data corresponding to the construction workers; if the construction area does not exist in the actual construction area or the construction time is longer than the actual construction time, the corresponding construction worker is marked as an abnormal construction worker; otherwise, it is marked as a normal construction worker; Obtain the number of devices corresponding to each regional node at the progress data summary time node, and compare it with the original number of devices. If they are different, mark the number of devices as abnormal, otherwise, mark it as normal. According to the regional building image and the preset actual regional building image, the progress data is summarized to obtain the regional building completion degree corresponding to the time node; and compared with the preset regional building completion degree threshold, if it is greater than the regional building completion degree threshold, the corresponding regional building completion degree is marked as normal completion degree; otherwise, it is marked as abnormal completion degree; An abnormal deviation progress data tree is established with regional nodes as the trunk, progress data summary time nodes as branches, and equipment movement trajectories, predicted concrete pouring volume, task completion data, equipment quantity and regional building images corresponding to the progress data summary time nodes as leaves.

[0009] Furthermore, the process of obtaining the abnormal deviation progress data includes: Abnormal trajectories, abnormal concrete pouring volumes, abnormal construction personnel, abnormal number of equipment, and abnormal completion rates are marked as abnormal deviation progress data, and the corresponding leaves are marked in red, thereby generating abnormal alarm signals.

[0010] Furthermore, the process of the response module performing real-time response according to the abnormal deviation progress data includes: Obtain abnormal alarm signals, and obtain abnormal deviation progress data corresponding to each abnormal deviation progress data tree according to the abnormal alarm signals, and then control the equipment movement trajectory, predicted concrete pouring volume and task completion data corresponding to the regional node according to the abnormal deviation progress data; If it is an abnormal trajectory, the abnormal equipment position corresponding to the equipment movement trajectory is obtained, and the construction staff is warned according to the abnormal equipment position; If it is an abnormal concrete pouring amount, the corresponding concrete number is obtained, and then the pouring speed, the number of concrete trucks and the concrete construction personnel are adjusted; If the construction worker is abnormal, the corresponding original construction worker personal information is obtained, and the construction worker is warned according to the original construction worker personal information; If the number of devices is abnormal, the number of devices corresponding to each regional node will be reviewed again; If the completion rate is abnormal, it is necessary to increase the number of construction personnel at the regional node or extend the construction time.

[0011] Compared with the prior art, the present invention has the following beneficial effects: 1. The present invention sets a progress data set through a progress data summary module, collects a corresponding progress data subset according to the progress data set; generates regional nodes for the original task area corresponding to the progress data set and constructs a progress data tracking model, and obtains the equipment movement trajectory corresponding to the regional node, the predicted concrete pouring volume and the task completion data based on the progress data tracking model; this process effectively improves the comprehensiveness of construction progress data monitoring.

[0012] 2. The progress data analysis module is used to analyze the equipment movement trajectory, predicted concrete pouring volume, and task completion data, and an abnormal deviation progress data tree is established. The abnormal deviation progress data is obtained according to the abnormal deviation progress data tree and sent to the response module, and then a real-time response is made based on the abnormal deviation progress data. This process effectively improves the timeliness of the response to the construction site. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0014] Figure 1 It is a schematic diagram of the present invention.

[0015] Figure 2 This is a schematic diagram of the task progress terminal. DETAILED DESCRIPTION

[0016] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0017] like Figure 1 As shown, an intelligent building progress data control system based on big data computing, the system includes a progress data aggregation module, a progress data processing module, a progress data analysis module and a response module; The progress data aggregation module sets a progress data set and collects a corresponding progress data subset according to the progress data set; The progress data processing module is used to generate regional nodes from the original task area corresponding to the progress data set and construct a progress data tracking model, and obtain the equipment movement trajectory, predicted concrete pouring volume and task completion data corresponding to the regional node based on the progress data tracking model; The progress data analysis module is used to analyze the equipment movement trajectory, the predicted concrete pouring volume and the task completion data and establish an abnormal deviation progress data tree, and obtain abnormal deviation progress data according to the abnormal deviation progress data tree; The response module is used to respond in real time according to the abnormal deviation progress data.

[0018] It should be further explained that the progress data aggregation module sets the progress data sets, and the process of collecting corresponding progress data subsets according to each progress data set includes: The progress data set includes a task, a task location, a task environment, and a task status; a corresponding modal code is set for the progress data set, and is marked as RW, RW-WZ, RW-HJ, and RW-ZT respectively; a corresponding modal acquisition device is set according to the modal code, and a progress data subset corresponding to the progress data set is acquired through the modal acquisition device; It should be further explained that the process of collecting the progress data subset corresponding to the task location, task environment and task status through the modal acquisition device includes: Deploy the deployment position of the modal acquisition device corresponding to the task position, task environment and task status at the construction site, and collect the task position subset, task environment subset and task status subset corresponding to the task position, task environment and task status at the deployment position according to the GPS positioning device, Internet of Things sensor device and camera device corresponding to the modal acquisition device; The task location subset includes equipment location and equipment quantity; the task environment subset includes pouring speed, number of concrete vehicles and concrete construction workers; the task status subset includes construction worker images and regional building images.

[0019] like Figure 2 As shown, it needs to be further explained that the task acquisition process includes: Setting a task progress terminal, wherein the task progress terminal is wirelessly connected to a plurality of mobile terminals corresponding to construction personnel; the task progress terminal comprises a schedule arrangement unit, a resource allocation unit and a task management unit; The original task area, original resource data and original construction personnel data corresponding to the construction site are stored in the resource allocation unit; and the original task area is numbered as i, i=1, 2, ..., n, and n is a positive integer; the original resource data includes the original equipment code, the original equipment location and the original equipment quantity; the original construction personnel data includes the original construction personnel quantity and the original construction personnel personal information; The resource allocation unit arranges the original resource data and the original construction personnel data corresponding to the original task area, generates the corresponding schedule task, sets the execution time period of the schedule task, connects with the schedule task to generate a schedule task set, and then sends it to the schedule unit for storage; and sends the schedule task set to the corresponding mobile terminal for reception through the personal information of each original construction personnel; The various scheduled task sets in the original task area are merged to generate tasks corresponding to the original task area.

[0020] It should be further explained that, in the specific implementation process, the original personal information of the construction personnel includes but is not limited to name, portrait, age, etc.

[0021] It should be further explained that the progress data processing module generates regional nodes from the original task area corresponding to the progress data set and constructs a progress data tracking model. The process of obtaining the equipment movement trajectory corresponding to the regional node, predicting the concrete pouring volume and the task completion data based on the progress data tracking model includes: The construction process of the progress data tracking model includes: Connecting the regional nodes corresponding to the construction site to generate a regional network of the construction site, setting the progress data summary days corresponding to each regional node, dividing the schedule execution period corresponding to each regional node according to the progress data summary days to obtain the corresponding progress data summary time node, and obtaining the corresponding progress data set in real time according to the progress data summary time node; and then connecting each progress data summary time node with the corresponding regional node; A regional spatial coordinate map is constructed with each regional node of the construction site regional network as the origin, and then a progress data tracking model is constructed.

[0022] The process of obtaining the device movement trajectory includes: Based on the progress data tracking model, the equipment position corresponding to each progress data summary time node of the regional node is obtained, marked on the corresponding regional spatial coordinate map, and mapped with the original equipment code; taking the original equipment position as the starting point, the equipment corresponding to each original equipment code is connected in sequence according to the regional number during the schedule execution period to generate the equipment movement trajectory of the equipment corresponding to the original equipment code during the schedule execution period.

[0023] The process of obtaining the predicted concrete pouring amount includes: According to each regional node, the corresponding pouring unit time is set, and the pouring speed, number of concrete vehicles and concrete construction personnel corresponding to each regional node are obtained, and then the formula is used to calculate the pouring speed, number of concrete vehicles and concrete construction personnel corresponding to each regional node. Get the concrete pouring rate corresponding to each regional node , where j represents the concrete number corresponding to the regional node, j=1, 2, ..., m, and m is a positive integer; V j 、N j and K j They are respectively represented by the pouring speed, number of concrete trucks and concrete construction workers corresponding to the concrete number; By formula Get the predicted concrete pouring volume for each regional node corresponding to the concrete number during the schedule execution period , where t i Indicates the schedule execution period corresponding to the regional node.

[0024] It should be further explained that in the specific implementation process, equipment and concrete are one of the most important tools for construction; by obtaining the equipment movement trajectory corresponding to the equipment, it can be determined whether the equipment has an abnormal movement trajectory at the construction site. Abnormal movement trajectory may cause the loss of construction equipment, changes in the equipment operation area, etc.; obtaining the predicted concrete pouring volume corresponding to the regional node can, on the one hand, determine whether the regional node is the peak period of concrete pouring; on the other hand, it can save the amount of concrete pouring, thereby controlling the concrete; The process of obtaining the task completion data includes: The construction personnel send the construction area and construction duration corresponding to the progress data summary time node to the task management unit through the mobile terminal, and map it with the original construction personnel's personal information to generate the task completion data corresponding to the construction personnel.

[0025] Send the equipment movement trajectory, predicted concrete pouring volume and task completion data to the task management unit for storage; It should be further explained that the process of the progress data analysis module analyzing the equipment movement trajectory, predicted concrete pouring volume and task completion data to obtain abnormal deviation progress data includes: According to the original device code, the regional critical position of the corresponding device is set and marked on the regional spatial coordinate map, and then compared with the device position involved in the corresponding device movement trajectory; if the device position exceeds the regional critical position, the corresponding device movement trajectory is marked as an abnormal trajectory; otherwise, it is marked as a normal trajectory; Set the concrete pouring volume corresponding to each regional node during the schedule execution period, and compare it with the predicted concrete pouring volume corresponding to each regional node; if the predicted concrete pouring volume is greater than the concrete pouring volume, the corresponding predicted concrete pouring volume is marked as abnormal concrete pouring volume; otherwise, it is marked as normal concrete pouring volume; By obtaining the images of construction workers corresponding to each regional node, the progress data is obtained, and the actual construction area and actual construction time of the construction workers corresponding to the time node are summarized, and compared with the task completion data corresponding to the construction workers; if the construction area does not exist in the actual construction area or the construction time is longer than the actual construction time, the corresponding construction worker is marked as an abnormal construction worker; otherwise, it is marked as a normal construction worker; Obtain the number of devices corresponding to each regional node at the progress data summary time node, and compare it with the original number of devices. If they are different, mark the number of devices as abnormal, otherwise, mark it as normal. According to the regional building image and the preset actual regional building image, the progress data is summarized to obtain the regional building completion degree corresponding to the time node; and compared with the preset regional building completion degree threshold, if it is greater than the regional building completion degree threshold, the corresponding regional building completion degree is marked as normal completion degree; otherwise, it is marked as abnormal completion degree; An abnormal deviation progress data tree is established with regional nodes as trunks, progress data summary time nodes as branches, and equipment movement trajectories, predicted concrete pouring volume, task completion data, equipment quantity, and regional building images corresponding to the progress data summary time nodes as leaves; Abnormal trajectories, abnormal concrete pouring volumes, abnormal construction personnel, abnormal number of equipment, and abnormal completion rates are marked as abnormal deviation progress data, and the corresponding leaves are marked in red, thereby generating abnormal alarm signals.

[0026] It should be further explained that, in a specific embodiment, the degree of completion of the regional construction can be displayed in multiple dimensions such as floors and construction sections.

[0027] It should be further explained that the process of the response module performing real-time response according to the abnormal deviation progress data includes: Obtain abnormal alarm signals, and obtain abnormal deviation progress data corresponding to each abnormal deviation progress data tree according to the abnormal alarm signals, and then control the equipment movement trajectory, predicted concrete pouring volume and task completion data corresponding to the regional node according to the abnormal deviation progress data; If it is an abnormal trajectory, the abnormal equipment position corresponding to the equipment movement trajectory is obtained, and the construction staff is warned according to the abnormal equipment position; If it is an abnormal concrete pouring amount, the corresponding concrete number is obtained, and then the pouring speed, the number of concrete trucks and the concrete construction personnel are adjusted; If the construction worker is abnormal, the corresponding original construction worker personal information is obtained, and the construction worker is warned according to the original construction worker personal information; If the number of devices is abnormal, the number of devices corresponding to each regional node will be reviewed again; If the completion rate is abnormal, it is necessary to increase the number of construction personnel at the regional node or extend the construction time.

[0028] It needs to be further explained that the preset management personnel monitor and manage the construction site through the scheduling unit, resource allocation unit and task management unit through the task progress terminal; the data corresponding to the scheduling unit, resource allocation unit and task management unit are connected through the original construction personnel's personal information, so as to intuitively judge the construction status of the construction personnel corresponding to the original construction personnel's personal information.

[0029] The features and exemplary embodiments of various aspects of the present application are described in detail above. In order to make the purpose, technical solutions and advantages of the present application clearer, the present application is further described in detail in combination with the drawings and specific embodiments. It should be understood that the specific embodiments described herein are only intended to explain the present application rather than to limit the present application. For those skilled in the art, the present application can be implemented without some of these specific details. The above description of the embodiments is merely to provide a better understanding of the present application by showing examples of the present application.

[0030] The above embodiments are only used to illustrate the technical method of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical method of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical method of the present invention.

Claims

1. An intelligent building progress data control system based on big data computing, characterized in that: The system includes a progress data aggregation module, a progress data processing module, a progress data analysis module and a response module; The progress data aggregation module is used to set a progress data set and collect corresponding progress data subsets according to the progress data set; The progress data processing module is used to generate regional nodes from the original task area corresponding to the progress data set and construct a progress data tracking model, and obtain the equipment movement trajectory, predicted concrete pouring volume and task completion data corresponding to the regional node based on the progress data tracking model; The progress data analysis module is used to analyze the equipment movement trajectory, the predicted concrete pouring volume and the task completion data and establish an abnormal deviation progress data tree, and obtain abnormal deviation progress data according to the abnormal deviation progress data tree; The response module is used to respond in real time according to the abnormal deviation progress data.

2. According to claim 1, an intelligent building progress data control system based on big data calculation is characterized in that: The progress data aggregation module sets a progress data set, and the process of collecting corresponding progress data subsets according to each progress data set includes: The progress data set includes tasks, task locations, task environments, and task states; a corresponding modal code is set for the progress data set, and a corresponding modal acquisition device is set according to the modal code; Deploy the deployment position of the modal acquisition device corresponding to the task position, task environment and task status at the construction site, and collect the task position subset, task environment subset and task status subset corresponding to the task position, task environment and task status at the deployment position according to the GPS positioning device, Internet of Things sensor device and camera device corresponding to the modal acquisition device; The task location subset includes equipment location and equipment quantity; the task environment subset includes pouring speed, number of concrete vehicles and concrete construction workers; the task status subset includes construction worker images and regional building images.

3. According to claim 2, the intelligent building progress data control system based on big data calculation is characterized in that: The process of obtaining the tasks corresponding to the progress dataset includes: Setting a task progress terminal, wherein the task progress terminal is wirelessly connected to a plurality of mobile terminals corresponding to construction personnel; the task progress terminal comprises a schedule arrangement unit, a resource allocation unit and a task management unit; The original task area, original resource data and original construction personnel data corresponding to the construction site are stored in the resource allocation unit; and the original task area is numbered as i, i=1, 2, ..., n, and n is a positive integer; the original resource data includes the original equipment code, the original equipment location and the original equipment quantity; the original construction personnel data includes the original construction personnel quantity and the original construction personnel personal information; The resource allocation unit arranges the original resource data and the original construction personnel data corresponding to the original task area, generates the corresponding schedule task, sets the execution time period of the schedule task, connects with the schedule task to generate a schedule task set, and then sends it to the schedule unit for storage; and sends the schedule task set to the corresponding mobile terminal for reception through the personal information of each original construction personnel; The various scheduled task sets in the original task area are merged to generate tasks corresponding to the original task area.

4. The intelligent building progress data control system based on big data calculation according to claim 3 is characterized in that: The process of constructing a progress data tracking model by the progress data processing module includes: The regional nodes corresponding to the construction site are connected to generate a regional network of the construction site, and the progress data summary days corresponding to each regional node are set. According to the progress data summary days, the schedule execution period corresponding to each regional node is divided to obtain the corresponding progress data summary time node, and the corresponding progress data set is obtained in real time according to the progress data summary time node; and the regional nodes of the construction site regional network are used as the origin to construct a regional spatial coordinate map, and then a progress data tracking model is constructed.

5. The intelligent building progress data control system based on big data calculation according to claim 4 is characterized in that: The process of obtaining the equipment movement trajectory, predicted concrete pouring volume and task completion data includes: Based on the progress data tracking model, the equipment position corresponding to each progress data summary time node of the regional node is obtained, marked on the corresponding regional spatial coordinate map, and mapped with the original equipment code; with the original equipment position as the starting point, the equipment corresponding to each original equipment code is sequentially connected according to the regional number during the schedule execution period to generate the equipment movement trajectory of the equipment corresponding to the original equipment code during the schedule execution period; According to each regional node, the corresponding pouring unit time is set, and the pouring speed, number of concrete vehicles and concrete construction personnel corresponding to each regional node are obtained, and then the formula is used to calculate the pouring speed, number of concrete vehicles and concrete construction personnel corresponding to each regional node. Get the concrete pouring rate corresponding to each regional node , where j represents the concrete number corresponding to the regional node, j=1, 2, ..., m, and m is a positive integer; V j 、N j and K j They are respectively represented by the pouring speed, number of concrete trucks and concrete construction workers corresponding to the concrete number; By formula Get the predicted concrete pouring volume for each regional node corresponding to the concrete number during the schedule execution period , where t i It represents the schedule execution period corresponding to the regional node; The construction personnel send the construction area and construction duration corresponding to the progress data summary time node to the task management unit through the mobile terminal, and map it with the original construction personnel’s personal information to generate the construction personnel’s corresponding task completion data; The equipment movement trajectory, predicted concrete pouring volume and task completion data are sent to the task management unit for storage.

6. The intelligent building progress data control system based on big data calculation according to claim 5 is characterized in that: The process of the progress data analysis module analyzing the equipment movement trajectory, predicted concrete pouring volume and task completion data to establish an abnormal deviation progress data tree includes: According to the original device code, the regional critical position of the corresponding device is set and marked on the regional spatial coordinate map, and then compared with the device position involved in the corresponding device movement trajectory; if the device position exceeds the regional critical position, the corresponding device movement trajectory is marked as an abnormal trajectory; otherwise, it is marked as a normal trajectory; Set the concrete pouring volume of each regional node corresponding to the concrete number during the schedule execution period, and compare it with the corresponding predicted concrete pouring volume; if the predicted concrete pouring volume is greater than the concrete pouring volume, the corresponding predicted concrete pouring volume is marked as abnormal concrete pouring volume; otherwise, it is marked as normal concrete pouring volume; By obtaining the images of construction workers corresponding to each regional node, the progress data is obtained, and the actual construction area and actual construction time of the construction workers corresponding to the time node are summarized, and compared with the task completion data corresponding to the construction workers; if the construction area does not exist in the actual construction area or the construction time is longer than the actual construction time, the corresponding construction worker is marked as an abnormal construction worker; otherwise, it is marked as a normal construction worker; Obtain the number of devices corresponding to each regional node at the progress data summary time node, and compare it with the original number of devices. If they are different, mark the number of devices as abnormal, otherwise, mark it as normal. According to the regional building image and the preset actual regional building image, the progress data is summarized to obtain the regional building completion degree corresponding to the time node; and compared with the preset regional building completion degree threshold, if it is greater than the regional building completion degree threshold, the corresponding regional building completion degree is marked as normal completion degree; otherwise, it is marked as abnormal completion degree; An abnormal deviation progress data tree is established with regional nodes as the trunk, progress data summary time nodes as branches, and equipment movement trajectories, predicted concrete pouring volume, task completion data, equipment quantity and regional building images corresponding to the progress data summary time nodes as leaves.

7. The intelligent building progress data control system based on big data calculation according to claim 6 is characterized in that: The process of obtaining the abnormal deviation progress data includes: Abnormal trajectories, abnormal concrete pouring volumes, abnormal construction personnel, abnormal number of equipment, and abnormal completion rates are marked as abnormal deviation progress data, and the corresponding leaves are marked in red, thereby generating abnormal alarm signals.

8. The intelligent building progress data control system based on big data calculation according to claim 7 is characterized in that: The process of the response module performing real-time response according to the abnormal deviation progress data includes: Obtain abnormal alarm signals, and obtain abnormal deviation progress data corresponding to each abnormal deviation progress data tree according to the abnormal alarm signals, and then control the equipment movement trajectory, predicted concrete pouring volume and task completion data corresponding to the regional node according to the abnormal deviation progress data.

Images