Cable machine full-path loading and unloading position recognition method

Abstract

The present application belongs to the field of intelligent construction of hydropower engineering and intelligent scheduling of engineering machinery, and relates to a cable machine full-path loading and unloading position identification method, comprising: 1) obtaining cable machine operation monitoring data from the database of the cable machine operation monitoring system; 2) preprocessing the cable machine operation monitoring data; 3) performing cluster analysis and constructing a cable machine operation data corresponding region division table based on the cluster analysis result; 4) performing first-order difference calculation and constructing a cable machine operation state judgment table; 5) based on the difference calculation result obtained from the cable machine operation state judgment table, performing cable machine non-empty load state operation data pattern recognition and outputting corresponding parameters; 6) combining the vector product and relative position comparison method to perform cable machine loading and unloading position pattern recognition. The present application has the advantages of high identification precision, being capable of greatly improving the accuracy of cable machine efficiency analysis, being capable of constructing a standardized data base for cable machine group collaborative scheduling and hoisting path optimization, and effectively eliminating information islands.

Description

Technical Field

[0001] This invention belongs to the field of intelligent construction of hydropower projects and intelligent scheduling of engineering machinery, and relates to a method for identifying the loading and unloading position of cable cranes along the entire path, and more particularly to a method for identifying the loading and unloading position of cable cranes along the entire path based on massive monitoring data. Background Technology

[0002] As my country's hydropower projects expand into high mountain and canyon areas, cable cranes have become a core transportation equipment in the construction of concrete arch dams. Their lifting tasks typically follow a basic operational cycle of lifting from the loading platform and unloading from the dam body. However, in complex actual construction scenarios, diverse lifting modes exist, including loading in the dam body area, unloading from the platform area, and internal transfer within the dam body area. In recent years, intelligent construction theory has been continuously integrated into engineering practice. To achieve standardized, refined, and intelligent cable crane operation management, real-time, high-precision, and high-refresh-rate cable crane operation monitoring systems have been gradually put into use, providing effective technical support for comprehensively understanding the dynamic operating conditions of cable cranes.

[0003] However, the massive amounts of data collected by the cable crane monitoring system are still in their raw state, and key parameters directly related to the operation process need to be further explored and analyzed. Although existing research has used pattern recognition technology to extract several operating modes and sub-modes from multidimensional data, laying the foundation for efficiency evaluation, current methods still have significant limitations: they cannot accurately identify the specific loading and unloading points of the hoisting task, and can only identify macroscopic areas such as the deck and platform.

[0004] On the other hand, some studies use high-precision positioning hardware such as RTK to directly obtain the unloading position of the cable crane. However, these methods rely on dedicated hardware, which is costly and difficult to promote in projects without such equipment; their recognition logic is only for concrete hoisting operations and cannot cover auxiliary hoisting tasks such as formwork and steel bars that are widely present in actual construction; in terms of recognition direction, they only support the identification of unloading points from the platform to the storage surface, and cannot realize the positioning of loading points from the storage surface to the platform, let alone handle the transfer scenarios from storage surface to storage surface, thus limiting their applicability.

[0005] In summary, existing methods either suffer from coarse-grained recognition due to reliance solely on trajectory data, resulting in an inability to output precise semantic locations; or they rely on specific hardware, supporting only a single operating mode and unidirectional recognition, leading to high costs and poor universality. Currently, there is a lack of a method that does not rely on dedicated hardware, but only on the cable crane's own multi-source monitoring data (such as fusion of lifting weight and coordinates) to automatically and accurately identify loading and unloading positions across multiple modes, including concrete and auxiliary lifting, covering the entire path of loading and unloading from platform to silo, silo to platform, and silo to silo. This technological gap restricts the in-depth mining of cable crane group operation data and the improvement of global collaborative scheduling capabilities. Therefore, achieving sub-meter-level accurate identification of lifting points can not only significantly improve the accuracy of cable crane efficiency analysis, but also build a high-confidence data foundation for collaborative scheduling and lifting path optimization of cable crane groups. Summary of the Invention

[0006] To address the aforementioned technical problems in the background art, this invention provides a method for identifying the loading and unloading positions of cable cranes along their entire path. This method offers high recognition accuracy, significantly improves the accuracy of cable crane efficiency analysis, establishes a standardized data base for collaborative scheduling and hoisting path optimization of cable crane groups, and effectively eliminates information silos.

[0007] To achieve the above objectives, the present invention adopts the following technical solution: A method for identifying the loading and unloading positions of a cable crane along its entire path, characterized in that the method includes the following steps: 1) Obtain cable crane operation monitoring data from the database of the cable crane operation monitoring system; 2) Preprocess the cable crane operation monitoring data obtained in step 1); 3) Perform cluster analysis on the cable crane operation monitoring data after the preprocessing in step 2), and construct a regional division table corresponding to the cable crane operation data based on the cluster analysis results; 4) Perform first-order difference calculation on the cable crane operation monitoring data after the preprocessing in step 2) to construct a cable crane operation status judgment table; 5) Based on the differential calculation results obtained from the cable crane operation status judgment table in step 4), perform cable crane non-no-load operation data pattern recognition and output the corresponding parameters; 6) Based on the corresponding parameters obtained in step 5) and the area division table corresponding to the cable crane operation data obtained in step 3), the loading and unloading position pattern recognition of the cable crane is performed by combining the vector product and relative position comparison method.

[0008] Preferably, the specific implementation of step 1) of the present invention is as follows: using SQL statements to extract cable crane operation monitoring data from the database of the cable crane operation monitoring system. The cable crane operation monitoring data includes the cable crane number, the cable crane operation data acquisition time point, the vector data acquisition point time interval, the cable crane X coordinate at the time of data acquisition, the cable crane Y coordinate at the time of data acquisition, the cable crane Z coordinate at the time of data acquisition, the load and number at the time of data acquisition.

[0009] Preferably, the preprocessing in step 2) of the present invention includes data conversion and cleaning, elevation data conversion, and the establishment of a pouring silo information table; The data conversion and cleaning includes cleaning up pre-construction data, deleting duplicate time data, verifying and processing coordinate data, converting time data formats, unifying coordinate systems, and reducing data noise. The elevation data conversion is performed after data conversion and cleaning. The elevation data conversion is to convert the cable length in the cable crane operation data into elevation data. The establishment of the casting silo information table includes the geometric information table of the arch dam casting silo and the opening schedule of the arch dam casting silo; the geometric information table of the arch dam casting silo includes the silo number, coordinates of the outline point of the casting silo, coordinates of the center point of the casting silo surface, and the elevation of the casting silo; the opening schedule of the arch dam casting silo includes the silo number, opening time, and closing time.

[0010] Preferably, the specific implementation of step 3) in this invention is as follows: 3.1) Extract the lifting weight data from the preprocessed cable crane operation monitoring data; the lifting weight data refers to the data in the target cable crane operation data where the lifting weight does not exceed 5t and the lifting weight exceeds 20t. 3.2) Perform differential calculation on the lifting weight data extracted in step 3.1). A difference value greater than 0 is marked as state 1, and a difference value less than 0 is marked as state 0. State 1 indicates the lifting weight is rising; state 0 indicates the lifting weight is falling. The formula for calculating the first-order differential value of the lifting weight is: DW=(W n+1 -W n ) / (t n+1 - t n ) in: W n It is the load in the nth row; W n+1 It is the weight of the (n+1)th row; t n It is the time point in the nth row; t n+1 It is the time point of the (n+1)th row; DW is the first-order difference value of the suspended weight; 3.3) Based on the results of step 3.2), filter the data marked as state 1; 3.4) Extract the X-coordinate data from the data obtained in step 3.3) and draw a histogram; the vertical axis of the histogram is the frequency of the loading point; the horizontal axis of the histogram is the X-coordinate data. 3.5) Repeat steps 3.1) to 3.4) to analyze the regional characteristics of the X-axis distribution histogram, construct a regional division table corresponding to the operating data of all cable cranes based on the regional characteristics, and determine the X-axis coordinate range of the platform area and the deck area of ​​each cable crane based on the regional division table.

[0011] Preferably, the specific implementation of step 4) in this invention is as follows: 4.1) Calculate the first-order difference values ​​for the suspended weight and X and Z coordinates in the cable crane operation monitoring data after the preprocessing in step 2); the first-order difference values ​​include the first-order difference value DX for horizontal displacement, the first-order difference value DZ for vertical displacement, and the first-order difference value DW for suspended weight. The expression for the first-order difference value DX of the horizontal displacement is: DX = (X n+1 -X n ) / (t n+1 - t n ) in: X n It is the X-coordinate of the nth row; X n+1 It is the X-coordinate of the (n+1)th row; t n It is the time point in the nth row; t n+1 It is the time point of the (n+1)th row; DX is the first-order difference value of the horizontal displacement; The expression for the first-order difference value DZ of the vertical displacement is: DZ = (Z) n+1 -Z n ) / (t n+1 - t n ) in: Z n It is the Z-coordinate of the nth row; Z n+1 It is the Z-coordinate of the (n+1)th row; t n It is the time point in the nth row; t n+1 It is the time point of the (n+1)th row; DZ is the first-order difference value of the vertical displacement; The expression for the first-order difference value DW of the suspended weight is: DW=(Wn+1 -W n ) / (t n+1 - t n ) in: W n It is the load in the nth row; W n+1 It is the weight of the (n+1)th row; t n It is the time point in the nth row; t n+1 It is the time point of the (n+1)th row; DW is the first-order difference value of the suspended weight; 4.2) Construct a cable crane operation status judgment table based on the first-order difference value calculated in step 4.1); the cable crane operation status judgment table includes operation status and operation status judgment conditions; the operation status includes no load, non-no load, transportation, return, stop, loading and unloading.

[0012] Preferably, the specific implementation of step 5) in this invention is as follows: 5.1) Based on the cable crane operation status judgment table constructed in step 4), each line of cable crane operation data points recorded in the cable crane operation monitoring data after preprocessing in step 2) is judged as either unloaded or not unloaded; the unloaded status label is 0, and the not unloaded status label is 1. 5.2) Based on the judgment result of step 5.1), the operation phase is divided into the cable crane operation monitoring data after the preprocessing in step 2) according to the state switching point. A unique group identifier Grp is assigned to each data subsequence with the same continuous state. Grp increases monotonically when the state switches. 5.3) For the data after step 5.2), use SQL statements to filter out all data groups marked as empty. For each data point in the remaining non-empty data groups, determine whether each data point belongs to the transportation, return or stop status according to the cable crane operation status judgment table. 5.4) Use SQL statements to extract the identification parameters of the average load weight during the transportation phase and return the identification parameters of the average load weight during the phase; 5.5) Identify the identification parameters obtained in step 5.4). If the average lifting weight of a data group during the transportation phase is greater than 22 tons and the average lifting weight during the return phase is between 5.5 tons and 9 tons, it is determined to be a concrete lifting operation mode. Other non-empty data are classified as auxiliary lifting modes. The threshold is set based on the measured data of the high arch dam cable crane construction and the characteristics of the equipment: the lower limit of transportation is 22t, which corresponds to a full load of about 28t for the lifting tank, taking into account the incomplete filling of the tank and measurement error; the empty tank range is 5.5t~9t, which corresponds to the self-weight of the lifting tank of about 6.5t, taking into account the concrete residue and measurement error, forming a precise identification closed loop for full load and empty tank.

[0013] 5.6) After the data from step 5.5) cable crane operation mode identification is completed, the corresponding parameters need to be output: the corresponding parameters include cable crane number, start time, end time, loading point coordinates, unloading point coordinates and cable crane operation mode.

[0014] Preferably, the specific implementation of step 6) in this invention is as follows: 6.1) Extract the relevant parameters obtained in step 5) as well as the geometric information table of the arch dam casting chamber and the opening schedule of the arch dam casting chamber obtained in step 2); 6.2) Determine the working area to which the loading point and unloading point belong based on the area division table corresponding to the cable crane operation data obtained in step 3); the working area is the area where the lifting point (loading or unloading point) is located on the warehouse surface or the area where the lifting point is located on the platform. 6.3) Based on the work area and hoisting mode in step 6.2), determine the platform level to which the hoisting point belongs; 6.4) Obtain the loading or unloading location based on the platform level of the lifting point obtained in step 6.3); 6.5) Based on the results obtained in step 6.4), generate structured record data, which includes cable crane number, start time, end time, operating mode, load, loading position, unloading position, and cable crane hoisting trajectory.

[0015] Preferably, in step 6.3) of the present invention, when the working area of ​​step 6.2) is the area where the lifting point is located on the warehouse surface, it is in the concrete lifting mode or the auxiliary lifting mode; when in the concrete lifting mode, the specific implementation of step 6.3) is as follows: 6.3.a.1) If the lifting point is located in the warehouse area and it is in concrete lifting mode, then extract the set S of warehouse opening status based on the task time period. K ; 6.3.a.2) Extract S using step 6.3.a.1) K Extract the coordinates of the surface outline points and center point from the geometric information table of the arch dam casting silo; 6.3.a.3) If |S K If | > 1, calculate the Euclidean distance between the unloading point and the center point of each bin, and select the bin with the closest distance as the target bin T; if |S K If |=1, then it is directly determined as the target warehouse T; When in auxiliary hoisting mode, the specific implementation of step 6.3) is as follows: 6.3.b.1) If the lifting point is located in the storage area and is in auxiliary lifting mode, then, based on the task time period, extract the set S of the top storage number of the currently constructed dam section from the storage opening schedule. Top ; 6.3.b.2) Extract S using step 6.3.b.1) Top Extract the surface outline and center point coordinates of the dam from the geometric information table of the arch dam casting silo; 6.3.b.3) Calculate the lifting point and S Top The Euclidean distance between the center points of each warehouse is used to select the three closest warehouses as the target warehouse set C = {C1, C2, C3}. When the working area of ​​step 6.2) is the platform area where the lifting point is located, step 6.3) is to use the relative distance comparison method to determine the platform level to which the lifting point belongs; Preferably, the specific implementation of the relative distance comparison method in step 6.3) is as follows: a) Extract the elevation data Z of the high-thread platform from the preprocessed cable crane operation monitoring data. High Low-thread platform elevation data Z LOW And the elevation data of the suspension point Z P ; b) Calculate the vertical displacement |Z| from the high-thread platform to the lifting point. High -Z P |and vertical displacement from the lifting point to the low-thread platform|Z LOW -Z P |; c) Determine |Z High -Z P Is |greater than|Z? LOW -Z P If yes, the sling point is located on the low-thread platform; otherwise, the sling point is located on the high-thread platform.

[0016] Preferably, the specific implementation of step 6.4) in this invention is as follows: 6.4.1) Use the vector product method to determine the spatial topological relationship between the lifting point and the target warehouse T. If the spatial topological relationship between the lifting point and the target warehouse T is inside the warehouse, then the unloading position of this lifting task is the target warehouse T; if the spatial topological relationship between the lifting point and the target warehouse T is not inside the warehouse, then the unloading warehouse record of this lifting task is outside the warehouse. 6.4.2) If there is one warehouse in the spatial topology of the target warehouse set C, then that warehouse is the loading or unloading position for this hoisting task; if there is no warehouse in the spatial topology of the target warehouse set C, then the topology of the warehouse closest to the hoisting point is selected as its loading or unloading position. Preferably, the formula for calculating the vector product in step 6.4.1) is: Cross i =[(x i -x p )×(y i -y p )-(x i+1 -xp )×(y i+1 -y p )] in: Cross i PQ i ×PQ i+1 (When calculating in a loop, i∈[1,n], when i=n, ​​Q) n+1 = Q n ); CroosS i This represents the result of the vector product calculation; n represents the total number of vertices of the polygon; x i Q represents the i-th vertex of the target warehouse outline. i x-coordinate of the point; y i Q represents the i-th vertex of the target warehouse outline. i The ordinate of the point; x i+1 The (i+1)th vertex Q of the target warehouse outline i+1 x-coordinate of the point; y i+1 The (i+1)th vertex Q of the target warehouse outline i+1 The ordinate of the point; x p Indicate the x-coordinate of the lifting point P; y p This represents the ordinate of the suspension point P.

[0017] Preferably, the logic for determining the cable crane hoisting trajectory in step 6.5) of the present invention is as follows: Type 1: When the operating mode is concrete hoisting or auxiliary hoisting, and the loading point is located on the platform and the unloading point is located on the storage surface, the hoisting trajectory is defined as from the platform to the storage surface; Type 2: When the operating mode is auxiliary hoisting, and both the loading and unloading points are located on the platform, the hoisting trajectory is defined as platform to platform; Type 3: When the operating mode is auxiliary hoisting, and the loading point is located on the warehouse surface and the unloading point is located on the platform, the hoisting trajectory is defined as from the warehouse surface to the platform; Type 4: When the operating mode is auxiliary hoisting and both the loading and unloading points are located on the warehouse surface, the hoisting trajectory is defined as warehouse surface to warehouse surface.

[0018] The advantages of this invention are: This invention discloses a method for identifying the loading and unloading positions of a cable crane along its entire path, comprising: 1) acquiring cable crane operation monitoring data from a database; 2) preprocessing the cable crane operation monitoring data acquired in step 1); 3) performing cluster analysis on the preprocessed cable crane operation monitoring data in step 2), and simultaneously constructing a regional division table corresponding to the cable crane operation data; 4) performing first-order difference calculation on the preprocessed data in step 2), and simultaneously constructing a cable crane operation status judgment table; 5) performing non-empty state operation data pattern recognition on the data after difference calculation in step 4), and outputting corresponding parameters; 6) performing cable crane loading and unloading position pattern recognition on the identified cable crane operation data in step 5). This invention is capable of automatically outputting the identified loading and unloading point position information under concrete hoisting and auxiliary hoisting modes, generating a structured position database, and constructing a standardized data foundation for the collaborative scheduling and hoisting path optimization of cable crane groups, effectively eliminating information silos. This invention provides a loading and unloading position identification method based on the fusion of multi-source monitoring data from the cable crane itself, without relying on external high-precision positioning hardware. It enables sub-meter-level accurate identification of loading and unloading positions across various operation modes, including concrete and auxiliary hoisting, covering the platform surface, the platform surface itself, and the entire path of the platform surface. This provides high-confidence position data support for the collaborative scheduling and intelligent management of cable crane groups. This invention can automatically and accurately identify the loading and unloading points of cable crane hoisting tasks from massive amounts of monitoring data, significantly improving the accuracy and efficiency of position identification in complex construction scenarios, and providing reliable data support for the collaborative scheduling and path optimization of cable crane groups. Attached Figure Description

[0019] Figure 1 This is a flowchart of the cable crane full-path loading and unloading position identification method provided by the present invention; Figure 2 It is a portion of the original data in the database; Figure 3 It involves collecting on-site data to establish an information representation of the pouring silo; Figure 4 This is a histogram of X-axis data for the No. 3 cable crane loading point from November 2017 to October 2018. Figure 5 This is a schematic diagram of the data processing flow used in this invention; Figure 6 This is partial cable crane operation mode identification data; Figure 7 This is a flowchart of the cable crane loading and unloading position identification process; Figure 8 This is a diagram showing the results of identifying the loading and unloading positions of some cable cranes; Figure 9 This is a schematic diagram illustrating the relationship between the loading (unloading) point and the position of the cargo hold surface using the vector product method. Figure 10 This is the identification result of the concrete hoisting data for units 1-7 from November to December 2017. Figure 11 This is a schematic diagram of the cable machine layout at the site of a certain arch dam project. Detailed Implementation

[0020] The technical solution provided by the present invention will be described in detail below with reference to the embodiments.

[0021] See Figure 1 This invention provides a method for identifying the loading and unloading location of a cable crane along its entire path, the method comprising: Step 1: Retrieve cable crane operation monitoring data from the cable crane operation monitoring system database. Specifically, the cable crane information monitoring system database contains approximately 16 million monitoring data entries for each cable crane from April 10, 2017 to December 15, 2020. Use SQL statements to extract the required data from the database. Specific data extraction information is shown in Table 1 below. Examples of some original data from the database are shown below. Figure 2 As shown.

[0022] Table 1 Cable crane operation data table Where X represents the direction across the river, with the direction from the left bank to the right bank being positive and the direction from the right bank to the left bank being negative; Y represents the direction along the river, with the direction from upstream to downstream being positive and the direction from downstream to upstream being negative; Z represents the length of the cable.

[0023] Step 2 involves preprocessing the acquired cable crane operation data, including: data conversion and cleaning, elevation data conversion, and establishing a pouring sump information table. Specifically: Data transformation and cleaning: cleaning up pre-construction data; deleting duplicate time data; verifying and processing coordinate data; converting time data formats; unifying coordinate systems; and reducing data noise.

[0024] It should be noted that the elevation data conversion is performed after data conversion and cleaning. The elevation data conversion is to convert the cable length in the cable crane operation data into elevation data. In the cable crane operation monitoring system, the vertical displacement of the cable crane is recorded in real time by recording the changes in cable length. In order to optimize the data analysis process, the elevation standardization method (Equation (1)) is adopted to realize the conversion of cable length data into relative elevation data.

[0025] (1) in: This is a relative elevation value. This refers to the length of the cable.

[0026] Establish a pouring silo information table: This table includes the geometric information table of the arch dam pouring silos and the pouring silo opening schedule. The pouring silo information table is used to collect data from the construction site. Specific information regarding the geometric information table of the arch dam pouring silos is shown in Table 2, and a schematic diagram of the silo geometry is shown below. Figure 3 As shown in Table 3, the schedule for the opening of the arch dam pouring chamber is as follows.

[0027] Table 2 Geometric Information of Arch Dam Casting Site In this diagram, the lower left indicates the downstream side of the left bank; the lower right indicates the downstream side of the right bank; the upper right indicates the upstream side of the right bank; and the upper left indicates the upstream side of the left bank.

[0028] Table 3. Schedule of Opening of Casting Compartments for Arch Dams The pouring start time of the pouring chamber indicates the time when the cable crane begins to transport concrete from the platform to a pouring chamber in the dam area; the pouring end time of the pouring chamber indicates the time when the cable crane stops transporting concrete from the platform to a pouring chamber in the dam area.

[0029] Step 3: Perform cluster analysis on the cable crane operation monitoring data after the preprocessing in Step 2), and construct a regional division table of the cable crane operation data based on the cluster analysis results. Specifically: Throughout the entire operating cycle of the cable crane, its activity space exhibits a significant spatial distribution pattern. The task process typically follows a platform loading-cabin unloading path, resulting in a significantly higher frequency of cable crane loading tasks in the platform area compared to other locations. Based on this spatial characteristic, spatial clustering of the cable crane's operating trajectory data can be achieved. Taking the monitoring data of cable crane #3 from November 2017 to October 2018 as an example, the specific process is as follows: 1) Extract data on the lifting weight of cable crane No. 3 from the database of pre-processed cable crane operation monitoring data, specifically data on lifting weights not exceeding 5t and exceeding 20t.

[0030] 2) Perform differential calculations on the extracted load data. A differential value greater than 0 (indicating the load is increasing) is marked as state 1; a differential value less than 0 (indicating the load is decreasing) is marked as state 0. The formula for calculating the first-order differential value of the load is: DW=(W n+1 -W n ) / (t n+1 - t n ) in: W n It is the load in the nth row; W n+1 It is the weight of the (n+1)th row; t n It is the time point in the nth row; t n+1 It is the time point of the (n+1)th row; DW is the first-order difference value of the suspended weight; 3) Filter the data marked as 1 (i.e., in the loading state).

[0031] 4) Extract the X-coordinate data from the filtered data and plot a histogram. The vertical axis of the histogram represents the frequency of loading points; the horizontal axis represents the X-coordinate data. Specific results are shown below. Figure 4 As shown. Then, repeat the aforementioned steps to parse the regional characteristics of the X-axis distribution histogram, construct a regional division table corresponding to the operating data of all cable cranes based on the regional characteristics, and determine the X-axis coordinate range of the platform area and deck area of ​​each cable crane based on the regional division table. For example, when parsing the histogram, from... Figure 4 The following distribution characteristics can be observed: Significant peaks appeared within the interval, and the distribution was highly concentrated; Within the interval, the histogram frequency approaches 0, indicating almost no effective data distribution; Within the interval, the distribution exhibits a multi-peaked, dispersed pattern, with the peak intensity significantly lower than that of the previous period. Interval.

[0032] The aforementioned X-axis spatial distribution characteristics closely match the actual operating patterns of the cable cranes—namely, frequent loading operations on the platform and relatively sparse loading operations on the deck area—confirming the feasibility of effectively dividing the platform and deck areas based on X-axis coordinates. Based on this conclusion, this invention systematically applies the above method to the operational data analysis of the remaining six cable cranes. By analyzing the regional characteristics of the X-axis distribution histograms of each cable crane, the final division results are systematically summarized in Table 4.

[0033] Table 4. Regional Division Table of Operation Data for Cable Cranes 1-7 Step 4: Perform first-order difference calculations on the cable crane operation monitoring data and construct a cable crane operation status judgment table. The cable crane operation status judgment table includes the operation status and the judgment conditions for the operation status; the operation status includes no-load, non-no-load, transport, return, stop, loading, and unloading. Specifically: This invention calculates the first-order difference values ​​of the suspended weight and X and Z coordinates in the cable crane operation data (the calculation formulas are shown below), and at the same time constructs a cable crane operation status judgment table, the specific judgment criteria of which are shown in Table 5.

[0034] The expression for the first-order difference value DX of the horizontal displacement is: DX = (X n+1 -X n ) / (t n+1 - t n ) in: X n It is the X coordinate of the nth row; X n+1 It is the X-coordinate of the (n+1)th row; t n It is the time point in the nth row; t n+1 It is the (n+1)th time point; DX is the first-order difference value of the horizontal displacement; The expression for the first-order difference value DZ of the vertical displacement is: DZ = (Z) n+1 -Z n ) / (t n+1 - t n ) in: Z n It is the Z-coordinate of the nth row; Z n+1 It is the Z-coordinate of the (n+1)th row; t n It is the time point in the nth row; t n+1 It is the (n+1)th time point; DZ is the first-order difference value of the vertical displacement; The expression for the first-order difference value DW of the suspended weight is: DW=(W n+1 -W n ) / (t n+1 - t n ) in: W n It is the load in the nth row; W n+1 It is the weight of the (n+1)th row; t n It is the time point in the nth row; t n+1 It is the time point of the (n+1)th row; DW is the first-order difference value of the suspended weight; Table 5. Cable crane operating status judgment table Step 5: Identify the cable crane's non-no-load operating data mode and output the corresponding parameters. Specifically: This invention aims to identify the loading and unloading positions of cable cranes under specific operating modes (i.e., concrete hoisting and auxiliary hoisting). The steps include: 5.1) Based on the cable crane operation status judgment table constructed in step 4), each line of cable crane operation data points recorded in the cable crane operation monitoring data after preprocessing in step 2) is judged as either unloaded or not unloaded; the status label for unloaded is 0, and the status label for not unloaded is 1. 5.2) Based on the judgment result of step 5.1), the operation phase is divided into the cable crane operation monitoring data after the preprocessing in step 2) according to the state switching point. A unique group identifier Grp is assigned to each data subsequence with the same continuous state. Grp increases monotonically when the state switches. 5.3) For the data after step 5.2), use SQL statements to filter out all data groups marked as empty. For each data point in the remaining non-empty data groups, determine whether each data point belongs to the transportation, return or stop status according to the cable crane operation status judgment table. 5.4) Use SQL statements to extract the identification parameters of the average load weight during the transportation phase and return the identification parameters of the average load weight during the phase; 5.5) Identify the identification parameters obtained in step 5.4). If the average lifting weight of a data group during the transportation phase is greater than 22 tons and the average lifting weight during the return phase is between 5.5 tons and 9 tons, it is determined to be a concrete lifting operation mode. The remaining non-empty data are classified as auxiliary lifting modes. 5.6) After the data from step 5.5) cable crane operation mode identification is completed, the corresponding parameters need to be output: the corresponding parameters include cable crane number, start time, end time, loading point coordinates, unloading point coordinates and cable crane operation mode.

[0035] Since non-empty operation data mainly consists of the two modes mentioned above, this invention has a clear focus. For example, to accurately identify the concrete lifting mode, based on its unique lifting weight and displacement characteristics, the following discrimination process was constructed, and the overall data processing flow is illustrated below. Figure 5 As shown: First, based on the trend of load variation, the continuous cable crane operation data sequence is divided into two binary states. Specifically, according to Table 5, the operation status of each data point is determined as either unloaded or not unloaded, and a status label of 0 and 1 is assigned respectively, thereby converting the original continuous signal into a discrete sequence T composed of 0 and 1.

[0036] Secondly, based on sequence T, the original data is further divided into operational stages according to state switching points (i.e., points where adjacent data points change from 0 to 1 or 1 to 0). A unique grouping identifier Grp is assigned to each continuous data subsequence in the same state, and this identifier monotonically increments with each state switch. Through this step, the entire sequence is divided into several continuous data segments, corresponding to empty and non-empty operation respectively. Subsequently, SQL statements are used to filter out all data groups marked as empty. For each data point in the remaining non-empty data groups, Table 5 is used to determine whether it belongs to the transportation, return, or stop state. Based on this, the average load weight of the transportation stage is extracted as a key parameter: this parameter uses a conditional mean calculation method based on a window function, using the grouping identifier Grp as the dividing basis, filtering out data records where the load change is stable and the horizontal movement state is transportation, and calculating their average load weight. The extraction logic for the average load weight of the return stage is the same, only the horizontal movement state in the filtering condition is changed to return.

[0037] Next, the following criteria were set: if the average lifting weight of a data group during the transportation phase is greater than 22 tons and the average lifting weight during the return phase is between 5.5 tons and 9 tons, then the data group is determined to belong to the concrete lifting operation mode; the remaining non-empty data are classified as auxiliary lifting mode.

[0038] The thresholds for the above-mentioned discrimination criteria are comprehensively set based on measured data from domestic high arch dam cable crane construction, equipment characteristics, and operating conditions. During the transportation phase, a lower limit of 22t is set, derived from the fact that a fully loaded canister weighs approximately 28t. This takes into account factors such as incomplete canister loading, sensor errors, and fluctuations, to cover effective concrete lifting and eliminate interference. During the return phase, the empty canister range is set at 5.5t~9t, based on the empty canister's own weight of approximately 6.5t. This considers residue, errors, and fluctuations to avoid missing empty canisters and eliminate interference from accompanying materials, ensuring a logical closed loop with the full-load determination.

[0039] Finally, after the cable crane operation mode is identified, the following parameters need to be output. Partially identified data is shown below. Figure 6 As shown: 1) Cable machine number: This refers to the cable machine number in the original database.

[0040] 2) Start time: Indicates the start time of this cable crane hoisting task.

[0041] 3) End time: This indicates the time when the cable crane hoisting task will end.

[0042] 4) Loading point: Indicates the X, Y, Z coordinates of the cable crane's position when it begins loading the object during this cable crane hoisting task.

[0043] 5) Unloading Point: Represents the X, Y, Z coordinates of the cable crane's position when it begins unloading the object during this cable crane hoisting operation. 6) Cable crane operation mode: indicates the type of task for this cable crane hoisting operation.

[0044] Step 6: Based on the recognition results of Step 5), combine the vector product and relative position comparison method to identify the cable crane loading and unloading position patterns, and output the corresponding parameters.

[0045] Specifically: 6.1) Extract the relevant parameters obtained in step 5) as well as the geometric information table of the arch dam casting chamber and the opening schedule of the arch dam casting chamber obtained in step 2); 6.2) Determine the working area to which the loading point and unloading point belong based on the area division table corresponding to the cable crane operation data obtained in step 3); the working area is the area where the lifting point (loading or unloading point) is located on the deck or the area where the lifting point is located on the platform. 6.3) Based on the work area and hoisting mode in step 6.2), determine the platform level to which the hoisting point belongs; In step 6.3), when the working area of ​​step 6.2) is the area where the lifting point is located on the warehouse surface, it is in concrete lifting mode or auxiliary lifting mode; when in concrete lifting mode, the specific implementation of step 6.3) is as follows: 6.3.a.1) If the lifting point is located in the warehouse area and it is in concrete lifting mode, then extract the set S of warehouse opening status based on the task time period. K ; 6.3.a.2) Extract S using step 6.3.a.1) K Extract the coordinates of the surface outline points and center point from the geometric information table of the arch dam casting silo; 6.3.a.3) If |S K If | > 1, calculate the Euclidean distance between the unloading point and the center point of each bin, and select the bin with the closest distance as the target bin T; if |S K If |=1, then it is directly determined as the target warehouse T; When in auxiliary hoisting mode, the specific implementation of step 6.3) is as follows: 6.3.b.1) If the lifting point is located in the storage area and is in auxiliary lifting mode, then, based on the task time period, extract the set S of the top storage number of the currently constructed dam section from the storage opening schedule. Top ; 6.3.b.2) Extract S using step 6.3.b.1) Top Extract the surface outline and center point coordinates of the dam from the geometric information table of the arch dam casting silo; 6.3.b.3) Calculate the lifting point and S Top The Euclidean distance between the center points of each warehouse is used to select the three closest warehouses as the target warehouse set C = {C1, C2, C3}. When the working area of ​​step 6.2) is the platform area where the lifting point is located, step 6.3) is to use the relative distance comparison method to determine the platform level to which the lifting point belongs; Preferably, the specific implementation of the relative distance comparison method in step 6.3) is as follows: a) Extract the elevation data Z of the high-thread platform from the preprocessed cable crane operation monitoring data. High Low-thread platform elevation data Z LOW And the elevation data of the suspension point Z P ; b) Calculate the vertical displacement |Z| from the high-thread platform to the lifting point. High -Z P |and vertical displacement from the lifting point to the low-thread platform|Z LOW -Z P |; c) Determine |Z High -Z P Is |greater than|Z? LOW -Z PIf yes, the sling point is located on the low-thread platform; otherwise, the sling point is located on the high-thread platform.

[0046] 6.4) Obtain the loading or unloading location based on the platform level of the lifting point obtained in step 6.3); The specific implementation method of step 6.4) is as follows: 6.4.1) Use the vector product method to determine the spatial topological relationship between the lifting point and the target warehouse T. If the spatial topological relationship between the lifting point and the target warehouse T is inside the warehouse, then the unloading position of this lifting task is the target warehouse T; if the spatial topological relationship between the lifting point and the target warehouse T is not inside the warehouse, then the unloading warehouse record of this lifting task is outside the warehouse. 6.4.2) If there is one warehouse in the spatial topology of the target warehouse set C, then that warehouse is the loading or unloading position for this hoisting task; if there is no warehouse in the spatial topology of the target warehouse set C, then the topology of the warehouse closest to the hoisting point is selected as its loading or unloading position. Preferably, the formula for calculating the vector product in step 6.4.1) is: Cross i =[(x i -x p )×(y i -y p )-(x i+1 -x p )×(y i+1 -y p )] in: Cross i PQ i ×PQ i+1 (When calculating in a loop, i∈[1,n], when i=n, ​​Q) n+1 = Q n ); CroosS i This represents the result of the vector product calculation; n represents the total number of vertices of the polygon; x i Q represents the i-th vertex of the target warehouse outline. i x-coordinate of the point; y i Q represents the i-th vertex of the target warehouse outline. i The ordinate of the point; x i+1 The (i+1)th vertex Q of the target warehouse outline i+1 x-coordinate of the point; y i+1 The (i+1)th vertex Q of the target warehouse outline i+1 The ordinate of the point; x pIndicate the x-coordinate of the lifting point P; y p This represents the ordinate of the suspension point P.

[0047] For example, in step 6), this embodiment only focuses on the concrete hoisting and auxiliary hoisting modes of cable cranes, constructing a method for identifying the location of loading and unloading points. Based on the spatial characteristics of the above tasks concentrated in the storage area and platform area, through Tables 2 and 3 and the cable crane operation mode identification data, combined with the vector product and relative position comparison method, the accurate positioning of loading or unloading points (hereinafter collectively referred to as hoisting points) is achieved. Figure 7 The specific process is as follows: Step 1: Extract cable crane hoisting task data: start time, end time, loading point Unload point Cable crane operating modes; Step 2: Determine based on Table 4 and The area to which it belongs; Step 3: Based on the results of Step 2, determine the area where the lifting point is located; Step 3.1: If the lifting point is located in the warehouse area and it is in concrete lifting mode, then extract the set of warehouse numbers in the open status from Table 3, based on the task time period. ; Step 3.1.1: Extract using Step 3.1 Extract the coordinates of the warehouse surface outline points and center point from Table 2; Step 3.1.2: If Then calculate the Euclidean distance between the unloading point and the center point of each bin, and select the bin with the closest distance as the target bin. ;like Then it is directly identified as the target warehouse. ; Step 3.2: If the lifting point is located in the storage area and it is in auxiliary lifting mode, then extract the set of storage compartment numbers at the top of the currently constructed dam section from Table 3, taking into account the task time period. ; Step 3.2.1: Extract using Step 3.2 Extract the warehouse surface outline and center point coordinates from Table 2; Step 3.2.2: Calculate the lifting point and The Euclidean distance between the center points of each warehouse is used to select the three closest warehouses as the target warehouse set. ; Step 3.3: Based on the results of Step 2, if the lifting point is located in the platform area, the platform level to which the lifting point belongs is determined by the relative distance comparison method; Step 3.4: Use the vector product method to determine the same lifting point or Spatial topological relationships; Step 3.4.1: If If the spatial topology is within the warehouse, then the unloading location for this hoisting task is... ;like If the spatial topology is not inside the warehouse, then the unloading warehouse record for this hoisting task is outside the warehouse; Step 3.4.2: If If a spatial topology indicates that one compartment is within a compartment, then that compartment is the loading or unloading location for that hoisting task; if If no compartment is within the spatial topology, the topology of the compartment closest to the lifting point is selected as its loading or unloading location. Step 5: Generate structured record data, including: cable crane number, start time, end time, operating mode, load, loading position, unloading position, and the cable crane hoisting trajectory identification results as follows: Figure 8 As shown.

[0048] It should be noted that this invention employs different location identification processes for concrete hoisting and auxiliary hoisting, primarily based on the difference in the completeness of task information between the two. Concrete hoisting tasks have clear task records (see Table 3), and their loading and unloading positions can be directly determined from the task list. Even when multiple pouring sections are opened simultaneously, the unloading position can still be accurately determined. In contrast, auxiliary hoisting tasks typically lack predefined task lists, thus lacking a clear corresponding target when determining the topological relationship between the hoisting point and the dam body. Therefore, this invention adopts an exhaustive strategy, calculating the vector product between all existing dam sections' crest sections and the hoisting point in the current time period, and selecting the vector product results corresponding to the three sections closest to the hoisting point as the basis for inferring the topological relationship.

[0049] The core purpose of using the vector product method is to determine the spatial relationship between the lifting points in the non-platform area and the polygonal region of the arch dam surface; essentially, it solves the problem of the topological relationship between points and polygons. Therefore, as... Figure 9 As shown, the boundary position determination algorithm based on vector cross product is used in this invention, which realizes position determination by calculating the symbol sequence of the vector cross product between the suspension point and the polygon edge. The vector cross product calculation formula (4) is defined as: (4) In the formula: express (During loop calculation) ,when hour ); This represents the total number of vertices in the polygon; express x-coordinate of the point; express The ordinate of the point; express x-coordinate of the point; express The ordinate of the point; express x-coordinate of the point; express The vertical coordinate of the point; counterclockwise is defined as the positive direction, 1 indicates that the cross product is positive, and -1 indicates that the cross product is negative.

[0050] After the cross product of the suspension point and the polygon vector is calculated, the topological relationship can be obtained based on the calculation results as shown in Table 6.

[0051] Table 6. The topological relationship between the vector product calculation results and the warehouse surface. The relative distance comparison method is used to determine the platform level to which a lifting point belongs within the platform area. Although the cable length in the cable crane operation data is converted into an elevation value during the data preprocessing stage, the accuracy of the converted elevation has a certain deviation due to the failure to consider the cable deflection effect. In the arch dam construction scenario where high and low-line platforms coexist, the platform level to which a lifting point belongs cannot be reliably determined solely based on absolute elevation. Therefore, this invention proposes a platform level determination method based on relative distance comparison, the specific process of which is as follows: 1) Extracting elevation data from the high-thread platform Low-thread platform elevation data and lifting point elevation data ; 2) Calculate the vertical displacement from the elevated platform to the lifting point respectively. Vertical displacement from low-thread platform to lifting point ; 3) Judgment Is it greater than If it is greater than , it means the suspension point is on a low-thread platform; if it is less than , it means the suspension point is on a high-thread platform.

[0052] The logic for determining the hoisting trajectory of the cable crane is as follows: According to the discrimination rules, the cable crane hoisting trajectory can be divided into the following four types based on the operating mode and the positional relationship between the loading and unloading points: When the operating mode is concrete hoisting or auxiliary hoisting, and the loading point is located on the platform and the unloading point is located on the storage surface, the hoisting trajectory is defined as platform to storage surface; when the operating mode is auxiliary hoisting, and both the loading and unloading points are located on the platform, the hoisting trajectory is defined as platform to platform; when the operating mode is auxiliary hoisting, and both the loading and unloading points are located on the platform, the hoisting trajectory is defined as storage surface to platform; when the operating mode is auxiliary hoisting, and both the loading and unloading points are located on the storage surface, the hoisting trajectory is defined as storage surface to storage surface.

[0053] Numerical Experiment: (1) Hoisting concrete To verify the effectiveness of the cable crane loading and unloading position identification method, concrete hoisting data from cable cranes 16#-008 and 21#-016 of an arch dam project in Southwest China were selected for identification. The identification results are as follows. Figure 10 As shown, the unloading point identification results for silos 16#-008 and 21#-016 indicate that the unloading points for concrete hoisted by cable cranes 1 to 7 were all accurately identified within the silos, with an accuracy rate as high as 99%. Furthermore, the distribution of the unloading points for cable cranes 1 to 7 is consistent with... Figure 11 The cable crane layout schemes are highly consistent. This result demonstrates that the method proposed in this paper can accurately identify the spatial relationship between the cable crane unloading point and the deck surface.

[0054] To further verify the effectiveness of the identification method, concrete lifting data from cable cranes #1 to #7 of an arch dam project in Southwest China from November to December 2017 were used for identification. The specific identification results are shown in Table 7. As shown in Table 7, the overall identification accuracy reached 99.5%, with dam sections #13, #14, #17, and #21-#23 achieving 100% accuracy. However, slight deviations were observed in some dam sections, mainly due to two factors: First, the geometric modeling of the arch dam surface simplified it to an ideal quadrilateral, neglecting the actual curved upstream and downstream surfaces. This resulted in identification deviations even though the physical unloading points at the edge of the surface were inside the dam due to model boundary contraction. Second, errors occurred during data preprocessing when converting from the geodetic coordinate system to the construction coordinate system, causing a certain offset between the cable crane lifting trajectory and the actual spatial position of the surface, resulting in some unloading points being misjudged as being outside the dam.

[0055] Table 7. Identification Results of Loading and Unloading Positions for Concrete Lifting Tasks of Cable Cranes #1-#7 from November to December 2017 The total number represents the total number of concrete hoisting tasks identified during the time from opening to closing the warehouse; the number of tasks located within the warehouse represents the number of concrete hoisting tasks identified during the time from opening to closing the warehouse, after the cable crane loading and unloading positions are identified and located within the warehouse.

[0056] (2) Assisting in hoisting To verify the effectiveness of the cable crane loading and unloading location identification method, the identification results based on the operation data of cable cranes #1 to #7 from November to December 2017 of a certain arch dam project in Southwest China are shown in Table 8. The results indicate that trajectories from platform to storage surface and platform to platform are dominant (approximately 70.0% in total), reflecting the core operational mode of materials being transported from the storage platform and transferred within the storage surface or platform during construction. Meanwhile, trajectories from storage surface to platform and storage surface to storage surface also account for a considerable proportion (approximately 30.0% in total), indicating that the storage surface area frequently serves as a loading starting point or transfer point, confirming the diversity of loading and unloading directions in actual construction.

[0057] It is worth noting that various trajectories are significantly distributed across different hoisting tasks, indicating that in complex construction scenarios, relying solely on trajectory patterns is insufficient to uniquely determine the nature of a hoisting task, and situations may exist where trajectories are similar but the operational purposes differ. Therefore, this invention, by further integrating temporal characteristics and load variation patterns during operation, can effectively distinguish between hoisting tasks with similar trajectories but fundamentally different objectives, thereby significantly improving the overall accuracy and robustness of loading and unloading position identification.

[0058] Table 8. Statistics on Auxiliary Lifting Trajectory Recognition Results for Cable Cranes #1 to #7 from November to December 2017 In summary, the cable crane loading and unloading point identification method proposed in this invention has demonstrated its efficiency, accuracy, and robustness through multi-dimensional experiments. This method can accurately identify the loading and unloading positions for both concrete lifting and auxiliary lifting operations simultaneously from massive amounts of operational data. It not only supports typical paths on platform surfaces but also comprehensively covers various actual lifting trajectories, fully reflecting the material flow process in complex construction. This invention possesses the ability to process large-scale data, exhibits strong adaptability, and can meet the identification needs under different working conditions. Building upon sub-meter-level high-precision identification of loading and unloading positions, this invention further provides reliable data support for the classification and identification of auxiliary lifting objects and the analysis of the construction process, thereby constructing a complete position-aware data foundation for the collaborative scheduling and intelligent management of cable crane groups.

Claims

1. A method for identifying the loading and unloading position of a cable crane along its entire path, characterized in that: The method for identifying the loading and unloading location of the cable crane along its entire path includes the following steps: 1) Obtain cable crane operation monitoring data from the database of the cable crane operation monitoring system; 2) Preprocess the cable crane operation monitoring data obtained in step 1); 3) Perform cluster analysis on the cable crane operation monitoring data after the preprocessing in step 2), and construct a regional division table of the cable crane operation data based on the cluster analysis results; 4) Perform first-order difference calculation on the cable crane operation monitoring data after the preprocessing in step 2) to construct a cable crane operation status judgment table; 5) Based on the differential calculation results obtained from the cable crane operation status judgment table in step 4), perform cable crane non-no-load operation data pattern recognition and output the corresponding parameters; 6) Based on the corresponding parameters obtained in step 5) and the area division table corresponding to the cable crane operation data obtained in step 3), the loading and unloading position pattern recognition of the cable crane is performed by combining the vector product and relative position comparison method.

2. The method for identifying the loading and unloading position of a cable crane along its entire path according to claim 1, characterized in that: The specific implementation method of step 1) is as follows: use SQL statements to extract cable crane operation monitoring data from the database of the cable crane operation monitoring system. The cable crane operation monitoring data includes cable crane number, cable crane operation data acquisition time point, vector data acquisition point time interval, cable crane X coordinate at the time of data acquisition, cable crane Y coordinate at the time of data acquisition, cable crane Z coordinate at the time of data acquisition, and the load and number at the time of data acquisition.

3. The method for identifying the loading and unloading position of a cable crane along its entire path according to claim 2, characterized in that: The preprocessing in step 2) includes data conversion and cleaning, elevation data conversion, and the creation of a pouring silo information table. The data conversion and cleaning includes cleaning up pre-construction data, deleting duplicate time data, verifying and processing coordinate data, converting time data formats, unifying coordinate systems, and reducing data noise. The elevation data conversion is performed after data conversion and cleaning. The elevation data conversion is to convert the cable length in the cable crane operation data into elevation data. The establishment of the casting silo information table includes the geometric information table of the arch dam casting silo and the opening schedule of the arch dam casting silo; the geometric information table of the arch dam casting silo includes the silo number, coordinates of the outline point of the casting silo, coordinates of the center point of the casting silo surface, and the elevation of the casting silo; the opening schedule of the arch dam casting silo includes the silo number, opening time, and closing time.

4. The method for identifying the loading and unloading position of a cable crane along its entire path according to claim 3, characterized in that: The specific implementation method of step 3) is as follows: 3.1) Extract the lifting weight data from the preprocessed cable crane operation monitoring data; the lifting weight data refers to the data in the target cable crane operation data where the lifting weight does not exceed 5t and the lifting weight exceeds 20t. 3.2) Perform differential calculation on the lifting weight data extracted in step 3.1). A difference value greater than 0 is marked as state 1, and a difference value less than 0 is marked as state 0. State 1 indicates the lifting weight is rising; state 0 indicates the lifting weight is falling. The formula for calculating the first-order differential value of the lifting weight is: DW=(W n+1 -IN n ) / (t n+1 - t n ) in: W n It is the load in the nth row; W n+1 It is the weight of the (n+1)th row; t n It is the time point in the nth row; t n+1 It is the time point of the (n+1)th row; DW is the first-order difference value of the suspended weight; 3.3) Based on the results of step 3.2), filter the data marked as state 1; 3.4) Extract the X-coordinate data from the data obtained in step 3.3) and draw a histogram; the vertical axis of the histogram is the frequency of the loading point; the horizontal axis of the histogram is the X-coordinate data. 3.5) Repeat steps 3.1) to 3.4) to analyze the regional characteristics of the X-axis distribution histogram, construct a regional division table corresponding to the operating data of all cable cranes based on the regional characteristics, and determine the X-axis coordinate range of the platform area and the deck area of ​​each cable crane based on the regional division table.

5. The method for identifying the loading and unloading position of a cable crane along its entire path according to claim 4, characterized in that: The specific implementation method of step 4) is as follows: 4.1) Calculate the first-order difference values ​​for the suspended weight and X and Z coordinates in the cable crane operation monitoring data after the preprocessing in step 2); the first-order difference values ​​include the first-order difference value DX for horizontal displacement, the first-order difference value DZ for vertical displacement, and the first-order difference value DW for suspended weight. The expression for the first-order difference value DX of the horizontal displacement is: DX=(X n+1 -X n ) / (t n+1 - t n ) in: X n It is the X-coordinate of the nth row; X n+1 It is the X-coordinate of the (n+1)th row; t n It is the time point in the nth row; t n+1 It is the time point of the (n+1)th row; DX is the first-order difference value of the horizontal displacement; The expression for the first-order difference value DZ of the vertical displacement is: DZ=(Z n+1 -Z n ) / (t n+1 - t n ) in: Z n It is the Z-coordinate of the nth row; Z n+1 It is the Z-coordinate of the (n+1)th row; t n It is the time point in the nth row; t n+1 It is the time point of the (n+1)th row; DZ is the first-order difference value of the vertical displacement; The expression for the first-order difference value DW of the suspended weight is: DW=(W n+1 -IN n ) / (t n+1 - t n ) in: W n It is the load in the nth row; W n+1 It is the weight of the (n+1)th row; t n It is the time point in the nth row; t n+1 It is the time point of the (n+1)th row; DW is the first-order difference value of the suspended weight; 4.2) Construct a cable crane operation status judgment table based on the first-order difference value calculated in step 4.1); the cable crane operation status judgment table includes operation status and operation status judgment conditions; the operation status includes no load, non-no load, transportation, return, stop, loading and unloading.

6. The method for identifying the loading and unloading position of a cable crane along its entire path according to claim 5, characterized in that: The specific implementation method of step 5) is as follows: 5.1) Based on the cable crane operation status judgment table constructed in step 4), each line of cable crane operation data points recorded in the cable crane operation monitoring data after preprocessing in step 2) is judged as either unloaded or not unloaded; the unloaded status label is 0, and the not unloaded status label is 1. 5.2) Based on the judgment result of step 5.1), the operation phase is divided into the cable crane operation monitoring data after the preprocessing in step 2) according to the state switching point. A unique group identifier Grp is assigned to each data subsequence with the same continuous state. Grp increases monotonically when the state switches. 5.3) For the data after step 5.2), use SQL statements to filter out all data groups marked as empty. For each data point in the remaining non-empty data groups, determine whether each data point belongs to the transportation, return or stop status according to the cable crane operation status judgment table. 5.4) Use SQL statements to extract the identification parameters of the average load weight during the transportation phase and return the identification parameters of the average load weight during the phase; 5.5) Identify the identification parameters obtained in step 5.4). If the average lifting weight of a data group during the transportation phase is greater than 22 tons and the average lifting weight during the return phase is between 5.5 tons and 9 tons, it is determined to be a concrete lifting operation mode. The remaining non-empty data are classified as auxiliary lifting modes. 5.6) After the data from step 5.5) cable crane operation mode identification is completed, the corresponding parameters need to be output: the corresponding parameters include cable crane number, start time, end time, loading point coordinates, unloading point coordinates and cable crane operation mode.

7. The method for identifying the loading and unloading position of a cable crane along its entire path according to claim 6, characterized in that: The specific implementation method of step 6) is as follows: 6.1) Extract the relevant parameters obtained in step 5) as well as the geometric information table of the arch dam casting chamber and the opening schedule of the arch dam casting chamber obtained in step 2); 6.2) Determine the working area to which the loading point and unloading point belong based on the area division table corresponding to the cable crane operation data obtained in step 3); the working area is either the area where the lifting point is located on the deck or the area where the lifting point is located on the platform. 6.3) Based on the work area and hoisting mode in step 6.2), determine the platform level to which the hoisting point belongs; 6.4) Obtain the loading or unloading location based on the platform level of the lifting point obtained in step 6.3); 6.5) Based on the results obtained in step 6.4), generate structured record data, which includes cable crane number, start time, end time, operating mode, load, loading position, unloading position, and cable crane hoisting trajectory.

8. The method for identifying the loading and unloading position of a cable crane along its entire path according to claim 7, characterized in that: In step 6.3), when the working area of ​​step 6.2) is the area where the lifting point is located on the warehouse surface, it is in concrete lifting mode or auxiliary lifting mode; when in concrete lifting mode, the specific implementation of step 6.3) is as follows: 6.3.a.1) If the lifting point is located in the warehouse area and it is in concrete lifting mode, then extract the set S of warehouse opening status warehouse numbers based on the task time period. K ; 6.3.a.2) Extract S using step 6.3.a.1) K Extract the coordinates of the surface outline points and center point from the geometric information table of the arch dam casting silo; 6.3.a.3) If |S K If | > 1, calculate the Euclidean distance between the unloading point and the center point of each bin, and select the bin with the closest distance as the target bin T; if |S K If |=1, then it is directly determined as the target warehouse T; When in auxiliary hoisting mode, the specific implementation of step 6.3) is as follows: 6.3.b.1) If the lifting point is located in the storage area and is in auxiliary lifting mode, then, based on the task time period, extract the set S of the top storage number of the currently constructed dam section from the storage opening schedule. Top ; 6.3.b.2) Extract S using step 6.3.b.1) Top Extract the surface outline and center point coordinates of the dam from the geometric information table of the arch dam casting silo; 6.3.b.3) Calculate the lifting point and S Top The Euclidean distance between the center points of each warehouse is used to select the three closest warehouses as the target warehouse set C = {C1, C2, C3}. When the working area of ​​step 6.2) is the platform area where the lifting point is located, step 6.3) is to use the relative distance comparison method to determine the platform level to which the lifting point belongs; Preferably, the specific implementation of the relative distance comparison method in step 6.3) is as follows: a) Extract the elevation data Z of the high-thread platform from the preprocessed cable crane operation monitoring data. High Low-thread platform elevation data Z LOW And the elevation data of the suspension point Z P ; b) Calculate the vertical displacement |Z| from the high-thread platform to the lifting point. High -Z P |and vertical displacement from the lifting point to the low-thread platform|Z LOW -Z P |; c) Determine |Z High -Z P Is |greater than|Z? LOW -Z P If yes, the sling point is located on the low-thread platform; otherwise, the sling point is located on the high-thread platform.

9. The method for identifying the loading and unloading position of a cable crane along its entire path according to claim 8, characterized in that: The specific implementation method of step 6.4) is as follows: 6.4.1) Use the vector product method to determine the spatial topological relationship between the lifting point and the target warehouse T. If the spatial topological relationship between the lifting point and the target warehouse T is inside the warehouse, then the unloading position of this lifting task is the target warehouse T; if the spatial topological relationship between the lifting point and the target warehouse T is not inside the warehouse, then the unloading warehouse record of this lifting task is outside the warehouse. 6.4.2) If there is one warehouse in the spatial topology of the target warehouse set C, then that warehouse is the loading or unloading position for this hoisting task; if there is no warehouse in the spatial topology of the target warehouse set C, then the topology of the warehouse closest to the hoisting point is selected as its loading or unloading position. Preferably, the formula for calculating the vector product in step 6.4.1) is: Cross i =[(x i -x p )×(y i -y p )-(x i+1 -x p )×(y i+1 -y p )] in: Cross i PQ i ×PQ i+1 When calculating in a loop, i∈[1,n], and when i=n, ​​Q n+1 = Q n ; CroosS i This represents the result of the vector product calculation; n represents the total number of vertices of the polygon; x i Q represents the i-th vertex of the target warehouse outline. i x-coordinate of the point; y i Q represents the i-th vertex of the target warehouse outline. i The ordinate of the point; x i+1 The (i+1)th vertex Q represents the outline of the target warehouse. i+1 x-coordinate of the point; y i+1 The (i+1)th vertex Q represents the outline of the target warehouse. i+1 The ordinate of the point; x p Indicate the x-coordinate of the lifting point P; y p This represents the ordinate of the suspension point P.

10. The method for identifying the loading and unloading position of a cable crane along its entire path according to claim 9, characterized in that: The logic for determining the hoisting trajectory in step 6.5) is as follows: Type 1: When the operating mode is concrete hoisting or auxiliary hoisting, and the loading point is located on the platform and the unloading point is located on the storage surface, the hoisting trajectory is defined as from the platform to the storage surface; Type 2: When the operating mode is auxiliary hoisting, and both the loading and unloading points are located on the platform, the hoisting trajectory is defined as platform to platform; Type 3: When the operating mode is auxiliary hoisting, and the loading point is located on the warehouse surface and the unloading point is located on the platform, the hoisting trajectory is defined as from the warehouse surface to the platform; Type 4: When the operating mode is auxiliary hoisting and both the loading and unloading points are located on the warehouse surface, the hoisting trajectory is defined as warehouse surface to warehouse surface.

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