A tower crane risk detection method, device, electronic equipment and storage medium

Abstract

The present application provides a method, device, electronic device and storage medium for tower crane risk detection, the method comprising: collecting multiple first data to be detected of the tower crane to be detected within a first detection time period; dividing the first data to be detected into multiple first data sets to be detected according to the location attribute data of the tower crane to be detected when each of the first data to be detected is collected; determining the sub-tilt risk of the tower crane to be detected at the corresponding position of the first data set to be detected according to the change trend between the first tilt angles in the first data set to be detected; wherein the first tilt angle is calculated through the first data to be detected; determining the overall tilt risk of the tower crane to be detected within the first detection time period according to the sub-tilt risk of the corresponding position of each of the first data sets to be detected. The present application can efficiently process a large amount of data and ensure the accuracy of tower crane detection.

Description

Technical Field

[0001] The present application relates to the field of tower crane safety technology, and in particular to a method, device, electronic equipment and storage medium for tower crane risk detection. Background Art

[0002] The verticality of a tower crane is an important indicator to ensure its safe operation. The ideal verticality of a tower crane should be zero deviation, but in actual operation, there is often a certain deviation due to the influence of various factors. These factors include foundation strength and stability, track installation flatness, embedded support foot accuracy, standard section size and shape error, and the influence of the upper load deviating from the center of the tower body. In the prior art, tower cranes are commonly used lifting equipment on construction sites, and their verticality after installation has an important impact on construction safety. Inaccurate verticality may make it impossible to promptly discover hidden dangers such as foundation settlement and loose connection bolts, resulting in tilting or even collapse, causing serious consequences.

[0003] At present, the detection of tower crane verticality generally relies on the use of laser rangefinders, total stations and other equipment for on-site surveys and data collection, and then results analysis. However, these methods have some problems, such as cumbersome operation, low efficiency, and inability to achieve real-time monitoring. In addition, due to changes in environmental conditions (such as wind speed, sunlight, etc.), the measurement results may be affected, resulting in increased errors. Summary of the invention

[0004] In view of this, the purpose of the present application is to provide a method, device, electronic device and storage medium for tower crane risk detection to overcome the problems in the prior art.

[0005] In a first aspect, an embodiment of the present application provides a method for tower crane risk detection, the method comprising:

[0006] In a first detection time period, collecting a plurality of first to-be-detected data of the to-be-detected tower crane;

[0007] Dividing the first data to be detected into a plurality of first data sets to be detected according to the location attribute data of the tower crane to be detected when each of the first data to be detected is collected;

[0008] According to the variation trend of the first tilt angles in the first data set to be detected, determining the sub-tilt risk of the tower crane to be detected at the corresponding position of the first data set to be detected; wherein the first tilt angle is calculated based on the first data to be detected;

[0009] The overall tilt risk of the tower crane to be detected within the first detection time period is determined according to the sub-tilt risks at the corresponding positions of the first data sets to be detected.

[0010] In some technical solutions of the present application, the plurality of first to-be-detected data of the tower crane to be detected are collected within the first detection time period, including:

[0011] In a first detection time period, collecting a plurality of first initial data of the tower crane to be detected;

[0012] The first data to be detected is screened out from the first initial data according to the working state of the tower crane to be detected.

[0013] In some technical solutions of the present application, the above-mentioned position attribute data includes rotation attribute data and arm length attribute data;

[0014] The first to-be-detected data are divided into a plurality of first to-be-detected data sets according to the location attribute data of the tower crane to be detected when each of the first to-be-detected data is collected, including:

[0015] According to the rotation attribute data / arm length attribute data, the first to-be-detected data is divided into an initial to-be-detected data set;

[0016] According to the arm length attribute data / rotation attribute data, the initial data set to be detected is further divided into a first data set to be detected.

[0017] In some technical solutions of the present application, the first data to be detected includes a first pitch angle and a first roll angle; the method calculates the first tilt angle in the following manner:

[0018] A first tilt angle is calculated according to the first pitch angle and the first roll angle.

[0019] In some technical solutions of the present application, the sub-tilt risk of the tower crane to be detected at the corresponding position of the first data set to be detected is determined according to the change trend between the first tilt angles in the first data set to be detected; wherein the first tilt angle is calculated through the first data to be detected, including:

[0020] The sub-tilt risk of the tower crane to be detected at the corresponding position of the first data set to be detected is determined by comparing the difference between two first tilt angles adjacent in acquisition time with a preset tilt threshold.

[0021] In some technical solutions of the present application, the above method further includes:

[0022] In the second detection time period, collecting a plurality of second to-be-detected data of the to-be-detected tower crane;

[0023] According to the location attribute data of the tower crane to be detected when each of the second data to be detected is collected, the second data to be detected is divided into a plurality of second data sets to be detected;

[0024] By comparing the first data set to be detected and the second data set to be detected under the same position attribute information, the tilt trend of the tower crane to be detected is predicted.

[0025] In some technical solutions of the present application, the above-mentioned prediction of the tilt trend of the tower crane to be detected by comparing the first data set to be detected and the second data set to be detected under the same position attribute information includes:

[0026] Calculating a first reference value according to each first tilt angle in the first to-be-detected data set;

[0027] Calculating a second reference value according to each second tilt angle in the second to-be-detected data set;

[0028] The tilt trend of the tower crane to be detected is predicted according to the first reference value and the second reference value.

[0029] In a second aspect, an embodiment of the present application provides a device for tower crane risk detection, the device comprising:

[0030] A collection module, used for collecting a plurality of first to-be-detected data of the tower crane to be detected within a first detection time period;

[0031] A division module, used for dividing the first data to be detected into a plurality of first data sets to be detected according to the location attribute data of the tower crane to be detected when each of the first data to be detected is collected;

[0032] A processing module, configured to determine the sub-tilt risk of the tower crane to be detected at a position corresponding to the first data set to be detected according to a change trend of the first tilt angles in the first data set to be detected; wherein the first tilt angle is calculated using the first data set to be detected;

[0033] The prediction module is used to determine the overall tilt risk of the tower crane to be detected within the first detection time period according to the sub-tilt risks at the corresponding positions of each of the first data sets to be detected.

[0034] In a third aspect, an embodiment of the present application provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above-mentioned tower crane risk detection method when executing the computer program.

[0035] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the above-mentioned tower crane risk detection method are executed.

[0036] The technical solution provided by the embodiments of the present application may have the following beneficial effects:

[0037] The method of the present application includes collecting a plurality of first data to be detected of the tower crane to be detected within a first detection time period; dividing the first data to be detected into a plurality of first data sets to be detected according to the location attribute data of the tower crane to be detected when each of the first data to be detected is collected; determining the sub-tilt risk of the tower crane to be detected at the corresponding position of the first data set to be detected according to the change trend between the first tilt angles in the first data set to be detected; wherein the first tilt angle is calculated by the first data to be detected; determining the overall tilt risk of the tower crane to be detected within the first detection time period according to the sub-tilt risk of the corresponding position of each of the first data sets to be detected. The present application can efficiently process a large amount of data and ensure the accuracy of tower crane detection.

[0038] In order to make the above-mentioned objects, features and advantages of the present application more obvious and easy to understand, preferred embodiments are specifically cited below and described in detail with reference to the attached drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the embodiments will be briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0040] Figure 1 A schematic flow chart of a tower crane risk detection method provided in an embodiment of the present application is shown;

[0041] Figure 2 A schematic diagram of a tower crane risk detection system provided by an embodiment of the present application is shown;

[0042] Figure 3 A schematic diagram of a tower crane risk detection device provided in an embodiment of the present application is shown;

[0043] Figure 4 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0044] To make the purpose, technical scheme and advantages of the embodiments of the present application clearer, the technical scheme in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. It should be understood that the drawings in the present application only serve the purpose of explanation and description and are not used to limit the scope of protection of the present application. In addition, it should be understood that the schematic drawings are not drawn in real proportion. The flowchart used in this application shows the operations implemented according to some embodiments of the present application. It should be understood that the operations of the flowchart can be implemented out of sequence, and the steps without logical context can be reversed in order or implemented simultaneously. In addition, those skilled in the art can add one or more other operations to the flowchart under the guidance of the content of the present application, or remove one or more operations from the flowchart.

[0045] In addition, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. The components of the embodiments of the present application described and shown in the drawings here can be arranged and designed in various configurations. Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the application claimed for protection, but merely represents the selected embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without making creative work belong to the scope of protection of the present application.

[0046] It should be noted that the term "comprising" will be used in the embodiments of the present application to indicate the existence of the features declared thereafter, but does not exclude the addition of other features.

[0047] The verticality of a tower crane is an important indicator to ensure its safe operation. The ideal verticality of a tower crane should be zero deviation, but in actual operation, there is often a certain deviation due to the influence of various factors. These factors include foundation strength and stability, track installation flatness, embedded support foot accuracy, standard section size and shape error, and the influence of the upper load deviating from the center of the tower body. In the prior art, tower cranes are commonly used lifting equipment on construction sites, and their verticality after installation has an important impact on construction safety. Inaccurate verticality may make it impossible to promptly discover hidden dangers such as foundation settlement and loose connection bolts, resulting in tilting or even collapse, causing serious consequences.

[0048] At present, the detection of tower crane verticality generally relies on the use of laser rangefinders, total stations and other equipment for on-site surveys and data collection, and then results analysis. However, these methods have some problems, such as cumbersome operation, low efficiency, and inability to achieve real-time monitoring. In addition, due to changes in environmental conditions (such as wind speed, sunlight, etc.), the measurement results may be affected, resulting in increased errors.

[0049] Based on this, the embodiments of the present application provide a method, device, electronic device and storage medium for tower crane risk detection, which are described below through embodiments.

[0050] Figure 1 A flow chart of a tower crane risk detection method provided in an embodiment of the present application is shown, wherein the method comprises steps S101-S104; specifically:

[0051] S101, collecting a plurality of first to-be-detected data of a tower crane to be detected within a first detection time period;

[0052] S102, dividing the first data to be detected into a plurality of first data sets to be detected according to the location attribute data of the tower crane to be detected when each of the first data to be detected is collected;

[0053] S103, determining the sub-tilt risk of the tower crane to be detected at a position corresponding to the first data set to be detected according to a change trend of the first tilt angles in the first data set to be detected; wherein the first tilt angle is calculated based on the first data set to be detected;

[0054] S104: Determine the overall tilt risk of the tower crane to be detected within the first detection time period according to the sub-tilt risks at the corresponding positions of the first data sets to be detected.

[0055] This application can efficiently process large amounts of data and ensure the accuracy of tower crane detection.

[0056] Some embodiments of the present application are described in detail below. In the absence of conflict, the following embodiments and features in the embodiments can be combined with each other.

[0057] The present application embodiment provides a tower crane risk detection method, which acts on a tower crane risk monitoring system, such as Figure 2 As shown, the system here includes the tower crane to be tested, a height encoder installed on the tower crane, a hub, a cross encoder, an intelligent machine, a rotary encoder and an inclination sensor, etc.

[0058] In order to determine whether there is a risk of tilting of the tower crane to be detected, the embodiment of the present application needs to collect data on the tower base to be detected. It should be emphasized that when collecting data on the tower crane to be detected in the embodiment of the present application, the collection frequency is higher than the data collection frequency for tower cranes in the prior art. For example, if the data collection frequency for tower cranes in the method in the prior art is the first frequency, the data collection frequency for the tower crane to be detected in the embodiment of the present application is the second frequency, and the second frequency is much greater than the first frequency. In other words, the embodiment of the present application can process a large amount of data efficiently. The first data to be detected is obtained by collecting data on the tower crane to be detected.

[0059] When collecting data for the tower crane to be inspected, the embodiment of the present application collects data within the first inspection time period. The length of the inspection time period here can be determined by the user according to actual conditions, and can be hours, days, weeks, months, quarters, years, etc. For example, the first inspection time period here is one day, and the collection frequency is ten seconds / time. By monitoring the tower crane to be inspected, 8640 pieces of data can be obtained.

[0060] In the process of monitoring the tower crane to be detected, it should be noted that the tower crane to be detected includes a working state and an idle state. When the tower crane to be detected is in the working state, it will be affected by the heavy object and will tilt. If the tower crane to be detected is also detected in the working state, the result obtained is likely to have a risk of tilting. Therefore, in order to avoid the normal tilt caused by heavy objects during work, the data collected from the tower crane to be detected in the first detection time period is referred to as the first initial data in the embodiment of the present application. The first initial data here includes the amplitude position, lifting height, rotation angle, roll angle, pitch angle and tower crane boom length. Among them, the amplitude position and lifting height here are the working state data of the tower crane to be detected, and the rotation angle and tower crane boom length are the position attribute data of the tower crane to be detected. The roll angle and pitch angle are the data to be detected. There are data collected in the working state in the first initial data. In order to ensure the detection result, the embodiment of the present application needs to exclude this part of the data. In other words, the embodiment of the present application only detects the data in the idle state of the tower crane to be detected.

[0061] The basis for screening the first initial data is the working state of the tower crane to be detected. The data that can reflect the working state of the tower crane to be detected in the first initial data are the luffing position and the lifting height. That is, according to the luffing position and the lifting height, the data of the tower crane to be detected in the idle state is selected. In the specific implementation, the data with a height less than 5m and a constant amplitude position for 30 consecutive minutes can be selected as the first data to be detected.

[0062] After obtaining the first data to be detected, due to the large amount of data, in order to improve data processing efficiency and accuracy, the embodiment of the present application does not directly detect the first data to be detected, but first groups the first data to be detected. By grouping the first data to be detected, multiple first data sets to be detected are obtained, and the first data sets to be detected include multiple first data to be detected. The basis for dividing the first data to be detected is the position attribute data when the first data to be detected is collected. That is, the rotation angle and tower crane arm length corresponding to the roll angle and pitch angle.

[0063] Furthermore, when dividing the first data to be detected, since the position attribute data includes two kinds of rotation attribute data (rotation angle) and arm length attribute data (tower crane arm length), when dividing the first data to be detected, it is necessary to first divide it into an initial data set to be detected, and then divide the initial data set to be detected again to obtain the first data set to be detected. For example, firstly divide the first data to be detected into an initial data set to be detected according to the rotation attribute data; and then further divide the initial data set to be detected into a first data set to be detected according to the arm length attribute data. It is also possible to first divide the first data to be detected into an initial data set to be detected according to the arm length attribute data; and then further divide the initial data set to be detected into a first data set to be detected according to the rotation attribute data.

[0064] When dividing the rotation attribute data, the embodiment of the present application needs to first calculate an angle threshold according to the force factors and the amount of effective data collected; then divide according to the angle threshold. For example, the main body of the tower crane tower force structure is a standard section, with 4 connecting pins on the upper and lower sides. Considering the influence of force and the range of rotation movement, the force of the 4 pins is more directly reflected through the verticality. Each quadrant is set with 3 rotation monitoring units on both sides with 45 degrees as the center line. For example, 1 degree, 5 degrees, and 9 degrees are used as a monitoring unit. If the granularity is too small, it may lead to too little effective sampling data, increasing the risk of measurement error. Comprehensive calculation shows that 15 degrees is a monitoring unit.

[0065] When the arm length attribute data is used for classification, the main basis is the length of the standard section of the tower crane boom. For example, the length of the standard section of the boom of most tower cranes on the market is 5m, and the main boom lengths are 40m, 45m, 50m, 55m, 60m, 65m, 70m, 75m, and 80m.

[0066] In the specific implementation, the collected rotation angle is defined as R (0 degrees at the north, and a clockwise rotation of 360 degrees returns to 0 degrees), with 15 degrees as a unit, divided into 24 detection units clockwise, named R1, R2...R24 in sequence. According to the installed arm length of the tower crane, starting from the 0m position of the amplitude change, every 5 meters is a unit, and the arm is divided into several monitoring units, named L1, L2...Ln in sequence. R1, R2...R24 are combined with L1, L2...Ln to obtain the first data set to be detected, which is R1L1, R1L2...R1Ln, R2L1, R2L2...R2Ln,...R24L1, R24L2...R24Ln.

[0067] Through the above division, the embodiment of the present application can obtain multiple first data sets to be detected. The division of the first data to be detected here is not useless. The embodiment of the present application needs to take into account the association between each first data to be detected, that is, if the data with the same position attribute are analyzed separately, more targeted results can be obtained.

[0068] In the prior art, when analyzing the collected data, all the data are taken as a whole to calculate and evaluate the risk of the tower crane to be detected, and the accuracy of the result is not enough. After collecting multiple first data to be detected, the embodiment of the present application needs to classify the first data to be detected, and then analyze them separately, so as to more accurately and specifically determine the tilt position of the tower crane to be detected.

[0069] After obtaining the first data to be detected, the embodiment of the present application needs to calculate the corresponding first tilt angle according to each first data to be detected in the first data set to be detected. The specific calculation is:

[0070] Calculate the verticality based on the monitored pitch angle and roll angle. Assume the roll angle is θ and the pitch angle is Φ, then the inclination angle α of the tower can be calculated by the following formula:

[0071]

[0072] By calculating in the above manner, the embodiment of the present application can obtain the first tilt angle corresponding to each first data to be detected in the first data set to be detected. The data are sorted according to the acquisition time of each first data to be detected to obtain a plurality of first tilt angles in order. The sub-tilt risk of the tower crane to be detected at this position can be determined through the change trend of these first tilt angles.

[0073] The specific determination process is: comparing the difference between two adjacent first tilt angles with a preset tilt threshold, if the difference is greater than the tilt threshold, there is a tilt risk, otherwise there is no tilt risk.

[0074] After the sub-tilt risks of each first set of data to be detected are obtained, all the sub-tilt risks are summarized to obtain the overall tilt risk of the tower crane to be detected in the first detection time period.

[0075] In an optional implementation, after the tower crane to be detected in the first detection time period is detected, the embodiment of the present application can also perform detection in other detection time periods. For example, the tower crane to be detected can also continue to be monitored, and the second data to be detected in the second detection time period is obtained (the process of obtaining the second data to be detected is the same as the process of obtaining the first data to be detected, which will not be repeated here). It should be emphasized that the second detection time period here can be the same as the first detection time period, or it can be different from the first detection time period. For example, the first detection time period is December 1, 2024, and the second detection time period is December 2, 2024. The first detection time period is October 1, 2024, and the second detection time period is the entire month of November 2024 (from the 1st to the 30th). The second detection time period is the third quarter of 2024 (July 1st to September 30th), and the second detection time period is October 1, 2024. That is to say, the second detection time period in the embodiment of the present application can be shorter than the first detection time period, or it can be longer than the first detection time period. Regardless of whether the second detection time period is long or short, since the embodiments of the present application include location attribute data when acquiring initial data (including first initial data and second initial data), the embodiments of the present application can analyze the second data to be detected and the first data to be detected together.

[0076] Specifically, after obtaining the second data to be detected in the second detection time period, the risk detection of the tower crane to be detected in the second detection time period can be carried out according to the same processing process as the first data to be detected. At the same time, after obtaining the second data to be detected, the data to be detected in the two time periods can be analyzed as a whole, and the tilt trend of the tower crane to be detected can be predicted according to the time sequence.

[0077] When predicting the tilt trend, the second data to be detected is first divided into a plurality of second data sets to be detected according to the division method of the first data to be detected, and the tilt trend of the tower crane to be detected is predicted by comparing the first data set to be detected and the second data set to be detected under the same position attribute information.

[0078] Furthermore, a first reference value is calculated based on each first tilt angle in the first data set to be detected; a second reference value is calculated based on each second tilt angle in the second data set to be detected; and a tilt trend of the tower crane to be detected is predicted based on the first reference value and the second reference value.

[0079] The reference value here can be calculated by the differential average method. If the difference between the first reference value and the second reference value is large (exceeding the preset warning value), a risk warning message is generated. The on-site operators are warned through sound and light, and the warning information is pushed to the management personnel.

[0080] Figure 3 A schematic diagram of the structure of a tower crane risk detection device provided in an embodiment of the present application is shown, and the device includes:

[0081] A collection module, used for collecting a plurality of first to-be-detected data of the tower crane to be detected within a first detection time period;

[0082] A division module, used for dividing the first data to be detected into a plurality of first data sets to be detected according to the location attribute data of the tower crane to be detected when each of the first data to be detected is collected;

[0083] A processing module, configured to determine the sub-tilt risk of the tower crane to be detected at a position corresponding to the first data set to be detected according to a change trend of the first tilt angles in the first data set to be detected; wherein the first tilt angle is calculated using the first data set to be detected;

[0084] The prediction module is used to determine the overall tilt risk of the tower crane to be detected within the first detection time period according to the sub-tilt risks at the corresponding positions of each of the first data sets to be detected.

[0085] The collecting of a plurality of first to-be-detected data of the to-be-detected tower crane within the first detection time period includes:

[0086] In a first detection time period, collecting a plurality of first initial data of the tower crane to be detected;

[0087] The first data to be detected is screened out from the first initial data according to the working state of the tower crane to be detected.

[0088] The position attribute data includes rotation attribute data and arm length attribute data;

[0089] The first to-be-detected data are divided into a plurality of first to-be-detected data sets according to the location attribute data of the tower crane to be detected when each of the first to-be-detected data is collected, including:

[0090] According to the rotation attribute data / arm length attribute data, the first to-be-detected data is divided into an initial to-be-detected data set;

[0091] According to the arm length attribute data / rotation attribute data, the initial data set to be detected is further divided into a first data set to be detected.

[0092] The first data to be detected includes a first pitch angle and a first roll angle; the method calculates the first tilt angle in the following manner:

[0093] A first tilt angle is calculated according to the first pitch angle and the first roll angle.

[0094] The method of determining the sub-tilt risk of the tower crane to be detected at a position corresponding to the first data set to be detected according to a change trend of the first tilt angles in the first data set to be detected, wherein the first tilt angle is calculated based on the first data to be detected, comprises:

[0095] The sub-tilt risk of the tower crane to be detected at the corresponding position of the first data set to be detected is determined by comparing the difference between two first tilt angles adjacent in acquisition time with a preset tilt threshold.

[0096] In the second detection time period, collecting a plurality of second to-be-detected data of the to-be-detected tower crane;

[0097] According to the location attribute data of the tower crane to be detected when each of the second data to be detected is collected, the second data to be detected is divided into a plurality of second data sets to be detected;

[0098] By comparing the first data set to be detected and the second data set to be detected under the same position attribute information, the tilt trend of the tower crane to be detected is predicted.

[0099] The step of comparing the first to-be-detected data set and the second to-be-detected data set under the same position attribute information to predict the tilt trend of the to-be-detected tower crane comprises:

[0100] Calculating a first reference value according to each first tilt angle in the first to-be-detected data set;

[0101] Calculating a second reference value according to each second tilt angle in the second to-be-detected data set;

[0102] The tilt trend of the tower crane to be detected is predicted according to the first reference value and the second reference value.

[0103] like Figure 4 As shown, an embodiment of the present application provides an electronic device for executing the tower crane risk detection method in the present application, the device includes a memory, a processor, a bus, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the tower crane risk detection method when executing the computer program.

[0104] Specifically, the above-mentioned memory and processor may be general-purpose memory and processor, which are not specifically limited here. When the processor runs the computer program stored in the memory, the above-mentioned tower crane risk detection method can be executed.

[0105] Corresponding to the tower crane risk detection method in the present application, an embodiment of the present application also provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the steps of the above-mentioned tower crane risk detection method are executed.

[0106] Specifically, the storage medium can be a general storage medium, such as a mobile disk, a hard disk, etc. When the computer program on the storage medium is run, the above-mentioned tower crane risk detection method can be executed.

[0107] In the embodiments provided in the present application, it should be understood that the disclosed systems and methods can be implemented in other ways. The system embodiments described above are merely schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some communication interfaces, indirect coupling or communication connection of systems or units, which can be electrical, mechanical or other forms.

[0108] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, and may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0109] In addition, each functional unit in the embodiments provided in the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0110] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions to enable a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk, and other media that can store program codes.

[0111] It should be noted that similar numbers and letters represent similar items in the following figures. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. In addition, the terms "first", "second", "third", etc. are only used to distinguish the description and are not to be understood as indicating or implying relative importance.

[0112] Finally, it should be noted that the above-described embodiments are only specific implementation methods of the present application, which are used to illustrate the technical solutions of the present application, rather than to limit them. The protection scope of the present application is not limited thereto. Although the present application is described in detail with reference to the above-mentioned embodiments, ordinary technicians in the field should understand that any technician familiar with the technical field can still modify the technical solutions recorded in the above-mentioned embodiments within the technical scope disclosed in the present application, or can easily think of changes, or make equivalent replacements for some of the technical features therein; and these modifications, changes or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application. They should all be included in the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.

Claims

1. A tower crane risk detection method, characterized in that: The method comprises: In a first detection time period, collecting a plurality of first to-be-detected data of the to-be-detected tower crane; Dividing the first data to be detected into a plurality of first data sets to be detected according to the location attribute data of the tower crane to be detected when each of the first data to be detected is collected; According to the variation trend of the first tilt angles in the first data set to be detected, determining the sub-tilt risk of the tower crane to be detected at the corresponding position of the first data set to be detected; wherein the first tilt angle is calculated based on the first data set to be detected; Determining the overall tilt risk of the tower crane to be detected within the first detection time period according to the sub-tilt risks at the corresponding positions of each of the first data sets to be detected; The position attribute data includes rotation angle data and arm length attribute data; The first to-be-detected data are divided into a plurality of first to-be-detected data sets according to the location attribute data of the tower crane to be detected when each of the first to-be-detected data is collected, including: According to the rotation angle data / arm length attribute data, the first to-be-detected data is divided into an initial to-be-detected data set; According to the arm length attribute data / rotation angle data, the initial data set to be detected is further divided into a first data set to be detected; In the second detection time period, collecting a plurality of second to-be-detected data of the to-be-detected tower crane; According to the location attribute data of the tower crane to be detected when each of the second data to be detected is collected, the second data to be detected is divided into a plurality of second data sets to be detected; By comparing the first data set to be detected and the second data set to be detected under the same position attribute information, the tilt trend of the tower crane to be detected is predicted.

2. The method according to claim 1, characterized in that The collecting of a plurality of first to-be-detected data of the to-be-detected tower crane within the first detection time period includes: In a first detection time period, collecting a plurality of first initial data of the tower crane to be detected; The first data to be detected is screened out from the first initial data according to the working state of the tower crane to be detected.

3. The method according to claim 1, characterized in that The first data to be detected includes a first pitch angle and a first roll angle; the method calculates the first tilt angle in the following manner: A first tilt angle is calculated according to the first pitch angle and the first roll angle.

4. The method according to claim 3, characterized in that The method of determining the sub-tilt risk of the tower crane to be detected at a position corresponding to the first data set to be detected according to a change trend of the first tilt angles in the first data set to be detected, wherein the first tilt angle is calculated based on the first data to be detected, comprises: The sub-tilt risk of the tower crane to be detected at the corresponding position of the first data set to be detected is determined by comparing the difference between two first tilt angles adjacent in acquisition time with a preset tilt threshold.

5. The method according to claim 1, characterized in that The step of comparing the first to-be-detected data set and the second to-be-detected data set under the same position attribute information to predict the tilt trend of the to-be-detected tower crane comprises: Calculating a first reference value according to each first tilt angle in the first to-be-detected data set; Calculating a second reference value according to each second tilt angle in the second to-be-detected data set; The tilt trend of the tower crane to be detected is predicted according to the first reference value and the second reference value.

6. A tower crane risk detection device, characterized in that: The device comprises: A collection module, used for collecting a plurality of first to-be-detected data of the tower crane to be detected within a first detection time period; A division module, used for dividing the first data to be detected into a plurality of first data sets to be detected according to the location attribute data of the tower crane to be detected when each of the first data to be detected is collected; A processing module, configured to determine the sub-tilt risk of the tower crane to be detected at a position corresponding to the first data set to be detected according to a change trend of the first tilt angles in the first data set to be detected; wherein the first tilt angle is calculated using the first data set to be detected; A prediction module, used to determine the overall tilt risk of the tower crane to be detected within the first detection time period according to the sub-tilt risks at the corresponding positions of each of the first data sets to be detected; The position attribute data includes rotation angle data and arm length attribute data; The first to-be-detected data are divided into a plurality of first to-be-detected data sets according to the location attribute data of the tower crane to be detected when each of the first to-be-detected data is collected, including: According to the rotation angle data / arm length attribute data, the first to-be-detected data is divided into an initial to-be-detected data set; According to the arm length attribute data / rotation angle data, the initial data set to be detected is further divided into a first data set to be detected; In the second detection time period, collecting a plurality of second to-be-detected data of the to-be-detected tower crane; According to the location attribute data of the tower crane to be detected when each of the second data to be detected is collected, the second data to be detected is divided into a plurality of second data sets to be detected; By comparing the first data set to be detected and the second data set to be detected under the same position attribute information, the tilt trend of the tower crane to be detected is predicted.

7. An electronic device, characterized in that: include: A processor, a memory and a bus, wherein the memory stores machine-readable instructions executable by the processor, and when the electronic device is running, the processor and the memory communicate via the bus, and when the machine-readable instructions are executed by the processor, the steps of the tower crane risk detection method as described in any one of claims 1 to 5 are performed.

8. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the tower crane risk detection method as claimed in any one of claims 1 to 5 are executed.

Images