Cutting path optimization method and system for cutting drum

Abstract

The invention relates to the technical field of design optimization, in particular to a cutting path optimization method and system for a cutting roller. The method comprises the following steps: firstly, acquiring the material cutting difficulty of a local reference time period according to the periodic distribution condition of amplitude values; in the historical reference time period corresponding to the current moment, according to the deviation standard condition of the material cutting difficulty of all the local reference time periods, the reference path comprehensive difficulty of the current moment is obtained; and obtaining an optimal cutting path according to the reference path comprehensive difficulty at the current moment, the spatial position at each sampling moment and the to-be-cut three-dimensional point cloud data. According to the method, the geological material condition experienced by the cutting roller in the operation process is fully considered, so that the cutting path is reasonably planned, and the working abrasion of the cutting roller is effectively reduced.

Description

Technical Field

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

[0002] The cutting drum is a cylindrical tool with picks or other coal-breaking tools on the outside, usually installed on mining equipment for cutting. The surface of the cutting drum is covered with cutting teeth or knives, and when the drum rotates, the cutting teeth cut into the material and break it. However, due to the continuous contact and friction between the cutting drum and the geological material during operation, it will wear. In order to extend the service life of the cutting drum and reduce the replacement cost, the cutting path of the cutting drum needs to be optimized.

[0003] The existing technology 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 of the cutting drum to a certain extent. However, when using the existing technology to determine the optimal cutting path, the geological material conditions experienced by the cutting drum during the operation are ignored. The geological material will 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 cutting path optimization 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 technical solution adopted is as follows: A method for optimizing a cutting path of a cutting drum, the method comprising: 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.

[0005] Furthermore, 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.

[0006] Furthermore, 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.

[0007] Furthermore, the method for obtaining the second cutting difficulty includes: The mean value 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.

[0008] Furthermore, 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.

[0009] Furthermore, 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.

[0010] Furthermore, 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.

[0011] Furthermore, the preset difficulty judgment threshold is 0.8.

[0012] Furthermore, the preset volatility value is 0.

[0013] The present invention proposes 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. When the processor executes the computer program, the steps of the cutting path optimization method for the cutting drum are implemented.

[0014] The present invention has the following beneficial effects: In order to analyze the difficulty of cutting geological materials in the cutting process in more detail, the entire historical reference time period is divided into multiple shorter local reference periods. Considering that the periodic changes in the amplitude value during the cutting process can characterize the stability of the interaction between the roller and the material, it can reflect the difficulty of cutting the material. In each local reference period, the periodic distribution of the amplitude value is analyzed, and the material cutting difficulty is constructed to reflect the difficulty of cutting geological materials in the operation process corresponding to the local reference period. Taking into account the deviation of the material cutting difficulty corresponding to all local reference periods from the standard, the comprehensive difficulty of the reference path at the current moment is obtained. The comprehensive difficulty of the reference path comprehensively reflects the overall cutting difficulty in the operation process corresponding to the 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, an optimal cutting path is determined. The optimal cutting path fully considers the geological material conditions experienced by the cutting roller during the operation process, so as to achieve reasonable planning of the cutting path and effectively reduce the working wear of the cutting roller. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0016] Figure 1 A flow chart of a cutting path optimization method for a cutting drum provided by one embodiment of the present invention; Figure 2 A flow chart of a method for obtaining material cutting difficulty provided by one embodiment of the present invention; Figure 3 A structural diagram of a cutting path optimization system for a cutting drum provided in one embodiment of the present invention. DETAILED DESCRIPTION

[0017] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the following is a detailed description of the cutting path optimization method and system for a cutting drum according to the present invention, its specific implementation, structure, features and effects, in combination with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment. In addition, specific features, structures or characteristics in one or more embodiments may be combined in any suitable form.

[0018] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0019] The specific scheme of a cutting path optimization method and system for a cutting drum provided by the present invention is described in detail below with reference to the accompanying drawings.

[0020] The 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 flow chart of a cutting path optimization method for a cutting drum provided by an embodiment of the present invention, the method comprising the following steps: Step S1: 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.

[0021] In order to determine the optimal cutting path, it is first necessary to obtain the amplitude value and spatial position of the cutting drum at each sampling moment in each historical reference time period, and the three-dimensional point cloud data to be cut.

[0022] When performing cutting operations, cutting equipment equipped with laser radar, vibration sensors and positioning sensors is used to synchronously sample the 3D point cloud data to be cut, the amplitude value and the spatial position according to the preset sampling frequency, and obtain the amplitude value and spatial position at each sampling moment, as well as the 3D point cloud data to be cut. The specific process includes: In order to obtain the three-dimensional structural imaging of the surrounding environment in real time, the laser radar is installed on the top or front of the cutting equipment to obtain a panoramic view and the best scanning effect. Among them, the laser radar has a 360-degree scanning capability, a scanning distance of about 100 meters, and a point cloud resolution of 1-5cm. Using the laser radar, sampling is performed according to the preset sampling frequency to obtain the three-dimensional point cloud data to be cut at each sampling time. 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 operation, the vibration sensor is installed on the base of the cutting equipment to accurately capture the vibration signal. Using the vibration sensor, sampling is performed according to the preset sampling frequency to obtain the amplitude value at each sampling time to evaluate the hardness of the material. In order to monitor the position change of the cutting drum during operation, the positioning sensor is used to sample according to the preset sampling frequency to obtain the spatial position at each sampling time. The amplitude value, spatial position, and three-dimensional point cloud data to be cut at each sampling time in each historical reference time period are counted to obtain the data set of the cutting drum.

[0023] In the implementation process of the present invention, once the amplitude value, spatial position and the collection of the 3D point cloud data to be cut at each sampling time in a historical reference time period are completed, a path planning will be performed immediately. In order to clearly explain the latest path planning process, the last sampling time of the latest historical reference time period in the present invention is the current time.

[0024] It should be noted that, in one embodiment of the present invention, the present invention adopts a synchronous sampling method, samples at the same time according to a preset frequency, takes each sampling as a sampling moment, the preset frequency is 5 times / second, and the historical reference time period is from the beginning to the end of each hour, which can be set by the implementer according to the implementation scenario. It should be noted that, in order to facilitate calculations, all indicator data involved in the calculations in the embodiments of the present invention are pre-processed to eliminate the dimensional effect. The specific means of removing the dimensional effect are technical means well known to those skilled in the art and are not limited here.

[0025] Considering that it may not be reasonable to determine the optimal cutting path based solely on the distance from the real-time position to the destination, it is difficult to effectively reduce the wear of 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 more intelligent path planning methods.

[0026] Step S2: 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 value 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 of the material cutting difficulty of all local reference time periods from the standard.

[0027] In order to analyze the difficulty of cutting geological materials in the cutting process in more detail, the entire historical reference time period is divided into multiple shorter local reference periods. Considering that the periodic changes in the amplitude value during the cutting operation can characterize the stability of the interaction between the roller and the material, it can reflect the difficulty of cutting the material. In each local reference period, the periodic distribution of the amplitude value is analyzed, and the material cutting difficulty is constructed to reflect the difficulty of cutting geological materials in the operation process corresponding to the local reference period. Taking into account the deviation of the material cutting difficulty corresponding to all local reference periods from the standard, the comprehensive difficulty of the reference path at the current moment is obtained. The comprehensive difficulty of the reference path comprehensively reflects the overall cutting difficulty in the operation process corresponding to the historical reference time period at the current moment.

[0028] Specifically, in order to more carefully analyze the data within the historical reference time period and improve the accuracy and adaptability of path planning, the entire historical reference time period is evenly divided into a preset number of local reference time periods. In one embodiment of the present invention, the preset number of divisions is 20, and the implementer can set it according to the implementation scenario.

[0029] In order to analyze the difficulty of cutting geological materials during the local reference period, it is considered that the difficulty of cutting materials directly affects the frequency and amplitude of the vibration signal during the cutting operation. Hard rocks such as granite and basalt require more force to cut due to their high hardness, so they will produce higher frequency vibrations and larger vibration amplitudes. On the contrary, softer materials such as sand or loose rock formations will produce relatively low vibration frequencies and amplitudes when cut. This characteristic of vibration signals provides an important basis for evaluating the difficulty of material cutting. Please refer to Figure 2 , which shows a flow chart of a method for obtaining material cutting difficulty in one embodiment of the present invention, the method for obtaining material cutting difficulty includes: Step S201: Obtain a first cutting difficulty of a local reference time period according to a periodic characteristic of an amplitude value in the local reference time period corresponding to a duration.

[0030] This step aims to construct the material cutting difficulty of the operation process corresponding to the local reference period of the first cutting difficulty assessment by analyzing the periodic characteristics of the amplitude value.

[0031] Preferably, in one embodiment of the present invention, the method for obtaining the first cutting difficulty includes: According to the order of sampling time, the amplitude values ​​corresponding to each sampling time in the local reference period are counted in turn to obtain the amplitude value sequence corresponding to the local reference period; the amplitude value sequence is decomposed using the STL decomposition algorithm to obtain all periodic components of the local reference period; the mean of the duration corresponding to all periodic components is calculated and negative correlation mapping is performed to obtain the first cutting difficulty of the local reference period. In one embodiment of the present invention, the negative correlation mapping can be in the form of inverse proportion or negative exponential power. It should be noted that the STL (Seasonal-Trend Decomposition using LOESS, seasonal-trend decomposition using local weighted regression scatter point smoothing) decomposition algorithm is a prior art well known to those skilled in the art, and the amplitude value sequence is decomposed to obtain the corresponding trend component, seasonal component and residual component; wherein the seasonal component is the periodic component.

[0032] For the above steps, all periodic components of the local reference period can be obtained through STL decomposition; the periodic components can reflect the periodic characteristics of the vibration signal. Calculate the mean of the corresponding time lengths of all periodic components, which can be regarded as the average period length of the vibration signal in the local reference period. Considering the negative correlation between the period length of the vibration signal and the difficulty of cutting the material: the longer the period length, that is, the lower the vibration frequency, the cut material is often looser or has a lower density, and the cutting difficulty is relatively small; conversely, the shorter the period length, that is, the higher the vibration frequency, the cut material may be harder, and the cutting difficulty is relatively large. Negatively correlated mapping is performed on the mean of the corresponding time lengths of all periodic components to obtain the first cutting difficulty. 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.

[0033] Step S202: Obtain a second segmentation difficulty of the local reference period according to the overall distribution of amplitude values ​​corresponding to all sampling moments in the local reference period.

[0034] The overall distribution of amplitude values ​​can reflect the average strength of the interaction between the roller and the material. This step aims to construct a second cutting difficulty through the overall distribution of amplitude values ​​to evaluate the material cutting difficulty of the operation process corresponding to the local reference period.

[0035] Preferably, in one embodiment of the present invention, the method for obtaining the second cutting difficulty includes: The mean of the amplitude values ​​corresponding to all sampling moments in the local reference period is calculated to obtain the second cutting difficulty of the local reference period.

[0036] For the above steps, considering that in the cutting operation, when the roller cuts a material that is more difficult to cut, due to the high hardness and high density of the material, the cutting tool needs to exert greater force to penetrate the material. This high-intensity interaction causes the friction and compression between the roller and the material to increase significantly, which in turn causes more intense energy transfer and vibration. Therefore, there is a direct correlation between the amplitude value and the difficulty of cutting the material. The mean of the amplitude values ​​corresponding to all sampling moments in the local reference period is calculated to obtain the second cutting difficulty of the local reference period. The second cutting difficulty reflects the average interaction strength between the roller and the material in the local reference period. The greater the second cutting difficulty, the more intense the interaction between the roller and the material in this period, and the higher the difficulty of cutting the material.

[0037] Step S203: forwardly fuse the first cutting difficulty and the second cutting difficulty to obtain the material cutting difficulty of the local reference time period.

[0038] 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.

[0039] It should be noted that forward fusion is an existing technology well known to those skilled in the art, and forward fusion can adopt simple product, arithmetic mean or other suitable fusion methods. In one 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 one embodiment of the present invention, the normalization process can adopt linear normalization, etc., which is not limited here.

[0040] For the above steps, the product of the first cutting difficulty and the second cutting difficulty is taken as the initial fusion result and normalized. Normalization helps to ensure that the material cutting difficulty between different local reference periods is comparable. After normalization, the material cutting difficulty of the local reference period is obtained. The material cutting difficulty combines the periodic characteristics of the amplitude value and the overall distribution information, and can more accurately reflect the difficulty of material cutting.

[0041] In order to evaluate the overall cutting difficulty during the operation process corresponding to the historical reference time period at the current moment, preferably, in one embodiment of the present invention, a 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 local reference time periods whose material cutting difficulty is greater than the preset standard difficulty value is counted to obtain a first comprehensive difficulty parameter; 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 average of the deviation standard parameters of all local reference periods to obtain the 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. In one embodiment of the present invention, the preset standard difficulty value is 0.78, and the implementer can set it according to the implementation scenario.

[0042] For the above steps, in the historical reference time period corresponding to the current moment, firstly select all local reference time periods in which the material cutting difficulty 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 in which the cutting difficulty exceeds the standard value during the operation process corresponding to the historical reference time period at the current moment, and is a basic indicator for evaluating the overall difficulty. For each selected 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 from the standard value, and is an important reference for evaluating the fluctuation of difficulty. Calculate the mean 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 from the preset standard difficulty value during the operation process corresponding to the historical reference time period at the current moment, and is another important indicator for evaluating the overall difficulty. The product of the first comprehensive difficulty parameter and the second comprehensive difficulty parameter is normalized 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 process corresponding to the historical reference time period at the current moment.

[0043] Step S3: Obtain the optimal cutting path according to the comprehensive difficulty of the reference path at the current moment, as well as the spatial position at each sampling moment and the three-dimensional point cloud data to be cut.

[0044] According to the current comprehensive difficulty of the path and the 3D point cloud data of the material to be cut, an optimal cutting path is determined. 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.

[0045] Preferably, in one embodiment of the present invention, 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 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 subjected to dimensionality reduction processing to obtain the reduced-dimensional point cloud data; the information volatility rate from the spatial position at the current moment to the actual cutting path corresponding to the loose position is set to the 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. In one 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 volatility value is 0. It should be noted that the PCA (Principal Component Analysis) algorithm and the ant colony algorithm are existing technologies well known to those skilled in the art and will not be described in detail here.

[0046] 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, the current path is considered to be 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, the current path is considered to be difficult, and an easier cutting time period needs to be found. At this time, the previous historical reference time period of 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, find the local reference time period with the lowest material cutting difficulty and regard it as the target time period. The spatial position corresponding to the first moment of the target time period is identified as a loose position, that is, because the loose position may be easier to handle, it can reduce the working wear of the cutting drum. The dimensionality reduction processing is performed on the three-dimensional point cloud data to be cut at the loose position, and the principal component analysis (PCA) algorithm is used to reduce the dimension of the data, thereby simplifying the subsequent processing and analysis. In this example, the information volatility 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 the path will evaporate quickly, thereby reducing the probability of ants choosing this path. Based on the reduced-dimensional point cloud data, the ant colony algorithm begins to search for the optimal cutting path. Due to the special information volatility, ants tend to explore other paths instead of directly choosing the actual cutting path from the current moment to the loose position.

[0047] Specifically, since the loose position is easier to handle and can reduce the wear of the cutting drum, the cutting device will first return to the loose position along the actual cutting path, and then perform cutting according to the planned optimal cutting path.

[0048] The present invention also proposes a cutting path optimization system for a cutting drum, see 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.

[0049] The data acquisition module 101 is used to 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.

[0050] 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 value in the local reference time period; in the historical reference time period corresponding to the current moment, the comprehensive difficulty of the reference path at the current moment is obtained according to the deviation of the material cutting difficulty of all local reference time periods from the standard.

[0051] The optimal cutting path analysis module 103 is used to obtain the optimal cutting path according to the comprehensive difficulty of the reference path at the current moment, as well as the spatial position at each sampling moment and the three-dimensional point cloud data to be cut.

[0052] It should be noted that: the system provided in the above embodiment is only illustrated by the division of the above functional modules. In actual applications, the above functions can be assigned to different functional modules as needed, 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 cutting drums and the cutting path optimization method for cutting drums provided in the above embodiment belong to the same concept. The specific implementation process is detailed in the method embodiment and will not be repeated here.

[0053] In summary, the embodiments of the present invention provide a cutting path optimization method and system for a cutting drum. The present invention first obtains the material cutting difficulty of a local reference time period according to the periodic distribution of the amplitude value; in the historical reference time period corresponding to the current moment, according to the deviation of the material cutting difficulty of all local reference time periods from the standard, 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 position of each sampling moment and the three-dimensional point cloud data to be cut, the optimal cutting path is obtained. The present invention 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.

[0054] It should be noted that the sequence of the above 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 accompanying 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.

[0055] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other, and 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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