Construction early warning method and device based on multi-source data space-time correlation

Abstract

The invention provides a construction early warning method and device based on multi-source data space-time correlation, belongs to the technical field of data processing, and aims at solving the problem that current construction prediction is insufficient in instantaneity and effectiveness through obtaining first construction state data of a target construction area; based on a first generation timestamp and a first generation space position of the first construction state data, performing space-time association on the first construction state data to obtain a plurality of first construction space-time data sets, and based on the first construction space-time data sets, performing risk identification on key construction risk sources of the target construction area to obtain a first risk identification result, and under the condition that the first risk identification result is that the risk exists, generating alarm prompt information and displaying the alarm prompt information in the target construction area, so that construction risk identification can be performed according to the time-space correlation of the multi-source heterogeneous data of the construction site, and prediction of future risks is realized. And the instantaneity, the reliability and the perspectiveness of construction early warning are obviously improved.

Description

Technical Field

[0001] This application relates to the field of data processing technology, and in particular to a construction early warning method and device based on the spatiotemporal correlation of multi-source data. Background Technology

[0002] Construction early warning is a crucial aspect of construction project management. It aims to identify potential risks in advance and implement countermeasures through real-time monitoring, data analysis, and information dissemination to ensure project safety, controllable progress, and quality standards. Modern construction early warning has shifted from traditional experience-based judgment to data-driven intelligent management, achieving proactive risk control through the Internet of Things (IoT), big data analytics, and closed-loop mechanisms. In bridge construction, the construction site involves a large number of construction machines, personnel, and a complex and ever-changing environment. How to acquire and process this heterogeneous data in real time and correlate it with the bridge project to achieve precise monitoring and control of the construction process is a pressing technical challenge. Specifically, the heterogeneous data at the construction site includes operational status data of machinery and equipment, behavioral trajectory data of construction personnel, and environmental meteorological and geological data. These data come from diverse sources, have various formats, are massive in volume, and require high real-time processing. How to achieve rapid access, analysis, and fusion of heterogeneous data, and realize construction risk early warning and control based on the construction site conditions, is a technical challenge that needs to be overcome. Summary of the Invention

[0003] In view of the above problems, embodiments of this application provide a construction early warning method, device, electronic device and readable storage medium based on spatiotemporal correlation of multi-source data, so as to overcome the above problems or at least partially solve the above problems.

[0004] In a first aspect, embodiments of this application provide a construction early warning method based on spatiotemporal correlation of multi-source data, the method comprising: Obtain the first construction status data of the target construction area; the first construction status data includes construction machinery and equipment status data, construction personnel trajectory data, construction environment meteorological data, and construction geological data; Based on the first generation timestamp and the first generation spatial location of the first construction status data, the first construction status data is spatiotemporally correlated to obtain multiple first construction spatiotemporal data groups; Based on the first construction spatiotemporal data group, risk identification is performed on key construction risk sources in the target construction area to obtain the first risk identification result; If the first risk identification result indicates the existence of a risk, an alarm message is generated and displayed in the target construction area.

[0005] Optionally, the step of identifying key construction risk sources in the target construction area based on the first construction spatiotemporal data group to obtain a first risk identification result includes: Based on the first construction spatiotemporal data set, a digital twin image sequence of the target construction area within a continuous historical time period is generated; The digital twin image sequence is input into a pre-trained spatiotemporal prediction neural network model, which processes the digital twin image sequence and outputs a predicted digital twin image for future times. The predicted digital twin image is processed using a semantic segmentation model to obtain a pixel-level classification map of construction elements, which includes personnel, machinery and equipment, and high-risk areas. The construction element classification map is logically matched with a pre-set safety rule base. If the matching result violates the safety rules, a warning message corresponding to a future time is generated.

[0006] Optionally, the spatiotemporal prediction neural network model is a PredRNN network; the PredRNN network models the spatiotemporal evolution features in the digital twin image sequence through its internal spatiotemporal long short-term memory units and cross-layer bidirectional recursive structure; the training objective of the PredRNN network is guided by a composite loss function, which includes the reconstruction loss between predicted image pixels and real image pixels, and the perceptual loss calculated based on the feature map difference of the feature extraction network.

[0007] Optionally, based on the first generation timestamp and the first generation spatial location of the first construction status data, the first construction status data is spatiotemporally correlated to obtain multiple first construction spatiotemporal data groups, including: Based on the first generation timestamps of the construction machinery and equipment status data, the construction personnel trajectory data, the construction environment meteorological data, and the construction geological data, a first generation time difference is determined between the construction machinery and equipment status data, the construction personnel trajectory data, the construction environment meteorological data, and the construction geological data; Based on the first generated spatial location of the construction machinery and equipment status data, the construction personnel trajectory data, the construction environment meteorological data, and the construction geological data, a first spatial Euclidean distance is determined between the construction machinery and equipment status data, the construction personnel trajectory data, the construction environment meteorological data, and the construction geological data; Based on the first generation time difference and the first spatial Euclidean distance, combined with the first construction status data, a first data mapping with spatiotemporal correlation is determined; If the first data mappings contain the same construction machinery and equipment status data, construction personnel trajectory data, construction environment meteorological data, or construction geological data, the first data mappings are merged to obtain multiple first construction spatiotemporal data groups.

[0008] Optionally, determining the first data mapping with spatiotemporal correlation based on the first generation time difference and the first spatial Euclidean distance, combined with the first construction status data, includes: When the first generation time difference is less than or equal to the first time threshold, and the first spatial Euclidean distance between the two data corresponding to the first generation time difference is less than or equal to the first distance threshold, a first data mapping is generated between the two first construction status data corresponding to the first generation time difference.

[0009] Optionally, determining the first data mapping with spatiotemporal correlation based on the first generation time difference and the first spatial Euclidean distance, combined with the first construction status data, includes: Based on the first generation time difference and the first spatial Euclidean distance between the two data corresponding to the first generation time difference, the first continuous correlation degree between the construction machinery and equipment status data, the construction personnel trajectory data, the construction environment meteorological data and the construction geological data is calculated; If the first continuous correlation degree is greater than or equal to the first threshold, a first data mapping is generated between the two first construction status data corresponding to the first continuous correlation degree.

[0010] Optionally, based on the first generation timestamp and the first generation spatial location of the first construction state, the first construction state data is spatiotemporally correlated to obtain multiple first construction spatiotemporal data groups, including: If there is data missing in the first construction status data at the first moment, determine the data collection frequency at the first moment; Based on the data acquisition frequency, the missing data at the first moment is filled in to obtain the second construction status data; Based on the first generation timestamp and the first generation spatial location of the second construction status data, the second construction status data is spatiotemporally correlated to obtain multiple first construction spatiotemporal data groups.

[0011] Optionally, the alarm message is a digital twin image that marks the construction risk source with high-risk area identification.

[0012] Optionally, the method further includes: If the first risk identification result indicates that there is no risk, the construction progress of the target construction area is obtained; If the construction progress is less than the planned progress, a construction progress warning message is generated and displayed.

[0013] Secondly, embodiments of this application provide a construction early warning device based on multi-source data spatiotemporal correlation, the device comprising: The first acquisition module is used to acquire the first construction status data of the target construction area; the first construction status data includes construction machinery and equipment status data, construction personnel trajectory data, construction environment meteorological data, and construction geological data. The spatiotemporal correlation module is used to perform spatiotemporal correlation on the first construction status data based on the first generation timestamp and the first generation spatial location of the first construction status data to obtain multiple first construction spatiotemporal data groups; The risk identification module is used to identify key construction risk sources in the target construction area based on the first construction spatiotemporal data group, and obtain the first risk identification result; The first generation module is used to generate an alarm message and display the alarm message in the target construction area when the first risk identification result indicates that there is a risk.

[0014] Optionally, the risk identification module includes: A generation submodule is used to generate a digital twin image sequence of the target construction area over a continuous historical time period based on the first construction spatiotemporal data group. The input / output submodule is used to input the digital twin image sequence into a pre-trained spatiotemporal prediction neural network model, which processes the digital twin image sequence and outputs the future... Predictive digital twin images of time; The image processing submodule is used to process the predicted digital twin image using a semantic segmentation model to obtain a pixel-level construction element classification map, wherein the construction elements include personnel, machinery and equipment, and high-risk areas. The logical matching submodule is used to logically match the construction element classification map with the preset safety rule base. If the matching result violates the safety rules, a warning message corresponding to a future time is generated.

[0015] Optionally, the spatiotemporal prediction neural network model is a PredRNN network; the PredRNN network models the spatiotemporal evolution features in the digital twin image sequence through its internal spatiotemporal long short-term memory units and cross-layer bidirectional recursive structure; the training objective of the PredRNN network is guided by a composite loss function, which includes the reconstruction loss between predicted image pixels and real image pixels, and the perceptual loss calculated based on the feature map difference of the feature extraction network.

[0016] Optionally, the spatiotemporal correlation module includes: The first determining submodule is used to determine the first generation time difference between the construction machinery and equipment status data, the construction personnel trajectory data, the construction environment meteorological data, and the construction geological data based on the first generation timestamp of the construction machinery and equipment status data, the construction personnel trajectory data, the construction environment meteorological data, and the construction geological data; The second determining submodule is used to determine the first spatial Euclidean distance between the construction machinery and equipment status data, the construction personnel trajectory data, the construction environment meteorological data, and the construction geological data based on the first generated spatial location of the construction machinery and equipment status data, the construction personnel trajectory data, the construction environment meteorological data, and the construction geological data. The third determining submodule is used to determine a first data mapping with spatiotemporal correlation based on the first generation time difference and the first spatial Euclidean distance, combined with the first construction status data; The data fusion submodule is used to fuse the first data mappings to obtain multiple first construction spatiotemporal data groups when the first data mappings contain the same construction machinery and equipment status data, construction personnel trajectory data, construction environment meteorological data, or construction geological data.

[0017] Optionally, the third determining submodule includes: The first generation unit is configured to generate a first data mapping between two first construction status data corresponding to the first generation time difference when the first generation time difference is less than or equal to a first time threshold and the first spatial Euclidean distance between the two data corresponding to the first generation time difference is less than or equal to a first distance threshold.

[0018] Optionally, the third determining submodule includes: The calculation unit is used to calculate the first degree of continuity between the construction machinery and equipment status data, the construction personnel trajectory data, the construction environment meteorological data and the construction geological data based on the first generation time difference and the first spatial Euclidean distance between the two data corresponding to the first generation time difference. The second generation unit is used to generate a first data mapping between two first construction status data corresponding to the first continuous correlation degree when the first continuous correlation degree is greater than or equal to the first threshold.

[0019] Optionally, the spatiotemporal correlation module includes: The fourth determination submodule is used to determine the data collection frequency at the first moment when there is a data gap in the first construction status data at the first moment. The data completion submodule is used to complete the missing data at the first moment based on the data acquisition frequency to obtain the second construction status data. The spatiotemporal correlation submodule is used to perform spatiotemporal correlation on the second construction status data based on the first generation timestamp and the first generation spatial location of the second construction status data, so as to obtain multiple first construction spatiotemporal data groups.

[0020] Optionally, the alarm message is a digital twin image that marks the construction risk source with high-risk area identification.

[0021] Optionally, the device further includes: The second acquisition module is used to acquire the construction progress of the target construction area when the first risk identification result is that there is no risk. The second generation module is used to generate and display construction progress warning information when the construction progress is less than the planned progress.

[0022] Thirdly, embodiments of this application provide an electronic device, including a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the construction early warning method based on multi-source data spatiotemporal correlation as described in any of the above.

[0023] Fourthly, embodiments of this application provide a readable storage medium storing a program or instructions, which, when executed by a processor, implement the construction early warning method based on spatiotemporal correlation of multi-source data as described above.

[0024] The specific beneficial effects are as follows: This application embodiment acquires first construction status data of the target construction area. This first construction status data includes construction machinery and equipment status data, construction personnel trajectory data, construction environment meteorological data, and construction geological data. Based on the first generation timestamp and first generation spatial location of the first construction status data, spatiotemporal correlation is performed to obtain multiple first construction spatiotemporal data groups. Based on these first construction spatiotemporal data groups, key construction risk sources in the target construction area are identified, resulting in a first risk identification result. If the first risk identification result indicates the existence of a risk, an alarm message is generated and displayed in the target construction area. This approach establishes the correlation between the aforementioned multi-source heterogeneous data based on the spatiotemporal correlation of the multi-source heterogeneous data at the construction site, and performs risk identification based on the correlated construction spatiotemporal data groups, thereby improving the timeliness and reliability of construction early warning to a certain extent. Furthermore, by introducing a digital twin image prediction method based on a spatiotemporal predictive neural network, predictive risk identification of the construction status at future moments can be achieved, generating advanced early warning information and significantly improving the foresight and proactivity of construction safety management. Attached Figure Description

[0025] To more clearly illustrate the technical solutions of the embodiments of this application, the drawings used in the description of the embodiments of this application 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.

[0026] Figure 1 This is a flowchart illustrating a construction early warning method based on spatiotemporal correlation of multi-source data provided in an embodiment of this application; Figure 2 This is a flowchart illustrating another construction early warning method based on spatiotemporal correlation of multi-source data provided in this application embodiment; Figure 3 This is a logic block diagram of a construction early warning device based on spatiotemporal correlation of multi-source data provided in an embodiment of this application; Figure 4 This is a schematic diagram of an electronic device provided in an embodiment of this application. Detailed Implementation

[0027] Exemplary embodiments of this application will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of this application are shown in the drawings, it should be understood that this application may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided to enable a more thorough understanding of this application and to fully convey the scope of this application to those skilled in the art.

[0028] Reference Figure 1 , Figure 1 A flowchart illustrating a construction early warning method based on spatiotemporal correlation of multi-source data, provided in this application embodiment, includes: Step 101: Obtain the first construction status data of the target construction area; the first construction status data includes construction machinery and equipment status data, construction personnel trajectory data, construction environment meteorological data, and construction geological data.

[0029] In the embodiments of this application, the first construction status data of the target construction area may include heterogeneous data such as construction machinery and equipment status data, construction personnel trajectory data, construction environment meteorological data, and construction geological data. Construction machinery and equipment status data can be obtained through equipment operation logs; construction personnel trajectory data can be obtained through locators; construction environment meteorological data can be determined based on current regional weather data or obtained from sensors (such as temperature, humidity, and dust sensors); and construction geological data can be obtained through geological analysis of the target construction area before construction. In this way, the first construction status data of the target construction area can be obtained. After obtaining the first construction status data, it can be preprocessed to delete obviously redundant data (i.e., data for which no other types of data existed at the time of its generation) and obviously erroneous data.

[0030] Step 102: Based on the first generation timestamp and the first generation spatial location of the first construction status data, perform spatiotemporal correlation on the first construction status data to obtain multiple first construction spatiotemporal data groups.

[0031] In the embodiments of this application, each first construction state data can have a first generation timestamp and a first generation spatial location. The first generation timestamp and the first generation spatial location serve as metadata for the first construction state data and are rigidly associated with it. Spatiotemporal association can be performed on each first construction state data based on the first generation timestamp and the first generation spatial location, thereby obtaining multiple first construction spatiotemporal data groups. For example, first construction state data corresponding to first generation timestamps can be associated when their first generation timestamps are close and their first generation spatial locations are similar; alternatively, the spatiotemporal correlation degree can be calculated using the first generation timestamp and the first generation spatial location, thereby determining whether the corresponding data have spatiotemporal correlation based on the spatiotemporal correlation degree.

[0032] Step 103: Based on the first construction spatiotemporal data group, identify the key construction risk sources in the target construction area to obtain the first risk identification result.

[0033] In the embodiments of this application, risk identification can be performed on key construction risk sources in the target construction area based on a first construction spatiotemporal data set, thereby obtaining a first risk identification result. Specifically, risk identification can be performed based on the values ​​of the first spatiotemporal data set itself. If more than half of the data in the first construction status data set corresponding to a key construction risk source deviates significantly from normal values, then the key construction risk source can be considered to pose a risk. Alternatively, if the value changes of two consecutive first construction spatiotemporal data sets corresponding to a key construction risk source exceed a preset range, then the key construction risk source can be considered to pose a risk. The first risk identification result includes two outcomes: presence of risk and absence of risk.

[0034] Step 104: If the first risk identification result indicates the existence of a risk, generate an alarm message and display the alarm message in the target construction area.

[0035] In the embodiments of this application, if the first risk identification result indicates the existence of a risk, an alarm notification can be generated and displayed in the target construction area. The alarm notification can be in the form of voice, text, images, animation, etc.

[0036] In the embodiments of this application, first construction status data of the target construction area is obtained. The first construction status data includes construction machinery and equipment status data, construction personnel trajectory data, construction environment meteorological data, and construction geological data. Based on the first generation timestamp and first generation spatial location of the first construction status data, spatiotemporal correlation is performed on the first construction status data to obtain multiple first construction spatiotemporal data groups. Based on the first construction spatiotemporal data groups, risk identification is performed on key construction risk sources in the target construction area to obtain a first risk identification result. If the first risk identification result indicates the existence of risk, an alarm prompt message is generated and displayed in the target construction area. This method can establish the correlation between the above-mentioned multi-source heterogeneous data based on the spatiotemporal correlation of multi-source heterogeneous data at the construction site, and perform risk identification based on the correlated construction spatiotemporal data groups, which can improve the timeliness and reliability of construction early warning to a certain extent.

[0037] Reference Figure 2 , Figure 2 A flowchart illustrating another construction early warning method based on spatiotemporal correlation of multi-source data provided in this application embodiment, the method may include: Step 201: Obtain the first construction status data of the target construction area; the first construction status data includes construction machinery and equipment status data, construction personnel trajectory data, construction environment meteorological data, and construction geological data.

[0038] In the embodiments of this application, the implementation of this step can be referred to the embodiment of step 101, and will not be repeated here.

[0039] Step 202: Based on the first generation timestamp and the first generation spatial location of the first construction status data, perform spatiotemporal correlation on the first construction status data to obtain multiple first construction spatiotemporal data groups.

[0040] In the embodiments of this application, the implementation of this step can be referred to the embodiment of step 102, and will not be repeated here.

[0041] Optionally, step 202 may include the following sub-steps: Sub-step 2021: If there is data missing in the first construction status data at the first moment, determine the data collection frequency at the first moment.

[0042] In the embodiments of this application, since multiple different first construction state data may correspond to any given signal acquisition time, and the acquisition sources for each type of construction state data are usually different, the first construction state data at the same time may be missing. The presence or absence of first construction state data can be determined based on the signal acquisition time. If the first construction state data is missing at the first time, the data acquisition frequency at the first time can be determined. This data acquisition frequency can be the data acquisition frequency corresponding to the data type of the missing data.

[0043] Sub-step 2022: Based on the data acquisition frequency, fill in the missing data at the first moment to obtain the second construction status data.

[0044] In the embodiments of this application, if the data acquisition frequency is greater than or equal to a preset frequency, a Long Short-Term Memory (LSTM) network can be used to predict the missing construction status data based on existing construction status data, thereby completing the missing data at the first moment. If the data acquisition frequency is less than the preset frequency, cubic spline interpolation can be used to complete the missing data. After completing the data, the second construction status data can be obtained.

[0045] Sub-step 2023: Based on the first generation timestamp and the first generation spatial location of the second construction status data, perform spatiotemporal correlation on the second construction status data to obtain multiple first construction spatiotemporal data groups.

[0046] In the embodiments of this application, the second construction status data can be spatiotemporally correlated based on the first generation timestamp and the first generation spatial location of the data-completed second construction status data to obtain multiple first construction spatiotemporal data groups. The specific implementation details of the spatiotemporal correlation can be found in step 102 regarding the embodiment of spatiotemporal correlation, and will not be repeated here.

[0047] In the embodiments of this application, when there is data loss in the first construction state data at the first moment, the data acquisition frequency at the first moment is determined. Based on the data acquisition frequency, the missing data at the first moment is filled in to obtain the second construction state data. Based on the first generation timestamp and the first generation spatial location of the second construction state data, the second construction state data is spatiotemporally correlated to obtain multiple first construction spatiotemporal data groups. This can fill in the first construction state data when there is data loss, and perform spatiotemporal correlation on the second construction state data obtained after filling in the data to obtain the first construction spatiotemporal data groups, which can improve the data integrity and accuracy of the first construction spatiotemporal data groups to a certain extent.

[0048] Sub-step 2024: Based on the first generation timestamps of the construction machinery and equipment status data, the construction personnel trajectory data, the construction environment meteorological data, and the construction geological data, determine the first generation time difference between the construction machinery and equipment status data, the construction personnel trajectory data, the construction environment meteorological data, and the construction geological data.

[0049] In the embodiments of this application, the first generation time difference between the construction machinery and equipment status data, construction personnel trajectory data, construction environment meteorological data, and construction geological data can be determined based on the first generation timestamp of the construction machinery and equipment status data, construction personnel trajectory data, construction environment meteorological data, and construction geological data. The calculation method is shown in Equation 1 below: (Equation 1); In Equation 1, Indicates the first generation time difference. and This refers to the first generation timestamp corresponding to any two data points from the following categories: construction machinery and equipment status data, construction personnel trajectory data, construction environment meteorological data, and construction geological data. Typically, the first generation time difference can be limited to calculations between different data types.

[0050] Sub-step 2025: Based on the first generated spatial location of the construction machinery and equipment status data, the construction personnel trajectory data, the construction environment meteorological data, and the construction geological data, determine the first spatial Euclidean distance between the construction machinery and equipment status data, the construction personnel trajectory data, the construction environment meteorological data, and the construction geological data.

[0051] In the embodiments of this application, the first spatial Euclidean distance between the construction machinery and equipment status data, construction personnel trajectory data, construction environment meteorological data, and construction geological data can be calculated based on the first generated spatial location of the construction machinery and equipment status data, construction personnel trajectory data, construction environment meteorological data, and construction geological data. The calculation method is shown in Equation 2 below: (Equation 2); In equation 2 above, This includes data on the status of construction machinery and equipment, the trajectory of construction personnel, meteorological data of the construction environment, and geological data of the construction site. and data First spatial Euclidean distance between them, data The first generated spatial location is ,data The first generated spatial location is Typically, the first-space Euclidean distance can be limited to calculations between different data types.

[0052] Sub-step 2026: Based on the first generation time difference and the first spatial Euclidean distance, and combined with the first construction status data, determine the first data mapping that has spatiotemporal correlation.

[0053] In the embodiments of this application, a first data mapping with spatiotemporal correlation can be determined based on a first generation time difference and a first spatial Euclidean distance, combined with first construction state data. That is, when the first generation time difference is less than a preset duration and the first spatial Euclidean distance is less than a preset distance, the two first construction state data corresponding to the first generation time difference or the first spatial Euclidean distance can be associated to obtain the first data mapping.

[0054] Optionally, sub-step 2026 may include the following sub-steps: Sub-step A1: When the first generation time difference is less than or equal to the first time threshold, and the first spatial Euclidean distance between the two data corresponding to the first generation time difference is less than or equal to the first distance threshold, generate the first data mapping between the two first construction state data corresponding to the first generation time difference.

[0055] In the embodiments of this application, if the first generation time difference is less than or equal to the first time threshold, and the first spatial Euclidean distance between the two data corresponding to the first generation time difference is less than or equal to the first distance threshold, then a first data mapping between the two first construction status data corresponding to the first generation time difference can be generated.

[0056] In the embodiments of this application, by generating a first data mapping between two first construction state data corresponding to the first generation time difference when the first generation time difference is less than or equal to a first time threshold and the first spatial Euclidean distance between the two data corresponding to the first generation time difference is less than or equal to a first distance threshold, a first data mapping for self-checking of two first construction state data can be generated by threshold judgment, which can improve the data accuracy of the first data mapping to a certain extent.

[0057] Sub-step A2: Based on the first generation time difference and the first spatial Euclidean distance between the two data corresponding to the first generation time difference, calculate the first continuous correlation between the construction machinery and equipment status data, the construction personnel trajectory data, the construction environment meteorological data and the construction geological data.

[0058] In the embodiments of this application, the degree of continuous correlation is defined as the degree of spatiotemporal correlation between two data points. The function of the degree of continuous correlation is shown in Equation 3 below: (Equation 3); In equation 3 above, The first degree of continuous association, As a time-weighted factor, The time decay coefficient, The spatial attenuation coefficient, The first generation time difference, Let be the first spatial Euclidean distance between the two data points corresponding to the first generation time difference. According to Equation 3, the first continuity correlation between construction machinery and equipment status data, construction personnel trajectory data, construction environment meteorological data, and construction geological data can be calculated by combining the first generation time difference and the first spatial Euclidean distance between the two data points corresponding to the first generation time difference.

[0059] Sub-step A3: If the first continuous correlation degree is greater than or equal to the first threshold, generate a first data mapping between the two first construction status data corresponding to the first continuous correlation degree.

[0060] In the embodiments of this application, if the calculated first continuous correlation degree is greater than or equal to the first threshold, a first data mapping between the two first construction status data corresponding to the first continuous correlation degree can be generated.

[0061] In the embodiments of this application, a first continuous correlation degree is calculated between construction machinery and equipment status data, construction personnel trajectory data, construction environment meteorological data and construction geological data based on a first generation time difference and a first spatial Euclidean distance between two data corresponding to the first generation time difference. When the first continuous correlation degree is greater than or equal to a first threshold, a first data mapping between two first construction status data corresponding to the first continuous correlation degree is generated. The continuous correlation degree can be used as a judgment index for spatiotemporal correlation, which can improve the data accuracy of the first data mapping to a certain extent.

[0062] Sub-step 2027: If the first data mappings contain the same construction machinery and equipment status data, construction personnel trajectory data, construction environment meteorological data or construction geological data, the first data mappings are fused to obtain multiple first construction spatiotemporal data groups.

[0063] In the embodiments of this application, a data matching method can be used to determine whether the various first data mappings contain the same construction machinery and equipment status data, construction personnel trajectory data, construction environment meteorological data, or construction geological data. If they do, the first data mappings containing the same construction machinery and equipment status data, construction personnel trajectory data, construction environment meteorological data, or construction geological data can be fused to obtain multiple first construction spatiotemporal data groups. Each first data mapping contains only two types of first construction status data. After one fusion, a data mapping containing three types of first construction status data can be obtained. After a second fusion, a first construction spatiotemporal data group containing four types of first construction status data can be obtained.

[0064] In the embodiments of this application, the methods described in sub-steps 2024 to 2027 and their sub-steps can be used as an embodiment of sub-step 2023 to perform spatiotemporal correlation on the second construction status data using the same method.

[0065] In the embodiments of this application, a first generation time difference is determined based on the first generation timestamp of construction machinery and equipment status data, construction personnel trajectory data, construction environment meteorological data, and construction geological data. A first spatial Euclidean distance is determined based on the first generation spatial location of these data. Based on the first generation time difference and the first spatial Euclidean distance, a first data mapping with spatiotemporal correlation is determined. When the first data mappings contain the same construction machinery and equipment status data, construction personnel trajectory data, construction environment meteorological data, or construction geological data, the first data mappings are fused to obtain multiple first construction spatiotemporal data groups. The first data mapping can be generated based on the first generation time difference and the first spatial Euclidean distance between the various construction state data. Then, data fusion is used to generate the first construction spatiotemporal data groups based on the first data mappings, which can improve the accuracy and reliability of the first construction spatiotemporal data groups to a certain extent.

[0066] Step 203: Based on the first construction spatiotemporal data group, identify the key construction risk sources in the target construction area to obtain the first risk identification result.

[0067] In the embodiments of this application, the implementation of this step can be referred to the embodiment of step 103, and will not be repeated here.

[0068] Optionally, step 203 may include the following sub-steps: Sub-step 2031: Based on the first construction spatiotemporal data set, generate a digital twin image sequence of the target construction area within a continuous historical time period. ,in, Indicates at time The previous That moment.

[0069] Sub-step 2032, the digital twin image sequence The input is fed into a pre-trained spatiotemporal prediction neural network model, which processes the digital twin image sequence and outputs the future... Predictive digital twin images of time .

[0070] Sub-step 2033: The predicted digital twin image is processed using a semantic segmentation model to obtain a pixel-level construction element classification map, wherein the construction elements include personnel, machinery and equipment, and high-risk areas.

[0071] Sub-step 2034 involves logically matching the construction element classification map with a pre-set safety rule library. If the matching result violates the safety rules, a warning message corresponding to a future time is generated.

[0072] In embodiments of this application, the security rule base may include: Mechanical collision risk: Simulates the sudden approach of personnel within the swing range of the load being lifted by the tower crane.

[0073] Area intrusion risk: Simulate personnel entering dynamically changing high-risk work areas (such as pouring areas).

[0074] Environmental risks: Simulate a sudden increase in wind speed during specific processes (such as working at height).

[0075] Equipment failure risk: Simulates a slow and abnormal increase in hydraulic pump pressure to a critical value.

[0076] Optionally, the spatiotemporal prediction neural network model is a PredRNN network; the PredRNN network models the spatiotemporal evolution features in the digital twin image sequence through its internal spatiotemporal long short-term memory units (ST-LSTM) and cross-layer bidirectional recursive structure; the training objective of the PredRNN network is guided by a composite loss function, which includes the reconstruction loss between predicted image pixels and real image pixels, and the perceptual loss calculated based on the feature map difference of the feature extraction network.

[0077] In the embodiments of this application, the composite loss function can be expressed as: ,in L Represents the composite loss function. surface Showing the reconstruction losses, Indicates perceived loss. and The balancing coefficient is used. The spatiotemporal prediction neural network model can be a PredRNN network. The PredRNN network can model the spatiotemporal evolution features in the digital twin image sequence through its internal spatiotemporal long short-term memory units (ST-LSTM) and cross-layer bidirectional recursive structure. This can improve the prediction performance of the spatiotemporal prediction neural network model, thereby improving the stability of the predicted digital twin image to a certain extent.

[0078] In the embodiments of this application, a digital twin image sequence of the target construction area within a continuous historical time period is generated based on the first construction spatiotemporal data group. The digital twin image sequence is input into a pre-trained spatiotemporal prediction neural network model, which processes the digital twin image sequence and outputs a predicted digital twin image for future moments. The predicted digital twin image is then processed using a semantic segmentation model to obtain a pixel-level construction element classification map. The construction elements include personnel, machinery and equipment, and high-risk areas. The construction element classification map is logically matched with a pre-set safety rule base. If the matching result violates the safety rules, a warning message corresponding to the future moment is generated. This enables predictive risk identification of the construction status at future moments and generates advanced warning information, significantly improving the foresight and initiative of construction safety management.

[0079] Step 204: If the first risk identification result indicates the existence of a risk, generate an alarm message and display the alarm message in the target construction area.

[0080] In the embodiments of this application, the implementation of this step can be referred to the embodiment of step 104, and will not be repeated here.

[0081] Optionally, the alarm message is a digital twin image that marks the construction risk source with high-risk area identification.

[0082] In the embodiments of this application, alarm notification information can be presented in the form of a digital twin image. The digital twin image can be a three-dimensional virtual image corresponding to the target construction area. When a key risk control source poses a risk, it can be marked in red, and the red color can flash. Simultaneously, a voice prompt can be issued to alert construction personnel to the construction risk in the target construction area. This approach can improve the intuitiveness and reference value of the alarm notification information to a certain extent, making it easier for construction personnel to identify risks.

[0083] Step 205: If the first risk identification result indicates that there is no risk, obtain the construction progress of the target construction area.

[0084] In the embodiments of this application, if the first risk identification result is that there is no risk, the construction progress of the target construction area can be calculated by the overlap between the current construction body model and the construction drawings.

[0085] Step 206: If the construction progress is less than the planned progress, generate and display the construction progress warning information.

[0086] In the embodiments of this application, if the construction progress is less than the planned progress, a construction progress warning message can be generated and displayed. Since the risk to personnel from a construction progress warning is relatively low, a milder warning method can be used, such as using flashing red text or a construction progress prompt pop-up message with a confirmation button.

[0087] In the embodiments of this application, by obtaining the construction progress of the target construction area when the first risk identification result is that there is no risk, and generating and displaying construction progress warning information when the construction progress is less than the planned progress, the construction progress can be identified and warned when there is no construction risk in the target construction area. This allows construction personnel to adjust the construction plan in a timely manner based on the construction progress warning, which can improve the stability of the construction progress to a certain extent.

[0088] In order to comprehensively and objectively verify the effectiveness, reliability and advancement of the embodiments of this application, we designed a rigorous experiment and conducted simulation and field tests during the construction process (6 months) of a large-scale cross-river bridge project.

[0089] 1. Experimental setup: Data sources: Real-time status data (oil pressure, speed, GPS location) of 10 key construction machines (such as tower cranes and pump trucks), location trajectory data of 50 construction workers, data from 5 meteorological stations deployed at the construction site (wind speed, wind direction, humidity, precipitation), and real-time data from geological subsidence monitoring points were collected.

[0090] Data preprocessing: The raw data was cleaned, including handling data loss caused by sensor communication interruptions, removing abnormal jump values ​​caused by signal interference, and synchronizing and aligning the data from multiple sources to the same timestamp.

[0091] Hardware and software platform: Deployed on a cloud virtual server, configured with an Intel Xeon Gold 6248R CPU, NVIDIA Tesla V100 GPU, and 128GB RAM.

[0092] Software environment: Operating system Ubuntu 18.04, programming language Python 3.8, deep learning framework PyTorch 1.9, digital twin engine Unity 3D 2020 LTS.

[0093] 2. Experimental methods and evaluation indicators: Test scenarios: covering three typical high-risk stages of bridge construction: foundation excavation, pier pouring, and beam erection.

[0094] Risk event injection: In collaboration with domain experts, we defined 20 representative risk scenarios across four categories for testing. Mechanical collision risk: Simulates the sudden approach of personnel within the swing range of the load being lifted by the tower crane.

[0095] Area intrusion risk: Simulate personnel entering dynamically changing high-risk work areas (such as pouring areas).

[0096] Environmental risks: Simulate a sudden increase in wind speed during specific processes (such as working at height).

[0097] Equipment failure risk: Simulates a slow and abnormal increase in hydraulic pump pressure to a critical value.

[0098] Evaluation indicators: Risk identification accuracy: including precision and F1-Score.

[0099] Early warning timeliness: The average time delay from when a risk condition is met to when the system issues an alarm.

[0100] False alarm rate: The frequency of false alarms from the system in a total of 500 hours of normal construction data.

[0101] Advanced early warning capability: The percentage of successfully predicted future risk events and the average lead time.

[0102] System performance: average time (ms) for a single inference run of the model and CPU / GPU utilization.

[0103] 3. Experimental Results: Table 1. Comparison of Early Warning Effects: ; 4. Results Analysis: As shown in Table 1 above, the F1 score of the early warning method in this application is 96.3 (average), which is higher than that of traditional methods and multi-sensor alarm methods. The average early warning delay is 2.8 minutes, which is lower than that of traditional methods and multi-sensor alarm methods. Regarding the false alarm rate, the false alarm rate of the early warning method in this application is 4.5%, significantly lower than the 19.2% of traditional methods and the 11.8% of multi-sensor alarm methods. In terms of the success rate of advance warning, traditional methods and multi-sensor alarms cannot provide advance warnings, while the early warning method in this application can achieve an 85% success rate. Due to the adoption of a more refined and intelligent early warning method, the upper limit of the early warning time of this application is 500ms, which is higher than that of traditional methods and multi-sensor alarm methods. However, considering the overall duration of the warning target, the slight increase in inference time will not affect its overall superiority.

[0104] Experimental data fully demonstrate that: Effectiveness: The present invention (Method A) significantly outperforms the comparative methods in all core metrics. Its high F1-Score demonstrates that the multi-source spatiotemporal correlation model can more comprehensively capture risk characteristics and significantly reduce false negatives and false negatives.

[0105] Forward-looking: Its unique early warning function provides an average decision window of more than 12 minutes for proactive safety management, which is impossible to achieve with traditional methods.

[0106] Practicality: Despite the higher model complexity, the average inference time is controlled within 500 milliseconds, which fully meets the real-time requirements of construction sites and proves the engineering application value of this invention.

[0107] Robustness: The invention maintained stable high performance and demonstrated good environmental adaptability at different stages of bridge construction.

[0108] Reference Figure 3 , Figure 3 This application provides a logic block diagram of a construction early warning device based on spatiotemporal correlation of multi-source data. The construction early warning device 300 based on spatiotemporal correlation of multi-source data may include: The first acquisition module 301 is used to acquire the first construction status data of the target construction area; the first construction status data includes construction machinery and equipment status data, construction personnel trajectory data, construction environment meteorological data, and construction geological data. The spatiotemporal correlation module 302 is used to perform spatiotemporal correlation on the first construction status data based on the first generation timestamp and the first generation spatial location of the first construction status data to obtain multiple first construction spatiotemporal data groups; Risk identification module 303 is used to identify key construction risk sources in the target construction area based on the first construction spatiotemporal data group, and obtain a first risk identification result; The first generation module 304 is used to generate alarm prompt information and display the alarm prompt information in the target construction area when the first risk identification result indicates that there is a risk.

[0109] Optionally, the spatiotemporal correlation module 302 includes: The first determining submodule is used to determine the first generation time difference between the construction machinery and equipment status data, the construction personnel trajectory data, the construction environment meteorological data, and the construction geological data based on the first generation timestamp of the construction machinery and equipment status data, the construction personnel trajectory data, the construction environment meteorological data, and the construction geological data; The second determining submodule is used to determine the first spatial Euclidean distance between the construction machinery and equipment status data, the construction personnel trajectory data, the construction environment meteorological data, and the construction geological data based on the first generated spatial location of the construction machinery and equipment status data, the construction personnel trajectory data, the construction environment meteorological data, and the construction geological data. The third determining submodule is used to determine a first data mapping with spatiotemporal correlation based on the first generation time difference and the first spatial Euclidean distance, combined with the first construction status data; The data fusion submodule is used to fuse the first data mappings to obtain multiple first construction spatiotemporal data groups when the first data mappings contain the same construction machinery and equipment status data, construction personnel trajectory data, construction environment meteorological data, or construction geological data.

[0110] Optionally, the third determining submodule includes: The first generation unit is configured to generate a first data mapping between two first construction status data corresponding to the first generation time difference when the first generation time difference is less than or equal to a first time threshold and the first spatial Euclidean distance between the two data corresponding to the first generation time difference is less than or equal to a first distance threshold.

[0111] Optionally, the third determining submodule includes: The calculation unit is used to calculate the first degree of continuity between the construction machinery and equipment status data, the construction personnel trajectory data, the construction environment meteorological data and the construction geological data based on the first generation time difference and the first spatial Euclidean distance between the two data corresponding to the first generation time difference. The second generation unit is used to generate a first data mapping between two first construction status data corresponding to the first continuous correlation degree when the first continuous correlation degree is greater than or equal to the first threshold.

[0112] Optionally, the spatiotemporal correlation module 302 includes: The fourth determination submodule is used to determine the data collection frequency at the first moment when there is a data gap in the first construction status data at the first moment. The data completion submodule is used to complete the missing data at the first moment based on the data acquisition frequency to obtain the second construction status data. The spatiotemporal correlation submodule is used to perform spatiotemporal correlation on the second construction status data based on the first generation timestamp and the first generation spatial location of the second construction status data, so as to obtain multiple first construction spatiotemporal data groups.

[0113] Optionally, the alarm message is a digital twin image that marks the construction risk source with high-risk area identification.

[0114] Optionally, the construction early warning device 300 based on multi-source data spatiotemporal correlation further includes: The second acquisition module is used to acquire the construction progress of the target construction area when the first risk identification result is that there is no risk. The second generation module is used to generate and display construction progress warning information when the construction progress is less than the planned progress.

[0115] The construction early warning device based on multi-source data spatiotemporal correlation in this application embodiment can be an electronic device or a component of an electronic device, such as an integrated circuit or a chip. The electronic device can be a terminal or other devices besides a terminal. For example, the electronic device can be a mobile phone, tablet computer, laptop computer, PDA, in-vehicle electronic device, mobile internet device (MID), augmented reality (AR) / virtual reality (VR) device, robot, wearable device, ultra-mobile personal computer (UMPC), netbook, or personal digital assistant (PDA), etc., and can also be a server, network attached storage (NAS), personal computer (PC), etc. This application embodiment does not specifically limit the specific implementation.

[0116] The construction early warning device based on multi-source data spatiotemporal correlation in this application embodiment can be a device with an operating system. This operating system can be Android, Linux, Windows, or other possible operating systems; this application embodiment does not specifically limit it.

[0117] The construction early warning device based on multi-source data spatiotemporal correlation provided in this application embodiment can achieve... Figures 1 to 2 The various processes implemented in the method implementation examples will not be described again here to avoid repetition.

[0118] This application provides an electronic device, see [link to relevant documentation] Figure 4The electronic device 40 includes a processor 401, a memory 402, and a computer program 4021 stored in the memory 402 and executable on the processor 401. When the processor 401 executes the program, it implements the construction early warning method based on multi-source data spatiotemporal correlation of the aforementioned embodiment.

[0119] This application also provides a computer-readable storage medium storing a computer program / instruction thereon, which, when executed by a processor, implements the steps in the construction early warning method based on multi-source data spatiotemporal correlation disclosed in this application.

[0120] This application also provides a computer program product that, when run on an electronic device, enables the processor to execute the steps in the construction early warning method based on multi-source data spatiotemporal correlation disclosed in this application.

[0121] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. The same or similar parts between the various embodiments can be referred to each other.

[0122] This application describes embodiments with reference to flowchart illustrations and / or block diagrams of methods, apparatuses, electronic devices, and computer program products according to embodiments of this application. It should be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing terminal device to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing terminal device, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0123] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing terminal device to operate in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0124] These computer program instructions can also be loaded onto a computer or other programmable data processing terminal equipment, causing a series of operational steps to be performed on the computer or other programmable terminal equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable terminal equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0125] Although preferred embodiments of the present application have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of the embodiments of this application.

[0126] Finally, it should be noted that in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or terminal device that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or terminal device. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or terminal device that includes said element.

[0127] The above provides a detailed description of a construction early warning method and apparatus based on spatiotemporal correlation of multi-source data provided in this application. Specific examples have been used to illustrate the principles and implementation methods of this application. The description of the above embodiments is only for the purpose of helping to understand the method and core ideas of this application. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application areas based on the ideas of this application. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A construction early warning method based on spatiotemporal correlation of multi-source data, characterized in that, The method includes: Obtain the first construction status data of the target construction area; the first construction status data includes construction machinery and equipment status data, construction personnel trajectory data, construction environment meteorological data, and construction geological data; Based on the first generation timestamp and the first generation spatial location of the first construction status data, the first construction status data is spatiotemporally correlated to obtain multiple first construction spatiotemporal data groups; Based on the first construction spatiotemporal data group, risk identification is performed on key construction risk sources in the target construction area to obtain the first risk identification result; If the first risk identification result indicates the existence of a risk, an alarm message is generated and displayed in the target construction area.

2. The method according to claim 1, characterized in that, The step of identifying key construction risk sources in the target construction area based on the first construction spatiotemporal data group, and obtaining a first risk identification result, includes: Based on the first construction spatiotemporal data set, a digital twin image sequence of the target construction area within a continuous historical time period is generated; The digital twin image sequence is input into a pre-trained spatiotemporal prediction neural network model, which processes the digital twin image sequence and outputs a predicted digital twin image for future times. The predicted digital twin image is processed using a semantic segmentation model to obtain a pixel-level classification map of construction elements, which includes personnel, machinery and equipment, and high-risk areas. The construction element classification map is logically matched with a pre-set safety rule base. If the matching result violates the safety rules, a warning message corresponding to a future time is generated.

3. The method according to claim 2, characterized in that, The spatiotemporal prediction neural network model is a PredRNN network; the PredRNN network models the spatiotemporal evolution features in the digital twin image sequence through its internal spatiotemporal long short-term memory units and cross-layer bidirectional recursive structure; the training objective of the PredRNN network is guided by a composite loss function, which includes the reconstruction loss between predicted image pixels and real image pixels, and the perceptual loss calculated based on the feature map difference of the feature extraction network.

4. The method according to claim 1, characterized in that, Based on the first generation timestamp and the first generation spatial location of the first construction status data, spatiotemporal correlation is performed on the first construction status data to obtain multiple first construction spatiotemporal data groups, including: Based on the first generation timestamps of the construction machinery and equipment status data, the construction personnel trajectory data, the construction environment meteorological data, and the construction geological data, a first generation time difference is determined between the construction machinery and equipment status data, the construction personnel trajectory data, the construction environment meteorological data, and the construction geological data; Based on the first generated spatial location of the construction machinery and equipment status data, the construction personnel trajectory data, the construction environment meteorological data, and the construction geological data, a first spatial Euclidean distance is determined between the construction machinery and equipment status data, the construction personnel trajectory data, the construction environment meteorological data, and the construction geological data; Based on the first generation time difference and the first spatial Euclidean distance, combined with the first construction status data, a first data mapping with spatiotemporal correlation is determined; If the first data mappings contain the same construction machinery and equipment status data, construction personnel trajectory data, construction environment meteorological data, or construction geological data, the first data mappings are merged to obtain multiple first construction spatiotemporal data groups.

5. The method according to claim 4, characterized in that, The step of determining a first data mapping with spatiotemporal correlation based on the first generation time difference and the first spatial Euclidean distance, combined with the first construction status data, includes: When the first generation time difference is less than or equal to the first time threshold, and the first spatial Euclidean distance between the two data corresponding to the first generation time difference is less than or equal to the first distance threshold, a first data mapping is generated between the two first construction status data corresponding to the first generation time difference.

6. The method according to claim 4, characterized in that, The step of determining a first data mapping with spatiotemporal correlation based on the first generation time difference and the first spatial Euclidean distance, combined with the first construction status data, includes: Based on the first generation time difference and the first spatial Euclidean distance between the two data corresponding to the first generation time difference, the first continuous correlation degree between the construction machinery and equipment status data, the construction personnel trajectory data, the construction environment meteorological data and the construction geological data is calculated; If the first continuous correlation degree is greater than or equal to the first threshold, a first data mapping is generated between the two first construction status data corresponding to the first continuous correlation degree.

7. The method according to claim 1, characterized in that, Based on the first generation timestamp and the first generation spatial location of the first construction state, the first construction state data is spatiotemporally correlated to obtain multiple first construction spatiotemporal data groups, including: In the case that there is a data gap in the first construction status data at the first moment, the data collection frequency at the first moment shall be determined; Based on the data acquisition frequency, the missing data at the first moment is filled in to obtain the second construction status data; Based on the first generation timestamp and the first generation spatial location of the second construction status data, the second construction status data is spatiotemporally correlated to obtain multiple first construction spatiotemporal data groups.

8. The method according to claim 1, characterized in that, The alarm message is a digital twin image that marks the construction risk sources with high-risk area identifiers.

9. The method according to claim 1, characterized in that, The method further includes: If the first risk identification result indicates that there is no risk, the construction progress of the target construction area is obtained; If the construction progress is less than the planned progress, a construction progress warning message is generated and displayed.

10. A construction early warning device based on spatiotemporal correlation of multi-source data, characterized in that, The device includes: The first acquisition module is used to acquire the first construction status data of the target construction area; the first construction status data includes construction machinery and equipment status data, construction personnel trajectory data, construction environment meteorological data, and construction geological data. The spatiotemporal correlation module is used to perform spatiotemporal correlation on the first construction status data based on the first generation timestamp and the first generation spatial location of the first construction status data to obtain multiple first construction spatiotemporal data groups; The risk identification module is used to identify key construction risk sources in the target construction area based on the first construction spatiotemporal data group, and obtain the first risk identification result; The first generation module is used to generate an alarm message and display the alarm message in the target construction area when the first risk identification result indicates that there is a risk.

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