Cutting Path Optimization Method and System for Cutting Drum

Abstract

The present invention relates to the technical field of design optimization, and particularly relates to a cutting path optimization method and system for a cutting drum. First, according to the periodic distribution of amplitude values, the cutting difficulty of materials in a local reference period is obtained; in the historical reference time period corresponding to the current moment, according to the deviation from the standard of the cutting difficulty of materials in all local reference periods, the comprehensive difficulty of the reference path at the current moment is obtained; according to the comprehensive difficulty of the reference path at the current moment, as well as the spatial positions and the three-dimensional point cloud data to be cut at each sampling moment, the optimal cutting path is obtained. By fully considering the geological material conditions experienced by the cutting drum during operation, the present invention realizes the reasonable planning of the cutting path and effectively reduces the working wear of the cutting drum.

Description

Technical Field

[0001] The present invention relates to the technical field of design optimization, and particularly relates to a cutting path optimization method and system for a cutting drum. Background Art

[0002] A cutting drum is a cylindrical tool with pick teeth or other coal-breaking tools installed on its periphery, and is usually installed on mining equipment for cutting. The surface of the cutting drum is covered with cutting teeth or tools. When the drum rotates, the cutting teeth cut into the material and break it. However, since the cutting drum constantly contacts geological materials and generates friction during operation, wear will occur. In order to extend the service life of the cutting drum and reduce the replacement cost, it is necessary to optimize the cutting path of the cutting drum.

[0003] The prior art usually directly determines the optimal cutting path based on the shortest distance from the real-time position to the destination, so as to reduce the wear generated by the cutting drum operation to a certain extent. However, when determining the optimal cutting path using the prior art, the geological material conditions experienced by the cutting drum during operation are ignored. Geological materials have a significant impact on the wear of the cutting drum, resulting in unreasonable determination of the optimal cutting path and difficulty in effectively reducing the working wear of the cutting drum. Summary of the Invention

[0004] In order to solve the technical problem of unreasonable optimization of the cutting path in the prior art, the purpose of the present invention is to provide a cutting path optimization method and system for a cutting drum, and the specific technical solutions adopted are as follows:

[0005] A cutting path optimization method for a cutting drum, the method includes:

[0006] Obtain a data set of the cutting drum, the data set including the amplitude values and spatial positions at each sampling moment in each historical reference time period, and the three-dimensional point cloud data to be cut;

[0007] Divide each of the historical reference time periods into respective local reference time periods, and obtain the material cutting difficulty of the local reference time period according to the periodic distribution of the amplitude values in the local reference time period; in the historical reference time period corresponding to the current moment, obtain the comprehensive difficulty of the reference path at the current moment according to the deviation from the standard of the material cutting difficulty of all the local reference time periods;

[0008] Obtain the optimal cutting path according to the comprehensive difficulty of the reference path at the current moment, and the spatial positions and the three-dimensional point cloud data to be cut at each sampling moment.

[0009] Further, the method for obtaining the material cutting difficulty includes:

[0010] Obtain the first cutting difficulty of the local reference period according to the corresponding duration of the periodic characteristics of the amplitude values in the local reference period;

[0011] Obtain the second cutting difficulty of the local reference period according to the overall distribution of the amplitude values corresponding to all the sampling moments in the local reference period;

[0012] Fusion the first cutting difficulty and the second cutting difficulty in a positive direction to obtain the material cutting difficulty of the local reference period.

[0013] Further, the method for obtaining the first cutting difficulty includes:

[0014] According to the sampling moment sequence, sequentially count the amplitude values corresponding to each sampling moment in the local reference period to obtain the amplitude value sequence corresponding to the local reference period;

[0015] Use the STL decomposition algorithm to decompose the amplitude value sequence to obtain all the periodic components of the local reference period;

[0016] Calculate the mean value of the durations corresponding to all the periodic components and perform a negative correlation mapping to obtain the first cutting difficulty of the local reference period.

[0017] Further, the method for obtaining the second cutting difficulty includes:

[0018] Calculate the mean value of the amplitude values corresponding to all the sampling moments in the local reference period to obtain the second cutting difficulty of the local reference period.

[0019] Further, the method for obtaining the material cutting difficulty of the local reference period includes:

[0020] Calculate the product of the first cutting difficulty and the second cutting difficulty and perform a normalization process to obtain the material cutting difficulty of the local reference period.

[0021] Further, the method for obtaining the comprehensive difficulty of the reference path includes:

[0022] In the historical reference time period corresponding to the current moment, count the total number of all the local reference periods in which the material cutting difficulty is greater than the preset standard difficulty value to obtain the first comprehensive difficulty parameter;

[0023] Calculate the ratio of the material cutting difficulty of each local reference period to the preset standard difficulty value to obtain the deviation standard parameter of each local reference period; calculate the mean value of the deviation standard parameters of all the local reference periods to obtain the second comprehensive difficulty parameter;

[0024] Calculate the product of the first comprehensive difficulty parameter and the first comprehensive difficulty parameter and perform normalization processing to obtain the comprehensive difficulty of the reference path at the current moment.

[0025] Further, the method for obtaining the optimal cutting path includes:

[0026] If the comprehensive difficulty of the reference path at the current moment is not greater than the preset difficulty judgment threshold, take the historical reference time period corresponding to the current moment as the target time period; if the comprehensive difficulty of the reference path at the current moment is greater than the preset difficulty judgment threshold, take the previous historical reference time period in the time sequence of the historical reference time period corresponding to the current moment as the target time period;

[0027] In the target time period, take the local reference time period corresponding to the minimum material cutting difficulty as the target time period, and take the first sampling moment of the target time period corresponding to the spatial position as the loose position;

[0028] Based on the PCA algorithm, perform dimensionality reduction processing on the three-dimensional point cloud data to be cut at the loose position to obtain the dimensionality-reduced point cloud data; set the information volatilization rate of the actual cutting path from the spatial position at the current moment to the loose position to the preset volatilization value, and based on the ant colony algorithm, obtain the optimal cutting path according to the dimensionality-reduced point cloud data at the loose position.

[0029] Further, the preset difficulty judgment threshold is 0.8.

[0030] Further, the preset volatilization value is 0.

[0031] The present invention provides a cutting path optimization system for a cutting drum, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the cutting path optimization method for the cutting drum are implemented.

[0032] The present invention has the following beneficial effects:

[0033] To more meticulously analyze the difficulty level of cutting geological materials during the cutting operation, the entire historical reference time period is divided into multiple shorter local reference time periods. Considering that the periodic change of the amplitude value during the cutting operation can characterize the stability of the interaction between the drum and the material, and further reflect the cutting difficulty of the material. Within each local reference time period, analyze the periodic distribution of the amplitude value, and construct the cutting difficulty of the material to reflect the difficulty level of cutting geological materials during the corresponding operation process of the local reference time period. Comprehensively consider the deviation standard of the cutting difficulty of the corresponding materials in all local reference time periods, and obtain the comprehensive difficulty of the reference path at the current moment. The comprehensive difficulty of the reference path comprehensively reflects the overall cutting difficulty during the operation process of the corresponding historical reference time period at the current moment. According to the current comprehensive difficulty of the path and the three-dimensional point cloud data of the material to be cut, determine an optimal cutting path. The optimal cutting path fully considers the geological material conditions experienced by the cutting drum during the operation process to achieve reasonable planning of the cutting path and effectively reduce the working wear of the cutting drum. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0035] Figure 1 It is a flowchart of a cutting path optimization method for a cutting drum provided by an embodiment of the present invention;

[0036] Figure 2 It is a flowchart of a method for obtaining the cutting difficulty of a material provided by an embodiment of the present invention;

[0037] Figure 3 It is a structural diagram of a cutting path optimization system for a cutting drum provided by an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0038] In order to further elaborate on the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the following, in combination with the drawings and preferred embodiments, details the specific implementation manners, structures, features, and effects of a cutting path optimization method and system for a cutting drum proposed according to the present invention. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. In addition, the specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0039] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the technical field to which this invention belongs.

[0040] The following specifically describes the specific solutions of a cutting path optimization method and system for a cutting drum provided by the present invention in conjunction with the accompanying drawings.

[0041] An embodiment of the present invention provides a cutting path optimization method and system for a cutting drum. Please refer to Figure 1 , which shows a flowchart of a cutting path optimization method for a cutting drum provided by an embodiment of the present invention. The method includes the following steps:

[0042] Step S1: Obtain a data set of the cutting drum. The data set includes the amplitude values and spatial positions at each sampling moment in each historical reference period, and the three-dimensional point cloud data to be cut.

[0043] In order to determine the optimal cutting path, it is first necessary to obtain the amplitude values and spatial positions at each sampling moment in each historical reference period of the cutting drum, as well as the three-dimensional point cloud data to be cut.

[0044] During the cutting operation, a cutting device equipped with a lidar, a vibration sensor, and a positioning sensor is used to synchronously sample the three-dimensional point cloud data to be cut, the amplitude value, and the spatial position at a preset sampling frequency to obtain the amplitude values and spatial positions at each sampling moment, and the three-dimensional point cloud data to be cut. The specific process includes:

[0045] In order to obtain a three-dimensional structure image of the surrounding environment in real time, the lidar is installed on the top or front of the cutting device to obtain a panoramic view and the best scanning effect. Among them, the lidar has a 360-degree scanning ability, the scanning distance is about 100 meters, and the point cloud resolution reaches 1-5 cm. Using the lidar, samples are taken at a preset sampling frequency to obtain the three-dimensional point cloud data to be cut at each sampling moment. The three-dimensional point cloud data to be cut provides accurate environmental information for planning the cutting path. In order to monitor the vibration signal of the cutting drum during the operation, the vibration sensor is installed on the base of the cutting device to accurately capture the vibration signal. Using the vibration sensor, samples are taken at a preset sampling frequency to obtain the amplitude values at each sampling moment to evaluate the hardness of the material. In order to monitor the position change of the cutting drum during the operation, the positioning sensor is used to sample at a preset sampling frequency to obtain the spatial positions at each sampling moment. The amplitude values and spatial positions at each sampling moment in each historical reference period are counted, and the data set of the cutting drum is obtained.

[0046] In the implementation process of the present invention, whenever the acquisition of the amplitude values, spatial positions, and the three-dimensional point cloud data to be cut at each sampling moment within a historical reference period is completed, a path planning will be immediately carried out. To clearly illustrate the process of the latest path planning, the last sampling moment of the latest historical reference period in the present invention is the current moment.

[0047] It should be noted that in an embodiment of the present invention, the present invention adopts a synchronous sampling method, and samples are taken simultaneously at a preset frequency. Each sampling is regarded as a sampling moment, and the preset frequency is 5 times per second. The historical reference period is from the start to the end of each hour, and the implementer can set it according to the implementation scenario. It should be noted that for the convenience of calculation, all the index data involved in the operation in the embodiment of the present invention have undergone data preprocessing, thereby canceling the influence of the dimension. The specific means of canceling the dimension influence are well-known technical means to those skilled in the art and will not be limited here.

[0048] Considering that it may not be reasonable to determine the optimal cutting path only based on the distance from the real-time position to the destination, and it is difficult to effectively reduce the wear generated by the cutting drum during operation. In order to more accurately determine the optimal cutting path, it is necessary to comprehensively consider the geological conditions during the operation and adopt a more intelligent path planning means.

[0049] Step S2: Divide each historical reference period into various local reference periods, and obtain the cutting difficulty of the material in the local reference period according to the periodic distribution of the amplitude values; in the historical reference period corresponding to the current moment, obtain the comprehensive difficulty of the reference path according to the deviation from the standard of the cutting difficulty of the material in all local reference periods.

[0050] In order to more carefully analyze the difficulty of cutting geological materials during the cutting operation, the entire historical reference period is divided into multiple shorter local reference periods. Considering that the periodic change of the amplitude value during the cutting operation can characterize the stability of the interaction between the drum and the material, and thus reflect the cutting difficulty of the material. In each local reference period, analyze the periodic distribution of the amplitude value, and construct the cutting difficulty of the material to reflect the difficulty of cutting geological materials in the corresponding operation process of the local reference period. Comprehensively consider the deviation from the standard of the cutting difficulty of the material corresponding to all local reference periods, and obtain the comprehensive difficulty of the reference path at the current moment. The comprehensive difficulty of the reference path comprehensively reflects the overall cutting difficulty during the operation process of the historical reference period corresponding to the current moment.

[0051] Specifically, in order to more carefully analyze the data within the historical reference period and improve the accuracy and adaptability of path planning, the entire historical reference period is evenly divided into a preset number of local reference periods. In an embodiment of the present invention, the preset number is 20, and the implementer can set it according to the implementation scenario.

[0052] To analyze the cutting difficulty of geological materials during the corresponding operation in the local reference period, considering that in the cutting operation, the cutting difficulty of materials directly affects the frequency and amplitude of vibration signals. Hard rocks such as granite and basalt, due to their high hardness, require more force to cut, so they will generate vibrations with higher frequencies and larger amplitudes. On the contrary, softer materials such as sand or loose rock formations will generate relatively lower vibration frequencies and amplitudes when cut. The characteristics of such vibration signals provide an important basis for evaluating the cutting difficulty of materials. Please refer to Figure 2 , which shows a flowchart of a method for obtaining the cutting difficulty of a material in an embodiment of the present invention. The method for obtaining the cutting difficulty of a material includes:

[0053] Step S201: Obtain the first cutting difficulty of the local reference period according to the duration of the periodic characteristics of the amplitude values in the local reference period.

[0054] This step aims to construct the first cutting difficulty to evaluate the cutting difficulty of the material in the operation corresponding to the local reference period by analyzing the periodic characteristics of the amplitude values.

[0055] Preferably, in an embodiment of the present invention, the method for obtaining the first cutting difficulty includes:

[0056] In the order of sampling times, successively count the amplitude values corresponding to each sampling time in the local reference period to obtain the amplitude value sequence corresponding to the local reference period; use the STL decomposition algorithm to decompose the amplitude value sequence to obtain all periodic components of the local reference period; calculate the mean of the durations corresponding to all periodic components and perform a negative correlation mapping to obtain the first cutting difficulty of the local reference period. In an embodiment of the present invention, the negative correlation mapping can adopt the form of inverse proportion or negative exponential power. It should be noted that the STL (Seasonal-Trend Decomposition using LOESS) decomposition algorithm is a well-known prior art to those skilled in the art. Decomposing the amplitude value sequence can obtain the corresponding trend component, seasonal component, and residual component; among them, the seasonal component is the periodic component.

[0057] For the above steps, through STL decomposition, all periodic components of the local reference period can be obtained; the periodic components can reflect the periodic characteristics of the vibration signal. Calculate the mean value of the durations corresponding to all periodic components, and this mean value can be regarded as the average cycle length of the vibration signal within the local reference period. Considering the negative correlation between the cycle length of the vibration signal and the cutting difficulty of the material: the longer the cycle length, that is, the lower the vibration frequency, the material to be cut is often looser or has a lower density, and the cutting difficulty is relatively small; conversely, the shorter the cycle length, that is, the higher the vibration frequency, the material to be cut may be harder, and the cutting difficulty is relatively large. Perform a negative correlation mapping on the mean value of the durations corresponding to all periodic components to obtain the first cutting difficulty, and the first cutting difficulty reflects the material cutting difficulty of the operation process corresponding to the local reference period. The larger the value, the greater the cutting difficulty.

[0058] Step S202: Obtain the second cutting difficulty of the local reference period according to the overall distribution of the amplitude values corresponding to all sampling moments in the local reference period.

[0059] The overall distribution of the amplitude values can reflect the average intensity of the interaction between the drum and the material. This step aims to construct the second cutting difficulty through the overall distribution of the amplitude values to evaluate the material cutting difficulty of the operation process corresponding to the local reference period.

[0060] Preferably, in an embodiment of the present invention, the method for obtaining the second cutting difficulty includes:

[0061] Calculate the mean value of the amplitude values corresponding to all sampling moments in the local reference period to obtain the second cutting difficulty of the local reference period.

[0062] For the above steps, considering that in the cutting operation, when the drum cuts materials with a higher cutting difficulty, due to the high hardness and high density characteristics of the materials, the cutting tool needs to apply greater force to penetrate the materials. This high-intensity interaction leads to a significant increase in the frictional force and compressive force between the drum and the materials, thereby triggering more intense energy transfer and vibration. Therefore, there is a direct correlation between the magnitude of the amplitude value and the cutting difficulty of the material. Calculate the mean value of the amplitude values corresponding to all sampling moments in the local reference period to obtain the second cutting difficulty of the local reference period. The second cutting difficulty reflects the average interaction intensity between the drum and the materials within the local reference period. When the second cutting difficulty is greater, it means that the interaction between the drum and the materials is more intense during this period, and the cutting difficulty of the materials is also higher.

[0063] Step S203: Positively fuse the first cutting difficulty and the second cutting difficulty to obtain the material cutting difficulty of the local reference period.

[0064] This step aims to integrate the information of the first cutting difficulty and the second cutting difficulty to more comprehensively evaluate the material cutting difficulty of the operation process corresponding to the local reference period.

[0065] It should be noted that forward fusion is a well-known prior art to those skilled in the art. Forward fusion can adopt simple multiplication, arithmetic mean or other suitable fusion methods. In an embodiment of the present invention, the product of the first cutting difficulty and the second cutting difficulty is calculated and normalized to obtain the material cutting difficulty of the local reference period. In an embodiment of the present invention, the normalization process can adopt linear normalization, etc., which is not limited here.

[0066] For the above step, the product of the first cutting difficulty and the second cutting difficulty is used as the preliminary fusion result and normalized. The normalization process helps to ensure the comparability of the material cutting difficulties between different local reference periods. After normalization, the material cutting difficulty of the local reference period is obtained. The material cutting difficulty integrates the periodic characteristics and overall distribution information of the amplitude value and can more accurately reflect the cutting difficulty of the material.

[0067] In order to evaluate the overall cutting difficulty in the operation process corresponding to the historical reference time period at the current moment, preferably, in an embodiment of the present invention, the method for obtaining the comprehensive difficulty of the reference path includes:

[0068] In the historical reference time period corresponding to the current moment, count the total number of all local reference periods in which the material cutting difficulty is greater than the preset standard difficulty value to obtain the first comprehensive difficulty parameter;

[0069] Calculate the ratio of the material cutting difficulty of each local reference period to the preset standard difficulty value to obtain the deviation standard parameter of each local reference period; calculate the mean value of the deviation standard parameters of all local reference periods to obtain the second comprehensive difficulty parameter;

[0070] Calculate the product of the first comprehensive difficulty parameter and the first comprehensive difficulty parameter and normalize it to obtain the comprehensive difficulty of the reference path at the current moment. In an embodiment of the present invention, the preset standard difficulty value is 0.78, and the implementer can set it according to the implementation scenario.

[0071] For the above steps, within the historical reference time period corresponding to the current moment, first screen out all local reference time periods in which the cutting difficulty of the material is greater than the preset standard difficulty value. Count the total number of these local reference time periods to obtain the first comprehensive difficulty parameter. The first comprehensive difficulty parameter reflects the number of time periods during the operation in the historical reference time period corresponding to the current moment when the cutting difficulty exceeds the standard value, and is a basic index for evaluating the overall difficulty. For each screened local reference time period, calculate the ratio of its material cutting difficulty to the preset standard difficulty value to obtain the deviation standard parameter. The deviation standard parameter reflects the degree of deviation of the cutting difficulty of each time period relative to the standard value, and is an important reference for evaluating the difficulty fluctuation. Calculate the mean value of the deviation standard parameters of all local reference time periods to obtain the second comprehensive difficulty parameter. The second comprehensive difficulty parameter reflects the overall deviation level of the cutting difficulty relative to the preset standard difficulty value during the operation in the historical reference time period corresponding to the current moment, and is another important index for evaluating the overall difficulty. Normalize the product of the first comprehensive difficulty parameter and the second comprehensive difficulty parameter to obtain the comprehensive difficulty of the reference path at the current moment. The greater the comprehensive difficulty of the reference path, the greater the overall cutting difficulty during the operation in the historical reference time period corresponding to the current moment.

[0072] Step S3: Obtain the optimal cutting path according to the comprehensive difficulty of the reference path at the current moment, the spatial positions at each sampling moment, and the three-dimensional point cloud data to be cut.

[0073] Determine an optimal cutting path based on the current comprehensive difficulty of the path and the three-dimensional point cloud data of the material to be cut. The optimal cutting path fully considers the geological material conditions experienced by the cutting drum during the operation, so as to realize the reasonable planning of the cutting path and effectively reduce the working wear of the cutting drum.

[0074] Preferably, in an embodiment of the present invention, the method for obtaining the optimal cutting path includes:

[0075] If the comprehensive difficulty of the reference path at the current moment is not greater than the preset difficulty judgment threshold, the historical reference time period corresponding to the current moment is used as the target time period; if the comprehensive difficulty of the reference path at the current moment is greater than the preset difficulty judgment threshold, the previous historical reference time period in the time sequence of the historical reference time period corresponding to the current moment is used as the target time period; in the target time period, the local reference time period corresponding to the minimum material cutting difficulty is used as the target time period, and the spatial position corresponding to the first sampling moment of the target time period is used as the loose position; based on the PCA algorithm, the three-dimensional point cloud data to be cut at the loose position is dimensionally reduced to obtain the dimensionally reduced point cloud data; let the information evaporation rate of the actual cutting path from the spatial position at the current moment to the loose position be the preset evaporation value, and based on the ant colony algorithm, the optimal cutting path is obtained according to the dimensionally reduced point cloud data at the loose position. Among them, in an embodiment of the present invention, the preset difficulty judgment threshold is 0.8, which is used to judge whether the cutting difficulty at the current moment is too high, so as to determine whether it is necessary to find an easier cutting path in the previous time period. The preset evaporation value is 0. It should be noted that the PCA (Principal Component Analysis) algorithm and the ant colony algorithm are well-known existing technologies in the art and will not be elaborated here.

[0076] For the above steps, if the comprehensive difficulty of the reference path at the current moment is not greater than the preset difficulty judgment threshold, it is considered that the current path is relatively easy, and the historical reference time period corresponding to the current moment can be directly used as the target time period. If the comprehensive difficulty of the reference path at the current moment is greater than the preset difficulty judgment threshold, it is considered that the current path is more difficult and an easier cutting time period needs to be found. At this time, the previous historical reference time period in the time sequence of the historical reference time period corresponding to the current moment is used as the target time period. In the target time period, the local reference time period with the minimum material cutting difficulty is found and regarded as the target time period. The spatial position corresponding to the first moment of the target time period is identified as the loose position, that is, because the loose position may be easier to process, thereby reducing the working wear of the cutting drum. The three-dimensional point cloud data to be cut at the loose position is dimensionally reduced, and the principal component analysis (PCA) algorithm is used to reduce the dimension of the data, thereby simplifying subsequent processing and analysis. In this example, the information evaporation rate from the spatial position at the current moment to the actual cutting path corresponding to the loose position is set to 0. This means that the pheromone on this path will quickly evaporate, thereby reducing the probability of ants choosing this path. Based on the dimensionally reduced point cloud data, the ant colony algorithm starts to search for the optimal cutting path. Due to the setting of the special information evaporation rate, ants will tend to explore other paths rather than directly choose the actual cutting path from the current moment to the loose position.

[0077] Specifically, since the loose position is easier to process and can reduce the wear of the cutting drum, the cutting equipment will first return to the loose position along the actual cutting path and then perform cutting according to the planned optimal cutting path.

[0078] The present invention also provides a cutting path optimization system for a cutting drum. Please refer to Figure 3 , which shows a structural diagram of a cutting path optimization system for a cutting drum provided by an embodiment of the present invention. The system includes: a data acquisition module 101, a reference path comprehensive difficulty analysis module 102, and an optimal cutting path analysis module 103.

[0079] The data acquisition module 101 is used to acquire a data set of the cutting drum. The data set includes the amplitude values and spatial positions at each sampling moment in each historical reference time period, as well as the three-dimensional point cloud data to be cut.

[0080] The reference path comprehensive difficulty analysis module 102 is used to divide each historical reference time period into local reference time periods, and obtain the material cutting difficulty of the local reference time period according to the periodic distribution of the amplitude values in the local reference time period; in the historical reference time period corresponding to the current moment, obtain the reference path comprehensive difficulty of the current moment according to the deviation standard of the material cutting difficulties of all local reference time periods.

[0081] The optimal cutting path analysis module 103 is used to obtain the optimal cutting path according to the reference path comprehensive difficulty of the current moment, as well as the spatial positions and three-dimensional point cloud data to be cut at each sampling moment.

[0082] It should be noted that: for the system provided in the above embodiment, only the above-mentioned division of each functional module is used for illustration. In actual applications, the above functions can be allocated to different functional modules according to needs, that is, the internal structure of the computer device is divided into different functional modules to complete all or part of the functions described above. In addition, the cutting path optimization system for a cutting drum provided in the above embodiment and the embodiment of the cutting path optimization method for a cutting drum belong to the same concept, and the specific implementation process is detailed in the method embodiment, which will not be repeated here.

[0083] In summary, the embodiments of the present invention provide a cutting path optimization method and system for a cutting drum. First, according to the periodic distribution of amplitude values, the cutting difficulty of materials in a local reference period is obtained; in the historical reference time period corresponding to the current moment, according to the deviation from the standard of the cutting difficulty of materials in all local reference periods, the comprehensive difficulty of the reference path at the current moment is obtained; according to the comprehensive difficulty of the reference path at the current moment, as well as the spatial positions and the three-dimensional point cloud data to be cut at each sampling moment, the optimal cutting path is obtained. By fully considering the geological material conditions experienced by the cutting drum during operation, the present invention realizes reasonable planning of the cutting path and effectively reduces the working wear of the cutting drum.

[0084] It should be noted that the above sequence of the embodiments of the present invention is only for description and does not represent the advantages and disadvantages of the embodiments. The processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0085] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between each embodiment can be referred to each other. Each embodiment focuses on the differences from other embodiments.

Claims

1. A cutting path optimization method for a cutting drum, characterized in that: The method comprises: Acquire a data set of the cutting drum, the data set including the amplitude value and spatial position at each sampling moment in each historical reference time period, and three-dimensional point cloud data to be cut; Divide each of the historical reference time periods into local reference time periods, and obtain the material cutting difficulty of the local reference time period according to the periodic distribution of the amplitude value in the local reference time period; obtain the comprehensive difficulty of the reference path at the current moment according to the deviation of the material cutting difficulty of all the local reference time periods in the historical reference time period corresponding to the current moment; The optimal cutting path is obtained according to the comprehensive difficulty of the reference path at the current moment, the spatial position at each sampling moment, and the three-dimensional point cloud data to be cut.

2. A cutting path optimization method for a cutting drum according to claim 1, characterized in that: The method for obtaining the material cutting difficulty includes: According to the duration corresponding to the periodic characteristic of the amplitude value in the local reference time period, obtaining a first cutting difficulty of the local reference time period; Obtaining a second segmentation difficulty of the local reference time period according to the overall distribution of the amplitude values ​​corresponding to all the sampling moments in the local reference time period; The first cutting difficulty and the second cutting difficulty are forwardly integrated to obtain the material cutting difficulty of the local reference time period.

3. A cutting path optimization method for a cutting drum according to claim 2, characterized in that: The method for obtaining the first cutting difficulty includes: According to the sampling time sequence, the amplitude values ​​corresponding to each sampling time in the local reference time period are counted in sequence to obtain the amplitude value sequence corresponding to the local reference time period; Decomposing the amplitude value sequence by using the STL decomposition algorithm to obtain all periodic components of the local reference period; The mean values ​​of the durations corresponding to all the periodic components are calculated and negative correlation mapping is performed to obtain the first cutting difficulty of the local reference time period.

4. A cutting path optimization method for a cutting drum according to claim 2, characterized in that: The method for obtaining the second cutting difficulty includes: The mean of the amplitude values ​​corresponding to all sampling moments in the local reference time period is calculated to obtain a second segmentation difficulty of the local reference time period.

5. A cutting path optimization method for a cutting drum according to claim 2, characterized in that: The method for obtaining the material cutting difficulty of the local reference time period includes: The product of the first cutting difficulty and the second cutting difficulty is calculated and normalized to obtain the material cutting difficulty of the local reference time period.

6. A cutting path optimization method for a cutting drum according to claim 1, characterized in that: The method for obtaining the comprehensive difficulty of the reference path includes: In the historical reference time period corresponding to the current moment, the total number of all the local reference time periods in which the material cutting difficulty is greater than the preset standard difficulty value is counted to obtain a first comprehensive difficulty parameter; Calculating the ratio of the material cutting difficulty of each local reference period to a preset standard difficulty value to obtain a deviation standard parameter of each local reference period; calculating the average of the deviation standard parameters of all the local reference periods to obtain a second comprehensive difficulty parameter; The product of the first comprehensive difficulty parameter and the second comprehensive difficulty parameter is calculated and normalized to obtain the comprehensive difficulty of the reference path at the current moment.

7. A cutting path optimization method for a cutting drum according to claim 1, characterized in that: The method for obtaining the optimal cutting path includes: If the comprehensive difficulty of the reference path at the current moment is not greater than the preset difficulty judgment threshold, the historical reference time period corresponding to the current moment is used as the target time period; if the comprehensive difficulty of the reference path at the current moment is greater than the preset difficulty judgment threshold, the historical reference time period immediately preceding the historical reference time period corresponding to the current moment in time sequence is used as the target time period; In the target time period, the local reference time period corresponding to the minimum material cutting difficulty is used as the target time period, and the first sampling moment of the target time period is used as the spatial position as the loose position; Based on the PCA algorithm, the three-dimensional point cloud data to be cut at the loose position is subjected to dimensionality reduction processing to obtain reduced-dimensional point cloud data; the information volatility rate of the actual cutting path corresponding to the spatial position at the current moment to the loose position is set to a preset volatility value, and based on the ant colony algorithm, the optimal cutting path is obtained according to the reduced-dimensional point cloud data at the loose position.

8. A cutting path optimization method for a cutting drum according to claim 7, characterized in that: The preset difficulty judgment threshold is 0.

8.

9. A cutting path optimization method for a cutting drum according to claim 7, characterized in that: The preset volatility value is 0.

10. A cutting path optimization system for a cutting drum, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of a cutting path optimization method for a cutting drum as described in any one of claims 1 to 9 are implemented.

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