Safety detection system and method for freight aerial cableway construction of power transmission line engineering

Abstract

The invention discloses a power transmission line engineering freight aerial cableway construction safety detection system and method, and relates to the technical field of cableway construction safety detection, and the method comprises the following steps: dividing a whole work cycle route of a power transmission line engineering freight aerial cableway into a certain number of work nodes, and carrying out the real-time monitoring and obtaining the equipment hidden danger data of each node; inputting the processed equipment hidden danger data into an equipment hidden danger model established based on machine learning, and generating an equipment hidden danger evaluation coefficient; drawing an equipment-hidden danger curve graph according to the equipment hidden danger evaluation coefficient and cableway transportation working nodes, judging whether hidden danger exists in the working node area or not, and performing maintenance in time; and collecting the equipment hidden danger assessment coefficient after maintenance, performing data analysis, and quantitatively assessing the equipment hidden danger maintenance measure effect. Through the safety detection technology, the problem that potential safety hazards existing in the long-distance work load of the cableway cannot be found and processed in time is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of optimal decision-making for power grid technical transformation projects, and more specifically, to the technical field of safety detection systems and methods for construction of aerial freight ropeways for power transmission line projects. Background Art

[0002] With the continuous growth of electricity demand, the construction and maintenance of power infrastructure are particularly important. In complex terrains (such as mountains, hills and remote areas), traditional road transportation often faces many difficulties. In order to solve these problems, freight aerial ropeway technology is increasingly used in power transmission line projects. This technology can effectively transport power equipment and building materials, improve construction efficiency and reduce environmental impact. The basic principle of aerial ropeway is to use steel cables suspended in the air as a transportation medium to transport goods from one location to another through mechanical devices. Compared with other modes of transportation, aerial ropeway has many significant advantages.

[0003] The above disclosed technical solutions have at least the following technical problems: Ultra-high voltage transmission line projects have long transmission distances and large regional spans. In addition, as land resources become increasingly scarce, more and more transmission line project design routes need to pass through mountainous terrain with poor transportation conditions, which brings great difficulties to the transportation of construction materials. Using aerial ropeways for material transportation is one of the effective ways to solve the transportation of construction materials in mountainous terrain for ultra-high voltage transmission line projects. However, during transportation, due to the long-distance workload, certain safety problems may occur during the construction process and cannot be discovered in time. In view of the above problems, the present invention proposes a solution. Summary of the invention

[0004] In order to overcome the above-mentioned defects of the prior art, the embodiments of the present invention provide a system and method for detecting the construction safety of a power transmission line engineering freight aerial ropeway, so as to solve the problem that the potential safety hazards existing in the construction process cannot be discovered and handled in time.

[0005] To achieve the above object, the present invention provides the following technical solutions: A system and method for detecting the construction safety of an aerial ropeway for freight transport in a power transmission line project, comprising the following steps: dividing the entire working cycle route of the aerial ropeway for freight transport in a power transmission line project into a certain number of working nodes, monitoring and acquiring equipment hidden danger data of each node in real time, wherein the equipment hidden danger data includes load-bearing hidden danger data and ropeway component hidden danger data, and cleaning and preprocessing the data; inputting the processed equipment hidden danger data into an equipment hidden danger model established based on machine learning to generate an equipment hidden danger assessment coefficient; drawing an equipment-hidden danger curve diagram according to the equipment hidden danger assessment coefficient combined with the ropeway transport working node, judging whether there are hidden dangers in the working node area through the equipment-hidden danger curve diagram, and making maintenance in time; after maintaining the node area with hidden dangers, collecting the equipment hidden danger assessment coefficient after maintenance and performing data analysis, and quantitatively evaluating the effect of equipment hidden danger maintenance measures.

[0006] In a preferred embodiment, the load-bearing hazard data includes a crane load-bearing hazard coefficient, and a specific method for obtaining the crane load-bearing hazard coefficient is as follows: when the crane arrives at a node area, images of people and goods on the crane are collected in real time; computer vision technology is used to denoise and enhance contrast of the collected images, and size and shape features are extracted from the processed images based on a deep learning model; the corresponding volume is obtained based on the extracted size and shape features and its real-time weight is obtained by calculating the density formula; the loss coefficient of the crane is obtained by calculating the ratio of the performance index of the actual material of the crane to the performance index of the standard material; the crane load-bearing hazard coefficient is obtained based on the crane load-bearing hazard formula according to the real-time weight carried by the crane combined with the loss coefficient.

[0007] In a preferred embodiment, the hidden danger data of the cableway components include a rope hidden danger coefficient and a winch hidden danger coefficient; the rope hidden danger coefficient is specifically obtained by the following method: real-time monitoring of the vibration mode of the rope during operation, recording the rope area with abnormal vibration characteristics, and obtaining the wear factor of the rope and the durability of the rope in this area; obtaining the first data of the traction rope, the first data including material parameters, dimensions and external load data; using the finite element method to analyze the stress distribution of the traction rope under the stress state, determining the stress value inside the traction rope, and combining the first data of the traction rope to obtain the strength value of the traction rope based on a preset strength formula; according to the strength value of the traction rope combined with the wear factor and durability of the rope, the rope hidden danger coefficient is calculated based on a preset rope hidden danger formula.

[0008] In a preferred embodiment, the winch hidden danger coefficient is specifically obtained as follows: Obtain the influencing data of power consumption, wherein the influencing data includes the heat loss of the motor when working and the loss of the reducer; monitor and obtain the power data of the winch during working hours in each node area in real time, wherein the power data includes power demand, voltage, current and resistance; obtain the power supply efficiency based on the power supply efficiency calculation formula of the winch according to the power data; obtain the winch hidden danger coefficient based on the preset winch hidden danger formula according to the influencing data of power consumption combined with the power supply efficiency.

[0009] In a preferred embodiment, the processed equipment hidden danger data is input into an equipment hidden danger model established based on machine learning to generate an equipment hidden danger assessment coefficient. The specific steps are as follows: collect construction data of the freight aerial ropeway during the construction process, the construction data including historical load-bearing hidden danger data, historical ropeway component hidden danger data, historical accident records, equipment operation parameters and equipment historical maintenance records, and perform data cleaning and preprocessing on the construction data; divide the processed data into a training set, and train the model based on machine learning using the training set data, adjust the model parameters to minimize the prediction error, adjust the weights, and assign trained weights to the equipment hidden danger data respectively; input the crane load hidden danger coefficient, rope hidden danger coefficient and winch hidden danger coefficient obtained in real time into the trained equipment hidden danger model to generate an equipment hidden danger assessment coefficient.

[0010] In a preferred embodiment, the equipment-hazard curve is drawn according to the equipment hazard assessment coefficient in combination with the cableway transportation working node, and data analysis is performed to determine whether there are hazards in the working node area and to deal with them in time. The specific steps are as follows: obtain the equipment hazard assessment coefficient of each node area in the entire working cycle of the transmission line engineering freight cableway from the equipment hazard model; use the equipment hazard assessment coefficient as the Y-axis and the node area as the X-axis to establish a rectangular coordinate system; set an equipment operation stability coefficient as a reference line based on a large amount of historical records and data during stable operation of the equipment; put the recorded equipment hazard assessment coefficients of all node areas into the rectangular coordinate system and connect them into a curve; perform real-time analysis based on the equipment-hazard curve in combination with the reference line. When the equipment hazard assessment coefficient of the node area is higher than the reference line of the equipment operation stability coefficient, the system will issue a risk warning and remind the on-site staff to stop working and wait for maintenance personnel to check the cranes, ropes and other equipment in the area and the power supply of the winch one by one to find out the parts with hazards for repair.

[0011] In a preferred embodiment, for the node area with hidden dangers after maintenance, the equipment hidden danger assessment coefficient after maintenance is collected and data analysis is performed to determine the effectiveness of the equipment hidden danger maintenance measures. The specific steps are as follows: after completing the maintenance, the equipment hidden danger assessment coefficient after maintenance is collected using the same data collection method, and a post-maintenance equipment-hidden danger curve graph is drawn, and the variances of the two equipment-hidden danger curve graphs before and after maintenance are obtained and compared; by calculating the variances of the curve graphs before and after maintenance, the degree of change of the equipment hidden danger coefficient in each node area is quantified to determine the effectiveness of the equipment hidden danger maintenance measures; if the variance of the curve graph after maintenance is lower than the variance of the curve graph before maintenance, the maintenance measure has a significant effect and improves the stability of the equipment; if the variance of the curve graph after maintenance is greater than or equal to the variance of the curve graph before maintenance, the maintenance measure has no effect and the maintenance strategy and method need to be reconsidered; if the variance of the curve graph after maintenance of a node is abnormally higher than the variance of the curve graph before maintenance, the node area has special problems or abnormal conditions and further inspection and analysis are required.

[0012] One or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages: 1. Use machine learning to build equipment hidden danger models, and train and evaluate them through historical data and real-time monitoring data to more accurately predict hidden danger risks. This data-driven approach improves the scientificity and effectiveness of decision-making.

[0013] 2. The analysis of hidden danger coefficient helps to identify potential risk points and formulate corresponding control measures and emergency plans. This preventive management reduces the possibility of accidents and improves the safety of the construction site. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 A schematic diagram of the structure of a method for safety detection of aerial freight ropeway construction for a power transmission line project provided in an embodiment of the present application.

[0015] Figure 2 A schematic diagram of the structure of a transmission line engineering freight aerial ropeway construction safety detection system provided in an embodiment of the present application. DETAILED DESCRIPTION

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

[0017] Embodiment 1, Figure 1The schematic diagram of the structure of the construction safety detection method of the aerial ropeway for freight transport in the power transmission line project provided in the embodiment of the present application includes the following steps: S1, divide the entire working cycle route of the transmission line engineering freight aerial ropeway into a certain number of working nodes, monitor and obtain the equipment hidden danger data of each node in real time, the equipment hidden danger data includes load-bearing hidden danger data and ropeway component hidden danger data, and clean and pre-process the data.

[0018] Due to the long distance of transportation and the large span of the area, during the transportation process, each time a work area is reached, unloading and loading must be carried out and work must continue. This puts an increasing load on the cableway, so this gap is divided into a certain number of node areas, and the safety of these areas is considered to ensure the safety of each transportation.

[0019] In this embodiment, the load-bearing hidden danger data includes the crane load-bearing hidden danger coefficient, and the cableway component hidden danger data includes the rope hidden danger coefficient and the winch hidden danger coefficient.

[0020] The crane load potential hazard coefficient can be used to evaluate the crane load capacity. It refers to the load impact of different degrees when the crane on the cableway works in different areas due to the increase in transported goods or personnel during the power transmission line project. Therefore, by monitoring and analyzing the crane load potential hazard 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 crane.

[0021] By analyzing the crane load hidden danger coefficient, it is found that the construction safety of the aerial ropeway for freight in power transmission line projects has the following advantages: In power transmission line projects, the construction safety of freight aerial ropeways is extremely important, and the analysis of the crane load potential hazard coefficient provides an important safety guarantee for this process. By analyzing the crane load potential hazard coefficient, the construction safety can be significantly improved in the following aspects.

[0022] First, improve safety and reliability. The load-bearing potential risk factor of a crane directly affects its safety performance. By analyzing this factor, potential overload risks can be identified, thereby ensuring the stability of the crane during operation. The analysis can help the construction team clarify the relationship between the maximum load capacity of the crane and the actual load, and avoid equipment failures or accidents caused by overload operations. This process not only ensures the safety of crane operators, but also reduces threats to surrounding construction workers.

[0023] Secondly, optimize construction plans and resource allocation. By analyzing the load-bearing potential risk coefficient of the crane, the construction unit can better plan the operation process. Knowing the type and number of cranes required for different construction stages can reasonably arrange resources and avoid unnecessary waiting and waste. In addition, the construction team can choose the appropriate crane model and configuration according to actual needs to ensure the best balance between construction efficiency and safety.

[0024] Secondly, it promotes risk management and the formulation of emergency plans. Analyzing the load-bearing potential risk factor can help project managers identify potential risk points and formulate corresponding risk control measures. This includes not only the assessment of the crane operating environment, but also the training and safety education of construction personnel. By establishing a detailed emergency plan, the construction team can respond quickly to emergencies and effectively reduce the probability of accidents.

[0025] In addition, it can enhance the safety awareness of construction workers. Through in-depth analysis of the potential risk coefficient of crane load, it can provide construction workers with specific data and cases to help them understand the importance of safe operation. The popularization of this knowledge can improve the safety literacy of construction workers, prompting them to consciously abide by safety regulations in their daily work and form a good safety culture.

[0026] Finally, it promotes technological innovation and equipment upgrades. With the in-depth analysis of the crane load-bearing potential hazard coefficient, construction companies will realize the limitations of existing equipment, thereby promoting technological innovation and equipment upgrades. This can not only improve construction efficiency, but also further ensure construction safety and reduce the occurrence of accidents.

[0027] In summary, analyzing the crane load risk coefficient has important advantages in improving the safety of the construction of the aerial ropeway for freight in power transmission line projects. This analysis not only helps to clarify risks, optimize resource allocation and strengthen safety education, but also promotes technological progress and ultimately achieves the goal of safe and efficient construction.

[0028] The specific method for obtaining the crane load hazard coefficient is as follows: When the crane arrives at a node area, high-quality camera equipment is used to capture images of people and goods on the crane in real time; Computer vision technology is used to denoise and enhance the contrast of the collected images, and the size and shape features of the processed images are extracted based on the deep learning model; Based on the extracted size and shape features, the corresponding volume is obtained and its weight is obtained by density formula calculation; The loss coefficient of the crane is obtained by calculating the ratio of the performance index of the actual material of the crane to the performance index of the standard material; The crane load hidden danger coefficient is obtained based on the crane load hidden danger formula according to the real-time weight carried by the crane combined with the loss coefficient.

[0029] The specific calculation formula of the loss coefficient of the crane is as follows:

[0030] The specific calculation formula of the crane load hidden danger coefficient is as follows:

[0031] In the formula, is the crane load hazard coefficient of the i-th node area, is the loss coefficient of the crane, is the weight of the cargo, is the quantity of goods, For the weight of the character, is the number of characters, is the actual tensile strength of the crane material, is the actual elongation at break, is the standard tensile strength, It is the standard elongation at break.

[0032] It should be noted that the quantity of goods and people will change with the change of node area, and the quantity of each node area will be different. When the safety regulations of personnel are not followed, the quantity of goods and people may increase, causing the potential risk coefficient of crane load to become larger and larger, thus leading to the risk of overloading during the construction process. By obtaining and analyzing the loss coefficient of the crane, the staff can also predict in advance some hidden dangers brought by the loss of equipment to the crane load.

[0033] The rope hidden danger coefficient refers to the fact that during the operation of the aerial ropeway for freight in power transmission line projects, the rope will gradually age due to long-term use and exposure to harsh environments, resulting in a decrease in its strength and durability. Frequent use and improper use will cause wear on the rope surface, affecting its carrying capacity. Therefore, by monitoring and analyzing the rope hidden danger 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 rope.

[0034] By analyzing the hidden danger coefficient of ropes, it is found that the construction safety of aerial ropeways for freight in power transmission line projects has the following advantages: In power transmission line projects, the construction safety of freight aerial ropeways is an important issue. By analyzing the potential hazards of ropes, the safety of the project can be effectively improved and the risk of accidents can be reduced. The advantages of this analysis method will be discussed from several aspects.

[0035] First, the analysis of the rope hidden danger coefficient can systematically identify potential safety hazards. In the construction process of the freight aerial ropeway, the rope is an important component for carrying cargo and construction equipment. By quantitatively analyzing factors such as the rope material, load capacity, and degree of wear, it is possible to identify which ropes have hidden dangers and take targeted measures. For example, when the hidden danger coefficient of the rope exceeds the safety standard, the construction team can replace the rope in time to avoid accidents caused by rope failure.

[0036] Secondly, this analysis method helps to optimize the construction plan. By analyzing the hidden danger coefficient of ropes, project managers can evaluate the safety of different construction plans. For example, when selecting the type and configuration of ropes, the plan with a lower hidden danger coefficient can be given priority, thereby improving safety while ensuring construction efficiency. In addition, dynamic monitoring of hidden danger coefficients can also provide a basis for real-time decision-making during the construction process, allowing managers to flexibly adjust construction plans and reduce risks.

[0037] Third, analyzing the rope hazard factor can also help improve the safety awareness of construction workers. Through the systematic analysis of rope hazards, the construction team can have a deeper understanding of potential risks and cultivate a safety culture. For example, regular training can be held to convey the relevant knowledge and countermeasures of the rope hazard factor to construction workers, improve their attention to safety, and enhance their self-protection awareness.

[0038] Finally, the analysis method of rope hidden danger coefficient can enhance the supervision ability of the project. By establishing a monitoring mechanism for hidden danger coefficient, real-time monitoring of the construction site can be achieved. Once an abnormal hidden danger coefficient is found, the relevant responsible person can be warned in time, so that effective measures can be taken to avoid accidents. This monitoring mechanism not only improves construction safety, but also provides data support for subsequent project management, which is convenient for summary and improvement.

[0039] In summary, by analyzing the rope hidden danger coefficient, we can not only effectively identify and manage risks and improve the safety of construction plans, but also enhance the safety awareness of construction workers and the supervision ability of construction sites. These advantages make the analysis of rope hidden danger coefficients of important application value in the construction safety of aerial cableways for freight in power transmission line projects.

[0040] The specific method for obtaining the rope hidden danger coefficient is as follows: The vibration mode of the rope in operation is monitored in real time by sensors. Wear will affect the vibration characteristics. The rope area with abnormal vibration characteristics is recorded, and the wear factor and durability of the rope in this area are obtained; Acquiring first data of the traction rope, wherein the first data includes material parameters, dimensions and external load data; The finite element method is used to analyze the stress distribution of the traction rope under the stress state, determine the stress value inside the traction rope, and obtain the strength value of the traction rope based on a preset strength formula in combination with the first data of the traction rope; The rope hidden danger coefficient is obtained based on the preset rope hidden danger formula according to the strength value of the traction rope combined with the wear factor and durability of the rope.

[0041] The strength value of the traction rope is specifically calculated as follows:

[0042] Q is the strength of the traction rope, is the stress value of the jth section, j is the stress of each section of the traction cable, j=1,2,3,...,R, R is an integer, q is the size of the material, c is the material parameter, K is the force value, and H is the external load.

[0043] The specific calculation formula of the rope hidden danger coefficient is as follows:

[0044] In the formula, is the hidden danger coefficient of the rope, Q is the strength value of the traction rope, N is the durability of the rope, m is the wear factor of the rope, T is the deformation time of the traction rope, is the stress value of the jth section, j is the stress of each section of the traction cable, j=1,2,3,...,R, R is an integer, q is the size of the material, c is the material parameter, K is the force value, and H is the external load.

[0045] It should be noted that the finite element method is a relatively accurate and flexible stress analysis method, which can take into account the complex geometry of the traction cable and various boundary conditions. First, the traction cable model is discretized into a finite number of units, each of which is connected by nodes. Then, after determining the external load and boundary conditions, the finite element calculation software is used to solve the stress in each unit. Finally, the stress distribution of the conveyor belt is obtained by post-processing and analyzing the stress.

[0046] The winch is the core power equipment of the freight aerial ropeway. Its basic structure includes engine, transmission device, drum, operating mechanism and chassis. The engine provides power, which is transmitted to the drum through the transmission device to rotate the drum, thereby pulling the steel cable and the sports car to run. The winch hidden danger coefficient refers to the equipment power failure that may occur during the operation of the winch, which may cause problems in the transportation process of the overhead transport ropeway of the power transmission line project. Therefore, by monitoring and analyzing the winch hidden danger 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 winch.

[0047] By analyzing the hidden danger coefficient of the winch machine, it is found that the construction safety of the aerial ropeway for freight in the power transmission line project has the following advantages: First of all, as a key equipment in the construction of aerial ropeways, the analysis of the hidden danger coefficient of the winch can help us identify potential safety risks. The hidden danger coefficient usually takes into account factors such as the technical status of the equipment, the operating environment, and the operating process. Through quantitative analysis, the safety performance of the winch in actual construction can be intuitively evaluated. Timely identification of hidden dangers will help to formulate targeted preventive measures and reduce the occurrence of accidents.

[0048] Secondly, analyzing the hidden danger coefficient can promote the standardization of construction management. By establishing a set of systematic hidden danger assessment standards, construction units can more strictly manage the operation of winches. Regular inspection and evaluation of hidden danger coefficients can form a mechanism for continuous improvement, prompting workers and managers to always pay attention to safety, thereby improving the overall construction safety level.

[0049] In addition, the analysis of the winch hidden danger coefficient can also help improve the safety awareness of construction workers. Through training and publicity, construction workers can understand the specific content and influencing factors of the hidden danger coefficient, which can enhance their attention to the safe operation of equipment. In actual operation, personnel can be more cautious and reduce the risk of accidents caused by improper operation.

[0050] Furthermore, from a technical perspective, the analysis of hidden danger coefficients provides a basis for equipment improvement. By comparing and analyzing the performance of different equipment in terms of hidden danger coefficients, construction units can better select suitable winches and carry out technical transformation or equipment replacement according to the level of hidden danger coefficients. This data-driven decision-making method not only improves the safety of equipment, but also reduces construction costs to a certain extent.

[0051] Finally, the analysis of winch hidden danger coefficient can also help improve construction efficiency. Effective identification and control of safety hazards can reduce downtime and resource waste caused by accidents and ensure the smooth progress of construction projects. By reducing the accident rate, construction units can improve the progress and economic benefits of the project while ensuring safety.

[0052] In summary, through in-depth analysis of the winch hidden danger coefficient, the safety of the construction of the aerial ropeway for freight in power transmission line projects can be significantly improved. This analysis not only helps to identify and manage risks, but also promotes the standardization of construction management, improves the safety awareness of construction personnel, promotes technical improvements in equipment, and ultimately improves construction efficiency and economic benefits. Therefore, the analysis of the winch hidden danger coefficient is indispensable in actual construction.

[0053] The specific method for obtaining the winch hidden danger coefficient is as follows: Acquiring the influencing data of the power consumption, wherein the influencing data includes the heat loss of the motor and the loss of the reducer when the motor is working; Real-time monitoring and acquisition of power data of winches during working hours in each node area, the power data including power demand, voltage, current and resistance; The power supply efficiency is obtained based on the power supply efficiency calculation formula of the winch according to the power data; The winch hidden danger coefficient is obtained based on the preset winch hidden danger formula according to the impact data of power consumption combined with the power supply efficiency.

[0054] The specific calculation formula of the power supply efficiency is as follows:

[0055] The specific calculation formula of the winch hidden danger coefficient is as follows:

[0056] In the formula, is the winch hidden danger coefficient, E is the power supply efficiency, Working hours for winch machine, is the heat loss, is the reducer loss, is the power demand, V is the voltage, is the current, CU is the resistance, d is the node area of ​​power supply, d=1,2,3,...,n, n is an integer, and D is the total number of nodes.

[0057] It should be noted that heat loss refers to the energy loss caused by friction, material heat conduction and other reasons during the operation of the machine. This part of energy is not effectively utilized, resulting in reduced working efficiency of the winch. Heat loss will cause the temperature of the winch and its components to rise, which may cause overheating problems. This not only affects the performance of the equipment, but may also shorten its service life. The reducer will also produce friction and energy loss during the transmission process, especially between gear contact and moving parts. Such losses will reduce the output power of the winch, resulting in a decrease in the lifting capacity of the winch. When the heat loss and reducer loss become larger and larger, the hidden danger coefficient of the winch will become larger and larger.

[0058] S2, input the processed equipment hidden danger data into the equipment hidden danger model established based on machine learning to generate the equipment hidden danger assessment coefficient. The specific steps are as follows: Collect construction data of the freight aerial ropeway during construction, including historical accident records, equipment operating parameters, and historical equipment maintenance records, and perform data cleaning and preprocessing on the construction data; Divide the processed data into training sets, use the training set data to train the model based on machine learning, adjust the model parameters to minimize the prediction error, and automatically adjust the weights to assign the trained weights to the equipment hidden danger data; The crane load hazard coefficient, rope hazard coefficient and winch hazard coefficient obtained in real time are input into the trained equipment hazard model to generate the equipment hazard assessment coefficient.

[0059] The specific calculation formula of the equipment hidden danger model is as follows: ‌

[0060] In the formula, is the equipment hidden danger assessment coefficient, , , is the weight coefficient after training, is the crane load hazard coefficient in the node area, is the rope hazard factor, is the hidden danger coefficient of the winch.

[0061] S3, according to the equipment hidden danger assessment coefficient and the cableway transportation working node, draw the equipment-hidden danger curve diagram, through the equipment-hidden danger curve diagram, determine whether there is a hidden danger in the working node area, and make maintenance in time. The specific steps are as follows: Obtain the equipment hidden danger assessment coefficient of each node area in the whole working cycle of the freight ropeway of the transmission line project from the equipment hidden danger model; The equipment hidden danger assessment coefficient is used as the Y-axis and the node area is used as the X-axis to establish a rectangular coordinate system; Based on a large amount of historical records and data from stable equipment operation, set an equipment operation stability coefficient as a reference line; Put the equipment hidden danger assessment coefficients of all recorded node areas into a rectangular coordinate system and connect them into an equipment-hidden danger curve graph; According to the equipment-hazard curve chart combined with the reference line, real-time analysis is performed. When the equipment hazard assessment coefficient of the node area is higher than the reference line of the equipment operation stability coefficient, it means that there are risk hazards in the node area at this time, and the node area with a higher equipment hazard coefficient is marked as a key maintenance object. The system will issue a risk warning and remind the on-site staff to stop working and wait for maintenance personnel to check the cranes, ropes and other equipment in the area and the power supply of the winch one by one, find out the parts with hidden dangers and repair them to ensure the safety of the aerial ropeway operation.

[0062] S4, after maintaining the node area with hidden dangers, collect the equipment hidden danger assessment coefficient after maintenance and perform data analysis to quantitatively evaluate the effect of equipment hidden danger maintenance. The specific steps are as follows: After the maintenance is completed, the same data collection method is used to collect the equipment hidden danger assessment coefficient after maintenance, and a equipment-hidden danger curve chart after maintenance is drawn. The two equipment-hidden danger curve charts before and after maintenance are compared and their variances are obtained. Variance is an important indicator to measure the degree of dispersion of data distribution. For the data points on the equipment-hidden danger curve chart, the variance can quantify the fluctuation of data at each node or time period. By calculating the variance of the curve charts before and after maintenance, we can quantify the degree of change of the equipment hidden danger coefficient in each node area. If the variance of the curve chart after maintenance is lower than the variance of the curve chart before maintenance, it means that the maintenance measures have effectively reduced the fluctuation of the equipment hidden danger coefficient, achieved significant results, and improved the stability of the equipment. If the variance of the curve chart after maintenance is greater than or equal to the variance of the curve chart before maintenance, the maintenance strategy and method need to be reconsidered. If the variance of the curve chart after maintenance of a node is abnormally higher than the variance of the curve chart before maintenance, it means that there are special problems or abnormal conditions in the node area. Comparing the influence of environmental weather, further inspection and analysis are required.

[0063] Embodiment 2, Figure 2 A schematic diagram of the structure of a transmission line engineering freight aerial ropeway construction safety detection system provided in an embodiment of the present application, including a data acquisition module, an equipment hidden danger model establishment module, a curve drawing and analysis module, and a maintenance effect evaluation module; A data acquisition module is used to divide the full working cycle route of the transmission line engineering freight aerial ropeway into a certain number of working nodes, monitor and acquire the equipment hidden danger data of each node in real time, the equipment hidden danger data includes load-bearing hidden danger data and ropeway component hidden danger data, and clean and pre-process the data; An equipment hidden danger model building module is used to input the processed equipment hidden danger data into the equipment hidden danger model built based on machine learning to generate an equipment hidden danger assessment coefficient; The curve drawing and analysis module is used to draw the equipment-hazard curve according to the equipment hazard assessment coefficient and the cableway transportation working node, and conduct data analysis on it to determine whether there are hazards in the working node area and deal with them in time; The maintenance effect evaluation module is used to collect the equipment hidden danger assessment coefficients after maintenance of the node area with hidden dangers and perform data analysis to quantitatively evaluate the equipment hidden danger maintenance effect.

[0064] 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.

[0065] 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.

[0066] 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.

[0067] 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.

[0068] 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.

[0069] 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 detection of aerial cableway construction for power transmission line engineering, characterized in that: The steps include: Divide the entire working cycle route of the aerial cableway for freight transportation of power transmission line projects into a certain number of working nodes, monitor and obtain equipment hidden danger data of each node in real time, the equipment hidden danger data includes load-bearing hidden danger data and cableway component hidden danger data, and clean and pre-process the data; Input the processed equipment hidden danger data into the equipment hidden danger model established based on machine learning to generate equipment hidden danger assessment coefficients; Draw an equipment-hazard curve diagram based on the equipment hazard assessment coefficient and the cableway transportation work node. Through the equipment-hazard curve diagram, determine whether there are hazards in the work node area and make maintenance in time; After maintaining the node areas with hidden dangers, collect the equipment hidden danger assessment coefficients after maintenance and conduct data analysis to quantitatively evaluate the effectiveness of equipment hidden danger maintenance measures.

2. The method for safety detection of aerial cableway construction for power transmission line engineering according to claim 1 is characterized in that: The load-bearing hidden danger data includes the crane load-bearing hidden danger coefficient, and the specific method for obtaining the crane load-bearing hidden danger coefficient is as follows: When the crane arrives at a node area, the images of people and goods on the crane are collected in real time; Computer vision technology is used to denoise and enhance contrast of the collected images, and size and shape features are extracted from the processed images based on a deep learning model; Based on the extracted size and shape features, the corresponding volume is obtained and its real-time weight is obtained by density formula calculation; The loss coefficient of the crane is obtained by calculating the ratio of the performance index of the actual material of the crane to the performance index of the standard material; The crane load hidden danger coefficient is obtained based on the crane load hidden danger formula according to the real-time weight carried by the crane and the loss coefficient.

3. The method for safety detection of aerial cableway construction for power transmission line engineering according to claim 1, characterized in that: The hidden danger data of the cableway components include the hidden danger coefficient of the rope and the hidden danger coefficient of the winch; the hidden danger coefficient of the rope is specifically obtained as follows: Real-time monitoring of the vibration mode of the rope during operation, recording the rope area with abnormal vibration characteristics, and obtaining the rope wear factor and rope durability in this area; Acquiring first data of the traction rope, wherein the first data includes material parameters, dimensions and external load data; The finite element method is used to analyze the stress distribution of the traction rope under the stress state, determine the stress value inside the traction rope, and obtain the strength value of the traction rope based on a preset strength formula in combination with the first data of the traction rope; The rope hidden danger coefficient is calculated based on the preset rope hidden danger formula according to the strength value of the traction rope combined with the wear factor and durability of the rope.

4. The method for safety detection of aerial cableway construction for power transmission line engineering according to claim 3 is characterized in that: The specific method for obtaining the winch hidden danger coefficient is as follows: Acquiring the influencing data of the power consumption, wherein the influencing data includes the heat loss of the motor and the loss of the reducer when the motor is working; Real-time monitoring and acquisition of power data of winches during working hours in each node area, the power data including power demand, voltage, current and resistance; The power supply efficiency is obtained based on the power supply efficiency calculation formula of the winch according to the power data; The winch hidden danger coefficient is obtained based on the preset winch hidden danger formula according to the impact data of power consumption combined with the power supply efficiency.

5. The system and method for detecting construction safety of aerial ropeway for freight transportation in power transmission line engineering according to claim 1, characterized in that: The processed equipment hidden danger data is input into the equipment hidden danger model established based on machine learning to generate the equipment hidden danger assessment coefficient. The specific steps are as follows: Collect the construction data of the freight aerial ropeway during the construction process, including historical load-bearing hidden danger data, historical ropeway component hidden danger data, historical accident records, equipment operating parameters and historical equipment maintenance records, and perform data cleaning and preprocessing on the construction data; Divide the processed data into training sets, use the training set data to train the model based on machine learning, adjust the model parameters to minimize the prediction error, adjust the weights, and assign the trained weights to the equipment hidden danger data; The crane load hazard coefficient, rope hazard coefficient and winch hazard coefficient obtained in real time are input into the trained equipment hazard model to generate the equipment hazard assessment coefficient.

6. The method for safety detection of aerial cableway construction for power transmission line engineering according to claim 1, characterized in that: According to the equipment hidden danger assessment coefficient combined with the cableway transportation working node, the equipment-hidden danger curve is drawn, and data analysis is performed on it to determine whether there are hidden dangers in the working node area and deal with them in time. The specific steps are as follows: Obtain the equipment hidden danger assessment coefficient of each node area in the whole working cycle of the freight ropeway of the transmission line project from the equipment hidden danger model; The equipment hidden danger assessment coefficient is used as the Y-axis and the node area is used as the X-axis to establish a rectangular coordinate system; Based on a large amount of historical records and data from stable equipment operation, set an equipment operation stability coefficient as a reference line; Put the equipment hidden danger assessment coefficients of all recorded node areas into a rectangular coordinate system and connect them into a curve graph; Real-time analysis is performed based on the equipment-hazard curve chart combined with the reference line. When the equipment hazard assessment coefficient of the node area is higher than the reference line of the equipment operation stability coefficient, the system will issue a risk warning and remind the on-site staff to stop working and wait for maintenance personnel to check the cranes, ropes and other equipment in the area and the power supply of the winch one by one to find out the parts with hazards and repair them.

7. The method for safety detection of aerial cableway construction for power transmission line engineering according to claim 1, characterized in that: For the node area with hidden dangers after maintenance, the equipment hidden danger assessment coefficient after maintenance is collected and data analysis is performed to determine the effect of the equipment hidden danger maintenance measures. The specific steps are as follows: After the maintenance is completed, the same data collection method is used to collect the equipment hidden danger assessment coefficient after maintenance, and a equipment-hidden danger curve after maintenance is drawn. The variance of the two equipment-hidden danger curves before and after maintenance is obtained and compared; By calculating the variance of the curves before and after maintenance, the degree of change of the equipment hidden danger coefficient in each node area can be quantified to determine the effectiveness of equipment hidden danger maintenance measures; If the variance of the curve after maintenance is lower than the variance of the curve before maintenance, then the maintenance measure has a significant effect and improves the stability of the equipment; If the variance of the post-maintenance graph is greater than or equal to the variance of the pre-maintenance graph, then the maintenance measure is ineffective and the maintenance strategy and method need to be reconsidered; If the variance of the graph after maintenance of a node is abnormally higher than the variance of the graph before maintenance, then there are special problems or abnormal conditions in the node area, which requires further inspection and analysis.

8. The method for safety detection of aerial cableway construction for power transmission line engineering according to claim 2, characterized in that: The specific calculation formula of the loss coefficient of the crane is as follows: The specific calculation formula of the crane load hidden danger coefficient is as follows: In the formula, is the crane load hazard coefficient of the i-th node area, is the loss coefficient of the crane, is the weight of the cargo, is the quantity of goods, For the weight of the character, is the number of characters, is the actual tensile strength of the crane material, is the actual elongation at break, is the standard tensile strength, It is the standard elongation at break.

9. The method for safety detection of aerial cableway construction for power transmission line engineering according to claim 4, characterized in that: The specific calculation formula of the power supply efficiency is as follows: The specific calculation formula of the winch hidden danger coefficient is as follows: In the formula, is the winch hidden danger coefficient, E is the power supply efficiency, Working hours for winch machine, is the heat loss, is the reducer loss, is the power demand, V is the voltage, is the current, CU is the resistance, d is the node area of ​​power supply, d=1,2,3,...,n, n is an integer, and D is the total number of nodes.

10. A system for detecting the construction safety of a power transmission line engineering freight aerial ropeway using any one of claims 1 to 9, comprising a data acquisition module, an equipment hidden danger model establishment module, a curve drawing and analysis module, and a maintenance effect evaluation module; A data acquisition module is used to divide the full working cycle route of the transmission line engineering freight aerial ropeway into a certain number of working nodes, monitor and acquire the equipment hidden danger data of each node in real time, the equipment hidden danger data includes load-bearing hidden danger data and ropeway component hidden danger data, and clean and pre-process the data; An equipment hidden danger model building module is used to input the processed equipment hidden danger data into the equipment hidden danger model built based on machine learning to generate an equipment hidden danger assessment coefficient; The curve drawing and analysis module is used to draw the equipment-hazard curve according to the equipment hazard assessment coefficient and the cableway transportation working node, and conduct data analysis on it to determine whether there are hazards in the working node area and deal with them in time; The maintenance effect evaluation module is used to collect the equipment hidden danger assessment coefficients after maintenance of the node area with hidden dangers and perform data analysis to quantitatively evaluate the equipment hidden danger maintenance effect.

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