Power construction safety management methods, devices, equipment and media

Abstract

The invention discloses a method, device, equipment and medium for electric power construction safety management, and relates to the technical field of electric power construction safety, comprising the following steps: obtaining foundation data and pole tower prediction data during pole tower foundation construction, constructing a pole tower foundation construction hidden danger assessment model based on a bp neural network algorithm according to the foundation data and the pole tower prediction data, and generating a pole tower foundation construction hidden danger assessment coefficient; obtaining progress data of foundation construction, and performing safety management of different maintenance types in combination with the pole tower foundation construction hidden danger assessment coefficient; obtaining the pole tower foundation construction hidden danger assessment coefficient during the safety management process, constructing a management assessment set, performing data analysis on the management assessment set, and judging the safety management result; the invention solves the problem of the hidden danger of tower foundation sinking or tilting in pole tower foundation construction through electric power construction safety management.

Description

Technical Field

[0001] The present invention relates to the field of electric power construction safety technology, and more specifically, to an electric power construction safety management method, device, equipment and medium. Background Art

[0002] Tower foundation construction is an important part of power transmission line engineering, which involves providing a stable support structure for transmission towers or poles to ensure the safety and stability of the entire transmission line. The complexity of this construction process is mainly reflected in the exploration and treatment of the foundation, especially in areas with complex or unstable geological conditions, such as soft soil, wetlands or mountainous areas. Before construction, a detailed geological survey is required to determine the bearing capacity and stability of the foundation and design a suitable foundation structure accordingly. If the foundation conditions are poor, pile foundations, foundation expansion or reinforcement measures may be required to prevent the tower foundation from sinking, tilting or other structural problems. In addition, during the construction process, earth excavation, foundation pouring and steel bar binding must be carried out in strict accordance with the design specifications to ensure the strength and durability of the foundation. At the same time, attention should be paid to the drainage and waterproofing treatment of the construction site to prevent the foundation from being eroded by water, so as to ensure the long-term safe operation of the tower. After the construction is completed, the settlement monitoring of the foundation is also required to ensure that the tower remains stable and does not deform during operation. The quality of tower foundation construction is directly related to the reliability of the entire transmission line. Therefore, special attention should be paid to the technical requirements and safety specifications of each link during the construction process.

[0003] For example, the invention patent with the publication number CN117011994A discloses a personnel safety management system for electric power construction sites, which uses a personnel identification device to identify personnel information entering the construction site; a monitoring device is used to obtain monitoring information of every corner of the construction site; an intelligent wearable device is used to obtain the physical sign information and location information of the construction personnel, and give early warning information based on the physical sign information and location information; a safety alarm device is used to issue an alarm prompt information; and a terminal control platform is used to receive and process data information sent by the personnel identification device, the monitoring device, the intelligent wearable device, and the safety alarm device, and send the processed data information to the intelligent wearable device and the safety alarm device. The present invention can timely discover personnel in danger in the construction site and warn them through the information interconnection between the terminal control platform and each device, so as to reduce the occurrence of personnel danger, improve personnel safety protection, and also improve personnel management efficiency.

[0004] For example, the invention patent with publication number CN118097888A discloses a power construction safety warning method, which determines the scope of the dangerous area based on on-site detection through regional planning; transports the safety warning device to the center of the dangerous area, and adjusts relevant parameters so that the safety warning device can detect the real-time situation in the dangerous area; the safety warning device can issue an alarm to the staff entering the dangerous area based on the detected information, and display eye-catching warning slogans to the staff. This method uses the safety warning device to avoid omissions and ensure the safety of the site. It uses ranging sensors to make judgments, which can effectively improve accuracy and avoid false triggering of the device.

[0005] The above disclosed technical solution has at least the following technical problems: due to unstable foundation soil, excessive water content of soft soil due to rainfall, and groundwater infiltration, the foundation construction becomes unstable. Such unstable foundation conditions will weaken the bearing capacity of the foundation, and thus affect the stability of the entire tower in the future, and may cause the tower foundation construction to have the hidden danger of sinking or tilting.

[0006] In view of the above problems, the present invention proposes a solution. Summary of the invention

[0007] In order to overcome the above-mentioned defects of the prior art, the embodiments of the present invention provide a method, device, equipment and medium for power construction safety management, which solves the problem of tower foundation sinking or tilting during tower foundation construction through power construction safety management.

[0008] To achieve the above object, the present invention provides the following technical solutions:

[0009] The electric power construction safety management method comprises the following steps: obtaining foundation data and tower prediction data during tower foundation construction, wherein the foundation data comprises an abnormal coefficient of settlement fluctuation, and the tower prediction data comprises an abnormal coefficient of deformation and an abnormal event hidden danger coefficient; constructing a tower foundation construction hidden danger assessment model based on a BP neural network algorithm according to the foundation data and the tower prediction data, and generating a tower foundation construction hidden danger assessment coefficient; obtaining progress data of foundation construction, and performing safety management of different maintenance types in combination with the tower foundation construction hidden danger assessment coefficient; obtaining the tower foundation construction hidden danger assessment coefficient during the safety management process, constructing a management assessment set, performing data analysis on the management assessment set, and judging the safety management result.

[0010] In a preferred embodiment, the method for obtaining the settlement fluctuation anomaly coefficient is as follows: obtain the basic settlement in real time; divide the basic settlement obtained in real time into arithmetic differences according to the time series to obtain several basic settlements based on the time series; filter the several basic settlements based on the time series based on the Kalman gain to obtain smoothed basic settlement data; calculate the settlement mean square error and standard deviation of the smoothed basic settlement data; calculate the settlement fluctuation anomaly coefficient based on the isolation forest algorithm according to the settlement mean square error and the settlement standard deviation.

[0011] In a preferred embodiment, the method for obtaining the deformation anomaly coefficient is as follows: set an initial coordinate system for recording the tower foundation deformation; define the initial deformation gradient as a 2*2 matrix, and record the original coordinates of the tower and the deformation coordinates of the tower respectively; calculate the deformation tensor of the deformation gradient according to the deformation gradient; approximate the deformation tensor at each measuring point based on the difference method to obtain two principal strain values; standardize the principal strain values ​​to obtain the deformation anomaly coefficients of several single measurement nodes; sum up the deformation anomaly coefficients of several single measurement nodes to obtain the deformation anomaly coefficient.

[0012] In a preferred embodiment, the method for obtaining the abnormal event hidden danger coefficient is as follows: based on the historical pole tower abnormal event types, an abnormal event set is obtained, and the abnormal event set includes various pole tower abnormal events and event results; various pole tower abnormal event types in the abnormal event set are assigned preset hidden danger scores; based on the abnormal event set and the corresponding hidden danger scores, a regression model is constructed to generate the abnormal event hidden danger coefficient.

[0013] In a preferred embodiment, the progress data of foundation construction is obtained, and combined with the hidden danger assessment coefficient of tower foundation construction, safety management of different maintenance types is carried out, specifically: various progress data in the foundation construction process are collected in real time, including but not limited to the completion degree of foundation pouring, material input, average daily construction progress, and the actual implementation of the construction schedule; the collected foundation construction progress data and the hidden danger assessment coefficient of tower foundation construction are correlated and analyzed; according to the correlation analysis results, corresponding maintenance types are formulated, and the maintenance types include preventive maintenance, corrective maintenance and emergency maintenance.

[0014] In a preferred embodiment, the method of obtaining the pole tower foundation construction hidden danger assessment coefficient in the safety management process, constructing a management assessment set, performing data analysis on the management assessment set, and judging the safety management result is specifically as follows: using the pole tower foundation construction hidden danger assessment coefficient in the management assessment set to calculate the pole tower foundation construction hidden danger assessment coefficient standard deviation and the pole tower foundation construction hidden danger assessment coefficient average value, and comparing and analyzing the pole tower foundation construction hidden danger assessment coefficient standard deviation and the pole tower foundation construction hidden danger assessment coefficient average value with a preset standard deviation reference threshold and a preset tower foundation construction hidden danger assessment coefficient reference threshold, respectively; if the pole tower foundation construction hidden danger assessment coefficient standard deviation is greater than the pole tower foundation construction hidden danger assessment coefficient reference threshold, an early warning is issued, and stability maintenance is performed; if the pole tower foundation construction hidden danger assessment coefficient standard deviation is less than the pole tower foundation construction hidden danger assessment coefficient reference threshold, and the pole tower foundation construction hidden danger assessment coefficient standard deviation is greater than the preset standard deviation reference threshold, continuous monitoring is performed; if the pole tower foundation construction hidden danger assessment coefficient standard deviation is less than the pole tower foundation construction hidden danger assessment coefficient reference threshold, and the pole tower foundation construction hidden danger assessment coefficient standard deviation is less than the preset standard deviation reference threshold, the maintenance is completed.

[0015] 1. The present invention effectively improves the level of monitoring and management of potential safety hazards in the construction process by acquiring foundation data and tower prediction data during the tower foundation construction process. The method uses a number of key indicators such as settlement fluctuation abnormal coefficient, deformation abnormal coefficient and abnormal event hidden danger coefficient to accurately identify the hidden dangers of tower foundation sinking or tilting that may occur during tower foundation construction. Real-time monitoring and analysis of these coefficients can not only provide early warning, timely discover and solve potential problems in the construction process, but also optimize construction quality control and maintenance strategies through data-driven decision support, and reduce the occurrence of safety accidents. In addition, these indicators also provide a scientific basis for subsequent engineering design improvements, further improving the safety and reliability of the overall project. In summary, this method significantly enhances the comprehensive assessment capability of construction site risks, ensures the long-term stability of the tower foundation and the safety management level of the construction process.

[0016] 2. The present invention collects foundation construction progress data in real time and combines it with the tower foundation construction hidden danger assessment coefficient to accurately identify potential hidden dangers in the construction process, thereby realizing dynamic safety management of different construction stages. The technical advantage is that it can automatically adjust the maintenance strategy according to the hidden danger assessment results to carry out preventive, corrective or emergency maintenance, thereby effectively reducing construction risks and improving construction safety and management efficiency. At the same time, by performing data analysis on the management evaluation set, it further ensures that hidden dangers in the construction process are responded to and controlled in a timely manner, greatly improving the accuracy and reliability of construction safety management. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 It is a structural schematic diagram of the power construction safety management method of the present invention.

[0018] Figure 2 It is a structural schematic diagram of the power construction safety management device of the present invention. DETAILED DESCRIPTION

[0019] 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.

[0020] Embodiment 1, Figure 1 The present invention provides a power construction safety management method, which includes the following steps:

[0021] S1, obtaining foundation data and tower prediction data during the tower foundation construction process, wherein the foundation data includes a settlement fluctuation abnormal coefficient, and the tower prediction data includes a deformation abnormal coefficient and an abnormal event hidden danger coefficient.

[0022] In this embodiment, the settlement fluctuation abnormal coefficient is a key indicator for evaluating the hidden dangers of tower foundation sinking or tilting during the construction of the tower foundation; this coefficient identifies whether there is an abnormal settlement trend or deformation pattern by analyzing the fluctuation of foundation settlement; a higher settlement fluctuation abnormal coefficient indicates that the foundation settlement may show an uneven or too fast trend, which may indicate unstable foundation or construction quality problems, thereby increasing the risk of tower foundation sinking or tilting; through the monitoring and evaluation of this coefficient, potential hidden dangers can be discovered in time and appropriate safety management measures can be taken to ensure the long-term stability of the tower foundation.

[0023] Furthermore, analyzing the settlement fluctuation anomaly coefficient has the following advantages and effects in discovering the hidden dangers of tower foundation sinking or tilting during tower foundation construction:

[0024] Early warning capability: The settlement fluctuation anomaly coefficient can monitor and detect the fluctuation of foundation settlement in real time. Once the settlement fluctuation is found to be beyond the normal range, the coefficient can be used as a warning signal to identify potential tower foundation sinking or tilting problems in advance, helping engineers take timely measures to prevent the problem from further deteriorating.

[0025] Accurately identify hidden dangers. By analyzing the settlement fluctuation anomaly coefficient, areas with uneven or abnormal settlement can be accurately identified. This accurate identification helps locate possible foundation problems, such as local soil instability or construction defects, so that targeted repairs and reinforcements can be carried out to reduce overall hidden dangers.

[0026] Data-driven decision support: The settlement fluctuation anomaly coefficient provides data-driven decision support to help engineers formulate safety management and maintenance plans based on objective data. Through continuous monitoring and analysis of settlement data, it can provide a scientific basis to guide subsequent engineering measures and resource allocation.

[0027] Improve construction quality control. During the construction process, the monitoring of settlement fluctuation abnormal coefficient can help timely discover possible quality problems in the construction process, such as uneven concrete pouring or insufficient soil compaction. This real-time feedback mechanism helps to improve construction quality control and ensure the stability of the foundation.

[0028] Optimize maintenance strategies. By analyzing the abnormal coefficient of settlement fluctuation, the long-term stability of the foundation can be evaluated and the maintenance strategy can be optimized. For example, different maintenance measures can be selected for different settlement fluctuations, such as strengthening the foundation or re-construction, thereby effectively ensuring the safety and reliability of the project.

[0029] Reduce safety accidents. Effective analysis of settlement fluctuation abnormal coefficient can prevent safety accidents caused by tower foundation sinking or tilting. By early detection and resolution of hidden dangers, safety problems caused by unstable foundation can be greatly reduced, ensuring the safety of construction personnel and equipment.

[0030] Improve subsequent engineering design. The results of analyzing the abnormal coefficient of settlement fluctuation can provide improvement suggestions for subsequent engineering design. If certain geological conditions or construction methods perform poorly in practice, engineers can optimize future design and construction specifications based on these data to improve the overall engineering quality.

[0031] In summary, the settlement fluctuation anomaly coefficient has significantly enhanced the detection capability and management level of tower base sinking or tilting hazards during tower foundation construction through early warning, accurate hidden danger identification, data-driven decision-making, improved construction quality control, optimized maintenance strategies, reduced safety accidents and improved subsequent design. This comprehensive analysis method helps to ensure the safety and stability of the project.

[0032] Furthermore, the method for obtaining the sedimentation fluctuation anomaly coefficient is as follows:

[0033] Obtain foundation settlement in real time;

[0034] The real-time basic settlement is divided into equal parts according to the time series to obtain a number of basic settlements based on the time series;

[0035] A number of basic sedimentation data based on time series are filtered based on Kalman gain to obtain smooth basic sedimentation data;

[0036] Calculate the mean square error and standard deviation of the settlement of the smoothed basic settlement data;

[0037] The settlement fluctuation anomaly coefficient is calculated based on the isolation forest algorithm according to the settlement mean square error and the settlement standard deviation.

[0038] The specific calculation formula of the smoothed basic settlement data is:

[0039]

[0040] The specific calculation formula of the settlement fluctuation anomaly coefficient is:

[0041]

[0042] In the formula, is the tth smoothed basic settlement data, is the t-1th smoothed basic settlement data, is the Kalman gain, is the tth basic settlement based on time series, is the sedimentation fluctuation anomaly coefficient, is the average value of basic sedimentation, is the total amount of smoothed basic settlement data, is the standard deviation of the smoothed basic settlement data.

[0043] In this embodiment, the deformation anomaly coefficient is a key indicator used to evaluate the potential tower foundation sinking or tilting hazards that may exist during tower foundation construction; the coefficient calculates the deviation between the actual deformation and the expected design value by monitoring the deformation of the tower foundation during construction or in the later stages of construction; specifically, the deformation anomaly coefficient is calculated by comparing the measured tower foundation deformation data with the design standard or historical data, and it reflects the degree of deviation between the actual tower foundation deformation and the theoretical model. If the deformation anomaly coefficient is significantly higher than the normal range, it indicates that the tower foundation may have abnormal settlement or tilting, and this deviation usually indicates that the foundation is unstable or there are problems during the construction process; therefore, by monitoring and analyzing the deformation anomaly coefficient, potential safety hazards can be discovered early, and timely measures can be taken to ensure construction safety and the long-term stability of the tower foundation.

[0044] Furthermore, analyzing the abnormal deformation coefficient has the following advantages and effects in discovering the hidden dangers of tower foundation sinking or tilting during tower foundation construction:

[0045] Accurately reflect the actual deformation. The deformation anomaly coefficient can accurately reflect the actual deformation of the tower foundation by comparing the actual deformation data with the design value. Compared with the traditional static detection method, it can capture tiny deformation changes in real time, thereby providing more accurate construction status information. It can identify the potential sinking or tilting of the tower foundation earlier, avoid safety problems caused by deformation not being discovered in time, and reduce construction risks. Real-time monitoring and early warning. The deformation anomaly coefficient can quickly calculate the anomaly coefficient by real-time monitoring and analysis of the tower foundation deformation data, thereby realizing dynamic monitoring and early warning of the construction process. It can respond to abnormal situations that occur during the construction process in a timely manner and take necessary corrective measures to ensure the safety of the construction process and prevent potential major safety hazards.

[0046] Early warning mechanism, using the monitoring results of abnormal deformation coefficient, can establish an early warning mechanism. When the coefficient exceeds the normal range, the system can automatically trigger an alarm to prompt engineering personnel to conduct inspections and interventions; through early warning, engineers can take action before the problem develops into a serious hidden danger, effectively reducing the risk of accidents caused by unstable tower foundation;

[0047] Quantitatively evaluate hidden dangers. The deformation anomaly coefficient provides a quantitative evaluation standard, which reflects the degree of abnormal deformation of the tower foundation in a numerical form. This quantitative analysis helps to objectively evaluate the severity of hidden dangers; engineering management personnel can prioritize areas with higher risks based on the specific value of the deformation anomaly coefficient, optimize resource allocation, and ensure construction safety and engineering quality.

[0048] Optimize construction management. By analyzing the deformation anomaly coefficient, different stages and conditions in the construction process can be evaluated to discover and correct deficiencies in the construction methods. This helps improve construction technology and management strategies, improve construction quality, reduce potential hidden dangers in the future, and enhance overall construction safety and stability.

[0049] Support decision-making and reporting. The calculation results of deformation anomaly coefficient can be used to generate detailed safety management reports, providing data support for construction quality assessment and decision-making. In the decision-making process, it provides objective data-based basis, which helps to formulate more scientific and reasonable safety management measures and improve the transparency and management level of engineering projects.

[0050] In summary, the deformation anomaly coefficient has significant advantages in the construction of tower foundations. Through real-time monitoring, quantitative evaluation and early warning mechanism, it can effectively detect the hidden dangers of tower foundation sinking or tilting; this not only improves the safety management level during the construction process, but also provides important data support for future construction quality improvements.

[0051] Furthermore, the method for obtaining the deformation anomaly coefficient is as follows:

[0052] Set the initial coordinate system to record the tower foundation deformation;

[0053] Define the initial deformation gradient as a 2*2 matrix to record the original coordinates of the tower and the deformation coordinates of the tower respectively;

[0054] Calculate a deformation tensor of the deformation gradient according to the deformation gradient;

[0055] Based on the difference method, the deformation tensor is approximately solved at each measuring point to obtain two principal strain values;

[0056] The principal strain values ​​are standardized to obtain the deformation anomaly coefficients of several single measurement nodes;

[0057] The deformation anomaly coefficients of several single measurement nodes are summed up to obtain the deformation anomaly coefficient.

[0058] The specific calculation formula of the initial deformation gradient is:

[0059]

[0060] make for , for , for , for , and same;

[0061] The specific calculation formula of the deformation tensor is:

[0062]

[0063] The specific calculation formula of the principal strain value is:

[0064]

[0065] The specific calculation formula of the deformation anomaly coefficient of the single measurement node is as follows:

[0066]

[0067] The specific calculation formula of the deformation anomaly coefficient is as follows:

[0068]

[0069] In the formula, is the deformed horizontal coordinate of the initial coordinate system, is the original horizontal coordinate of the initial coordinate system, is the deformed ordinate of the initial coordinate system, is the original ordinate of the initial coordinate system, is the deformation tensor, is the deformation gradient, The principal strain values ​​include and , is the deformation anomaly coefficient of a single measurement node, and is the preset principal strain reference value, is the deformation anomaly coefficient of a single measurement node of the hth node, is the total number of measurement nodes, is the deformation anomaly coefficient.

[0070] In this embodiment, the abnormal event hidden danger coefficient is a key indicator for evaluating the potential safety risks in the construction process of the pole tower foundation; the coefficient identifies abnormal events that may cause the pole tower foundation to sink or tilt by analyzing the historical data and real-time monitoring data of the construction site, such as settlement, deformation, geological conditions, etc.; the calculation of the abnormal event hidden danger coefficient combines a variety of factors, including sudden settlement acceleration, abnormal deformation trend, and geological mutations that may occur during the construction process, which can provide early warning signals for construction personnel to indicate potential structural instability risks. By timely identifying and evaluating these abnormal event hidden danger coefficients, the risk of safety accidents caused by sinking or tilting in the construction of the pole tower foundation can be effectively reduced.

[0071] Furthermore, analyzing the hidden danger coefficient of abnormal events has the following advantages in discovering the hidden dangers of tower foundation sinking or tilting during tower foundation construction:

[0072] Real-time monitoring and early warning: The abnormal event hidden danger coefficient can comprehensively analyze the real-time monitoring data and historical construction data to timely discover potential risks that may cause the tower foundation to sink or tilt. This real-time nature helps to intervene in the early stages of the problem and prevent the situation from deteriorating;

[0073] Comprehensive analysis: This coefficient takes multiple factors, such as settlement acceleration, deformation trend, geological conditions, etc., into account to provide a comprehensive risk assessment. This comprehensive analysis can more accurately reflect the actual situation of the construction site and avoid misjudgment that may be caused by a single data source;

[0074] High sensitivity: Since the coefficient can capture sudden abnormal changes during the construction process, it is particularly sensitive to small deformations or settlement fluctuations; this high sensitivity allows construction managers to take measures in advance to reduce the possibility of the expansion of hidden dangers;

[0075] Decision support: The abnormal event hidden danger coefficient provides a quantitative basis for risk management during the construction process. It can be used to guide the adjustment of the construction progress, the implementation of reinforcement measures and the deployment of personnel, thereby ensuring the safety and stability of the construction;

[0076] Reduce false alarm rate: Through multi-factor joint analysis, the abnormal event hidden danger coefficient can effectively reduce the false alarm rate, avoid unnecessary intervention caused by fluctuations in a single indicator, and improve the efficiency of risk management;

[0077] In general, the abnormal event hidden danger coefficient provides construction personnel with a scientific and sensitive tool, which can effectively evaluate and prevent the hidden dangers of tower base sinking or tilting during tower foundation construction, and ensure the safety and stability of construction.

[0078] Furthermore, the method for obtaining the abnormal event hidden danger coefficient is as follows:

[0079] Based on the historical tower abnormal event types, an abnormal event set is obtained, wherein the abnormal event set includes various tower abnormal events and event results;

[0080] Assign preset hidden danger scores to various tower abnormal event types in the abnormal event set;

[0081] A regression model is constructed based on the abnormal event set and the corresponding hidden danger scores to generate the abnormal event hidden danger coefficient.

[0082] The specific calculation formula of the abnormal event hidden danger coefficient is as follows:

[0083]

[0084] In the formula, is the potential risk coefficient of abnormal events, … is the preset hidden danger score corresponding to the abnormal event of the tower, … is the proportion of abnormal tower events in all abnormal tower events, is the type of abnormal tower event, n is the number of the abnormal tower event, and e is a natural number.

[0085] S2, constructs a pole tower foundation construction hidden danger assessment model based on the BP neural network algorithm according to the foundation data and the pole tower prediction data, and generates a pole tower foundation construction hidden danger assessment coefficient.

[0086] In this embodiment, the tower foundation construction hidden danger assessment model is constructed based on the BP neural network algorithm according to the foundation data and the tower prediction data to generate the tower foundation construction hidden danger assessment coefficient, which is specifically:

[0087] The acquired settlement fluctuation abnormal coefficient, deformation abnormal coefficient and abnormal event hidden danger coefficient are used to establish a hidden danger assessment model for tower foundation construction, and generate hidden danger assessment coefficients for tower foundation construction;

[0088] The specific calculation formula of the tower foundation construction hidden danger assessment coefficient is as follows:

[0089]

[0090] In the formula, is the traffic shaping risk assessment coefficient, is the preset proportional coefficient of the settlement fluctuation anomaly coefficient, is the preset proportional coefficient of the deformation anomaly coefficient, is the preset proportional coefficient of the abnormal event hidden danger coefficient, is the sedimentation fluctuation anomaly coefficient, is the preset proportional coefficient of the deformation anomaly coefficient, is the potential risk coefficient of abnormal events.

[0091] This embodiment effectively improves the level of monitoring and management of potential safety hazards during the construction process by acquiring foundation data and tower prediction data during the tower foundation construction process. This method uses a number of key indicators such as settlement fluctuation anomaly coefficient, deformation anomaly coefficient, and abnormal event hidden danger coefficient to accurately identify the potential tower foundation sinking or tilting hazards that may occur during tower foundation construction. Real-time monitoring and analysis of these coefficients can not only provide early warnings, timely discover and solve potential problems in the construction process, but also optimize construction quality control and maintenance strategies through data-driven decision support to reduce the occurrence of safety accidents. In addition, these indicators also provide a scientific basis for subsequent engineering design improvements, further improving the safety and reliability of the overall project. In summary, this method significantly enhances the comprehensive assessment capability of construction site risks, ensuring the long-term stability of the tower foundation and the safety management level of the construction process.

[0092] Embodiment 2, S3, obtains progress data of foundation construction, and performs safety management of different maintenance types in combination with the hidden danger assessment coefficient of tower foundation construction.

[0093] In this embodiment, the progress data of the foundation construction is obtained, and the safety management of different maintenance types is performed in combination with the hidden danger assessment coefficient of the tower foundation construction, specifically:

[0094] First, real-time collection of various progress data during the foundation construction process, including but not limited to the completion of foundation pouring, material input, average daily construction progress, and the actual implementation of the construction schedule. These data can be obtained through automated monitoring equipment at the construction site, manual input by workers, and records in the construction management system;

[0095] Conduct correlation analysis on the collected foundation construction progress data and the tower foundation construction hidden danger assessment coefficient;

[0096] The hidden danger assessment coefficient has integrated multiple key indicators such as settlement fluctuations and deformation anomalies. By associating these assessment coefficients with foundation progress data, potential hidden dangers that may exist at different construction stages can be identified; for example, if the hidden danger assessment coefficient increases abnormally at a certain stage, but the foundation construction progress data does not meet expectations, it may indicate that there are potential risks in the construction.

[0097] According to the results of correlation analysis, formulate corresponding maintenance types; according to different hidden danger assessment results, the following typical safety management types can be adopted:

[0098] Preventive maintenance: If the assessment finds that there may be minor risks in certain construction stages, but they have not yet reached the level of serious hidden dangers, preventive maintenance measures will be taken;

[0099] Corrective maintenance: When the hidden danger assessment coefficient shows a high risk and the foundation construction progress is delayed or abnormal, corrective maintenance measures should be taken immediately;

[0100] Emergency maintenance: In the event of serious hidden dangers, emergency maintenance measures must be initiated.

[0101] S4, obtain the tower foundation construction hidden danger assessment coefficient in the safety management process, build a management assessment set, perform data analysis on the management assessment set, and judge the safety management results.

[0102] The obtaining of the tower foundation construction hidden danger assessment coefficient in the safety management process, constructing a management assessment set, performing data analysis on the management assessment set, and determining the safety management result are specifically as follows:

[0103] Using the pole tower foundation construction hidden danger assessment coefficients in the management assessment set, the standard deviation of the pole tower foundation construction hidden danger assessment coefficients and the average value of the pole tower foundation construction hidden danger assessment coefficients are calculated, and the standard deviation of the pole tower foundation construction hidden danger assessment coefficients and the average value of the pole tower foundation construction hidden danger assessment coefficients are compared and analyzed with the pre-set standard deviation reference threshold and the pre-set pole tower foundation construction hidden danger assessment coefficient reference threshold respectively;

[0104] If the standard deviation of the tower foundation construction hazard assessment coefficient is greater than the reference threshold of the tower foundation construction hazard assessment coefficient, an early warning is issued and stability maintenance is performed;

[0105] If the standard deviation of the tower foundation construction hazard assessment coefficient is less than the reference threshold of the tower foundation construction hazard assessment coefficient, and the standard deviation of the tower foundation construction hazard assessment coefficient is greater than the preset standard deviation reference threshold, continuous monitoring is performed;

[0106] If the standard deviation of the tower foundation construction hazard assessment coefficient is less than the reference threshold of the tower foundation construction hazard assessment coefficient, and the standard deviation of the tower foundation construction hazard assessment coefficient is less than the preset standard deviation reference threshold, the maintenance is completed.

[0107] This embodiment collects foundation construction progress data in real time, and combines it with the tower foundation construction hidden danger assessment coefficient to accurately identify potential hidden dangers in the construction process, thereby realizing dynamic safety management of different construction stages. The technical advantage is that it can automatically adjust the maintenance strategy according to the hidden danger assessment results to perform preventive, corrective or emergency maintenance, thereby effectively reducing construction risks and improving construction safety and management efficiency. At the same time, by performing data analysis on the management assessment set, it further ensures that hidden dangers in the construction process are responded to and controlled in a timely manner, greatly improving the accuracy and reliability of construction safety management.

[0108] Embodiment 3, Figure 2 The invention provides an electric power construction safety management device, which includes a data acquisition module, a data analysis module, a maintenance classification module and a safety management module, and there are connections between the modules.

[0109] A data acquisition module, used to acquire foundation data and tower prediction data during the tower foundation construction process, wherein the foundation data includes a settlement fluctuation abnormal coefficient, and the tower prediction data includes a deformation abnormal coefficient and an abnormal event hidden danger coefficient;

[0110] The data analysis module is used to construct a tower foundation construction hidden danger assessment model based on the BP neural network algorithm according to the foundation data and tower prediction data, and generate a tower foundation construction hidden danger assessment coefficient;

[0111] The maintenance classification module is used to obtain the progress data of foundation construction and conduct safety management of different maintenance types in combination with the hidden danger assessment coefficient of tower foundation construction;

[0112] The safety management module is used to obtain the evaluation coefficient of the hidden dangers of the tower foundation construction in the process of safety management, build a management evaluation set, perform data analysis on the management evaluation set, and judge the safety management results.

[0113] The present invention also includes an electronic device, the electronic device comprising:

[0114] At least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the power construction safety management method.

[0115] The present invention includes a computer-readable storage medium storing a computer program, and the computer program implements a power construction safety management method when executed by a processor.

[0116] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0117] The above embodiments may be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented by software, the above embodiments may be implemented in whole or in part in the form of a computer program product.

[0118] Those of ordinary skill in the art will appreciate that the modules and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0119] In addition, each functional module in each embodiment of the present application may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one module.

[0120] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0121] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for safety management of electric power construction, characterized in that: The steps include: Obtain foundation data and tower prediction data during tower foundation construction; The tower prediction data includes deformation anomaly coefficient; The method for obtaining the deformation anomaly coefficient is as follows: Set the initial coordinate system to record the tower foundation deformation; Define the initial deformation gradient as a 2*2 matrix to record the original coordinates of the tower and the deformation coordinates of the tower respectively; Calculate a deformation tensor of the deformation gradient according to the deformation gradient; Based on the difference method, the deformation tensor is approximately solved at each measuring point to obtain two principal strain values; The principal strain values ​​are standardized to obtain the deformation anomaly coefficients of several single measurement nodes; The deformation anomaly coefficients of several single measurement nodes are summed up to obtain the deformation anomaly coefficient; The specific calculation formula of the initial deformation gradient is: make for , for , for , for , and same; The specific calculation formula of the deformation tensor is: The specific calculation formula of the principal strain value is: The specific calculation formula of the deformation anomaly coefficient of the single measurement node is as follows: The specific calculation formula of the deformation anomaly coefficient is as follows: In the formula, is the deformed horizontal coordinate of the initial coordinate system, is the original horizontal coordinate of the initial coordinate system, is the deformed ordinate of the initial coordinate system, is the original ordinate of the initial coordinate system, is the deformation tensor, is the deformation gradient, The principal strain values ​​include and , is the deformation anomaly coefficient of a single measurement node, and is the preset principal strain reference value, is the deformation anomaly coefficient of a single measurement node of the hth node, is the total number of measurement nodes, is the deformation anomaly coefficient; According to the foundation data and tower prediction data, a tower foundation construction hidden danger assessment model is constructed based on the BP neural network algorithm to generate a tower foundation construction hidden danger assessment coefficient; Obtain progress data of foundation construction and conduct safety management of different maintenance types in combination with the hidden danger assessment coefficient of tower foundation construction; Obtain the hidden danger assessment coefficient of tower foundation construction in the process of safety management, build a management assessment set, perform data analysis on the management assessment set, and judge the safety management results.

2. The power construction safety management method according to claim 1, characterized in that: The foundation data include settlement fluctuation anomaly coefficient; The method for obtaining the settlement fluctuation anomaly coefficient is as follows: Obtain foundation settlement in real time; The real-time basic settlement is divided into equal parts according to the time series to obtain several basic settlements based on the time series; A number of basic sedimentation data based on time series are filtered based on Kalman gain to obtain smooth basic sedimentation data; Calculate the mean square error and standard deviation of the settlement data from the smoothed basic settlement data; The settlement fluctuation anomaly coefficient is calculated based on the isolation forest algorithm according to the settlement mean square error and the settlement standard deviation.

3. The power construction safety management method according to claim 2, characterized in that: The tower prediction data includes deformation anomaly coefficient and abnormal event hidden danger coefficient; The method for obtaining the abnormal event hidden danger coefficient is as follows: Based on the historical tower abnormal event types, an abnormal event set is obtained, wherein the abnormal event set includes various tower abnormal events and event results; Assign preset hidden danger scores to various tower abnormal event types in the abnormal event set; A regression model is constructed based on the abnormal event set and the corresponding hidden danger scores to generate the abnormal event hidden danger coefficient.

4. The power construction safety management method according to claim 3, characterized in that: The progress data of foundation construction is obtained, and safety management of different maintenance types is carried out in combination with the hidden danger assessment coefficient of tower foundation construction, specifically: Real-time collection of various progress data during foundation construction, including but not limited to the completion of foundation pouring, material input, average daily construction progress, and the actual implementation of the construction schedule; Conduct correlation analysis on the collected foundation construction progress data and the tower foundation construction hidden danger assessment coefficient; According to the correlation analysis results, corresponding maintenance types are formulated, and the maintenance types include preventive maintenance, corrective maintenance and emergency maintenance.

5. The power construction safety management method according to claim 4, characterized in that: The obtaining of the tower foundation construction hidden danger assessment coefficient in the safety management process, constructing a management assessment set, performing data analysis on the management assessment set, and determining the safety management result are specifically as follows: Using the pole tower foundation construction hidden danger assessment coefficients in the management assessment set, the standard deviation of the pole tower foundation construction hidden danger assessment coefficients and the average value of the pole tower foundation construction hidden danger assessment coefficients are calculated, and the standard deviation of the pole tower foundation construction hidden danger assessment coefficients and the average value of the pole tower foundation construction hidden danger assessment coefficients are compared and analyzed with the pre-set standard deviation reference threshold and the pre-set pole tower foundation construction hidden danger assessment coefficient reference threshold respectively; If the standard deviation of the tower foundation construction hazard assessment coefficient is greater than the reference threshold of the tower foundation construction hazard assessment coefficient, an early warning is issued and stability maintenance is performed; If the standard deviation of the tower foundation construction hazard assessment coefficient is less than the reference threshold of the tower foundation construction hazard assessment coefficient, and the standard deviation of the tower foundation construction hazard assessment coefficient is greater than the preset standard deviation reference threshold, continuous monitoring is performed; If the standard deviation of the tower foundation construction hazard assessment coefficient is less than the reference threshold of the tower foundation construction hazard assessment coefficient, and the standard deviation of the tower foundation construction hazard assessment coefficient is less than the preset standard deviation reference threshold, the maintenance is completed.

6. A device using the power construction safety management method according to any one of claims 1 to 5, characterized in that: It includes data acquisition module, data analysis module, maintenance classification module and security management module, and there are connections between the modules; A data acquisition module, used to acquire foundation data and tower prediction data during the tower foundation construction process, wherein the foundation data includes a settlement fluctuation abnormal coefficient, and the tower prediction data includes a deformation abnormal coefficient and an abnormal event hidden danger coefficient; The data analysis module is used to construct a tower foundation construction hidden danger assessment model based on the BP neural network algorithm according to the foundation data and tower prediction data, and generate a tower foundation construction hidden danger assessment coefficient; The maintenance classification module is used to obtain the progress data of foundation construction and conduct safety management of different maintenance types in combination with the hidden danger assessment coefficient of tower foundation construction; The safety management module is used to obtain the evaluation coefficient of the hidden dangers of the tower foundation construction in the process of safety management, build a management evaluation set, perform data analysis on the management evaluation set, and judge the safety management results.

7. An electronic device, characterized in that: The electronic device comprises: at least one processor; and, a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the power construction safety management method as described in any one of claims 1 to 5.

8. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the power construction safety management method as described in any one of claims 1 to 5 is implemented.

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